R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ############################################################# > # > # DESCRIPTION: Unit tests in the HIBAG package > # > > # load the HIBAG package > library(HIBAG) HIBAG (HLA Genotype Imputation with Attribute Bagging) Kernel Version: v1.4 > > > ############################################################# > > # a list of HLA genes > hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1") > > # pre-defined lower bound of prediction accuracy > hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7) > > > for (hla.idx in seq_len(length(hla.list))) + { + hla.id <- hla.list[hla.idx] + + # make a "hlaAlleleClass" object + hla <- hlaAllele(HLA_Type_Table$sample.id, + H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], + H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], + locus=hla.id, assembly="hg19") + + # divide HLA types randomly + set.seed(100) + hlatab <- hlaSplitAllele(hla, train.prop=0.5) + + # SNP predictors within the flanking region on each side + region <- 500 # kb + snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, + HapMap_CEU_Geno$snp.position, + hla.id, region*1000, assembly="hg19") + + # training and validation genotypes + train.geno <- hlaGenoSubset(HapMap_CEU_Geno, + snp.sel=match(snpid, HapMap_CEU_Geno$snp.id), + samp.sel=match(hlatab$training$value$sample.id, + HapMap_CEU_Geno$sample.id)) + test.geno <- hlaGenoSubset(HapMap_CEU_Geno, + samp.sel=match(hlatab$validation$value$sample.id, + HapMap_CEU_Geno$sample.id)) + + + # train a HIBAG model + set.seed(100) + model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10) + summary(model) + + # validation + pred <- predict(model, test.geno) + summary(pred) + + # compare + comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model, + call.threshold=0) + print(comp$overall) + + # check + if (comp$overall$acc.haplo < hla.acc[hla.idx]) + { + stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ", + hla.acc[hla.idx], ".") + } + + cat("\n\n") + } Exclude 11 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:25 [1] 2019-10-16 04:25:25, OOB Acc: 77.27%, # of SNPs: 14, # of Haplo: 31 [2] 2019-10-16 04:25:25, OOB Acc: 84.62%, # of SNPs: 16, # of Haplo: 84 [3] 2019-10-16 04:25:25, OOB Acc: 82.35%, # of SNPs: 14, # of Haplo: 65 [4] 2019-10-16 04:25:25, OOB Acc: 69.23%, # of SNPs: 12, # of Haplo: 19 [5] 2019-10-16 04:25:26, OOB Acc: 91.67%, # of SNPs: 15, # of Haplo: 20 [6] 2019-10-16 04:25:26, OOB Acc: 95.83%, # of SNPs: 14, # of Haplo: 28 [7] 2019-10-16 04:25:26, OOB Acc: 92.86%, # of SNPs: 16, # of Haplo: 29 [8] 2019-10-16 04:25:26, OOB Acc: 93.33%, # of SNPs: 15, # of Haplo: 25 [9] 2019-10-16 04:25:26, OOB Acc: 86.36%, # of SNPs: 14, # of Haplo: 48 [10] 2019-10-16 04:25:26, OOB Acc: 95.45%, # of SNPs: 15, # of Haplo: 35 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0009786010 0.0009841353 0.0010339440 0.0042716135 0.0094062927 0.0220411814 Max. Mean SD 0.3213246156 0.0347390661 0.0745369844 Accuracy with training data: 98.5% Out-of-bag accuracy: 86.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 10 total # of SNPs used: 89 avg. # of SNPs in an individual classifier: 14.50 (sd: 1.18, min: 12, max: 16, median: 14.50) avg. # of haplotypes in an individual classifier: 38.40 (sd: 21.18, min: 19, max: 84, median: 30.00) avg. out-of-bag accuracy: 86.90% (sd: 8.72%, min: 69.23%, max: 95.83%, median: 89.02%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0009786010 0.0009841353 0.0010339440 0.0042716135 0.0094062927 0.0220411814 Max. Mean SD 0.3213246156 0.0347390661 0.0745369844 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:26) 0% Predicting (2019-10-16 04:25:26) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000001 0.0024114 0.0090509 0.0292197 0.0400998 0.3213246 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 26 25 51 0.9615385 0.9807692 0 n.call call.rate 1 26 1 Exclude 1 monomorphic SNP Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 340, # of samples: 28 # of unique HLA alleles: 22 [-] 2019-10-16 04:25:26 [1] 2019-10-16 04:25:26, OOB Acc: 58.33%, # of SNPs: 19, # of Haplo: 77 [2] 2019-10-16 04:25:27, OOB Acc: 62.50%, # of SNPs: 18, # of Haplo: 95 [3] 2019-10-16 04:25:27, OOB Acc: 75.00%, # of SNPs: 15, # of Haplo: 115 [4] 2019-10-16 04:25:28, OOB Acc: 65.00%, # of SNPs: 11, # of Haplo: 90 [5] 2019-10-16 04:25:28, OOB Acc: 60.00%, # of SNPs: 14, # of Haplo: 39 [6] 2019-10-16 04:25:28, OOB Acc: 78.57%, # of SNPs: 23, # of Haplo: 133 [7] 2019-10-16 04:25:29, OOB Acc: 63.64%, # of SNPs: 9, # of Haplo: 41 [8] 2019-10-16 04:25:29, OOB Acc: 83.33%, # of SNPs: 13, # of Haplo: 76 [9] 2019-10-16 04:25:29, OOB Acc: 68.18%, # of SNPs: 20, # of Haplo: 67 [10] 2019-10-16 04:25:29, OOB Acc: 70.00%, # of SNPs: 19, # of Haplo: 67 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001594481 0.0001635380 0.0002003470 0.0013990924 0.0055325715 0.0126663222 Max. Mean SD 0.1628913557 0.0137053758 0.0306449898 Accuracy with training data: 100.0% Out-of-bag accuracy: 68.5% Gene: B Training dataset: 28 samples X 340 SNPs # of HLA alleles: 22 # of individual classifiers: 10 total # of SNPs used: 118 avg. # of SNPs in an individual classifier: 16.10 (sd: 4.41, min: 9, max: 23, median: 16.50) avg. # of haplotypes in an individual classifier: 80.00 (sd: 29.67, min: 39, max: 133, median: 76.50) avg. out-of-bag accuracy: 68.46% (sd: 8.26%, min: 58.33%, max: 83.33%, median: 66.59%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001594481 0.0001635380 0.0002003470 0.0013990924 0.0055325715 0.0126663222 Max. Mean SD 0.1628913557 0.0137053758 0.0306449898 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 15 Predicting (2019-10-16 04:25:29) 0% Predicting (2019-10-16 04:25:29) 100% Gene: B Range: [31321649bp, 31324989bp] on hg19 # of samples: 15 # of unique HLA alleles: 10 # of unique HLA genotypes: 13 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 3 (20.0%) 5 (33.3%) 3 (20.0%) 4 (26.7%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 2.000e-08 3.309e-04 2.997e-03 2.910e-02 1.250e-02 2.415e-01 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 15 12 27 0.8 0.9 0 n.call call.rate 1 15 1 Exclude 2 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 354, # of samples: 36 # of unique HLA alleles: 17 [-] 2019-10-16 04:25:29 [1] 2019-10-16 04:25:30, OOB Acc: 80.77%, # of SNPs: 19, # of Haplo: 40 [2] 2019-10-16 04:25:30, OOB Acc: 90.91%, # of SNPs: 32, # of Haplo: 32 [3] 2019-10-16 04:25:30, OOB Acc: 85.71%, # of SNPs: 19, # of Haplo: 44 [4] 2019-10-16 04:25:30, OOB Acc: 86.36%, # of SNPs: 18, # of Haplo: 51 [5] 2019-10-16 04:25:30, OOB Acc: 93.33%, # of SNPs: 17, # of Haplo: 32 [6] 2019-10-16 04:25:31, OOB Acc: 95.83%, # of SNPs: 17, # of Haplo: 44 [7] 2019-10-16 04:25:31, OOB Acc: 95.45%, # of SNPs: 21, # of Haplo: 45 [8] 2019-10-16 04:25:31, OOB Acc: 79.17%, # of SNPs: 14, # of Haplo: 21 [9] 2019-10-16 04:25:31, OOB Acc: 89.29%, # of SNPs: 24, # of Haplo: 27 [10] 2019-10-16 04:25:31, OOB Acc: 85.71%, # of SNPs: 32, # of Haplo: 41 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0006172852 0.0006263998 0.0007084311 0.0019338438 0.0049160876 0.0123872299 Max. Mean SD 0.0457619810 0.0084777629 0.0094487847 Accuracy with training data: 100.0% Out-of-bag accuracy: 88.3% Gene: C Training dataset: 36 samples X 354 SNPs # of HLA alleles: 17 # of individual classifiers: 10 total # of SNPs used: 131 avg. # of SNPs in an individual classifier: 21.30 (sd: 6.22, min: 14, max: 32, median: 19.00) avg. # of haplotypes in an individual classifier: 37.70 (sd: 9.33, min: 21, max: 51, median: 40.50) avg. out-of-bag accuracy: 88.25% (sd: 5.76%, min: 79.17%, max: 95.83%, median: 87.82%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0006172852 0.0006263998 0.0007084311 0.0019338438 0.0049160876 0.0123872299 Max. Mean SD 0.0457619810 0.0084777629 0.0094487847 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 24 Predicting (2019-10-16 04:25:31) 0% Predicting (2019-10-16 04:25:31) 100% Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 24 # of unique HLA alleles: 14 # of unique HLA genotypes: 18 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (4.2%) 4 (16.7%) 5 (20.8%) 14 (58.3%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0001839 0.0014275 0.0060595 0.0063168 0.0360898 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 24 18 41 0.75 0.8541667 0 n.call call.rate 1 24 1 Exclude 4 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 345, # of samples: 31 # of unique HLA alleles: 7 [-] 2019-10-16 04:25:31 [1] 2019-10-16 04:25:31, OOB Acc: 95.45%, # of SNPs: 11, # of Haplo: 22 [2] 2019-10-16 04:25:31, OOB Acc: 100.00%, # of SNPs: 13, # of Haplo: 22 [3] 2019-10-16 04:25:31, OOB Acc: 83.33%, # of SNPs: 15, # of Haplo: 23 [4] 2019-10-16 04:25:32, OOB Acc: 82.14%, # of SNPs: 9, # of Haplo: 18 [5] 2019-10-16 04:25:32, OOB Acc: 88.46%, # of SNPs: 11, # of Haplo: 34 [6] 2019-10-16 04:25:32, OOB Acc: 90.00%, # of SNPs: 11, # of Haplo: 21 [7] 2019-10-16 04:25:32, OOB Acc: 92.31%, # of SNPs: 14, # of Haplo: 23 [8] 2019-10-16 04:25:32, OOB Acc: 96.15%, # of SNPs: 11, # of Haplo: 16 [9] 2019-10-16 04:25:32, OOB Acc: 89.29%, # of SNPs: 12, # of Haplo: 19 [10] 2019-10-16 04:25:32, OOB Acc: 86.36%, # of SNPs: 13, # of Haplo: 18 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007414827 0.0007769916 0.0010965719 0.0048388463 0.0073448966 0.0134756751 Max. Mean SD 0.4553289404 0.0238336045 0.0803512596 Accuracy with training data: 98.4% Out-of-bag accuracy: 90.4% Gene: DQA1 Training dataset: 31 samples X 345 SNPs # of HLA alleles: 7 # of individual classifiers: 10 total # of SNPs used: 85 avg. # of SNPs in an individual classifier: 12.00 (sd: 1.76, min: 9, max: 15, median: 11.50) avg. # of haplotypes in an individual classifier: 21.60 (sd: 4.97, min: 16, max: 34, median: 21.50) avg. out-of-bag accuracy: 90.35% (sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007414827 0.0007769916 0.0010965719 0.0048388463 0.0073448966 0.0134756751 Max. Mean SD 0.4553289404 0.0238336045 0.0803512596 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 345 SNPs, 7 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 29 Predicting (2019-10-16 04:25:32) 0% Predicting (2019-10-16 04:25:32) 100% Gene: DQA1 Range: [32605169bp, 32612152bp] on hg19 # of samples: 29 # of unique HLA alleles: 6 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 5 (17.2%) 5 (17.2%) 3 (10.3%) 16 (55.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000001 0.0010406 0.0047349 0.0418692 0.0128254 0.4624373 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 29 21 49 0.7241379 0.8448276 0 n.call call.rate 1 29 1 Exclude 6 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 350, # of samples: 34 # of unique HLA alleles: 12 [-] 2019-10-16 04:25:32 [1] 2019-10-16 04:25:32, OOB Acc: 86.36%, # of SNPs: 13, # of Haplo: 34 [2] 2019-10-16 04:25:32, OOB Acc: 76.92%, # of SNPs: 14, # of Haplo: 53 [3] 2019-10-16 04:25:32, OOB Acc: 76.67%, # of SNPs: 8, # of Haplo: 17 [4] 2019-10-16 04:25:32, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 22 [5] 2019-10-16 04:25:33, OOB Acc: 90.91%, # of SNPs: 13, # of Haplo: 30 [6] 2019-10-16 04:25:33, OOB Acc: 96.43%, # of SNPs: 16, # of Haplo: 70 [7] 2019-10-16 04:25:33, OOB Acc: 96.15%, # of SNPs: 13, # of Haplo: 22 [8] 2019-10-16 04:25:33, OOB Acc: 88.46%, # of SNPs: 14, # of Haplo: 30 [9] 2019-10-16 04:25:33, OOB Acc: 83.33%, # of SNPs: 18, # of Haplo: 32 [10] 2019-10-16 04:25:33, OOB Acc: 87.50%, # of SNPs: 14, # of Haplo: 27 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0005947372 0.0006431624 0.0010789892 0.0044426248 0.0097359490 0.0166306770 Max. Mean SD 0.1760153593 0.0197506687 0.0349045874 Accuracy with training data: 98.5% Out-of-bag accuracy: 85.8% Gene: DQB1 Training dataset: 34 samples X 350 SNPs # of HLA alleles: 12 # of individual classifiers: 10 total # of SNPs used: 84 avg. # of SNPs in an individual classifier: 13.60 (sd: 2.55, min: 8, max: 18, median: 13.50) avg. # of haplotypes in an individual classifier: 33.70 (sd: 16.05, min: 17, max: 70, median: 30.00) avg. out-of-bag accuracy: 85.77% (sd: 7.76%, min: 75.00%, max: 96.43%, median: 86.93%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0005947372 0.0006431624 0.0010789892 0.0044426248 0.0097359490 0.0166306770 Max. Mean SD 0.1760153593 0.0197506687 0.0349045874 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 350 SNPs, 12 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:33) 0% Predicting (2019-10-16 04:25:33) 100% Gene: DQB1 Range: [32627241bp, 32634466bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 19 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 5 (19.2%) 4 (15.4%) 3 (11.5%) 14 (53.8%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0003853 0.0024404 0.0285739 0.0144724 0.3277489 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 26 19 45 0.7307692 0.8653846 0 n.call call.rate 1 26 1 Exclude 5 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 18 # of SNPs: 322, # of samples: 35 # of unique HLA alleles: 20 [-] 2019-10-16 04:25:33 [1] 2019-10-16 04:25:35, OOB Acc: 66.67%, # of SNPs: 21, # of Haplo: 132 [2] 2019-10-16 04:25:35, OOB Acc: 65.38%, # of SNPs: 12, # of Haplo: 45 [3] 2019-10-16 04:25:35, OOB Acc: 87.50%, # of SNPs: 16, # of Haplo: 84 [4] 2019-10-16 04:25:36, OOB Acc: 59.09%, # of SNPs: 19, # of Haplo: 40 [5] 2019-10-16 04:25:36, OOB Acc: 76.92%, # of SNPs: 18, # of Haplo: 79 [6] 2019-10-16 04:25:37, OOB Acc: 75.00%, # of SNPs: 18, # of Haplo: 65 [7] 2019-10-16 04:25:37, OOB Acc: 63.64%, # of SNPs: 15, # of Haplo: 38 [8] 2019-10-16 04:25:38, OOB Acc: 68.18%, # of SNPs: 19, # of Haplo: 115 [9] 2019-10-16 04:25:38, OOB Acc: 87.50%, # of SNPs: 25, # of Haplo: 120 [10] 2019-10-16 04:25:39, OOB Acc: 75.00%, # of SNPs: 19, # of Haplo: 78 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0003265455 0.0003313808 0.0003748983 0.0013687905 0.0023664730 0.0054255595 Max. Mean SD 0.5194548714 0.0467172966 0.1419418110 Accuracy with training data: 94.3% Out-of-bag accuracy: 72.5% Gene: DRB1 Training dataset: 35 samples X 322 SNPs # of HLA alleles: 20 # of individual classifiers: 10 total # of SNPs used: 126 avg. # of SNPs in an individual classifier: 18.20 (sd: 3.49, min: 12, max: 25, median: 18.50) avg. # of haplotypes in an individual classifier: 79.60 (sd: 33.93, min: 38, max: 132, median: 78.50) avg. out-of-bag accuracy: 72.49% (sd: 9.68%, min: 59.09%, max: 87.50%, median: 71.59%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0003265455 0.0003313808 0.0003748983 0.0013687905 0.0023664730 0.0054255595 Max. Mean SD 0.5194548714 0.0467172966 0.1419418110 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 322 SNPs, 20 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 25 Predicting (2019-10-16 04:25:39) 0% Predicting (2019-10-16 04:25:39) 100% Gene: DRB1 Range: [32546546bp, 32557613bp] on hg19 # of samples: 25 # of unique HLA alleles: 11 # of unique HLA genotypes: 17 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 3 (12.0%) 8 (32.0%) 5 (20.0%) 9 (36.0%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 1.000e-08 4.083e-05 4.910e-04 1.251e-02 2.714e-03 2.748e-01 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 25 15 39 0.6 0.78 0 n.call call.rate 1 25 1 > > > > ############################################################# > > { + function.list <- readRDS( + system.file("Meta", "Rd.rds", package="HIBAG"))$Name + + sapply(function.list, FUN = function(func.name) + { + args <- list( + topic = func.name, + package = "HIBAG", + echo = FALSE, + verbose = FALSE, + ask = FALSE + ) + suppressWarnings(do.call(example, args)) + NULL + }) + invisible() + } SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:39 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 247, Loss: 46.5098, OOB Acc: 77.27%, # of Haplo: 16 7, SNP: 165, Loss: 35.9871, OOB Acc: 77.27%, # of Haplo: 18 8, SNP: 6, Loss: 30.6466, OOB Acc: 77.27%, # of Haplo: 25 9, SNP: 143, Loss: 27.8092, OOB Acc: 77.27%, # of Haplo: 25 10, SNP: 225, Loss: 25.7755, OOB Acc: 77.27%, # of Haplo: 25 11, SNP: 73, Loss: 13.9831, OOB Acc: 77.27%, # of Haplo: 26 12, SNP: 113, Loss: 13.9649, OOB Acc: 77.27%, # of Haplo: 27 13, SNP: 229, Loss: 13.0193, OOB Acc: 77.27%, # of Haplo: 31 14, SNP: 151, Loss: 11.5207, OOB Acc: 77.27%, # of Haplo: 31 [1] 2019-10-16 04:25:39, OOB Acc: 77.27%, # of SNPs: 14, # of Haplo: 31 1, SNP: 87, Loss: 211.966, OOB Acc: 61.54%, # of Haplo: 15 2, SNP: 126, Loss: 158.007, OOB Acc: 69.23%, # of Haplo: 17 3, SNP: 160, Loss: 111.322, OOB Acc: 76.92%, # of Haplo: 22 4, SNP: 212, Loss: 79.9917, OOB Acc: 80.77%, # of Haplo: 23 5, SNP: 178, Loss: 45.9693, OOB Acc: 80.77%, # of Haplo: 35 6, SNP: 215, Loss: 37.1742, OOB Acc: 80.77%, # of Haplo: 44 7, SNP: 217, Loss: 27.4102, OOB Acc: 80.77%, # of Haplo: 45 8, SNP: 7, Loss: 20.5019, OOB Acc: 80.77%, # of Haplo: 63 9, SNP: 251, Loss: 11.3399, OOB Acc: 80.77%, # of Haplo: 80 10, SNP: 17, Loss: 10.9368, OOB Acc: 80.77%, # of Haplo: 80 11, SNP: 154, Loss: 10.8236, OOB Acc: 80.77%, # of Haplo: 81 12, SNP: 186, Loss: 10.7894, OOB Acc: 80.77%, # of Haplo: 82 13, SNP: 151, Loss: 8.4893, OOB Acc: 80.77%, # of Haplo: 82 14, SNP: 73, Loss: 7.72281, OOB Acc: 80.77%, # of Haplo: 83 15, SNP: 199, Loss: 7.29465, OOB Acc: 80.77%, # of Haplo: 83 16, SNP: 13, Loss: 7.29358, OOB Acc: 84.62%, # of Haplo: 84 [2] 2019-10-16 04:25:39, OOB Acc: 84.62%, # of SNPs: 16, # of Haplo: 84 1, SNP: 215, Loss: 205.058, OOB Acc: 52.94%, # of Haplo: 13 2, SNP: 118, Loss: 190.319, OOB Acc: 58.82%, # of Haplo: 15 3, SNP: 208, Loss: 153.803, OOB Acc: 61.76%, # of Haplo: 17 4, SNP: 127, Loss: 132.579, OOB Acc: 67.65%, # of Haplo: 21 5, SNP: 151, Loss: 118.432, OOB Acc: 76.47%, # of Haplo: 22 6, SNP: 86, Loss: 86.6901, OOB Acc: 79.41%, # of Haplo: 24 7, SNP: 198, Loss: 52.9314, OOB Acc: 82.35%, # of Haplo: 24 8, SNP: 106, Loss: 44.3078, OOB Acc: 82.35%, # of Haplo: 32 9, SNP: 137, Loss: 21.1637, OOB Acc: 82.35%, # of Haplo: 32 10, SNP: 189, Loss: 13.3231, OOB Acc: 82.35%, # of Haplo: 33 11, SNP: 51, Loss: 10.5502, OOB Acc: 82.35%, # of Haplo: 33 12, SNP: 23, Loss: 9.7067, OOB Acc: 82.35%, # of Haplo: 39 13, SNP: 256, Loss: 9.24939, OOB Acc: 82.35%, # of Haplo: 65 14, SNP: 199, Loss: 5.54551, OOB Acc: 82.35%, # of Haplo: 65 [3] 2019-10-16 04:25:40, OOB Acc: 82.35%, # of SNPs: 14, # of Haplo: 65 1, SNP: 183, Loss: 171.32, OOB Acc: 38.46%, # of Haplo: 11 2, SNP: 149, Loss: 108.998, OOB Acc: 46.15%, # of Haplo: 13 3, SNP: 132, Loss: 75.176, OOB Acc: 50.00%, # of Haplo: 15 4, SNP: 170, Loss: 58.2249, OOB Acc: 57.69%, # of Haplo: 15 5, SNP: 171, Loss: 36.0762, OOB Acc: 61.54%, # of Haplo: 16 6, SNP: 142, Loss: 24.0609, OOB Acc: 65.38%, # of Haplo: 16 7, SNP: 87, Loss: 18.165, OOB Acc: 65.38%, # of Haplo: 16 8, SNP: 83, Loss: 17.5062, OOB Acc: 65.38%, # of Haplo: 18 9, SNP: 140, Loss: 17.5057, OOB Acc: 69.23%, # of Haplo: 18 10, SNP: 73, Loss: 12.0729, OOB Acc: 69.23%, # of Haplo: 18 11, SNP: 151, Loss: 9.12969, OOB Acc: 69.23%, # of Haplo: 19 12, SNP: 199, Loss: 8.4128, OOB Acc: 69.23%, # of Haplo: 19 [4] 2019-10-16 04:25:40, OOB Acc: 69.23%, # of SNPs: 12, # of Haplo: 19 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.900181e-07 1.970182e-06 1.799165e-05 2.514069e-03 9.121147e-03 1.794765e-02 Max. Mean SD 3.913180e-01 3.547382e-02 9.060872e-02 Accuracy with training data: 92.6% Out-of-bag accuracy: 78.4% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 46 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.63, min: 12, max: 16, median: 14.00) avg. # of haplotypes in an individual classifier: 49.75 (sd: 30.02, min: 19, max: 84, median: 48.00) avg. out-of-bag accuracy: 78.37% (sd: 6.82%, min: 69.23%, max: 84.62%, median: 79.81%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.900181e-07 1.970182e-06 1.799165e-05 2.514069e-03 9.121147e-03 1.794765e-02 Max. Mean SD 3.913180e-01 3.547382e-02 9.060872e-02 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:40) 0% Predicting (2019-10-16 04:25:40) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 13 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 2 (7.7%) 5 (19.2%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0006488 0.0118488 0.0267265 0.0249375 0.3913180 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:41) 0% Predicting (2019-10-16 04:25:41) 100% Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 90 Predicting (2019-10-16 04:25:41) 0% Predicting (2019-10-16 04:25:41) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 using the default genome assembly (assembly="hg19") Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 12 # of unique HLA genotypes: 28 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 100 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 dominant model: [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 additive model: [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p 01:01 95 25 50.5 48.0 0.0000 1.000 1.000 02:01 77 43 48.1 53.5 0.1450 0.703 0.704 02:06 119 1 50.4 0.0 0.0000 1.000 1.000 03:01 111 9 49.5 55.6 0.0000 1.000 1.000 11:01 115 5 50.4 40.0 0.0000 1.000 1.000 23:01 117 3 50.4 33.3 0.0000 1.000 1.000 24:02 109 11 46.8 81.8 3.6030 0.058 0.053 24:03 119 1 50.4 0.0 0.0000 1.000 1.000 25:01 115 5 51.3 20.0 0.8348 0.361 0.364 26:01 117 3 51.3 0.0 1.3675 0.242 0.244 29:02 116 4 50.9 25.0 0.2586 0.611 0.619 31:01 117 3 49.6 66.7 0.0000 1.000 1.000 32:01 116 4 48.3 100.0 2.3276 0.127 0.119 68:01 117 3 51.3 0.0 1.3675 0.242 0.244 recessive model: [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p 01:01 59 1 50.8 0 0.000 1.000 1.000 02:01 52 8 46.2 75 1.298 0.255 0.254 02:06 60 0 50.0 . . . . 03:01 60 0 50.0 . . . . 11:01 60 0 50.0 . . . . 23:01 59 1 50.8 0 0.000 1.000 1.000 24:02 60 0 50.0 . . . . 24:03 60 0 50.0 . . . . 25:01 60 0 50.0 . . . . 26:01 60 0 50.0 . . . . 29:02 60 0 50.0 . . . . 31:01 60 0 50.0 . . . . 32:01 60 0 50.0 . . . . 68:01 60 0 50.0 . . . . genotype model: [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p 24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042* ----- 01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000 02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271 02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000 11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000 23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000 24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353 26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612 31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000 32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112 68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 dominant model: [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p 01:01 36 24 -0.14684 -0.117427 0.909 02:01 25 35 -0.32331 -0.000618 0.190 02:06 59 1 -0.14024 0.170057 . 03:01 51 9 -0.05600 -0.583178 0.147 11:01 55 5 -0.19188 0.489815 0.287 23:01 58 2 -0.15400 0.413687 0.281 24:02 49 11 -0.10486 -0.269664 0.537 24:03 59 1 -0.11409 -1.373118 . 25:01 55 5 -0.12237 -0.274749 0.742 26:01 57 3 -0.12473 -0.331558 0.690 29:02 56 4 -0.13044 -0.199941 0.789 31:01 57 3 -0.10097 -0.783003 0.607 32:01 56 4 -0.07702 -0.947791 0.092 68:01 57 3 -0.16915 0.512457 0.196 genotype model: [-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p 01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784 02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446 02:06 59 1 0 -0.14024 0.17006 . 0.756 03:01 51 9 0 -0.05600 -0.58318 . 0.138 11:01 55 5 0 -0.19188 0.48981 . 0.137 23:01 58 1 1 -0.15400 0.10762 0.71975 0.663 24:02 49 11 0 -0.10486 -0.26966 . 0.618 24:03 59 1 0 -0.11409 -1.37312 . 0.205 25:01 55 5 0 -0.12237 -0.27475 . 0.742 26:01 57 3 0 -0.12473 -0.33156 . 0.725 29:02 56 4 0 -0.13044 -0.19994 . 0.892 31:01 57 3 0 -0.10097 -0.78300 . 0.243 32:01 56 4 0 -0.07702 -0.94779 . 0.086 68:01 57 3 0 -0.16915 0.51246 . 0.243 Logistic regression (dominant model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 4.657e-17 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.686e-16 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 h.2.5% h.97.5% h.pval 24:02 0.1585 3.4251 0.032* ----- 01:01 -1.0330 1.0330 1.000 02:01 -1.1643 0.8899 0.793 02:06 -2868.1268 2836.9268 0.991 03:01 -1.1624 1.6872 0.718 11:01 -2.3074 1.4237 0.643 23:01 -2.8192 2.8192 1.000 24:03 -2868.1268 2836.9268 0.991 25:01 -3.7498 0.7588 0.194 26:01 -2731.9621 2698.6192 0.990 29:02 -3.4931 1.1530 0.324 31:01 -1.7277 3.1842 0.561 32:01 -3859.2763 3894.6947 0.993 68:01 -2731.9621 2698.6192 0.990 Logistic regression (dominant model) with 60 individuals: glm(case ~ h + pc1, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01 h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968 ----- 01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982 02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992 02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912 03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964 11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975 23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982 24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965 25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923 26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957 29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899 31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974 32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650 68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764 Logistic regression (dominant model) with 60 individuals: glm(case ~ h + pc1, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08 h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968 ----- 01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982 02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992 02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912 03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964 11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975 23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982 24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965 25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923 26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957 29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899 31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974 32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650 68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764 Linear regression (dominant model) with 60 individuals: glm(y ~ h, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393 02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226 02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603 03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592 11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685 23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518 24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795 24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655 25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507 26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383 29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319 31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508 32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054 68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145 h.pval 01:01 0.910 02:01 0.211 02:06 0.756 03:01 0.138 11:01 0.137 23:01 0.425 24:02 0.618 24:03 0.205 25:01 0.742 26:01 0.725 29:02 0.892 31:01 0.243 32:01 0.086 68:01 0.243 Linear regression (dominant model) with 60 individuals: glm(y ~ h + pc1, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773 02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891 02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722 03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884 11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520 23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598 24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034 24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708 25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388 26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460 29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496 31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254 32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862 68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749 h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386 02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414 02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371 03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539 11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474 23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343 24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393 24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395 25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396 26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390 29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372 31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371 32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210 68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294 Linear regression (dominant model) with 60 individuals: glm(y ~ h + pc1, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773 02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891 02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722 03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884 11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520 23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598 24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034 24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708 25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388 26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460 29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496 31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254 32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862 68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749 h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386 02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414 02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371 03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539 11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474 23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343 24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393 24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395 25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396 26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390 29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372 31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371 32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210 68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294 Logistic regression (additive model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5% 24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585 ----- 01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843 02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289 02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268 03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624 11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074 23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435 24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268 25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498 26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621 29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931 31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277 32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763 68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621 h.97.5% h.pval 24:02 3.4251 0.032* ----- 01:01 0.8430 0.806 02:01 0.9563 0.573 02:06 2836.9268 0.991 03:01 1.6872 0.718 11:01 1.4237 0.643 23:01 1.4789 0.658 24:03 2836.9268 0.991 25:01 0.7588 0.194 26:01 2698.6192 0.990 29:02 1.1530 0.324 31:01 3.1842 0.561 32:01 3894.6947 0.993 68:01 2698.6192 0.990 Logistic regression (recessive model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est 01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600 02:01 52 8 46.2 75 1.298 0.255 0.254 1.253 02:06 60 0 50.0 . . . . . 03:01 60 0 50.0 . . . . . 11:01 60 0 50.0 . . . . . 23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600 24:02 60 0 50.0 . . . . . 24:03 60 0 50.0 . . . . . 25:01 60 0 50.0 . . . . . 26:01 60 0 50.0 . . . . . 29:02 60 0 50.0 . . . . . 31:01 60 0 50.0 . . . . . 32:01 60 0 50.0 . . . . . 68:01 60 0 50.0 . . . . . h.2.5% h.97.5% h.pval 01:01 -2868.1268 2836.927 0.991 02:01 -0.4379 2.943 0.146 02:06 . . . 03:01 . . . 11:01 . . . 23:01 -2868.1268 2836.927 0.991 24:02 . . . 24:03 . . . 25:01 . . . 26:01 . . . 29:02 . . . 31:01 . . . 32:01 . . . 68:01 . . . Logistic regression (genotype model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p 24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042* ----- 01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000 02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271 02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000 11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000 23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000 24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353 26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612 31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000 32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112 68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5% 24:02 1.79176 0.1585 3.4251 0.032* . . . ----- 01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961 02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801 02:06 -15.59997 -2868.1268 2836.9268 0.991 . . . 03:01 0.26236 -1.1624 1.6872 0.718 . . . 11:01 -0.44183 -2.3074 1.4237 0.643 . . . 23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455 24:03 -15.59997 -2868.1268 2836.9268 0.991 . . . 25:01 -1.49549 -3.7498 0.7588 0.194 . . . 26:01 -16.67143 -2731.9621 2698.6192 0.990 . . . 29:02 -1.17007 -3.4931 1.1530 0.324 . . . 31:01 0.72824 -1.7277 3.1842 0.561 . . . 32:01 17.70917 -3859.2763 3894.6947 0.993 . . . 68:01 -16.67143 -2731.9621 2698.6192 0.990 . . . h2.pval 24:02 . ----- 01:01 0.991 02:01 0.263 02:06 . 03:01 . 11:01 . 23:01 0.994 24:03 . 25:01 . 26:01 . 29:02 . 31:01 . 32:01 . 68:01 . Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:43 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 247, Loss: 46.5098, OOB Acc: 77.27%, # of Haplo: 16 7, SNP: 165, Loss: 35.9871, OOB Acc: 77.27%, # of Haplo: 18 8, SNP: 6, Loss: 30.6466, OOB Acc: 77.27%, # of Haplo: 25 9, SNP: 143, Loss: 27.8092, OOB Acc: 77.27%, # of Haplo: 25 10, SNP: 225, Loss: 25.7755, OOB Acc: 77.27%, # of Haplo: 25 11, SNP: 73, Loss: 13.9831, OOB Acc: 77.27%, # of Haplo: 26 12, SNP: 113, Loss: 13.9649, OOB Acc: 77.27%, # of Haplo: 27 13, SNP: 229, Loss: 13.0193, OOB Acc: 77.27%, # of Haplo: 31 14, SNP: 151, Loss: 11.5207, OOB Acc: 77.27%, # of Haplo: 31 [1] 2019-10-16 04:25:43, OOB Acc: 77.27%, # of SNPs: 14, # of Haplo: 31 1, SNP: 87, Loss: 211.966, OOB Acc: 61.54%, # of Haplo: 15 2, SNP: 126, Loss: 158.007, OOB Acc: 69.23%, # of Haplo: 17 3, SNP: 160, Loss: 111.322, OOB Acc: 76.92%, # of Haplo: 22 4, SNP: 212, Loss: 79.9917, OOB Acc: 80.77%, # of Haplo: 23 5, SNP: 178, Loss: 45.9693, OOB Acc: 80.77%, # of Haplo: 35 6, SNP: 215, Loss: 37.1742, OOB Acc: 80.77%, # of Haplo: 44 7, SNP: 217, Loss: 27.4102, OOB Acc: 80.77%, # of Haplo: 45 8, SNP: 7, Loss: 20.5019, OOB Acc: 80.77%, # of Haplo: 63 9, SNP: 251, Loss: 11.3399, OOB Acc: 80.77%, # of Haplo: 80 10, SNP: 17, Loss: 10.9368, OOB Acc: 80.77%, # of Haplo: 80 11, SNP: 154, Loss: 10.8236, OOB Acc: 80.77%, # of Haplo: 81 12, SNP: 186, Loss: 10.7894, OOB Acc: 80.77%, # of Haplo: 82 13, SNP: 151, Loss: 8.4893, OOB Acc: 80.77%, # of Haplo: 82 14, SNP: 73, Loss: 7.72281, OOB Acc: 80.77%, # of Haplo: 83 15, SNP: 199, Loss: 7.29465, OOB Acc: 80.77%, # of Haplo: 83 16, SNP: 13, Loss: 7.29358, OOB Acc: 84.62%, # of Haplo: 84 [2] 2019-10-16 04:25:44, OOB Acc: 84.62%, # of SNPs: 16, # of Haplo: 84 1, SNP: 215, Loss: 205.058, OOB Acc: 52.94%, # of Haplo: 13 2, SNP: 118, Loss: 190.319, OOB Acc: 58.82%, # of Haplo: 15 3, SNP: 208, Loss: 153.803, OOB Acc: 61.76%, # of Haplo: 17 4, SNP: 127, Loss: 132.579, OOB Acc: 67.65%, # of Haplo: 21 5, SNP: 151, Loss: 118.432, OOB Acc: 76.47%, # of Haplo: 22 6, SNP: 86, Loss: 86.6901, OOB Acc: 79.41%, # of Haplo: 24 7, SNP: 198, Loss: 52.9314, OOB Acc: 82.35%, # of Haplo: 24 8, SNP: 106, Loss: 44.3078, OOB Acc: 82.35%, # of Haplo: 32 9, SNP: 137, Loss: 21.1637, OOB Acc: 82.35%, # of Haplo: 32 10, SNP: 189, Loss: 13.3231, OOB Acc: 82.35%, # of Haplo: 33 11, SNP: 51, Loss: 10.5502, OOB Acc: 82.35%, # of Haplo: 33 12, SNP: 23, Loss: 9.7067, OOB Acc: 82.35%, # of Haplo: 39 13, SNP: 256, Loss: 9.24939, OOB Acc: 82.35%, # of Haplo: 65 14, SNP: 199, Loss: 5.54551, OOB Acc: 82.35%, # of Haplo: 65 [3] 2019-10-16 04:25:44, OOB Acc: 82.35%, # of SNPs: 14, # of Haplo: 65 1, SNP: 183, Loss: 171.32, OOB Acc: 38.46%, # of Haplo: 11 2, SNP: 149, Loss: 108.998, OOB Acc: 46.15%, # of Haplo: 13 3, SNP: 132, Loss: 75.176, OOB Acc: 50.00%, # of Haplo: 15 4, SNP: 170, Loss: 58.2249, OOB Acc: 57.69%, # of Haplo: 15 5, SNP: 171, Loss: 36.0762, OOB Acc: 61.54%, # of Haplo: 16 6, SNP: 142, Loss: 24.0609, OOB Acc: 65.38%, # of Haplo: 16 7, SNP: 87, Loss: 18.165, OOB Acc: 65.38%, # of Haplo: 16 8, SNP: 83, Loss: 17.5062, OOB Acc: 65.38%, # of Haplo: 18 9, SNP: 140, Loss: 17.5057, OOB Acc: 69.23%, # of Haplo: 18 10, SNP: 73, Loss: 12.0729, OOB Acc: 69.23%, # of Haplo: 18 11, SNP: 151, Loss: 9.12969, OOB Acc: 69.23%, # of Haplo: 19 12, SNP: 199, Loss: 8.4128, OOB Acc: 69.23%, # of Haplo: 19 [4] 2019-10-16 04:25:44, OOB Acc: 69.23%, # of SNPs: 12, # of Haplo: 19 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.900181e-07 1.970182e-06 1.799165e-05 2.514069e-03 9.121147e-03 1.794765e-02 Max. Mean SD 3.913180e-01 3.547382e-02 9.060872e-02 Accuracy with training data: 92.6% Out-of-bag accuracy: 78.4% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 46 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.63, min: 12, max: 16, median: 14.00) avg. # of haplotypes in an individual classifier: 49.75 (sd: 30.02, min: 19, max: 84, median: 48.00) avg. out-of-bag accuracy: 78.37% (sd: 6.82%, min: 69.23%, max: 84.62%, median: 79.81%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.900181e-07 1.970182e-06 1.799165e-05 2.514069e-03 9.121147e-03 1.794765e-02 Max. Mean SD 3.913180e-01 3.547382e-02 9.060872e-02 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:44) 0% Predicting (2019-10-16 04:25:44) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 13 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 2 (7.7%) 5 (19.2%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0006488 0.0118488 0.0267265 0.0249375 0.3913180 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:44) 0% Predicting (2019-10-16 04:25:44) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 13 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 2 (7.7%) 5 (19.2%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0006488 0.0118488 0.0267265 0.0249375 0.3913180 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. SNP genotypes: 90 samples X 3932 SNPs SNPs range from 28694391bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489 Missing rate per sample: min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554 Minor allele frequency: min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144 Allelic information: A/G C/T G/T A/C C/G A/T 1567 1510 348 332 111 64 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 5316 SNPs from chromosome 6. SNP genotypes: 90 samples X 5316 SNPs SNPs range from 25651262bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674 Missing rate per sample: min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735 Minor allele frequency: min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475 Allelic information: A/G C/T G/T A/C C/G A/T 2102 2046 480 471 134 83 Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 9 # of SNPs: 77, # of samples: 60 # of unique HLA alleles: 12 [-] 2019-10-16 04:25:44 [1] 2019-10-16 04:25:44, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20 [2] 2019-10-16 04:25:44, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Accuracy with training data: 95.0% Out-of-bag accuracy: 94.5% Gene: DQB1 Training dataset: 60 samples X 77 SNPs # of HLA alleles: 12 # of individual classifiers: 2 total # of SNPs used: 20 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.41, min: 13, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 20.50 (sd: 0.71, min: 20, max: 21, median: 20.50) avg. out-of-bag accuracy: 94.45% (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Genome assembly: hg19 The HIBAG model: There are 77 SNP predictors in total. There are 2 individual classifiers. Summarize the missing fractions of SNP predictors per classifier: Min. 1st Qu. Median Mean 3rd Qu. Max. 0 0 0 0 0 0 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 60 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 100 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 100 # of unique HLA alleles: 0 # of unique HLA genotypes: 0 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 200 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Exclude 9 monomorphic SNPs Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:44 [1] 2019-10-16 04:25:44, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.898504e-09 2.973325e-09 3.646720e-09 1.495845e-03 1.516334e-02 6.687055e-02 Max. Mean SD 9.834029e-01 9.608437e-02 2.420052e-01 Accuracy with training data: 92.5% Out-of-bag accuracy: 87.0% Exclude 9 monomorphic SNPs Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:44 [1] 2019-10-16 04:25:45, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 6.946528e-15 6.575829e-14 5.950641e-13 1.660680e-03 6.378961e-03 1.518505e-02 Max. Mean SD 6.400032e-01 4.484927e-02 1.420419e-01 Accuracy with training data: 92.5% Out-of-bag accuracy: 82.6% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 23 avg. # of SNPs in an individual classifier: 12.50 (sd: 2.12, min: 11, max: 14, median: 12.50) avg. # of haplotypes in an individual classifier: 51.00 (sd: 4.24, min: 48, max: 54, median: 51.00) avg. out-of-bag accuracy: 84.78% (sd: 3.07%, min: 82.61%, max: 86.96%, median: 84.78%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.006377e-08 1.077445e-08 1.717056e-08 3.680759e-03 1.272457e-02 4.469180e-02 Max. Mean SD 8.117031e-01 7.046682e-02 1.896453e-01 Genome assembly: hg19 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:45 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 247, Loss: 46.5098, OOB Acc: 77.27%, # of Haplo: 16 7, SNP: 165, Loss: 35.9871, OOB Acc: 77.27%, # of Haplo: 18 8, SNP: 6, Loss: 30.6466, OOB Acc: 77.27%, # of Haplo: 25 9, SNP: 143, Loss: 27.8092, OOB Acc: 77.27%, # of Haplo: 25 10, SNP: 225, Loss: 25.7755, OOB Acc: 77.27%, # of Haplo: 25 11, SNP: 73, Loss: 13.9831, OOB Acc: 77.27%, # of Haplo: 26 12, SNP: 113, Loss: 13.9649, OOB Acc: 77.27%, # of Haplo: 27 13, SNP: 229, Loss: 13.0193, OOB Acc: 77.27%, # of Haplo: 31 14, SNP: 151, Loss: 11.5207, OOB Acc: 77.27%, # of Haplo: 31 [1] 2019-10-16 04:25:45, OOB Acc: 77.27%, # of SNPs: 14, # of Haplo: 31 1, SNP: 87, Loss: 211.966, OOB Acc: 61.54%, # of Haplo: 15 2, SNP: 126, Loss: 158.007, OOB Acc: 69.23%, # of Haplo: 17 3, SNP: 160, Loss: 111.322, OOB Acc: 76.92%, # of Haplo: 22 4, SNP: 212, Loss: 79.9917, OOB Acc: 80.77%, # of Haplo: 23 5, SNP: 178, Loss: 45.9693, OOB Acc: 80.77%, # of Haplo: 35 6, SNP: 215, Loss: 37.1742, OOB Acc: 80.77%, # of Haplo: 44 7, SNP: 217, Loss: 27.4102, OOB Acc: 80.77%, # of Haplo: 45 8, SNP: 7, Loss: 20.5019, OOB Acc: 80.77%, # of Haplo: 63 9, SNP: 251, Loss: 11.3399, OOB Acc: 80.77%, # of Haplo: 80 10, SNP: 17, Loss: 10.9368, OOB Acc: 80.77%, # of Haplo: 80 11, SNP: 154, Loss: 10.8236, OOB Acc: 80.77%, # of Haplo: 81 12, SNP: 186, Loss: 10.7894, OOB Acc: 80.77%, # of Haplo: 82 13, SNP: 151, Loss: 8.4893, OOB Acc: 80.77%, # of Haplo: 82 14, SNP: 73, Loss: 7.72281, OOB Acc: 80.77%, # of Haplo: 83 15, SNP: 199, Loss: 7.29465, OOB Acc: 80.77%, # of Haplo: 83 16, SNP: 13, Loss: 7.29358, OOB Acc: 84.62%, # of Haplo: 84 [2] 2019-10-16 04:25:45, OOB Acc: 84.62%, # of SNPs: 16, # of Haplo: 84 1, SNP: 215, Loss: 205.058, OOB Acc: 52.94%, # of Haplo: 13 2, SNP: 118, Loss: 190.319, OOB Acc: 58.82%, # of Haplo: 15 3, SNP: 208, Loss: 153.803, OOB Acc: 61.76%, # of Haplo: 17 4, SNP: 127, Loss: 132.579, OOB Acc: 67.65%, # of Haplo: 21 5, SNP: 151, Loss: 118.432, OOB Acc: 76.47%, # of Haplo: 22 6, SNP: 86, Loss: 86.6901, OOB Acc: 79.41%, # of Haplo: 24 7, SNP: 198, Loss: 52.9314, OOB Acc: 82.35%, # of Haplo: 24 8, SNP: 106, Loss: 44.3078, OOB Acc: 82.35%, # of Haplo: 32 9, SNP: 137, Loss: 21.1637, OOB Acc: 82.35%, # of Haplo: 32 10, SNP: 189, Loss: 13.3231, OOB Acc: 82.35%, # of Haplo: 33 11, SNP: 51, Loss: 10.5502, OOB Acc: 82.35%, # of Haplo: 33 12, SNP: 23, Loss: 9.7067, OOB Acc: 82.35%, # of Haplo: 39 13, SNP: 256, Loss: 9.24939, OOB Acc: 82.35%, # of Haplo: 65 14, SNP: 199, Loss: 5.54551, OOB Acc: 82.35%, # of Haplo: 65 [3] 2019-10-16 04:25:45, OOB Acc: 82.35%, # of SNPs: 14, # of Haplo: 65 1, SNP: 183, Loss: 171.32, OOB Acc: 38.46%, # of Haplo: 11 2, SNP: 149, Loss: 108.998, OOB Acc: 46.15%, # of Haplo: 13 3, SNP: 132, Loss: 75.176, OOB Acc: 50.00%, # of Haplo: 15 4, SNP: 170, Loss: 58.2249, OOB Acc: 57.69%, # of Haplo: 15 5, SNP: 171, Loss: 36.0762, OOB Acc: 61.54%, # of Haplo: 16 6, SNP: 142, Loss: 24.0609, OOB Acc: 65.38%, # of Haplo: 16 7, SNP: 87, Loss: 18.165, OOB Acc: 65.38%, # of Haplo: 16 8, SNP: 83, Loss: 17.5062, OOB Acc: 65.38%, # of Haplo: 18 9, SNP: 140, Loss: 17.5057, OOB Acc: 69.23%, # of Haplo: 18 10, SNP: 73, Loss: 12.0729, OOB Acc: 69.23%, # of Haplo: 18 11, SNP: 151, Loss: 9.12969, OOB Acc: 69.23%, # of Haplo: 19 12, SNP: 199, Loss: 8.4128, OOB Acc: 69.23%, # of Haplo: 19 [4] 2019-10-16 04:25:45, OOB Acc: 69.23%, # of SNPs: 12, # of Haplo: 19 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.900181e-07 1.970182e-06 1.799165e-05 2.514069e-03 9.121147e-03 1.794765e-02 Max. Mean SD 3.913180e-01 3.547382e-02 9.060872e-02 Accuracy with training data: 92.6% Out-of-bag accuracy: 78.4% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 46 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.63, min: 12, max: 16, median: 14.00) avg. # of haplotypes in an individual classifier: 49.75 (sd: 30.02, min: 19, max: 84, median: 48.00) avg. out-of-bag accuracy: 78.37% (sd: 6.82%, min: 69.23%, max: 84.62%, median: 79.81%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.900181e-07 1.970182e-06 1.799165e-05 2.514069e-03 9.121147e-03 1.794765e-02 Max. Mean SD 3.913180e-01 3.547382e-02 9.060872e-02 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:45) 0% Predicting (2019-10-16 04:25:45) 100% Allelic ambiguity: 01:01, 02:02 Allelic ambiguity: 01:01, 02:02 Allelic ambiguity: 09:01 Allelic ambiguity: 09:01 Allelic ambiguity: 05:01, 06:01 Allelic ambiguity: 05:01, 06:01 Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01 Pos Num * - A D E F G H I K L M N Q R S T V W Y 1 120 120 . . . . . . . . . . . . . . . . . . . 9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17 44 120 . 25 . . . . . . . . . . . . 95 . . . . . 56 120 . 117 . . . . . . . . . . . . 3 . . . . . 62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . . 63 120 . 105 . . . . . . . . . . 11 4 . . . . . . 65 120 . 105 . . . . 15 . . . . . . . . . . . . . 66 120 . 61 . . . . . . . 59 . . . . . . . . . . 67 120 . 25 . . . . . . . . . . . . . . . 95 . . 70 120 . 99 . . . . . . . . . . . 21 . . . . . . 73 120 . 117 . . . . . . 3 . . . . . . . . . . . 74 120 . 76 . . . . . 44 . . . . . . . . . . . . 76 120 . 32 . . 24 . . . . . . . . . . . . 64 . . 77 120 . 47 . 64 . . . . . . . . . . . 9 . . . . 79 120 . 96 . . . . . . . . . . . . 24 . . . . . 80 120 . 96 . . . . . . 24 . . . . . . . . . . . 81 120 . 96 24 . . . . . . . . . . . . . . . . . 82 120 . 96 . . . . . . . . 24 . . . . . . . . . 83 120 . 96 . . . . . . . . . . . . 24 . . . . . 90 120 . 38 82 . . . . . . . . . . . . . . . . . 95 120 . 61 . . . . . . . . 15 . . . . . . 44 . . 97 120 . 39 . . . . . . . . . 29 . . 52 . . . . . 99 120 . 105 . . . 15 . . . . . . . . . . . . . . 105 120 . 42 . . . . . . . . . . . . . 78 . . . . 107 120 . 76 . . . . . . . . . . . . . . . . 44 . 109 120 . 116 . . . . . . . . 4 . . . . . . . . . 114 120 . 46 . . . . . 59 . . . . . 15 . . . . . . 116 120 . 61 . . . . . . . . . . . . . . . . . 59 127 120 . 58 . . . . . . . 62 . . . . . . . . . . 142 120 . 73 . . . . . . . . . . . . . . 47 . . . 144 120 . 98 . . . . . . . . . . . 22 . . . . . . 145 120 . 73 . . . . . 47 . . . . . . . . . . . . 149 120 . 112 . . . . . . . . . . . . . . 8 . . . 150 120 . 25 95 . . . . . . . . . . . . . . . . . 151 120 . 106 . . . . . . . . . . . . 14 . . . . . 152 120 . 30 . . 17 . . . . . . . . . . . . 73 . . 156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 . 158 120 . 25 95 . . . . . . . . . . . . . . . . . 161 120 . 111 . 9 . . . . . . . . . . . . . . . . 163 120 . 38 . . . . . . . . . . . . . . 82 . . . 166 120 . 39 . . 81 . . . . . . . . . . . . . . . 167 120 . 39 . . . . . . . . . . . . . . . . 81 . 183 120 120 . . . . . . . . . . . . . . . . . . . Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01 Pos Num * - A D E F G H I K L M N Q R S T V W Y -23 120 120 . . . . . . . . . . . . . . . . . . . -22 120 120 . . . . . . . . . . . . . . . . . . . -21 120 120 . . . . . . . . . . . . . . . . . . . -20 120 120 . . . . . . . . . . . . . . . . . . . -19 120 120 . . . . . . . . . . . . . . . . . . . -18 120 120 . . . . . . . . . . . . . . . . . . . -17 120 120 . . . . . . . . . . . . . . . . . . . -16 120 120 . . . . . . . . . . . . . . . . . . . -15 120 120 . . . . . . . . . . . . . . . . . . . -14 120 120 . . . . . . . . . . . . . . . . . . . -13 120 120 . . . . . . . . . . . . . . . . . . . -12 120 120 . . . . . . . . . . . . . . . . . . . -11 120 120 . . . . . . . . . . . . . . . . . . . -10 120 120 . . . . . . . . . . . . . . . . . . . -9 120 120 . . . . . . . . . . . . . . . . . . . -8 120 120 . . . . . . . . . . . . . . . . . . . -7 120 120 . . . . . . . . . . . . . . . . . . . -6 120 120 . . . . . . . . . . . . . . . . . . . -5 120 120 . . . . . . . . . . . . . . . . . . . -4 120 120 . . . . . . . . . . . . . . . . . . . -3 120 120 . . . . . . . . . . . . . . . . . . . -2 120 120 . . . . . . . . . . . . . . . . . . . -1 120 120 . . . . . . . . . . . . . . . . . . . . 120 120 . . . . . . . . . . . . . . . . . . . 1 120 120 . . . . . . . . . . . . . . . . . . . 9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17 44 120 . 25 . . . . . . . . . . . . 95 . . . . . 56 120 . 117 . . . . . . . . . . . . 3 . . . . . 62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . . 63 120 . 105 . . . . . . . . . . 11 4 . . . . . . 65 120 . 105 . . . . 15 . . . . . . . . . . . . . 66 120 . 61 . . . . . . . 59 . . . . . . . . . . 67 120 . 25 . . . . . . . . . . . . . . . 95 . . 70 120 . 99 . . . . . . . . . . . 21 . . . . . . 73 120 . 117 . . . . . . 3 . . . . . . . . . . . 74 120 . 76 . . . . . 44 . . . . . . . . . . . . 76 120 . 32 . . 24 . . . . . . . . . . . . 64 . . 77 120 . 47 . 64 . . . . . . . . . . . 9 . . . . 79 120 . 96 . . . . . . . . . . . . 24 . . . . . 80 120 . 96 . . . . . . 24 . . . . . . . . . . . 81 120 . 96 24 . . . . . . . . . . . . . . . . . 82 120 . 96 . . . . . . . . 24 . . . . . . . . . 83 120 . 96 . . . . . . . . . . . . 24 . . . . . 90 120 . 38 82 . . . . . . . . . . . . . . . . . 95 120 . 61 . . . . . . . . 15 . . . . . . 44 . . 97 120 . 39 . . . . . . . . . 29 . . 52 . . . . . 99 120 . 105 . . . 15 . . . . . . . . . . . . . . 105 120 . 42 . . . . . . . . . . . . . 78 . . . . 107 120 . 76 . . . . . . . . . . . . . . . . 44 . 109 120 . 116 . . . . . . . . 4 . . . . . . . . . 114 120 . 46 . . . . . 59 . . . . . 15 . . . . . . 116 120 . 61 . . . . . . . . . . . . . . . . . 59 127 120 . 58 . . . . . . . 62 . . . . . . . . . . 142 120 . 73 . . . . . . . . . . . . . . 47 . . . 144 120 . 98 . . . . . . . . . . . 22 . . . . . . 145 120 . 73 . . . . . 47 . . . . . . . . . . . . 149 120 . 112 . . . . . . . . . . . . . . 8 . . . 150 120 . 25 95 . . . . . . . . . . . . . . . . . 151 120 . 106 . . . . . . . . . . . . 14 . . . . . 152 120 . 30 . . 17 . . . . . . . . . . . . 73 . . 156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 . 158 120 . 25 95 . . . . . . . . . . . . . . . . . 161 120 . 111 . 9 . . . . . . . . . . . . . . . . 163 120 . 38 . . . . . . . . . . . . . . 82 . . . 166 120 . 39 . . 81 . . . . . . . . . . . . . . . 167 120 . 39 . . . . . . . . . . . . . . . . 81 . 183 120 120 . . . . . . . . . . . . . . . . . . . 184 120 120 . . . . . . . . . . . . . . . . . . . 185 120 120 . . . . . . . . . . . . . . . . . . . 186 120 120 . . . . . . . . . . . . . . . . . . . 187 120 120 . . . . . . . . . . . . . . . . . . . 188 120 120 . . . . . . . . . . . . . . . . . . . 189 120 120 . . . . . . . . . . . . . . . . . . . 190 120 120 . . . . . . . . . . . . . . . . . . . 191 120 120 . . . . . . . . . . . . . . . . . . . 192 120 120 . . . . . . . . . . . . . . . . . . . 193 120 120 . . . . . . . . . . . . . . . . . . . 194 120 120 . . . . . . . . . . . . . . . . . . . 195 120 120 . . . . . . . . . . . . . . . . . . . 196 120 120 . . . . . . . . . . . . . . . . . . . 197 120 120 . . . . . . . . . . . . . . . . . . . 198 120 120 . . . . . . . . . . . . . . . . . . . 199 120 120 . . . . . . . . . . . . . . . . . . . 200 120 120 . . . . . . . . . . . . . . . . . . . 201 120 120 . . . . . . . . . . . . . . . . . . . 202 120 120 . . . . . . . . . . . . . . . . . . . 203 120 120 . . . . . . . . . . . . . . . . . . . 204 120 120 . . . . . . . . . . . . . . . . . . . 205 120 120 . . . . . . . . . . . . . . . . . . . 206 120 120 . . . . . . . . . . . . . . . . . . . 207 120 120 . . . . . . . . . . . . . . . . . . . 208 120 120 . . . . . . . . . . . . . . . . . . . 209 120 120 . . . . . . . . . . . . . . . . . . . 210 120 120 . . . . . . . . . . . . . . . . . . . 211 120 120 . . . . . . . . . . . . . . . . . . . 212 120 120 . . . . . . . . . . . . . . . . . . . 213 120 120 . . . . . . . . . . . . . . . . . . . 214 120 120 . . . . . . . . . . . . . . . . . . . 215 120 120 . . . . . . . . . . . . . . . . . . . 216 120 120 . . . . . . . . . . . . . . . . . . . 217 120 120 . . . . . . . . . . . . . . . . . . . 218 120 120 . . . . . . . . . . . . . . . . . . . 219 120 120 . . . . . . . . . . . . . . . . . . . 220 120 120 . . . . . . . . . . . . . . . . . . . 221 120 120 . . . . . . . . . . . . . . . . . . . 222 120 120 . . . . . . . . . . . . . . . . . . . 223 120 120 . . . . . . . . . . . . . . . . . . . 224 120 120 . . . . . . . . . . . . . . . . . . . 225 120 120 . . . . . . . . . . . . . . . . . . . 226 120 120 . . . . . . . . . . . . . . . . . . . 227 120 120 . . . . . . . . . . . . . . . . . . . 228 120 120 . . . . . . . . . . . . . . . . . . . 229 120 120 . . . . . . . . . . . . . . . . . . . 230 120 120 . . . . . . . . . . . . . . . . . . . 231 120 120 . . . . . . . . . . . . . . . . . . . 232 120 120 . . . . . . . . . . . . . . . . . . . 233 120 120 . . . . . . . . . . . . . . . . . . . 234 120 120 . . . . . . . . . . . . . . . . . . . 235 120 120 . . . . . . . . . . . . . . . . . . . 236 120 120 . . . . . . . . . . . . . . . . . . . 237 120 120 . . . . . . . . . . . . . . . . . . . 238 120 120 . . . . . . . . . . . . . . . . . . . 239 120 120 . . . . . . . . . . . . . . . . . . . 240 120 120 . . . . . . . . . . . . . . . . . . . 241 120 120 . . . . . . . . . . . . . . . . . . . 242 120 120 . . . . . . . . . . . . . . . . . . . 243 120 120 . . . . . . . . . . . . . . . . . . . 244 120 120 . . . . . . . . . . . . . . . . . . . 245 120 120 . . . . . . . . . . . . . . . . . . . 246 120 120 . . . . . . . . . . . . . . . . . . . 247 120 120 . . . . . . . . . . . . . . . . . . . 248 120 120 . . . . . . . . . . . . . . . . . . . 249 120 120 . . . . . . . . . . . . . . . . . . . 250 120 120 . . . . . . . . . . . . . . . . . . . 251 120 120 . . . . . . . . . . . . . . . . . . . 252 120 120 . . . . . . . . . . . . . . . . . . . 253 120 120 . . . . . . . . . . . . . . . . . . . 254 120 120 . . . . . . . . . . . . . . . . . . . 255 120 120 . . . . . . . . . . . . . . . . . . . 256 120 120 . . . . . . . . . . . . . . . . . . . 257 120 120 . . . . . . . . . . . . . . . . . . . 258 120 120 . . . . . . . . . . . . . . . . . . . 259 120 120 . . . . . . . . . . . . . . . . . . . 260 120 120 . . . . . . . . . . . . . . . . . . . 261 120 120 . . . . . . . . . . . . . . . . . . . 262 120 120 . . . . . . . . . . . . . . . . . . . 263 120 120 . . . . . . . . . . . . . . . . . . . 264 120 120 . . . . . . . . . . . . . . . . . . . 265 120 120 . . . . . . . . . . . . . . . . . . . 266 120 120 . . . . . . . . . . . . . . . . . . . 267 120 120 . . . . . . . . . . . . . . . . . . . 268 120 120 . . . . . . . . . . . . . . . . . . . 269 120 120 . . . . . . . . . . . . . . . . . . . 270 120 120 . . . . . . . . . . . . . . . . . . . 271 120 120 . . . . . . . . . . . . . . . . . . . 272 120 120 . . . . . . . . . . . . . . . . . . . 273 120 120 . . . . . . . . . . . . . . . . . . . 274 120 120 . . . . . . . . . . . . . . . . . . . 275 120 120 . . . . . . . . . . . . . . . . . . . 276 120 120 . . . . . . . . . . . . . . . . . . . 277 120 120 . . . . . . . . . . . . . . . . . . . 278 120 120 . . . . . . . . . . . . . . . . . . . 279 120 120 . . . . . . . . . . . . . . . . . . . 280 120 120 . . . . . . . . . . . . . . . . . . . 281 120 120 . . . . . . . . . . . . . . . . . . . 282 120 120 . . . . . . . . . . . . . . . . . . . 283 120 120 . . . . . . . . . . . . . . . . . . . 284 120 120 . . . . . . . . . . . . . . . . . . . 285 120 120 . . . . . . . . . . . . . . . . . . . 286 120 120 . . . . . . . . . . . . . . . . . . . 287 120 120 . . . . . . . . . . . . . . . . . . . 288 120 120 . . . . . . . . . . . . . . . . . . . 289 120 120 . . . . . . . . . . . . . . . . . . . 290 120 120 . . . . . . . . . . . . . . . . . . . 291 120 120 . . . . . . . . . . . . . . . . . . . 292 120 120 . . . . . . . . . . . . . . . . . . . 293 120 120 . . . . . . . . . . . . . . . . . . . 294 120 120 . . . . . . . . . . . . . . . . . . . 295 120 120 . . . . . . . . . . . . . . . . . . . 296 120 120 . . . . . . . . . . . . . . . . . . . 297 120 120 . . . . . . . . . . . . . . . . . . . 298 120 120 . . . . . . . . . . . . . . . . . . . 299 120 120 . . . . . . . . . . . . . . . . . . . 300 120 120 . . . . . . . . . . . . . . . . . . . 301 120 120 . . . . . . . . . . . . . . . . . . . 302 120 120 . . . . . . . . . . . . . . . . . . . 303 120 120 . . . . . . . . . . . . . . . . . . . 304 120 120 . . . . . . . . . . . . . . . . . . . 305 120 120 . . . . . . . . . . . . . . . . . . . 306 120 120 . . . . . . . . . . . . . . . . . . . 307 120 120 . . . . . . . . . . . . . . . . . . . 308 120 120 . . . . . . . . . . . . . . . . . . . 309 120 120 . . . . . . . . . . . . . . . . . . . 310 120 120 . . . . . . . . . . . . . . . . . . . 311 120 120 . . . . . . . . . . . . . . . . . . . 312 120 120 . . . . . . . . . . . . . . . . . . . 313 120 120 . . . . . . . . . . . . . . . . . . . 314 120 120 . . . . . . . . . . . . . . . . . . . 315 120 120 . . . . . . . . . . . . . . . . . . . 316 120 120 . . . . . . . . . . . . . . . . . . . 317 120 120 . . . . . . . . . . . . . . . . . . . 318 120 120 . . . . . . . . . . . . . . . . . . . 319 120 120 . . . . . . . . . . . . . . . . . . . 320 120 120 . . . . . . . . . . . . . . . . . . . 321 120 120 . . . . . . . . . . . . . . . . . . . 322 120 120 . . . . . . . . . . . . . . . . . . . 323 120 120 . . . . . . . . . . . . . . . . . . . 324 120 120 . . . . . . . . . . . . . . . . . . . 325 120 120 . . . . . . . . . . . . . . . . . . . 326 120 120 . . . . . . . . . . . . . . . . . . . 327 120 120 . . . . . . . . . . . . . . . . . . . 328 120 120 . . . . . . . . . . . . . . . . . . . 329 120 120 . . . . . . . . . . . . . . . . . . . 330 120 120 . . . . . . . . . . . . . . . . . . . 331 120 120 . . . . . . . . . . . . . . . . . . . 332 120 120 . . . . . . . . . . . . . . . . . . . 333 120 120 . . . . . . . . . . . . . . . . . . . 334 120 120 . . . . . . . . . . . . . . . . . . . 335 120 120 . . . . . . . . . . . . . . . . . . . 336 120 120 . . . . . . . . . . . . . . . . . . . 337 120 120 . . . . . . . . . . . . . . . . . . . 338 120 120 . . . . . . . . . . . . . . . . . . . 339 120 120 . . . . . . . . . . . . . . . . . . . 340 120 120 . . . . . . . . . . . . . . . . . . . 341 120 120 . . . . . . . . . . . . . . . . . . . Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02 Pos Num * - A D E F G I K L M N P Q R S T Y 5 120 112 . . . . . . . . . . 8 . . . . . . 6 120 20 92 8 . . . . . . . . . . . . . . . 7 112 20 92 . . . . . . . . . . . . . . . . 8 112 20 92 . . . . . . . . . . . . . . . . 9 112 3 76 . . . 33 . . . . . . . . . . . . 10 112 3 109 . . . . . . . . . . . . . . . . 11 112 3 109 . . . . . . . . . . . . . . . . 12 112 3 109 . . . . . . . . . . . . . . . . 13 112 3 93 16 . . . . . . . . . . . . . . . 14 112 3 14 . . . . . . . . 95 . . . . . . . 15 112 3 109 . . . . . . . . . . . . . . . . 16 112 3 109 . . . . . . . . . . . . . . . . 17 112 3 109 . . . . . . . . . . . . . . . . 18 112 3 109 . . . . . . . . . . . . . . . . 19 112 3 109 . . . . . . . . . . . . . . . . 20 112 3 109 . . . . . . . . . . . . . . . . 26 112 . 20 . . . . . . . 76 . . . . . . . 16 28 112 . 100 . . . . . . . . . . . . . 12 . . 30 112 . 24 . . . . . . . . . . . . . 12 . 76 37 112 . 100 . . . . . 12 . . . . . . . . . . 38 112 . 29 83 . . . . . . . . . . . . . . . 45 112 . 96 . . 16 . . . . . . . . . . . . . 46 112 . 100 . . 12 . . . . . . . . . . . . . 47 112 . 100 . . . 12 . . . . . . . . . . . . 52 112 . 100 . . . . . . . 12 . . . . . . . . 53 112 . 54 . . . . . . . 58 . . . . . . . . 55 112 . 57 . . . . . . . 12 . . 43 . . . . . 56 112 . 109 . . . . . . . 3 . . . . . . . . 57 112 . 14 33 64 . . . . . . . . . . . 1 . . 66 112 . 97 . 15 . . . . . . . . . . . . . . 67 112 . 97 . . . . . 15 . . . . . . . . . . 70 112 3 50 . . 3 . . . . . . . . . 56 . . . 71 112 3 14 . 3 . . . . 12 . . . . . . . 80 . 72 112 3 109 . . . . . . . . . . . . . . . . 73 112 3 109 . . . . . . . . . . . . . . . . 74 112 3 17 12 . 80 . . . . . . . . . . . . . 75 112 3 29 . . . . . . . 80 . . . . . . . . 76 112 3 109 . . . . . . . . . . . . . . . . 77 112 3 26 . . . . . . . . . . . . . . 83 . 78 112 3 109 . . . . . . . . . . . . . . . . 79 112 3 109 . . . . . . . . . . . . . . . . 80 112 3 109 . . . . . . . . . . . . . . . . 81 112 3 109 . . . . . . . . . . . . . . . . 82 112 3 109 . . . . . . . . . . . . . . . . 83 112 3 109 . . . . . . . . . . . . . . . . 84 112 3 51 . . . . . . . . . . . 58 . . . . 85 112 3 51 . . . . . . . 58 . . . . . . . . 86 112 3 50 . . 58 . 1 . . . . . . . . . . . 87 112 3 15 . . . 36 . . . 58 . . . . . . . . 88 112 3 109 . . . . . . . . . . . . . . . . 89 112 3 51 . . . . . . . . . . . . . . 58 . 90 112 3 51 . . . . . . . . . . . . . . 58 . 91 112 3 109 . . . . . . . . . . . . . . . . 92 112 3 109 . . . . . . . . . . . . . . . . 93 112 3 109 . . . . . . . . . . . . . . . . 94 112 17 95 . . . . . . . . . . . . . . . . 95 112 112 . . . . . . . . . . . . . . . . . Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02 Pos Num * - A D E F G I K L M N P Q R S T Y -31 120 112 . . . . . . . . . . 8 . . . . . . -30 120 112 . 8 . . . . . . . . . . . . . . . -29 112 112 . . . . . . . . . . . . . . . . . -28 112 112 . . . . . . . . . . . . . . . . . -27 112 112 . . . . . . . . . . . . . . . . . -26 112 112 . . . . . . . . . . . . . . . . . -25 112 112 . . . . . . . . . . . . . . . . . -24 112 112 . . . . . . . . . . . . . . . . . -23 112 112 . . . . . . . . . . . . . . . . . -22 112 112 . . . . . . . . . . . . . . . . . -21 112 112 . . . . . . . . . . . . . . . . . -20 112 112 . . . . . . . . . . . . . . . . . -19 112 112 . . . . . . . . . . . . . . . . . -18 112 112 . . . . . . . . . . . . . . . . . -17 112 112 . . . . . . . . . . . . . . . . . -16 112 112 . . . . . . . . . . . . . . . . . -15 112 112 . . . . . . . . . . . . . . . . . -14 112 112 . . . . . . . . . . . . . . . . . -13 112 112 . . . . . . . . . . . . . . . . . -12 112 112 . . . . . . . . . . . . . . . . . -11 112 112 . . . . . . . . . . . . . . . . . -10 112 112 . . . . . . . . . . . . . . . . . -9 112 112 . . . . . . . . . . . . . . . . . -8 112 112 . . . . . . . . . . . . . . . . . -7 112 112 . . . . . . . . . . . . . . . . . -6 112 112 . . . . . . . . . . . . . . . . . -5 112 112 . . . . . . . . . . . . . . . . . -4 112 112 . . . . . . . . . . . . . . . . . -3 112 112 . . . . . . . . . . . . . . . . . -2 112 112 . . . . . . . . . . . . . . . . . -1 112 112 . . . . . . . . . . . . . . . . . . 112 112 . . . . . . . . . . . . . . . . . 1 112 112 . . . . . . . . . . . . . . . . . 2 112 112 . . . . . . . . . . . . . . . . . 3 112 112 . . . . . . . . . . . . . . . . . 4 112 112 . . . . . . . . . . . . . . . . . 5 112 112 . . . . . . . . . . . . . . . . . 6 112 20 92 . . . . . . . . . . . . . . . . 7 112 20 92 . . . . . . . . . . . . . . . . 8 112 20 92 . . . . . . . . . . . . . . . . 9 112 3 76 . . . 33 . . . . . . . . . . . . 10 112 3 109 . . . . . . . . . . . . . . . . 11 112 3 109 . . . . . . . . . . . . . . . . 12 112 3 109 . . . . . . . . . . . . . . . . 13 112 3 93 16 . . . . . . . . . . . . . . . 14 112 3 14 . . . . . . . . 95 . . . . . . . 15 112 3 109 . . . . . . . . . . . . . . . . 16 112 3 109 . . . . . . . . . . . . . . . . 17 112 3 109 . . . . . . . . . . . . . . . . 18 112 3 109 . . . . . . . . . . . . . . . . 19 112 3 109 . . . . . . . . . . . . . . . . 20 112 3 109 . . . . . . . . . . . . . . . . 26 112 . 20 . . . . . . . 76 . . . . . . . 16 28 112 . 100 . . . . . . . . . . . . . 12 . . 30 112 . 24 . . . . . . . . . . . . . 12 . 76 37 112 . 100 . . . . . 12 . . . . . . . . . . 38 112 . 29 83 . . . . . . . . . . . . . . . 45 112 . 96 . . 16 . . . . . . . . . . . . . 46 112 . 100 . . 12 . . . . . . . . . . . . . 47 112 . 100 . . . 12 . . . . . . . . . . . . 52 112 . 100 . . . . . . . 12 . . . . . . . . 53 112 . 54 . . . . . . . 58 . . . . . . . . 55 112 . 57 . . . . . . . 12 . . 43 . . . . . 56 112 . 109 . . . . . . . 3 . . . . . . . . 57 112 . 14 33 64 . . . . . . . . . . . 1 . . 66 112 . 97 . 15 . . . . . . . . . . . . . . 67 112 . 97 . . . . . 15 . . . . . . . . . . 70 112 3 50 . . 3 . . . . . . . . . 56 . . . 71 112 3 14 . 3 . . . . 12 . . . . . . . 80 . 72 112 3 109 . . . . . . . . . . . . . . . . 73 112 3 109 . . . . . . . . . . . . . . . . 74 112 3 17 12 . 80 . . . . . . . . . . . . . 75 112 3 29 . . . . . . . 80 . . . . . . . . 76 112 3 109 . . . . . . . . . . . . . . . . 77 112 3 26 . . . . . . . . . . . . . . 83 . 78 112 3 109 . . . . . . . . . . . . . . . . 79 112 3 109 . . . . . . . . . . . . . . . . 80 112 3 109 . . . . . . . . . . . . . . . . 81 112 3 109 . . . . . . . . . . . . . . . . 82 112 3 109 . . . . . . . . . . . . . . . . 83 112 3 109 . . . . . . . . . . . . . . . . 84 112 3 51 . . . . . . . . . . . 58 . . . . 85 112 3 51 . . . . . . . 58 . . . . . . . . 86 112 3 50 . . 58 . 1 . . . . . . . . . . . 87 112 3 15 . . . 36 . . . 58 . . . . . . . . 88 112 3 109 . . . . . . . . . . . . . . . . 89 112 3 51 . . . . . . . . . . . . . . 58 . 90 112 3 51 . . . . . . . . . . . . . . 58 . 91 112 3 109 . . . . . . . . . . . . . . . . 92 112 3 109 . . . . . . . . . . . . . . . . 93 112 3 109 . . . . . . . . . . . . . . . . 94 112 17 95 . . . . . . . . . . . . . . . . 95 112 112 . . . . . . . . . . . . . . . . . 96 112 112 . . . . . . . . . . . . . . . . . 97 112 112 . . . . . . . . . . . . . . . . . 98 112 112 . . . . . . . . . . . . . . . . . 99 112 112 . . . . . . . . . . . . . . . . . 100 112 112 . . . . . . . . . . . . . . . . . 101 112 112 . . . . . . . . . . . . . . . . . 102 112 112 . . . . . . . . . . . . . . . . . 103 112 112 . . . . . . . . . . . . . . . . . 104 112 112 . . . . . . . . . . . . . . . . . 105 112 112 . . . . . . . . . . . . . . . . . 106 112 112 . . . . . . . . . . . . . . . . . 107 112 112 . . . . . . . . . . . . . . . . . 108 112 112 . . . . . . . . . . . . . . . . . 109 112 112 . . . . . . . . . . . . . . . . . 110 112 112 . . . . . . . . . . . . . . . . . 111 112 112 . . . . . . . . . . . . . . . . . 112 112 112 . . . . . . . . . . . . . . . . . 113 112 112 . . . . . . . . . . . . . . . . . 114 112 112 . . . . . . . . . . . . . . . . . 115 112 112 . . . . . . . . . . . . . . . . . 116 112 112 . . . . . . . . . . . . . . . . . 117 112 112 . . . . . . . . . . . . . . . . . 118 112 112 . . . . . . . . . . . . . . . . . 119 112 112 . . . . . . . . . . . . . . . . . 120 112 112 . . . . . . . . . . . . . . . . . 121 112 112 . . . . . . . . . . . . . . . . . 122 112 112 . . . . . . . . . . . . . . . . . 123 112 112 . . . . . . . . . . . . . . . . . 124 112 112 . . . . . . . . . . . . . . . . . 125 112 112 . . . . . . . . . . . . . . . . . 126 112 112 . . . . . . . . . . . . . . . . . 127 112 112 . . . . . . . . . . . . . . . . . 128 112 112 . . . . . . . . . . . . . . . . . 129 112 112 . . . . . . . . . . . . . . . . . 130 112 112 . . . . . . . . . . . . . . . . . 131 112 112 . . . . . . . . . . . . . . . . . 132 112 112 . . . . . . . . . . . . . . . . . 133 112 112 . . . . . . . . . . . . . . . . . 134 112 112 . . . . . . . . . . . . . . . . . 135 112 112 . . . . . . . . . . . . . . . . . 136 112 112 . . . . . . . . . . . . . . . . . 137 112 112 . . . . . . . . . . . . . . . . . 138 112 112 . . . . . . . . . . . . . . . . . 139 112 112 . . . . . . . . . . . . . . . . . 140 112 112 . . . . . . . . . . . . . . . . . 141 112 112 . . . . . . . . . . . . . . . . . 142 112 112 . . . . . . . . . . . . . . . . . 143 112 112 . . . . . . . . . . . . . . . . . 144 112 112 . . . . . . . . . . . . . . . . . 145 112 112 . . . . . . . . . . . . . . . . . 146 112 112 . . . . . . . . . . . . . . . . . 147 112 112 . . . . . . . . . . . . . . . . . 148 112 112 . . . . . . . . . . . . . . . . . 149 112 112 . . . . . . . . . . . . . . . . . 150 112 112 . . . . . . . . . . . . . . . . . 151 112 112 . . . . . . . . . . . . . . . . . 152 112 112 . . . . . . . . . . . . . . . . . 153 112 112 . . . . . . . . . . . . . . . . . 154 112 112 . . . . . . . . . . . . . . . . . 155 112 112 . . . . . . . . . . . . . . . . . 156 112 112 . . . . . . . . . . . . . . . . . 157 112 112 . . . . . . . . . . . . . . . . . 158 112 112 . . . . . . . . . . . . . . . . . 159 112 112 . . . . . . . . . . . . . . . . . 160 112 112 . . . . . . . . . . . . . . . . . 161 112 112 . . . . . . . . . . . . . . . . . 162 112 112 . . . . . . . . . . . . . . . . . 163 112 112 . . . . . . . . . . . . . . . . . 164 112 112 . . . . . . . . . . . . . . . . . 165 112 112 . . . . . . . . . . . . . . . . . 166 112 112 . . . . . . . . . . . . . . . . . 167 112 112 . . . . . . . . . . . . . . . . . 168 112 112 . . . . . . . . . . . . . . . . . 169 112 112 . . . . . . . . . . . . . . . . . 170 112 112 . . . . . . . . . . . . . . . . . 171 112 112 . . . . . . . . . . . . . . . . . 172 112 112 . . . . . . . . . . . . . . . . . 173 112 112 . . . . . . . . . . . . . . . . . 174 112 112 . . . . . . . . . . . . . . . . . 175 112 112 . . . . . . . . . . . . . . . . . 176 112 112 . . . . . . . . . . . . . . . . . 177 112 112 . . . . . . . . . . . . . . . . . 178 112 112 . . . . . . . . . . . . . . . . . 179 112 112 . . . . . . . . . . . . . . . . . 180 112 112 . . . . . . . . . . . . . . . . . 181 112 112 . . . . . . . . . . . . . . . . . 182 112 112 . . . . . . . . . . . . . . . . . 183 112 112 . . . . . . . . . . . . . . . . . 184 112 112 . . . . . . . . . . . . . . . . . 185 112 112 . . . . . . . . . . . . . . . . . 186 112 112 . . . . . . . . . . . . . . . . . 187 112 112 . . . . . . . . . . . . . . . . . 188 112 112 . . . . . . . . . . . . . . . . . 189 112 112 . . . . . . . . . . . . . . . . . 190 112 112 . . . . . . . . . . . . . . . . . 191 112 112 . . . . . . . . . . . . . . . . . 192 112 112 . . . . . . . . . . . . . . . . . 193 112 112 . . . . . . . . . . . . . . . . . 194 112 112 . . . . . . . . . . . . . . . . . 195 112 112 . . . . . . . . . . . . . . . . . 196 112 112 . . . . . . . . . . . . . . . . . 197 112 112 . . . . . . . . . . . . . . . . . 198 112 112 . . . . . . . . . . . . . . . . . 199 112 112 . . . . . . . . . . . . . . . . . 200 112 112 . . . . . . . . . . . . . . . . . 201 112 112 . . . . . . . . . . . . . . . . . 202 112 112 . . . . . . . . . . . . . . . . . 203 112 112 . . . . . . . . . . . . . . . . . 204 112 112 . . . . . . . . . . . . . . . . . 205 112 112 . . . . . . . . . . . . . . . . . 206 112 112 . . . . . . . . . . . . . . . . . 207 112 112 . . . . . . . . . . . . . . . . . 208 112 112 . . . . . . . . . . . . . . . . . 209 112 112 . . . . . . . . . . . . . . . . . 210 112 112 . . . . . . . . . . . . . . . . . 211 112 112 . . . . . . . . . . . . . . . . . 212 112 112 . . . . . . . . . . . . . . . . . 213 112 112 . . . . . . . . . . . . . . . . . 214 112 112 . . . . . . . . . . . . . . . . . 215 112 112 . . . . . . . . . . . . . . . . . 216 112 112 . . . . . . . . . . . . . . . . . 217 112 112 . . . . . . . . . . . . . . . . . 218 112 112 . . . . . . . . . . . . . . . . . 219 112 112 . . . . . . . . . . . . . . . . . 220 112 112 . . . . . . . . . . . . . . . . . 221 112 112 . . . . . . . . . . . . . . . . . 222 112 112 . . . . . . . . . . . . . . . . . 223 112 112 . . . . . . . . . . . . . . . . . 224 112 112 . . . . . . . . . . . . . . . . . 225 112 112 . . . . . . . . . . . . . . . . . 226 112 112 . . . . . . . . . . . . . . . . . 227 112 112 . . . . . . . . . . . . . . . . . 228 112 112 . . . . . . . . . . . . . . . . . 229 112 112 . . . . . . . . . . . . . . . . . 230 112 112 . . . . . . . . . . . . . . . . . 231 112 112 . . . . . . . . . . . . . . . . . 232 112 112 . . . . . . . . . . . . . . . . . 233 112 112 . . . . . . . . . . . . . . . . . 234 112 112 . . . . . . . . . . . . . . . . . 235 112 112 . . . . . . . . . . . . . . . . . 236 112 112 . . . . . . . . . . . . . . . . . 237 112 112 . . . . . . . . . . . . . . . . . SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 Exclude 9 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:48 [1] 2019-10-16 04:25:48, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48 [2] 2019-10-16 04:25:48, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54 [3] 2019-10-16 04:25:48, OOB Acc: 97.22%, # of SNPs: 21, # of Haplo: 50 [4] 2019-10-16 04:25:49, OOB Acc: 90.38%, # of SNPs: 16, # of Haplo: 34 [5] 2019-10-16 04:25:49, OOB Acc: 95.45%, # of SNPs: 20, # of Haplo: 38 [6] 2019-10-16 04:25:49, OOB Acc: 95.83%, # of SNPs: 18, # of Haplo: 83 [7] 2019-10-16 04:25:50, OOB Acc: 100.00%, # of SNPs: 24, # of Haplo: 100 [8] 2019-10-16 04:25:51, OOB Acc: 100.00%, # of SNPs: 18, # of Haplo: 48 [9] 2019-10-16 04:25:51, OOB Acc: 94.74%, # of SNPs: 15, # of Haplo: 28 [10] 2019-10-16 04:25:51, OOB Acc: 83.33%, # of SNPs: 12, # of Haplo: 20 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002170206 0.0002218348 0.0002651623 0.0035806631 0.0108171268 0.0260212634 Max. Mean SD 0.4900436399 0.0436176972 0.1079908676 Accuracy with training data: 97.5% Out-of-bag accuracy: 92.7% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 10 total # of SNPs used: 104 avg. # of SNPs in an individual classifier: 16.90 (sd: 4.09, min: 11, max: 24, median: 17.00) avg. # of haplotypes in an individual classifier: 50.30 (sd: 24.49, min: 20, max: 100, median: 48.00) avg. out-of-bag accuracy: 92.65% (sd: 6.47%, min: 82.61%, max: 100.00%, median: 95.10%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002170206 0.0002218348 0.0002651623 0.0035806631 0.0108171268 0.0260212634 Max. Mean SD 0.4900436399 0.0436176972 0.1079908676 Genome assembly: hg19 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 Loading required namespace: gdsfmt Loading required namespace: SNPRelate Import 2348 SNPs from chromosome 6. 3 SNPs with invalid alleles have been removed. SNP genotypes: 165 samples X 2345 SNPs SNPs range from 24652946bp to 33524089bp on hg18 Missing rate per SNP: min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919 Missing rate per sample: min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105 Minor allele frequency: min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319 Allelic information: A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A 556 467 410 400 116 109 103 81 30 29 28 16 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. No allelic strand orders are switched. SNP genotypes: 150 samples X 1214 SNPs SNPs range from 28695148bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841 Missing rate per sample: min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119 Minor allele frequency: min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855 Allelic information: A/G C/T G/T A/C 505 496 109 104 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1197 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446 Missing rate per sample: min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786 Minor allele frequency: min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338 Allelic information: A/G C/T A/C G/T 511 476 105 105 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. SNP genotypes: 90 samples X 3932 SNPs SNPs range from 28694391bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489 Missing rate per sample: min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554 Minor allele frequency: min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144 Allelic information: A/G C/T G/T A/C C/G A/T 1567 1510 348 332 111 64 No allelic strand orders are switched. SNP genotypes: 60 samples X 1214 SNPs SNPs range from 28695148bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381 Missing rate per sample: min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373 Minor allele frequency: min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833 Allelic information: A/G C/T G/T A/C 505 496 109 104 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 MAF filter (>=0.01), excluding 9 SNP(s) using the default genome assembly (assembly="hg19") SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:25:53 [1] 2019-10-16 04:25:53, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02 Max. Mean SD 9.105734e-02 2.054649e-02 2.598603e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 92.0% Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:25:53 [1] 2019-10-16 04:25:53, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02 Max. Mean SD 9.784316e-02 1.490504e-02 1.947399e-02 Accuracy with training data: 97.5% Out-of-bag accuracy: 97.5% Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:25:53 [1] 2019-10-16 04:25:53, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02 Max. Mean SD 1.808372e-01 2.422083e-02 3.699146e-02 Accuracy with training data: 95.8% Out-of-bag accuracy: 88.9% Gene: C Training dataset: 60 samples X 83 SNPs # of HLA alleles: 17 # of individual classifiers: 3 total # of SNPs used: 40 avg. # of SNPs in an individual classifier: 18.67 (sd: 5.03, min: 14, max: 24, median: 18.00) avg. # of haplotypes in an individual classifier: 33.67 (sd: 4.51, min: 29, max: 38, median: 34.00) avg. out-of-bag accuracy: 92.80% (sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02 Max. Mean SD 1.210500e-01 1.989079e-02 2.507466e-02 Genome assembly: hg19 Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 9 # of SNPs: 77, # of samples: 60 # of unique HLA alleles: 12 [-] 2019-10-16 04:25:53 [1] 2019-10-16 04:25:53, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20 [2] 2019-10-16 04:25:53, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Accuracy with training data: 95.0% Out-of-bag accuracy: 94.5% Gene: DQB1 Training dataset: 60 samples X 77 SNPs # of HLA alleles: 12 # of individual classifiers: 2 total # of SNPs used: 20 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.41, min: 13, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 20.50 (sd: 0.71, min: 20, max: 21, median: 20.50) avg. out-of-bag accuracy: 94.45% (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Genome assembly: hg19 Exclude 9 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:53 [1] 2019-10-16 04:25:53, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48 [2] 2019-10-16 04:25:53, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54 [3] 2019-10-16 04:25:54, OOB Acc: 97.22%, # of SNPs: 21, # of Haplo: 50 [4] 2019-10-16 04:25:54, OOB Acc: 90.38%, # of SNPs: 16, # of Haplo: 34 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654 Max. Mean SD 0.5667222637 0.0472474454 0.1286381649 Accuracy with training data: 98.3% Out-of-bag accuracy: 89.3% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 50 avg. # of SNPs in an individual classifier: 15.50 (sd: 4.20, min: 11, max: 21, median: 15.00) avg. # of haplotypes in an individual classifier: 46.50 (sd: 8.70, min: 34, max: 54, median: 49.00) avg. out-of-bag accuracy: 89.29% (sd: 6.17%, min: 82.61%, max: 97.22%, median: 88.67%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654 Max. Mean SD 0.5667222637 0.0472474454 0.1286381649 Genome assembly: hg19 Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 50 avg. # of SNPs in an individual classifier: 15.50 (sd: 4.20, min: 11, max: 21, median: 15.00) avg. # of haplotypes in an individual classifier: 46.50 (sd: 8.70, min: 34, max: 54, median: 49.00) avg. out-of-bag accuracy: 89.29% (sd: 6.17%, min: 82.61%, max: 97.22%, median: 88.67%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654 Max. Mean SD 0.5667222637 0.0472474454 0.1286381649 Genome assembly: hg19 Wed Oct 16 04:25:54 2019, passing the 1/4 classifiers. Wed Oct 16 04:25:54 2019, passing the 2/4 classifiers. Wed Oct 16 04:25:54 2019, passing the 3/4 classifiers. Wed Oct 16 04:25:54 2019, passing the 4/4 classifiers. Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall Valid. Valid. (%) (%) (%) (%) (%) (%) (%) ---- Overall accuracy: 88.1%, Call rate: 100.0% 01:01 25 0.2083 100.0 99.5 100.0 99.4 95.8 100.0 -- 02:01 43 0.3583 100.0 96.1 100.0 94.2 89.0 100.0 -- 02:06 1 0.0083 50.0 97.6 0.0 100.0 -- 97.6 02:01 (100) 03:01 9 0.0750 100.0 96.2 93.8 96.4 72.5 99.4 24:02 (100) 11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 -- 23:01 3 0.0250 75.0 99.4 83.3 100.0 100.0 99.3 24:02 (100) 24:02 11 0.0917 100.0 98.3 91.7 98.9 90.8 99.3 03:01 (100) 24:03 1 0.0083 50.0 97.8 0.0 100.0 -- 97.8 01:01 (50) 25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 -- 26:01 3 0.0250 100.0 97.8 50.0 100.0 100.0 97.8 25:01 (62) 29:02 4 0.0333 75.0 93.6 33.3 99.3 50.0 94.2 02:01 (50) 31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 -- 32:01 4 0.0333 50.0 100.0 100.0 100.0 100.0 100.0 -- 68:01 3 0.0250 100.0 98.6 75.0 100.0 100.0 98.6 03:01 (67) \title{Imputation Evaluation} \documentclass[12pt]{article} \usepackage{fullpage} \usepackage{longtable} \begin{document} \maketitle \setlength{\LTcapwidth}{6.5in} % -------- BEGIN TABLE -------- \begin{longtable}{rrr | rrrrrrl} \caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).} \label{tab:accuracy} \\ Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endfirsthead \multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\ Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endhead \hline \multicolumn{10}{r}{Continued on next page ...} \\ \hline \endfoot \hline\hline \endlastfoot \multicolumn{10}{l}{\it Overall accuracy: 88.1\%, Call rate: 100.0\%} \\ 01:01 & 25 & 0.2083 & 100.0 & 99.5 & 100.0 & 99.4 & 95.8 & 100.0 & -- \\ 02:01 & 43 & 0.3583 & 100.0 & 96.1 & 100.0 & 94.2 & 89.0 & 100.0 & -- \\ 02:06 & 1 & 0.0083 & 50.0 & 97.6 & 0.0 & 100.0 & -- & 97.6 & 02:01 (100) \\ 03:01 & 9 & 0.0750 & 100.0 & 96.2 & 93.8 & 96.4 & 72.5 & 99.4 & 24:02 (100) \\ 11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 23:01 & 3 & 0.0250 & 75.0 & 99.4 & 83.3 & 100.0 & 100.0 & 99.3 & 24:02 (100) \\ 24:02 & 11 & 0.0917 & 100.0 & 98.3 & 91.7 & 98.9 & 90.8 & 99.3 & 03:01 (100) \\ 24:03 & 1 & 0.0083 & 50.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 01:01 (50) \\ 25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\ 26:01 & 3 & 0.0250 & 100.0 & 97.8 & 50.0 & 100.0 & 100.0 & 97.8 & 25:01 (62) \\ 29:02 & 4 & 0.0333 & 75.0 & 93.6 & 33.3 & 99.3 & 50.0 & 94.2 & 02:01 (50) \\ 31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 32:01 & 4 & 0.0333 & 50.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 68:01 & 3 & 0.0250 & 100.0 & 98.6 & 75.0 & 100.0 & 100.0 & 98.6 & 03:01 (67) \\ \end{longtable} % -------- END TABLE -------- \end{document} Imputation Evaluation

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Allele	Num. Valid.	Freq. Valid.	CR (%)	ACC (%)	SEN (%)	SPE (%)	PPV (%)	NPV (%)	Miscall (%)
Overall accuracy: 88.1%, Call rate: 100.0%
01:01	25	0.2083	100.0	99.5	100.0	99.4	95.8	100.0	--
02:01	43	0.3583	100.0	96.1	100.0	94.2	89.0	100.0	--
02:06	1	0.0083	50.0	97.6	0.0	100.0	--	97.6	02:01 (100)
03:01	9	0.0750	100.0	96.2	93.8	96.4	72.5	99.4	24:02 (100)
11:01	5	0.0417	100.0	100.0	100.0	100.0	100.0	100.0	--
23:01	3	0.0250	75.0	99.4	83.3	100.0	100.0	99.3	24:02 (100)
24:02	11	0.0917	100.0	98.3	91.7	98.9	90.8	99.3	03:01 (100)
24:03	1	0.0083	50.0	97.8	0.0	100.0	--	97.8	01:01 (50)
25:01	5	0.0417	100.0	98.4	100.0	98.3	84.7	100.0	--
26:01	3	0.0250	100.0	97.8	50.0	100.0	100.0	97.8	25:01 (62)
29:02	4	0.0333	75.0	93.6	33.3	99.3	50.0	94.2	02:01 (50)
31:01	3	0.0250	75.0	100.0	100.0	100.0	100.0	100.0	--
32:01	4	0.0333	50.0	100.0	100.0	100.0	100.0	100.0	--
68:01	3	0.0250	100.0	98.6	75.0	100.0	100.0	98.6	03:01 (67)

Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes: 2019-10-16 04:25:55, 1, job 2, # of SNPs: 12, # of haplo: 53, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91% 2019-10-16 04:25:55, 2, job 2, # of SNPs: 14, # of haplo: 26, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91% 2019-10-16 04:25:55, 3, job 1, # of SNPs: 17, # of haplo: 56, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91% 2019-10-16 04:25:56, 4, job 2, # of SNPs: 15, # of haplo: 51, acc: 70.8% -- avg out-of-bag acc: 85.89%, sd: 10.04%, min: 70.83%, max: 90.91% Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 5.407034e-05 7.372297e-05 2.505967e-04 2.439435e-03 8.691516e-03 3.543591e-02 Max. Mean SD 3.768538e-01 4.247874e-02 8.803447e-02 Accuracy with training data: 98.5% Out-of-bag accuracy: 85.9% Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes: The model is autosaved in 'tmp_model.RData'. 2019-10-16 04:25:56, 1, job 1, # of SNPs: 11, # of haplo: 24, acc: 87.5% -- avg out-of-bag acc: 87.50%, sd: NA%, min: 87.50%, max: 87.50% 2019-10-16 04:25:56, 2, job 2, # of SNPs: 15, # of haplo: 38, acc: 83.3% -- avg out-of-bag acc: 85.42%, sd: 2.95%, min: 83.33%, max: 87.50% 2019-10-16 04:25:57, 3, job 2, # of SNPs: 14, # of haplo: 52, acc: 83.3% Stop "job 2". -- avg out-of-bag acc: 84.72%, sd: 2.41%, min: 83.33%, max: 87.50% 2019-10-16 04:25:57, 4, job 1, # of SNPs: 19, # of haplo: 57, acc: 93.8% Stop "job 1". -- avg out-of-bag acc: 86.98%, sd: 4.92%, min: 83.33%, max: 93.75% Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007569497 0.0007640860 0.0008283122 0.0040042727 0.0078497196 0.0198451501 Max. Mean SD 0.3613806225 0.0352260138 0.0821735834 Accuracy with training data: 97.1% Out-of-bag accuracy: 87.0% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 48 avg. # of SNPs in an individual classifier: 14.75 (sd: 3.30, min: 11, max: 19, median: 14.50) avg. # of haplotypes in an individual classifier: 42.75 (sd: 14.86, min: 24, max: 57, median: 45.00) avg. out-of-bag accuracy: 86.98% (sd: 4.92%, min: 83.33%, max: 93.75%, median: 85.42%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007569497 0.0007640860 0.0008283122 0.0040042727 0.0078497196 0.0198451501 Max. Mean SD 0.3613806225 0.0352260138 0.0821735834 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:57) 0% Predicting (2019-10-16 04:25:57) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.001269 0.003803 0.024627 0.016455 0.361381 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Run in parallel with 2 compute nodes. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.001269 0.003803 0.024627 0.016455 0.361381 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:57 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:25:57, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:25:58, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.000000e+00 4.210645e-09 4.210645e-08 2.324904e-03 8.109951e-03 1.989621e-02 Max. Mean SD 5.990492e-02 1.420983e-02 1.652472e-02 Accuracy with training data: 100.0% Out-of-bag accuracy: 94.9% Exclude 11 monomorphic SNPs Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:58 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [1] 2019-10-16 04:25:58, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [2] 2019-10-16 04:25:58, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002703521 0.0002971139 0.0005379705 0.0035684045 0.0131584084 0.0413920204 Max. Mean SD 0.5762364122 0.0439769026 0.0999476134 Accuracy with training data: 97.1% Out-of-bag accuracy: 90.8% HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting by voting from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:58) 0% Predicting (2019-10-16 04:25:58) 100% HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting by voting from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:25:58) 0% Predicting (2019-10-16 04:25:58) 100% Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 13 # of SNPs: 158, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:58 1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14 2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15 3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19 4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23 5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23 6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27 7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35 8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51 9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55 10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55 11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56 12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59 13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58 14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60 [1] 2019-10-16 04:25:59, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60 1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15 2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18 3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23 4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29 5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40 6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40 7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41 8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41 9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43 10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43 11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43 12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44 13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45 14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45 15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45 16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47 17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47 18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50 [2] 2019-10-16 04:25:59, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Accuracy with training data: 96.7% Out-of-bag accuracy: 93.2% Gene: A Training dataset: 60 samples X 158 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 28 avg. # of SNPs in an individual classifier: 16.00 (sd: 2.83, min: 14, max: 18, median: 16.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: 7.07, min: 50, max: 60, median: 55.00) avg. out-of-bag accuracy: 93.20% (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Genome assembly: hg19 Remove 130 unused SNPs. Gene: A Training dataset: 60 samples X 28 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 28 avg. # of SNPs in an individual classifier: 16.00 (sd: 2.83, min: 14, max: 18, median: 16.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: 7.07, min: 50, max: 60, median: 55.00) avg. out-of-bag accuracy: 93.20% (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Genome assembly: hg19 Platform: Illumina 1M Duo Information: Training set -- HapMap Phase II HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 60 Predicting (2019-10-16 04:25:59) 0% Predicting (2019-10-16 04:25:59) 100% HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 60 Predicting (2019-10-16 04:25:59) 0% Predicting (2019-10-16 04:25:59) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:25:59 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:25:59, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:25:59, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 04:25:59, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 04:26:00, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:00) 0% Predicting (2019-10-16 04:26:00) 100% Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%) ---- Overall accuracy: 88.5%, Call rate: 100.0% 01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 -- 02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100) 02:06 1 0.0147 0 0 -- -- -- -- -- -- -- 03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 -- 11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 -- 23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100) 24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50) 24:03 1 0.0147 0 0 -- -- -- -- -- -- -- 25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50) 31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 -- 68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 -- \title{Imputation Evaluation} \documentclass[12pt]{article} \usepackage{fullpage} \usepackage{longtable} \begin{document} \maketitle \setlength{\LTcapwidth}{6.5in} % -------- BEGIN TABLE -------- \begin{longtable}{rrrrr | rrrrrrl} \caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).} \label{tab:accuracy} \\ Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endfirsthead \multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\ Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endhead \hline \multicolumn{12}{r}{Continued on next page ...} \\ \hline \endfoot \hline\hline \endlastfoot \multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\ 01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\ 02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\ 02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\ 03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\ 11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\ 24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\ 24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\ 25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\ 31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\ \end{longtable} % -------- END TABLE -------- \end{document} Imputation Evaluation

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Allele	Num. Train	Freq. Train	Num. Valid.	Freq. Valid.	CR (%)	ACC (%)	SEN (%)	SPE (%)	PPV (%)	NPV (%)	Miscall (%)
Overall accuracy: 88.5%, Call rate: 100.0%
01:01	13	0.1912	12	0.2308	100.0	96.2	100.0	95.0	85.7	100.0	--
02:01	25	0.3676	18	0.3462	100.0	98.1	94.4	100.0	100.0	97.1	01:01 (100)
02:06	1	0.0147	0	0	--	--	--	--	--	--	--
03:01	4	0.0588	5	0.0962	100.0	98.1	100.0	97.9	83.3	100.0	--
11:01	2	0.0294	3	0.0577	100.0	100.0	100.0	100.0	100.0	100.0	--
23:01	1	0.0147	2	0.0385	100.0	96.2	0.0	100.0	--	96.2	24:02 (100)
24:02	6	0.0882	5	0.0962	100.0	92.3	60.0	95.7	60.0	95.7	01:01 (50)
24:03	1	0.0147	0	0	--	--	--	--	--	--	--
25:01	4	0.0588	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
26:01	2	0.0294	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
29:02	3	0.0441	1	0.0192	100.0	98.1	0.0	100.0	--	98.1	03:01 (50)
31:01	2	0.0294	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
32:01	2	0.0294	2	0.0385	100.0	100.0	100.0	100.0	100.0	100.0	--
68:01	2	0.0294	1	0.0192	100.0	98.1	100.0	98.0	50.0	100.0	--

**Overall accuracy: 88.5%, Call rate: 100.0%** | Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) | |:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--| | 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- | | 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) | | 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- | | 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- | | 11:01 | 2 | 0.0294 | 3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 23:01 | 1 | 0.0147 | 2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) | | 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) | | 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- | | 25:01 | 4 | 0.0588 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) | | 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 32:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- | Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:00 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:26:00, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:26:00, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 04:26:00, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 04:26:00, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:00) 0% Predicting (2019-10-16 04:26:01) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 8 # of SNPs: 51, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:01 1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18 3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19 4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20 5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20 6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23 7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25 8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29 9, SNP: 10, Loss: 40.6216, OOB Acc: 83.33%, # of Haplo: 29 10, SNP: 37, Loss: 38.9199, OOB Acc: 83.33%, # of Haplo: 30 11, SNP: 26, Loss: 32.7795, OOB Acc: 83.33%, # of Haplo: 38 12, SNP: 5, Loss: 31.6837, OOB Acc: 83.33%, # of Haplo: 44 13, SNP: 29, Loss: 31.6837, OOB Acc: 85.42%, # of Haplo: 44 [1] 2019-10-16 04:26:01, OOB Acc: 85.42%, # of SNPs: 13, # of Haplo: 44 1, SNP: 18, Loss: 467.346, OOB Acc: 42.50%, # of Haplo: 17 2, SNP: 51, Loss: 397.716, OOB Acc: 50.00%, # of Haplo: 18 3, SNP: 4, Loss: 286.978, OOB Acc: 55.00%, # of Haplo: 18 4, SNP: 50, Loss: 224.305, OOB Acc: 62.50%, # of Haplo: 19 5, SNP: 34, Loss: 147.479, OOB Acc: 70.00%, # of Haplo: 19 6, SNP: 29, Loss: 139.44, OOB Acc: 75.00%, # of Haplo: 19 7, SNP: 20, Loss: 74.4196, OOB Acc: 80.00%, # of Haplo: 20 8, SNP: 49, Loss: 72.6051, OOB Acc: 82.50%, # of Haplo: 20 9, SNP: 1, Loss: 41.7053, OOB Acc: 85.00%, # of Haplo: 20 10, SNP: 32, Loss: 38.8112, OOB Acc: 85.00%, # of Haplo: 20 11, SNP: 7, Loss: 43.6126, OOB Acc: 87.50%, # of Haplo: 21 12, SNP: 36, Loss: 44.1728, OOB Acc: 92.50%, # of Haplo: 26 13, SNP: 37, Loss: 43.1334, OOB Acc: 92.50%, # of Haplo: 27 14, SNP: 11, Loss: 41.7533, OOB Acc: 92.50%, # of Haplo: 25 [2] 2019-10-16 04:26:01, OOB Acc: 92.50%, # of SNPs: 14, # of Haplo: 25 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02 Max. Mean SD 1.595681e-01 3.334436e-02 3.729916e-02 Accuracy with training data: 93.3% Out-of-bag accuracy: 89.0% Gene: C Training dataset: 60 samples X 51 SNPs # of HLA alleles: 17 # of individual classifiers: 2 total # of SNPs used: 23 avg. # of SNPs in an individual classifier: 13.50 (sd: 0.71, min: 13, max: 14, median: 13.50) avg. # of haplotypes in an individual classifier: 34.50 (sd: 13.44, min: 25, max: 44, median: 34.50) avg. out-of-bag accuracy: 88.96% (sd: 5.01%, min: 85.42%, max: 92.50%, median: 88.96%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02 Max. Mean SD 1.595681e-01 3.334436e-02 3.729916e-02 Genome assembly: hg19 Gene: C Training dataset: 60 samples X 51 SNPs # of HLA alleles: 17 # of individual classifiers: 1 total # of SNPs used: 13 avg. # of SNPs in an individual classifier: 13.00 (sd: NA, min: 13, max: 13, median: 13.00) avg. # of haplotypes in an individual classifier: 44.00 (sd: NA, min: 44, max: 44, median: 44.00) avg. out-of-bag accuracy: 85.42% (sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02 Max. Mean SD 1.595681e-01 3.334436e-02 3.729916e-02 Genome assembly: hg19 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:01 1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17 3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20 4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20 5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22 6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24 7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24 8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22 9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24 10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24 11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28 12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29 13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37 14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38 15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39 16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40 17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41 18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43 19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43 [1] 2019-10-16 04:26:01, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43 1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19 2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21 3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21 4, SNP: 59, Loss: 209.932, OOB Acc: 76.47%, # of Haplo: 21 5, SNP: 74, Loss: 153.579, OOB Acc: 82.35%, # of Haplo: 22 6, SNP: 69, Loss: 108.679, OOB Acc: 88.24%, # of Haplo: 22 7, SNP: 72, Loss: 81.3125, OOB Acc: 91.18%, # of Haplo: 22 8, SNP: 24, Loss: 67.8072, OOB Acc: 91.18%, # of Haplo: 22 9, SNP: 70, Loss: 56.7061, OOB Acc: 91.18%, # of Haplo: 22 10, SNP: 82, Loss: 55.1169, OOB Acc: 94.12%, # of Haplo: 26 11, SNP: 3, Loss: 53.5756, OOB Acc: 94.12%, # of Haplo: 27 12, SNP: 2, Loss: 45.9376, OOB Acc: 94.12%, # of Haplo: 29 13, SNP: 27, Loss: 26.806, OOB Acc: 94.12%, # of Haplo: 29 14, SNP: 5, Loss: 25.3407, OOB Acc: 94.12%, # of Haplo: 30 15, SNP: 32, Loss: 22.6034, OOB Acc: 94.12%, # of Haplo: 31 16, SNP: 78, Loss: 21.7748, OOB Acc: 94.12%, # of Haplo: 37 [2] 2019-10-16 04:26:01, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 37 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02 Max. Mean SD 7.898042e-02 1.807739e-02 2.114254e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 91.9% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:01 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:26:01, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:26:01, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 04:26:01, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 04:26:02, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:02) 0% Predicting (2019-10-16 04:26:02) 100% Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:02 1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17 3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20 4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20 5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22 6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24 7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24 8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22 9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24 10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24 11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28 12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29 13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37 14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38 15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39 16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40 17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41 18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43 19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43 [1] 2019-10-16 04:26:02, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43 1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19 2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21 3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21 4, SNP: 59, Loss: 209.932, OOB Acc: 76.47%, # of Haplo: 21 5, SNP: 74, Loss: 153.579, OOB Acc: 82.35%, # of Haplo: 22 6, SNP: 69, Loss: 108.679, OOB Acc: 88.24%, # of Haplo: 22 7, SNP: 72, Loss: 81.3125, OOB Acc: 91.18%, # of Haplo: 22 8, SNP: 24, Loss: 67.8072, OOB Acc: 91.18%, # of Haplo: 22 9, SNP: 70, Loss: 56.7061, OOB Acc: 91.18%, # of Haplo: 22 10, SNP: 82, Loss: 55.1169, OOB Acc: 94.12%, # of Haplo: 26 11, SNP: 3, Loss: 53.5756, OOB Acc: 94.12%, # of Haplo: 27 12, SNP: 2, Loss: 45.9376, OOB Acc: 94.12%, # of Haplo: 29 13, SNP: 27, Loss: 26.806, OOB Acc: 94.12%, # of Haplo: 29 14, SNP: 5, Loss: 25.3407, OOB Acc: 94.12%, # of Haplo: 30 15, SNP: 32, Loss: 22.6034, OOB Acc: 94.12%, # of Haplo: 31 16, SNP: 78, Loss: 21.7748, OOB Acc: 94.12%, # of Haplo: 37 [2] 2019-10-16 04:26:02, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 37 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02 Max. Mean SD 7.898042e-02 1.807739e-02 2.114254e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 91.9% Gene: C Training dataset: 60 samples X 83 SNPs # of HLA alleles: 17 # of individual classifiers: 2 total # of SNPs used: 27 avg. # of SNPs in an individual classifier: 17.50 (sd: 2.12, min: 16, max: 19, median: 17.50) avg. # of haplotypes in an individual classifier: 40.00 (sd: 4.24, min: 37, max: 43, median: 40.00) avg. out-of-bag accuracy: 91.85% (sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02 Max. Mean SD 7.898042e-02 1.807739e-02 2.114254e-02 Genome assembly: hg19 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 > > proc.time() user system elapsed 32.64 0.43 37.82

R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ############################################################# > # > # DESCRIPTION: Unit tests in the HIBAG package > # > > # load the HIBAG package > library(HIBAG) HIBAG (HLA Genotype Imputation with Attribute Bagging) Kernel Version: v1.4 Supported by Streaming SIMD Extensions (SSE2) > > > ############################################################# > > # a list of HLA genes > hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1") > > # pre-defined lower bound of prediction accuracy > hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7) > > > for (hla.idx in seq_len(length(hla.list))) + { + hla.id <- hla.list[hla.idx] + + # make a "hlaAlleleClass" object + hla <- hlaAllele(HLA_Type_Table$sample.id, + H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], + H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], + locus=hla.id, assembly="hg19") + + # divide HLA types randomly + set.seed(100) + hlatab <- hlaSplitAllele(hla, train.prop=0.5) + + # SNP predictors within the flanking region on each side + region <- 500 # kb + snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, + HapMap_CEU_Geno$snp.position, + hla.id, region*1000, assembly="hg19") + + # training and validation genotypes + train.geno <- hlaGenoSubset(HapMap_CEU_Geno, + snp.sel=match(snpid, HapMap_CEU_Geno$snp.id), + samp.sel=match(hlatab$training$value$sample.id, + HapMap_CEU_Geno$sample.id)) + test.geno <- hlaGenoSubset(HapMap_CEU_Geno, + samp.sel=match(hlatab$validation$value$sample.id, + HapMap_CEU_Geno$sample.id)) + + + # train a HIBAG model + set.seed(100) + model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10) + summary(model) + + # validation + pred <- predict(model, test.geno) + summary(pred) + + # compare + comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model, + call.threshold=0) + print(comp$overall) + + # check + if (comp$overall$acc.haplo < hla.acc[hla.idx]) + { + stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ", + hla.acc[hla.idx], ".") + } + + cat("\n\n") + } Exclude 11 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:03 [1] 2019-10-16 04:26:03, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 [2] 2019-10-16 04:26:03, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 [3] 2019-10-16 04:26:03, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 [4] 2019-10-16 04:26:03, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 [5] 2019-10-16 04:26:03, OOB Acc: 79.41%, # of SNPs: 13, # of Haplo: 34 [6] 2019-10-16 04:26:03, OOB Acc: 100.00%, # of SNPs: 19, # of Haplo: 72 [7] 2019-10-16 04:26:03, OOB Acc: 100.00%, # of SNPs: 17, # of Haplo: 37 [8] 2019-10-16 04:26:03, OOB Acc: 84.62%, # of SNPs: 14, # of Haplo: 58 [9] 2019-10-16 04:26:04, OOB Acc: 89.29%, # of SNPs: 13, # of Haplo: 34 [10] 2019-10-16 04:26:04, OOB Acc: 80.77%, # of SNPs: 14, # of Haplo: 24 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136 Max. Mean SD 0.5364225707 0.0500771527 0.1262815588 Accuracy with training data: 98.5% Out-of-bag accuracy: 86.1% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 10 total # of SNPs used: 93 avg. # of SNPs in an individual classifier: 13.90 (sd: 2.38, min: 11, max: 19, median: 13.00) avg. # of haplotypes in an individual classifier: 36.70 (sd: 17.93, min: 14, max: 72, median: 34.00) avg. out-of-bag accuracy: 86.05% (sd: 8.68%, min: 75.00%, max: 100.00%, median: 85.16%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136 Max. Mean SD 0.5364225707 0.0500771527 0.1262815588 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:04) 0% Predicting (2019-10-16 04:26:04) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002746 0.006607 0.033131 0.023928 0.536423 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 26 25 51 0.9615385 0.9807692 0 n.call call.rate 1 26 1 Exclude 1 monomorphic SNP Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 340, # of samples: 28 # of unique HLA alleles: 22 [-] 2019-10-16 04:26:04 [1] 2019-10-16 04:26:04, OOB Acc: 58.33%, # of SNPs: 17, # of Haplo: 52 [2] 2019-10-16 04:26:04, OOB Acc: 63.64%, # of SNPs: 18, # of Haplo: 51 [3] 2019-10-16 04:26:04, OOB Acc: 50.00%, # of SNPs: 15, # of Haplo: 29 [4] 2019-10-16 04:26:04, OOB Acc: 59.09%, # of SNPs: 12, # of Haplo: 57 [5] 2019-10-16 04:26:04, OOB Acc: 63.64%, # of SNPs: 15, # of Haplo: 86 [6] 2019-10-16 04:26:04, OOB Acc: 79.17%, # of SNPs: 18, # of Haplo: 66 [7] 2019-10-16 04:26:05, OOB Acc: 70.83%, # of SNPs: 15, # of Haplo: 86 [8] 2019-10-16 04:26:05, OOB Acc: 77.78%, # of SNPs: 16, # of Haplo: 117 [9] 2019-10-16 04:26:05, OOB Acc: 77.78%, # of SNPs: 18, # of Haplo: 92 [10] 2019-10-16 04:26:05, OOB Acc: 61.11%, # of SNPs: 15, # of Haplo: 72 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 9.567411e-05 9.631975e-05 1.021306e-04 3.122433e-03 7.264975e-03 1.191013e-02 Max. Mean SD 2.325640e-01 1.687210e-02 4.316862e-02 Accuracy with training data: 100.0% Out-of-bag accuracy: 66.1% Gene: B Training dataset: 28 samples X 340 SNPs # of HLA alleles: 22 # of individual classifiers: 10 total # of SNPs used: 118 avg. # of SNPs in an individual classifier: 15.90 (sd: 1.91, min: 12, max: 18, median: 15.50) avg. # of haplotypes in an individual classifier: 70.80 (sd: 25.28, min: 29, max: 117, median: 69.00) avg. out-of-bag accuracy: 66.14% (sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 9.567411e-05 9.631975e-05 1.021306e-04 3.122433e-03 7.264975e-03 1.191013e-02 Max. Mean SD 2.325640e-01 1.687210e-02 4.316862e-02 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 15 Predicting (2019-10-16 04:26:05) 0% Predicting (2019-10-16 04:26:05) 100% Gene: B Range: [31321649bp, 31324989bp] on hg19 # of samples: 15 # of unique HLA alleles: 9 # of unique HLA genotypes: 12 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 3 (20.0%) 5 (33.3%) 3 (20.0%) 4 (26.7%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 2.000e-08 4.068e-05 2.934e-03 3.316e-02 6.112e-03 2.412e-01 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 15 11 25 0.7333333 0.8333333 0 n.call call.rate 1 15 1 Exclude 2 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 354, # of samples: 36 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:05 [1] 2019-10-16 04:26:05, OOB Acc: 80.77%, # of SNPs: 19, # of Haplo: 40 [2] 2019-10-16 04:26:05, OOB Acc: 90.91%, # of SNPs: 32, # of Haplo: 32 [3] 2019-10-16 04:26:06, OOB Acc: 89.29%, # of SNPs: 19, # of Haplo: 43 [4] 2019-10-16 04:26:06, OOB Acc: 84.62%, # of SNPs: 19, # of Haplo: 72 [5] 2019-10-16 04:26:06, OOB Acc: 90.00%, # of SNPs: 19, # of Haplo: 66 [6] 2019-10-16 04:26:06, OOB Acc: 95.00%, # of SNPs: 21, # of Haplo: 59 [7] 2019-10-16 04:26:06, OOB Acc: 90.62%, # of SNPs: 18, # of Haplo: 25 [8] 2019-10-16 04:26:06, OOB Acc: 89.29%, # of SNPs: 23, # of Haplo: 57 [9] 2019-10-16 04:26:06, OOB Acc: 84.62%, # of SNPs: 18, # of Haplo: 39 [10] 2019-10-16 04:26:07, OOB Acc: 89.29%, # of SNPs: 35, # of Haplo: 62 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007716165 0.0007743171 0.0007986223 0.0017413935 0.0044732464 0.0093175730 Max. Mean SD 0.0723014524 0.0089787922 0.0135525777 Accuracy with training data: 100.0% Out-of-bag accuracy: 88.4% Gene: C Training dataset: 36 samples X 354 SNPs # of HLA alleles: 17 # of individual classifiers: 10 total # of SNPs used: 135 avg. # of SNPs in an individual classifier: 22.30 (sd: 6.13, min: 18, max: 35, median: 19.00) avg. # of haplotypes in an individual classifier: 49.50 (sd: 15.74, min: 25, max: 72, median: 50.00) avg. out-of-bag accuracy: 88.44% (sd: 4.04%, min: 80.77%, max: 95.00%, median: 89.29%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007716165 0.0007743171 0.0007986223 0.0017413935 0.0044732464 0.0093175730 Max. Mean SD 0.0723014524 0.0089787922 0.0135525777 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 24 Predicting (2019-10-16 04:26:07) 0% Predicting (2019-10-16 04:26:07) 100% Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 24 # of unique HLA alleles: 14 # of unique HLA genotypes: 19 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (8.3%) 3 (12.5%) 6 (25.0%) 13 (54.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0000000 0.0002058 0.0060220 0.0035911 0.0497611 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 24 16 39 0.6666667 0.8125 0 n.call call.rate 1 24 1 Exclude 4 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 345, # of samples: 31 # of unique HLA alleles: 7 [-] 2019-10-16 04:26:07 [1] 2019-10-16 04:26:07, OOB Acc: 95.45%, # of SNPs: 11, # of Haplo: 22 [2] 2019-10-16 04:26:07, OOB Acc: 100.00%, # of SNPs: 13, # of Haplo: 22 [3] 2019-10-16 04:26:07, OOB Acc: 83.33%, # of SNPs: 15, # of Haplo: 23 [4] 2019-10-16 04:26:07, OOB Acc: 82.14%, # of SNPs: 8, # of Haplo: 14 [5] 2019-10-16 04:26:07, OOB Acc: 88.46%, # of SNPs: 11, # of Haplo: 34 [6] 2019-10-16 04:26:07, OOB Acc: 90.00%, # of SNPs: 11, # of Haplo: 21 [7] 2019-10-16 04:26:07, OOB Acc: 92.31%, # of SNPs: 14, # of Haplo: 23 [8] 2019-10-16 04:26:07, OOB Acc: 96.15%, # of SNPs: 11, # of Haplo: 16 [9] 2019-10-16 04:26:07, OOB Acc: 89.29%, # of SNPs: 12, # of Haplo: 19 [10] 2019-10-16 04:26:07, OOB Acc: 86.36%, # of SNPs: 8, # of Haplo: 13 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 Max. Mean SD 0.557800372 0.029648068 0.098391249 Accuracy with training data: 96.8% Out-of-bag accuracy: 90.4% Gene: DQA1 Training dataset: 31 samples X 345 SNPs # of HLA alleles: 7 # of individual classifiers: 10 total # of SNPs used: 80 avg. # of SNPs in an individual classifier: 11.40 (sd: 2.27, min: 8, max: 15, median: 11.00) avg. # of haplotypes in an individual classifier: 20.70 (sd: 5.96, min: 13, max: 34, median: 21.50) avg. out-of-bag accuracy: 90.35% (sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 Max. Mean SD 0.557800372 0.029648068 0.098391249 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 345 SNPs, 7 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 29 Predicting (2019-10-16 04:26:07) 0% Predicting (2019-10-16 04:26:07) 100% Gene: DQA1 Range: [32605169bp, 32612152bp] on hg19 # of samples: 29 # of unique HLA alleles: 6 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 5 (17.2%) 5 (17.2%) 2 (6.9%) 17 (58.6%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000001 0.0019253 0.0069908 0.0562248 0.0167536 0.5649087 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 29 21 49 0.7241379 0.8448276 0 n.call call.rate 1 29 1 Exclude 6 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 350, # of samples: 34 # of unique HLA alleles: 12 [-] 2019-10-16 04:26:07 [1] 2019-10-16 04:26:07, OOB Acc: 86.36%, # of SNPs: 13, # of Haplo: 34 [2] 2019-10-16 04:26:07, OOB Acc: 76.92%, # of SNPs: 21, # of Haplo: 42 [3] 2019-10-16 04:26:07, OOB Acc: 80.77%, # of SNPs: 10, # of Haplo: 17 [4] 2019-10-16 04:26:07, OOB Acc: 92.31%, # of SNPs: 22, # of Haplo: 78 [5] 2019-10-16 04:26:07, OOB Acc: 92.31%, # of SNPs: 11, # of Haplo: 40 [6] 2019-10-16 04:26:07, OOB Acc: 71.43%, # of SNPs: 8, # of Haplo: 22 [7] 2019-10-16 04:26:08, OOB Acc: 71.43%, # of SNPs: 14, # of Haplo: 53 [8] 2019-10-16 04:26:08, OOB Acc: 86.36%, # of SNPs: 14, # of Haplo: 40 [9] 2019-10-16 04:26:08, OOB Acc: 100.00%, # of SNPs: 16, # of Haplo: 56 [10] 2019-10-16 04:26:08, OOB Acc: 88.46%, # of SNPs: 14, # of Haplo: 34 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0003282346 0.0003687353 0.0007332412 0.0039826881 0.0073528147 0.0148594626 Max. Mean SD 0.2905810098 0.0221828682 0.0553213757 Accuracy with training data: 98.5% Out-of-bag accuracy: 84.6% Gene: DQB1 Training dataset: 34 samples X 350 SNPs # of HLA alleles: 12 # of individual classifiers: 10 total # of SNPs used: 99 avg. # of SNPs in an individual classifier: 14.30 (sd: 4.45, min: 8, max: 22, median: 14.00) avg. # of haplotypes in an individual classifier: 41.60 (sd: 17.55, min: 17, max: 78, median: 40.00) avg. out-of-bag accuracy: 84.64% (sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0003282346 0.0003687353 0.0007332412 0.0039826881 0.0073528147 0.0148594626 Max. Mean SD 0.2905810098 0.0221828682 0.0553213757 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 350 SNPs, 12 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:08) 0% Predicting (2019-10-16 04:26:08) 100% Gene: DQB1 Range: [32627241bp, 32634466bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 17 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 3 (11.5%) 7 (26.9%) 5 (19.2%) 11 (42.3%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0002253 0.0018486 0.0289671 0.0099906 0.3724278 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 26 21 46 0.8076923 0.8846154 0 n.call call.rate 1 26 1 Exclude 5 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 18 # of SNPs: 322, # of samples: 35 # of unique HLA alleles: 20 [-] 2019-10-16 04:26:08 [1] 2019-10-16 04:26:08, OOB Acc: 70.00%, # of SNPs: 17, # of Haplo: 77 [2] 2019-10-16 04:26:08, OOB Acc: 68.75%, # of SNPs: 22, # of Haplo: 119 [3] 2019-10-16 04:26:09, OOB Acc: 73.33%, # of SNPs: 19, # of Haplo: 33 [4] 2019-10-16 04:26:09, OOB Acc: 84.62%, # of SNPs: 18, # of Haplo: 67 [5] 2019-10-16 04:26:09, OOB Acc: 86.36%, # of SNPs: 24, # of Haplo: 127 [6] 2019-10-16 04:26:10, OOB Acc: 66.67%, # of SNPs: 18, # of Haplo: 102 [7] 2019-10-16 04:26:10, OOB Acc: 75.00%, # of SNPs: 15, # of Haplo: 71 [8] 2019-10-16 04:26:10, OOB Acc: 70.00%, # of SNPs: 15, # of Haplo: 32 [9] 2019-10-16 04:26:10, OOB Acc: 91.67%, # of SNPs: 20, # of Haplo: 93 [10] 2019-10-16 04:26:11, OOB Acc: 66.67%, # of SNPs: 15, # of Haplo: 57 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.041155e-05 3.565321e-05 1.728281e-04 1.616937e-03 2.836751e-03 7.180633e-03 Max. Mean SD 5.019208e-01 4.576117e-02 1.379081e-01 Accuracy with training data: 94.3% Out-of-bag accuracy: 75.3% Gene: DRB1 Training dataset: 35 samples X 322 SNPs # of HLA alleles: 20 # of individual classifiers: 10 total # of SNPs used: 129 avg. # of SNPs in an individual classifier: 18.30 (sd: 3.06, min: 15, max: 24, median: 18.00) avg. # of haplotypes in an individual classifier: 77.80 (sd: 32.72, min: 32, max: 127, median: 74.00) avg. out-of-bag accuracy: 75.31% (sd: 9.00%, min: 66.67%, max: 91.67%, median: 71.67%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.041155e-05 3.565321e-05 1.728281e-04 1.616937e-03 2.836751e-03 7.180633e-03 Max. Mean SD 5.019208e-01 4.576117e-02 1.379081e-01 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 322 SNPs, 20 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 25 Predicting (2019-10-16 04:26:11) 0% Predicting (2019-10-16 04:26:11) 100% Gene: DRB1 Range: [32546546bp, 32557613bp] on hg19 # of samples: 25 # of unique HLA alleles: 10 # of unique HLA genotypes: 17 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 4 (16.0%) 5 (20.0%) 9 (36.0%) 7 (28.0%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0001451 0.0007388 0.0111563 0.0028980 0.2302289 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 25 16 40 0.64 0.8 0 n.call call.rate 1 25 1 > > > > ############################################################# > > { + function.list <- readRDS( + system.file("Meta", "Rd.rds", package="HIBAG"))$Name + + sapply(function.list, FUN = function(func.name) + { + args <- list( + topic = func.name, + package = "HIBAG", + echo = FALSE, + verbose = FALSE, + ask = FALSE + ) + suppressWarnings(do.call(example, args)) + NULL + }) + invisible() + } SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:11 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13 7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16 8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18 9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19 10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21 11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23 12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23 13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23 [1] 2019-10-16 04:26:11, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17 2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23 3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31 4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31 5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32 6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34 7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38 8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36 9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42 10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42 11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44 12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48 [2] 2019-10-16 04:26:11, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11 2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12 3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12 4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12 5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12 6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12 7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12 8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14 9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14 10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14 11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14 [3] 2019-10-16 04:26:11, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12 2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13 3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16 4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18 5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18 6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20 7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20 8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21 9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21 10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21 11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22 12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23 13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23 [4] 2019-10-16 04:26:11, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Accuracy with training data: 97.1% Out-of-bag accuracy: 81.6% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 38 avg. # of SNPs in an individual classifier: 12.25 (sd: 0.96, min: 11, max: 13, median: 12.50) avg. # of haplotypes in an individual classifier: 27.00 (sd: 14.63, min: 14, max: 48, median: 23.00) avg. out-of-bag accuracy: 81.61% (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:11) 0% Predicting (2019-10-16 04:26:11) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:11) 0% Predicting (2019-10-16 04:26:11) 100% Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 90 Predicting (2019-10-16 04:26:11) 0% Predicting (2019-10-16 04:26:11) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 using the default genome assembly (assembly="hg19") Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 12 # of unique HLA genotypes: 28 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 100 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 dominant model: [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 additive model: [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p 01:01 95 25 50.5 48.0 0.0000 1.000 1.000 02:01 77 43 48.1 53.5 0.1450 0.703 0.704 02:06 119 1 50.4 0.0 0.0000 1.000 1.000 03:01 111 9 49.5 55.6 0.0000 1.000 1.000 11:01 115 5 50.4 40.0 0.0000 1.000 1.000 23:01 117 3 50.4 33.3 0.0000 1.000 1.000 24:02 109 11 46.8 81.8 3.6030 0.058 0.053 24:03 119 1 50.4 0.0 0.0000 1.000 1.000 25:01 115 5 51.3 20.0 0.8348 0.361 0.364 26:01 117 3 51.3 0.0 1.3675 0.242 0.244 29:02 116 4 50.9 25.0 0.2586 0.611 0.619 31:01 117 3 49.6 66.7 0.0000 1.000 1.000 32:01 116 4 48.3 100.0 2.3276 0.127 0.119 68:01 117 3 51.3 0.0 1.3675 0.242 0.244 recessive model: [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p 01:01 59 1 50.8 0 0.000 1.000 1.000 02:01 52 8 46.2 75 1.298 0.255 0.254 02:06 60 0 50.0 . . . . 03:01 60 0 50.0 . . . . 11:01 60 0 50.0 . . . . 23:01 59 1 50.8 0 0.000 1.000 1.000 24:02 60 0 50.0 . . . . 24:03 60 0 50.0 . . . . 25:01 60 0 50.0 . . . . 26:01 60 0 50.0 . . . . 29:02 60 0 50.0 . . . . 31:01 60 0 50.0 . . . . 32:01 60 0 50.0 . . . . 68:01 60 0 50.0 . . . . genotype model: [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p 24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042* ----- 01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000 02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271 02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000 11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000 23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000 24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353 26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612 31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000 32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112 68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 dominant model: [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p 01:01 36 24 -0.14684 -0.117427 0.909 02:01 25 35 -0.32331 -0.000618 0.190 02:06 59 1 -0.14024 0.170057 . 03:01 51 9 -0.05600 -0.583178 0.147 11:01 55 5 -0.19188 0.489815 0.287 23:01 58 2 -0.15400 0.413687 0.281 24:02 49 11 -0.10486 -0.269664 0.537 24:03 59 1 -0.11409 -1.373118 . 25:01 55 5 -0.12237 -0.274749 0.742 26:01 57 3 -0.12473 -0.331558 0.690 29:02 56 4 -0.13044 -0.199941 0.789 31:01 57 3 -0.10097 -0.783003 0.607 32:01 56 4 -0.07702 -0.947791 0.092 68:01 57 3 -0.16915 0.512457 0.196 genotype model: [-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p 01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784 02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446 02:06 59 1 0 -0.14024 0.17006 . 0.756 03:01 51 9 0 -0.05600 -0.58318 . 0.138 11:01 55 5 0 -0.19188 0.48981 . 0.137 23:01 58 1 1 -0.15400 0.10762 0.71975 0.663 24:02 49 11 0 -0.10486 -0.26966 . 0.618 24:03 59 1 0 -0.11409 -1.37312 . 0.205 25:01 55 5 0 -0.12237 -0.27475 . 0.742 26:01 57 3 0 -0.12473 -0.33156 . 0.725 29:02 56 4 0 -0.13044 -0.19994 . 0.892 31:01 57 3 0 -0.10097 -0.78300 . 0.243 32:01 56 4 0 -0.07702 -0.94779 . 0.086 68:01 57 3 0 -0.16915 0.51246 . 0.243 Logistic regression (dominant model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -8.777e-16 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.874e-15 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 h.2.5% h.97.5% h.pval 24:02 0.1585 3.4251 0.032* ----- 01:01 -1.0330 1.0330 1.000 02:01 -1.1643 0.8899 0.793 02:06 -2868.1268 2836.9268 0.991 03:01 -1.1624 1.6872 0.718 11:01 -2.3074 1.4237 0.643 23:01 -2.8192 2.8192 1.000 24:03 -2868.1268 2836.9268 0.991 25:01 -3.7498 0.7588 0.194 26:01 -2731.9621 2698.6192 0.990 29:02 -3.4931 1.1530 0.324 31:01 -1.7277 3.1842 0.561 32:01 -3859.2763 3894.6947 0.993 68:01 -2731.9621 2698.6192 0.990 Logistic regression (dominant model) with 60 individuals: glm(case ~ h + pc1, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01 h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968 ----- 01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982 02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992 02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912 03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964 11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975 23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982 24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965 25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923 26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957 29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899 31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974 32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650 68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764 Logistic regression (dominant model) with 60 individuals: glm(case ~ h + pc1, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08 h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968 ----- 01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982 02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992 02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912 03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964 11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975 23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982 24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965 25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923 26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957 29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899 31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974 32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650 68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764 Linear regression (dominant model) with 60 individuals: glm(y ~ h, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393 02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226 02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603 03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592 11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685 23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518 24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795 24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655 25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507 26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383 29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319 31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508 32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054 68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145 h.pval 01:01 0.910 02:01 0.211 02:06 0.756 03:01 0.138 11:01 0.137 23:01 0.425 24:02 0.618 24:03 0.205 25:01 0.742 26:01 0.725 29:02 0.892 31:01 0.243 32:01 0.086 68:01 0.243 Linear regression (dominant model) with 60 individuals: glm(y ~ h + pc1, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773 02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891 02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722 03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884 11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520 23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598 24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034 24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708 25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388 26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460 29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496 31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254 32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862 68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749 h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386 02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414 02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371 03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539 11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474 23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343 24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393 24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395 25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396 26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390 29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372 31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371 32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210 68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294 Linear regression (dominant model) with 60 individuals: glm(y ~ h + pc1, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773 02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891 02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722 03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884 11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520 23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598 24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034 24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708 25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388 26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460 29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496 31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254 32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862 68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749 h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386 02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414 02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371 03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539 11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474 23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343 24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393 24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395 25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396 26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390 29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372 31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371 32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210 68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294 Logistic regression (additive model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5% 24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585 ----- 01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843 02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289 02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268 03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624 11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074 23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435 24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268 25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498 26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621 29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931 31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277 32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763 68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621 h.97.5% h.pval 24:02 3.4251 0.032* ----- 01:01 0.8430 0.806 02:01 0.9563 0.573 02:06 2836.9268 0.991 03:01 1.6872 0.718 11:01 1.4237 0.643 23:01 1.4789 0.658 24:03 2836.9268 0.991 25:01 0.7588 0.194 26:01 2698.6192 0.990 29:02 1.1530 0.324 31:01 3.1842 0.561 32:01 3894.6947 0.993 68:01 2698.6192 0.990 Logistic regression (recessive model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est 01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600 02:01 52 8 46.2 75 1.298 0.255 0.254 1.253 02:06 60 0 50.0 . . . . . 03:01 60 0 50.0 . . . . . 11:01 60 0 50.0 . . . . . 23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600 24:02 60 0 50.0 . . . . . 24:03 60 0 50.0 . . . . . 25:01 60 0 50.0 . . . . . 26:01 60 0 50.0 . . . . . 29:02 60 0 50.0 . . . . . 31:01 60 0 50.0 . . . . . 32:01 60 0 50.0 . . . . . 68:01 60 0 50.0 . . . . . h.2.5% h.97.5% h.pval 01:01 -2868.1268 2836.927 0.991 02:01 -0.4379 2.943 0.146 02:06 . . . 03:01 . . . 11:01 . . . 23:01 -2868.1268 2836.927 0.991 24:02 . . . 24:03 . . . 25:01 . . . 26:01 . . . 29:02 . . . 31:01 . . . 32:01 . . . 68:01 . . . Logistic regression (genotype model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p 24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042* ----- 01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000 02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271 02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000 11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000 23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000 24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353 26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612 31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000 32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112 68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5% 24:02 1.79176 0.1585 3.4251 0.032* . . . ----- 01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961 02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801 02:06 -15.59997 -2868.1268 2836.9268 0.991 . . . 03:01 0.26236 -1.1624 1.6872 0.718 . . . 11:01 -0.44183 -2.3074 1.4237 0.643 . . . 23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455 24:03 -15.59997 -2868.1268 2836.9268 0.991 . . . 25:01 -1.49549 -3.7498 0.7588 0.194 . . . 26:01 -16.67143 -2731.9621 2698.6192 0.990 . . . 29:02 -1.17007 -3.4931 1.1530 0.324 . . . 31:01 0.72824 -1.7277 3.1842 0.561 . . . 32:01 17.70917 -3859.2763 3894.6947 0.993 . . . 68:01 -16.67143 -2731.9621 2698.6192 0.990 . . . h2.pval 24:02 . ----- 01:01 0.991 02:01 0.263 02:06 . 03:01 . 11:01 . 23:01 0.994 24:03 . 25:01 . 26:01 . 29:02 . 31:01 . 32:01 . 68:01 . Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:12 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13 7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16 8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18 9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19 10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21 11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23 12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23 13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23 [1] 2019-10-16 04:26:12, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17 2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23 3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31 4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31 5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32 6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34 7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38 8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36 9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42 10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42 11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44 12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48 [2] 2019-10-16 04:26:12, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11 2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12 3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12 4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12 5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12 6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12 7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12 8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14 9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14 10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14 11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14 [3] 2019-10-16 04:26:12, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12 2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13 3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16 4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18 5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18 6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20 7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20 8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21 9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21 10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21 11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22 12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23 13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23 [4] 2019-10-16 04:26:12, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Accuracy with training data: 97.1% Out-of-bag accuracy: 81.6% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 38 avg. # of SNPs in an individual classifier: 12.25 (sd: 0.96, min: 11, max: 13, median: 12.50) avg. # of haplotypes in an individual classifier: 27.00 (sd: 14.63, min: 14, max: 48, median: 23.00) avg. out-of-bag accuracy: 81.61% (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:12) 0% Predicting (2019-10-16 04:26:12) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:13) 0% Predicting (2019-10-16 04:26:13) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. SNP genotypes: 90 samples X 3932 SNPs SNPs range from 28694391bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489 Missing rate per sample: min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554 Minor allele frequency: min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144 Allelic information: A/G C/T G/T A/C C/G A/T 1567 1510 348 332 111 64 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 5316 SNPs from chromosome 6. SNP genotypes: 90 samples X 5316 SNPs SNPs range from 25651262bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674 Missing rate per sample: min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735 Minor allele frequency: min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475 Allelic information: A/G C/T G/T A/C C/G A/T 2102 2046 480 471 134 83 Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 9 # of SNPs: 77, # of samples: 60 # of unique HLA alleles: 12 [-] 2019-10-16 04:26:13 [1] 2019-10-16 04:26:13, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20 [2] 2019-10-16 04:26:13, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Accuracy with training data: 95.0% Out-of-bag accuracy: 94.5% Gene: DQB1 Training dataset: 60 samples X 77 SNPs # of HLA alleles: 12 # of individual classifiers: 2 total # of SNPs used: 20 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.41, min: 13, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 20.50 (sd: 0.71, min: 20, max: 21, median: 20.50) avg. out-of-bag accuracy: 94.45% (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Genome assembly: hg19 The HIBAG model: There are 77 SNP predictors in total. There are 2 individual classifiers. Summarize the missing fractions of SNP predictors per classifier: Min. 1st Qu. Median Mean 3rd Qu. Max. 0 0 0 0 0 0 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 60 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 100 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 100 # of unique HLA alleles: 0 # of unique HLA genotypes: 0 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 200 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Exclude 9 monomorphic SNPs Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:13 [1] 2019-10-16 04:26:13, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02 Max. Mean SD 6.267394e-01 6.664806e-02 1.405453e-01 Accuracy with training data: 94.2% Out-of-bag accuracy: 87.0% Exclude 9 monomorphic SNPs Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:13 [1] 2019-10-16 04:26:13, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02 Max. Mean SD 2.755151e-01 2.949891e-02 6.162169e-02 Accuracy with training data: 95.0% Out-of-bag accuracy: 87.5% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 24 avg. # of SNPs in an individual classifier: 13.50 (sd: 2.12, min: 12, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 36.00 (sd: 5.66, min: 32, max: 40, median: 36.00) avg. out-of-bag accuracy: 87.23% (sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02 Max. Mean SD 4.511273e-01 4.807348e-02 1.006148e-01 Genome assembly: hg19 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:13 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13 7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16 8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18 9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19 10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21 11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23 12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23 13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23 [1] 2019-10-16 04:26:13, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17 2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23 3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31 4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31 5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32 6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34 7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38 8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36 9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42 10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42 11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44 12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48 [2] 2019-10-16 04:26:13, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11 2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12 3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12 4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12 5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12 6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12 7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12 8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14 9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14 10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14 11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14 [3] 2019-10-16 04:26:13, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12 2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13 3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16 4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18 5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18 6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20 7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20 8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21 9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21 10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21 11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22 12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23 13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23 [4] 2019-10-16 04:26:13, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Accuracy with training data: 97.1% Out-of-bag accuracy: 81.6% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 38 avg. # of SNPs in an individual classifier: 12.25 (sd: 0.96, min: 11, max: 13, median: 12.50) avg. # of haplotypes in an individual classifier: 27.00 (sd: 14.63, min: 14, max: 48, median: 23.00) avg. out-of-bag accuracy: 81.61% (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:13) 0% Predicting (2019-10-16 04:26:13) 100% Allelic ambiguity: 01:01, 02:02 Allelic ambiguity: 01:01, 02:02 Allelic ambiguity: 09:01 Allelic ambiguity: 09:01 Allelic ambiguity: 05:01, 06:01 Allelic ambiguity: 05:01, 06:01 Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01 Pos Num * - A D E F G H I K L M N Q R S T V W Y 1 120 120 . . . . . . . . . . . . . . . . . . . 9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17 44 120 . 25 . . . . . . . . . . . . 95 . . . . . 56 120 . 117 . . . . . . . . . . . . 3 . . . . . 62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . . 63 120 . 105 . . . . . . . . . . 11 4 . . . . . . 65 120 . 105 . . . . 15 . . . . . . . . . . . . . 66 120 . 61 . . . . . . . 59 . . . . . . . . . . 67 120 . 25 . . . . . . . . . . . . . . . 95 . . 70 120 . 99 . . . . . . . . . . . 21 . . . . . . 73 120 . 117 . . . . . . 3 . . . . . . . . . . . 74 120 . 76 . . . . . 44 . . . . . . . . . . . . 76 120 . 32 . . 24 . . . . . . . . . . . . 64 . . 77 120 . 47 . 64 . . . . . . . . . . . 9 . . . . 79 120 . 96 . . . . . . . . . . . . 24 . . . . . 80 120 . 96 . . . . . . 24 . . . . . . . . . . . 81 120 . 96 24 . . . . . . . . . . . . . . . . . 82 120 . 96 . . . . . . . . 24 . . . . . . . . . 83 120 . 96 . . . . . . . . . . . . 24 . . . . . 90 120 . 38 82 . . . . . . . . . . . . . . . . . 95 120 . 61 . . . . . . . . 15 . . . . . . 44 . . 97 120 . 39 . . . . . . . . . 29 . . 52 . . . . . 99 120 . 105 . . . 15 . . . . . . . . . . . . . . 105 120 . 42 . . . . . . . . . . . . . 78 . . . . 107 120 . 76 . . . . . . . . . . . . . . . . 44 . 109 120 . 116 . . . . . . . . 4 . . . . . . . . . 114 120 . 46 . . . . . 59 . . . . . 15 . . . . . . 116 120 . 61 . . . . . . . . . . . . . . . . . 59 127 120 . 58 . . . . . . . 62 . . . . . . . . . . 142 120 . 73 . . . . . . . . . . . . . . 47 . . . 144 120 . 98 . . . . . . . . . . . 22 . . . . . . 145 120 . 73 . . . . . 47 . . . . . . . . . . . . 149 120 . 112 . . . . . . . . . . . . . . 8 . . . 150 120 . 25 95 . . . . . . . . . . . . . . . . . 151 120 . 106 . . . . . . . . . . . . 14 . . . . . 152 120 . 30 . . 17 . . . . . . . . . . . . 73 . . 156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 . 158 120 . 25 95 . . . . . . . . . . . . . . . . . 161 120 . 111 . 9 . . . . . . . . . . . . . . . . 163 120 . 38 . . . . . . . . . . . . . . 82 . . . 166 120 . 39 . . 81 . . . . . . . . . . . . . . . 167 120 . 39 . . . . . . . . . . . . . . . . 81 . 183 120 120 . . . . . . . . . . . . . . . . . . . Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01 Pos Num * - A D E F G H I K L M N Q R S T V W Y -23 120 120 . . . . . . . . . . . . . . . . . . . -22 120 120 . . . . . . . . . . . . . . . . . . . -21 120 120 . . . . . . . . . . . . . . . . . . . -20 120 120 . . . . . . . . . . . . . . . . . . . -19 120 120 . . . . . . . . . . . . . . . . . . . -18 120 120 . . . . . . . . . . . . . . . . . . . -17 120 120 . . . . . . . . . . . . . . . . . . . -16 120 120 . . . . . . . . . . . . . . . . . . . -15 120 120 . . . . . . . . . . . . . . . . . . . -14 120 120 . . . . . . . . . . . . . . . . . . . -13 120 120 . . . . . . . . . . . . . . . . . . . -12 120 120 . . . . . . . . . . . . . . . . . . . -11 120 120 . . . . . . . . . . . . . . . . . . . -10 120 120 . . . . . . . . . . . . . . . . . . . -9 120 120 . . . . . . . . . . . . . . . . . . . -8 120 120 . . . . . . . . . . . . . . . . . . . -7 120 120 . . . . . . . . . . . . . . . . . . . -6 120 120 . . . . . . . . . . . . . . . . . . . -5 120 120 . . . . . . . . . . . . . . . . . . . -4 120 120 . . . . . . . . . . . . . . . . . . . -3 120 120 . . . . . . . . . . . . . . . . . . . -2 120 120 . . . . . . . . . . . . . . . . . . . -1 120 120 . . . . . . . . . . . . . . . . . . . . 120 120 . . . . . . . . . . . . . . . . . . . 1 120 120 . . . . . . . . . . . . . . . . . . . 9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17 44 120 . 25 . . . . . . . . . . . . 95 . . . . . 56 120 . 117 . . . . . . . . . . . . 3 . . . . . 62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . . 63 120 . 105 . . . . . . . . . . 11 4 . . . . . . 65 120 . 105 . . . . 15 . . . . . . . . . . . . . 66 120 . 61 . . . . . . . 59 . . . . . . . . . . 67 120 . 25 . . . . . . . . . . . . . . . 95 . . 70 120 . 99 . . . . . . . . . . . 21 . . . . . . 73 120 . 117 . . . . . . 3 . . . . . . . . . . . 74 120 . 76 . . . . . 44 . . . . . . . . . . . . 76 120 . 32 . . 24 . . . . . . . . . . . . 64 . . 77 120 . 47 . 64 . . . . . . . . . . . 9 . . . . 79 120 . 96 . . . . . . . . . . . . 24 . . . . . 80 120 . 96 . . . . . . 24 . . . . . . . . . . . 81 120 . 96 24 . . . . . . . . . . . . . . . . . 82 120 . 96 . . . . . . . . 24 . . . . . . . . . 83 120 . 96 . . . . . . . . . . . . 24 . . . . . 90 120 . 38 82 . . . . . . . . . . . . . . . . . 95 120 . 61 . . . . . . . . 15 . . . . . . 44 . . 97 120 . 39 . . . . . . . . . 29 . . 52 . . . . . 99 120 . 105 . . . 15 . . . . . . . . . . . . . . 105 120 . 42 . . . . . . . . . . . . . 78 . . . . 107 120 . 76 . . . . . . . . . . . . . . . . 44 . 109 120 . 116 . . . . . . . . 4 . . . . . . . . . 114 120 . 46 . . . . . 59 . . . . . 15 . . . . . . 116 120 . 61 . . . . . . . . . . . . . . . . . 59 127 120 . 58 . . . . . . . 62 . . . . . . . . . . 142 120 . 73 . . . . . . . . . . . . . . 47 . . . 144 120 . 98 . . . . . . . . . . . 22 . . . . . . 145 120 . 73 . . . . . 47 . . . . . . . . . . . . 149 120 . 112 . . . . . . . . . . . . . . 8 . . . 150 120 . 25 95 . . . . . . . . . . . . . . . . . 151 120 . 106 . . . . . . . . . . . . 14 . . . . . 152 120 . 30 . . 17 . . . . . . . . . . . . 73 . . 156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 . 158 120 . 25 95 . . . . . . . . . . . . . . . . . 161 120 . 111 . 9 . . . . . . . . . . . . . . . . 163 120 . 38 . . . . . . . . . . . . . . 82 . . . 166 120 . 39 . . 81 . . . . . . . . . . . . . . . 167 120 . 39 . . . . . . . . . . . . . . . . 81 . 183 120 120 . . . . . . . . . . . . . . . . . . . 184 120 120 . . . . . . . . . . . . . . . . . . . 185 120 120 . . . . . . . . . . . . . . . . . . . 186 120 120 . . . . . . . . . . . . . . . . . . . 187 120 120 . . . . . . . . . . . . . . . . . . . 188 120 120 . . . . . . . . . . . . . . . . . . . 189 120 120 . . . . . . . . . . . . . . . . . . . 190 120 120 . . . . . . . . . . . . . . . . . . . 191 120 120 . . . . . . . . . . . . . . . . . . . 192 120 120 . . . . . . . . . . . . . . . . . . . 193 120 120 . . . . . . . . . . . . . . . . . . . 194 120 120 . . . . . . . . . . . . . . . . . . . 195 120 120 . . . . . . . . . . . . . . . . . . . 196 120 120 . . . . . . . . . . . . . . . . . . . 197 120 120 . . . . . . . . . . . . . . . . . . . 198 120 120 . . . . . . . . . . . . . . . . . . . 199 120 120 . . . . . . . . . . . . . . . . . . . 200 120 120 . . . . . . . . . . . . . . . . . . . 201 120 120 . . . . . . . . . . . . . . . . . . . 202 120 120 . . . . . . . . . . . . . . . . . . . 203 120 120 . . . . . . . . . . . . . . . . . . . 204 120 120 . . . . . . . . . . . . . . . . . . . 205 120 120 . . . . . . . . . . . . . . . . . . . 206 120 120 . . . . . . . . . . . . . . . . . . . 207 120 120 . . . . . . . . . . . . . . . . . . . 208 120 120 . . . . . . . . . . . . . . . . . . . 209 120 120 . . . . . . . . . . . . . . . . . . . 210 120 120 . . . . . . . . . . . . . . . . . . . 211 120 120 . . . . . . . . . . . . . . . . . . . 212 120 120 . . . . . . . . . . . . . . . . . . . 213 120 120 . . . . . . . . . . . . . . . . . . . 214 120 120 . . . . . . . . . . . . . . . . . . . 215 120 120 . . . . . . . . . . . . . . . . . . . 216 120 120 . . . . . . . . . . . . . . . . . . . 217 120 120 . . . . . . . . . . . . . . . . . . . 218 120 120 . . . . . . . . . . . . . . . . . . . 219 120 120 . . . . . . . . . . . . . . . . . . . 220 120 120 . . . . . . . . . . . . . . . . . . . 221 120 120 . . . . . . . . . . . . . . . . . . . 222 120 120 . . . . . . . . . . . . . . . . . . . 223 120 120 . . . . . . . . . . . . . . . . . . . 224 120 120 . . . . . . . . . . . . . . . . . . . 225 120 120 . . . . . . . . . . . . . . . . . . . 226 120 120 . . . . . . . . . . . . . . . . . . . 227 120 120 . . . . . . . . . . . . . . . . . . . 228 120 120 . . . . . . . . . . . . . . . . . . . 229 120 120 . . . . . . . . . . . . . . . . . . . 230 120 120 . . . . . . . . . . . . . . . . . . . 231 120 120 . . . . . . . . . . . . . . . . . . . 232 120 120 . . . . . . . . . . . . . . . . . . . 233 120 120 . . . . . . . . . . . . . . . . . . . 234 120 120 . . . . . . . . . . . . . . . . . . . 235 120 120 . . . . . . . . . . . . . . . . . . . 236 120 120 . . . . . . . . . . . . . . . . . . . 237 120 120 . . . . . . . . . . . . . . . . . . . 238 120 120 . . . . . . . . . . . . . . . . . . . 239 120 120 . . . . . . . . . . . . . . . . . . . 240 120 120 . . . . . . . . . . . . . . . . . . . 241 120 120 . . . . . . . . . . . . . . . . . . . 242 120 120 . . . . . . . . . . . . . . . . . . . 243 120 120 . . . . . . . . . . . . . . . . . . . 244 120 120 . . . . . . . . . . . . . . . . . . . 245 120 120 . . . . . . . . . . . . . . . . . . . 246 120 120 . . . . . . . . . . . . . . . . . . . 247 120 120 . . . . . . . . . . . . . . . . . . . 248 120 120 . . . . . . . . . . . . . . . . . . . 249 120 120 . . . . . . . . . . . . . . . . . . . 250 120 120 . . . . . . . . . . . . . . . . . . . 251 120 120 . . . . . . . . . . . . . . . . . . . 252 120 120 . . . . . . . . . . . . . . . . . . . 253 120 120 . . . . . . . . . . . . . . . . . . . 254 120 120 . . . . . . . . . . . . . . . . . . . 255 120 120 . . . . . . . . . . . . . . . . . . . 256 120 120 . . . . . . . . . . . . . . . . . . . 257 120 120 . . . . . . . . . . . . . . . . . . . 258 120 120 . . . . . . . . . . . . . . . . . . . 259 120 120 . . . . . . . . . . . . . . . . . . . 260 120 120 . . . . . . . . . . . . . . . . . . . 261 120 120 . . . . . . . . . . . . . . . . . . . 262 120 120 . . . . . . . . . . . . . . . . . . . 263 120 120 . . . . . . . . . . . . . . . . . . . 264 120 120 . . . . . . . . . . . . . . . . . . . 265 120 120 . . . . . . . . . . . . . . . . . . . 266 120 120 . . . . . . . . . . . . . . . . . . . 267 120 120 . . . . . . . . . . . . . . . . . . . 268 120 120 . . . . . . . . . . . . . . . . . . . 269 120 120 . . . . . . . . . . . . . . . . . . . 270 120 120 . . . . . . . . . . . . . . . . . . . 271 120 120 . . . . . . . . . . . . . . . . . . . 272 120 120 . . . . . . . . . . . . . . . . . . . 273 120 120 . . . . . . . . . . . . . . . . . . . 274 120 120 . . . . . . . . . . . . . . . . . . . 275 120 120 . . . . . . . . . . . . . . . . . . . 276 120 120 . . . . . . . . . . . . . . . . . . . 277 120 120 . . . . . . . . . . . . . . . . . . . 278 120 120 . . . . . . . . . . . . . . . . . . . 279 120 120 . . . . . . . . . . . . . . . . . . . 280 120 120 . . . . . . . . . . . . . . . . . . . 281 120 120 . . . . . . . . . . . . . . . . . . . 282 120 120 . . . . . . . . . . . . . . . . . . . 283 120 120 . . . . . . . . . . . . . . . . . . . 284 120 120 . . . . . . . . . . . . . . . . . . . 285 120 120 . . . . . . . . . . . . . . . . . . . 286 120 120 . . . . . . . . . . . . . . . . . . . 287 120 120 . . . . . . . . . . . . . . . . . . . 288 120 120 . . . . . . . . . . . . . . . . . . . 289 120 120 . . . . . . . . . . . . . . . . . . . 290 120 120 . . . . . . . . . . . . . . . . . . . 291 120 120 . . . . . . . . . . . . . . . . . . . 292 120 120 . . . . . . . . . . . . . . . . . . . 293 120 120 . . . . . . . . . . . . . . . . . . . 294 120 120 . . . . . . . . . . . . . . . . . . . 295 120 120 . . . . . . . . . . . . . . . . . . . 296 120 120 . . . . . . . . . . . . . . . . . . . 297 120 120 . . . . . . . . . . . . . . . . . . . 298 120 120 . . . . . . . . . . . . . . . . . . . 299 120 120 . . . . . . . . . . . . . . . . . . . 300 120 120 . . . . . . . . . . . . . . . . . . . 301 120 120 . . . . . . . . . . . . . . . . . . . 302 120 120 . . . . . . . . . . . . . . . . . . . 303 120 120 . . . . . . . . . . . . . . . . . . . 304 120 120 . . . . . . . . . . . . . . . . . . . 305 120 120 . . . . . . . . . . . . . . . . . . . 306 120 120 . . . . . . . . . . . . . . . . . . . 307 120 120 . . . . . . . . . . . . . . . . . . . 308 120 120 . . . . . . . . . . . . . . . . . . . 309 120 120 . . . . . . . . . . . . . . . . . . . 310 120 120 . . . . . . . . . . . . . . . . . . . 311 120 120 . . . . . . . . . . . . . . . . . . . 312 120 120 . . . . . . . . . . . . . . . . . . . 313 120 120 . . . . . . . . . . . . . . . . . . . 314 120 120 . . . . . . . . . . . . . . . . . . . 315 120 120 . . . . . . . . . . . . . . . . . . . 316 120 120 . . . . . . . . . . . . . . . . . . . 317 120 120 . . . . . . . . . . . . . . . . . . . 318 120 120 . . . . . . . . . . . . . . . . . . . 319 120 120 . . . . . . . . . . . . . . . . . . . 320 120 120 . . . . . . . . . . . . . . . . . . . 321 120 120 . . . . . . . . . . . . . . . . . . . 322 120 120 . . . . . . . . . . . . . . . . . . . 323 120 120 . . . . . . . . . . . . . . . . . . . 324 120 120 . . . . . . . . . . . . . . . . . . . 325 120 120 . . . . . . . . . . . . . . . . . . . 326 120 120 . . . . . . . . . . . . . . . . . . . 327 120 120 . . . . . . . . . . . . . . . . . . . 328 120 120 . . . . . . . . . . . . . . . . . . . 329 120 120 . . . . . . . . . . . . . . . . . . . 330 120 120 . . . . . . . . . . . . . . . . . . . 331 120 120 . . . . . . . . . . . . . . . . . . . 332 120 120 . . . . . . . . . . . . . . . . . . . 333 120 120 . . . . . . . . . . . . . . . . . . . 334 120 120 . . . . . . . . . . . . . . . . . . . 335 120 120 . . . . . . . . . . . . . . . . . . . 336 120 120 . . . . . . . . . . . . . . . . . . . 337 120 120 . . . . . . . . . . . . . . . . . . . 338 120 120 . . . . . . . . . . . . . . . . . . . 339 120 120 . . . . . . . . . . . . . . . . . . . 340 120 120 . . . . . . . . . . . . . . . . . . . 341 120 120 . . . . . . . . . . . . . . . . . . . Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02 Pos Num * - A D E F G I K L M N P Q R S T Y 5 120 112 . . . . . . . . . . 8 . . . . . . 6 120 20 92 8 . . . . . . . . . . . . . . . 7 112 20 92 . . . . . . . . . . . . . . . . 8 112 20 92 . . . . . . . . . . . . . . . . 9 112 3 76 . . . 33 . . . . . . . . . . . . 10 112 3 109 . . . . . . . . . . . . . . . . 11 112 3 109 . . . . . . . . . . . . . . . . 12 112 3 109 . . . . . . . . . . . . . . . . 13 112 3 93 16 . . . . . . . . . . . . . . . 14 112 3 14 . . . . . . . . 95 . . . . . . . 15 112 3 109 . . . . . . . . . . . . . . . . 16 112 3 109 . . . . . . . . . . . . . . . . 17 112 3 109 . . . . . . . . . . . . . . . . 18 112 3 109 . . . . . . . . . . . . . . . . 19 112 3 109 . . . . . . . . . . . . . . . . 20 112 3 109 . . . . . . . . . . . . . . . . 26 112 . 20 . . . . . . . 76 . . . . . . . 16 28 112 . 100 . . . . . . . . . . . . . 12 . . 30 112 . 24 . . . . . . . . . . . . . 12 . 76 37 112 . 100 . . . . . 12 . . . . . . . . . . 38 112 . 29 83 . . . . . . . . . . . . . . . 45 112 . 96 . . 16 . . . . . . . . . . . . . 46 112 . 100 . . 12 . . . . . . . . . . . . . 47 112 . 100 . . . 12 . . . . . . . . . . . . 52 112 . 100 . . . . . . . 12 . . . . . . . . 53 112 . 54 . . . . . . . 58 . . . . . . . . 55 112 . 57 . . . . . . . 12 . . 43 . . . . . 56 112 . 109 . . . . . . . 3 . . . . . . . . 57 112 . 14 33 64 . . . . . . . . . . . 1 . . 66 112 . 97 . 15 . . . . . . . . . . . . . . 67 112 . 97 . . . . . 15 . . . . . . . . . . 70 112 3 50 . . 3 . . . . . . . . . 56 . . . 71 112 3 14 . 3 . . . . 12 . . . . . . . 80 . 72 112 3 109 . . . . . . . . . . . . . . . . 73 112 3 109 . . . . . . . . . . . . . . . . 74 112 3 17 12 . 80 . . . . . . . . . . . . . 75 112 3 29 . . . . . . . 80 . . . . . . . . 76 112 3 109 . . . . . . . . . . . . . . . . 77 112 3 26 . . . . . . . . . . . . . . 83 . 78 112 3 109 . . . . . . . . . . . . . . . . 79 112 3 109 . . . . . . . . . . . . . . . . 80 112 3 109 . . . . . . . . . . . . . . . . 81 112 3 109 . . . . . . . . . . . . . . . . 82 112 3 109 . . . . . . . . . . . . . . . . 83 112 3 109 . . . . . . . . . . . . . . . . 84 112 3 51 . . . . . . . . . . . 58 . . . . 85 112 3 51 . . . . . . . 58 . . . . . . . . 86 112 3 50 . . 58 . 1 . . . . . . . . . . . 87 112 3 15 . . . 36 . . . 58 . . . . . . . . 88 112 3 109 . . . . . . . . . . . . . . . . 89 112 3 51 . . . . . . . . . . . . . . 58 . 90 112 3 51 . . . . . . . . . . . . . . 58 . 91 112 3 109 . . . . . . . . . . . . . . . . 92 112 3 109 . . . . . . . . . . . . . . . . 93 112 3 109 . . . . . . . . . . . . . . . . 94 112 17 95 . . . . . . . . . . . . . . . . 95 112 112 . . . . . . . . . . . . . . . . . Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02 Pos Num * - A D E F G I K L M N P Q R S T Y -31 120 112 . . . . . . . . . . 8 . . . . . . -30 120 112 . 8 . . . . . . . . . . . . . . . -29 112 112 . . . . . . . . . . . . . . . . . -28 112 112 . . . . . . . . . . . . . . . . . -27 112 112 . . . . . . . . . . . . . . . . . -26 112 112 . . . . . . . . . . . . . . . . . -25 112 112 . . . . . . . . . . . . . . . . . -24 112 112 . . . . . . . . . . . . . . . . . -23 112 112 . . . . . . . . . . . . . . . . . -22 112 112 . . . . . . . . . . . . . . . . . -21 112 112 . . . . . . . . . . . . . . . . . -20 112 112 . . . . . . . . . . . . . . . . . -19 112 112 . . . . . . . . . . . . . . . . . -18 112 112 . . . . . . . . . . . . . . . . . -17 112 112 . . . . . . . . . . . . . . . . . -16 112 112 . . . . . . . . . . . . . . . . . -15 112 112 . . . . . . . . . . . . . . . . . -14 112 112 . . . . . . . . . . . . . . . . . -13 112 112 . . . . . . . . . . . . . . . . . -12 112 112 . . . . . . . . . . . . . . . . . -11 112 112 . . . . . . . . . . . . . . . . . -10 112 112 . . . . . . . . . . . . . . . . . -9 112 112 . . . . . . . . . . . . . . . . . -8 112 112 . . . . . . . . . . . . . . . . . -7 112 112 . . . . . . . . . . . . . . . . . -6 112 112 . . . . . . . . . . . . . . . . . -5 112 112 . . . . . . . . . . . . . . . . . -4 112 112 . . . . . . . . . . . . . . . . . -3 112 112 . . . . . . . . . . . . . . . . . -2 112 112 . . . . . . . . . . . . . . . . . -1 112 112 . . . . . . . . . . . . . . . . . . 112 112 . . . . . . . . . . . . . . . . . 1 112 112 . . . . . . . . . . . . . . . . . 2 112 112 . . . . . . . . . . . . . . . . . 3 112 112 . . . . . . . . . . . . . . . . . 4 112 112 . . . . . . . . . . . . . . . . . 5 112 112 . . . . . . . . . . . . . . . . . 6 112 20 92 . . . . . . . . . . . . . . . . 7 112 20 92 . . . . . . . . . . . . . . . . 8 112 20 92 . . . . . . . . . . . . . . . . 9 112 3 76 . . . 33 . . . . . . . . . . . . 10 112 3 109 . . . . . . . . . . . . . . . . 11 112 3 109 . . . . . . . . . . . . . . . . 12 112 3 109 . . . . . . . . . . . . . . . . 13 112 3 93 16 . . . . . . . . . . . . . . . 14 112 3 14 . . . . . . . . 95 . . . . . . . 15 112 3 109 . . . . . . . . . . . . . . . . 16 112 3 109 . . . . . . . . . . . . . . . . 17 112 3 109 . . . . . . . . . . . . . . . . 18 112 3 109 . . . . . . . . . . . . . . . . 19 112 3 109 . . . . . . . . . . . . . . . . 20 112 3 109 . . . . . . . . . . . . . . . . 26 112 . 20 . . . . . . . 76 . . . . . . . 16 28 112 . 100 . . . . . . . . . . . . . 12 . . 30 112 . 24 . . . . . . . . . . . . . 12 . 76 37 112 . 100 . . . . . 12 . . . . . . . . . . 38 112 . 29 83 . . . . . . . . . . . . . . . 45 112 . 96 . . 16 . . . . . . . . . . . . . 46 112 . 100 . . 12 . . . . . . . . . . . . . 47 112 . 100 . . . 12 . . . . . . . . . . . . 52 112 . 100 . . . . . . . 12 . . . . . . . . 53 112 . 54 . . . . . . . 58 . . . . . . . . 55 112 . 57 . . . . . . . 12 . . 43 . . . . . 56 112 . 109 . . . . . . . 3 . . . . . . . . 57 112 . 14 33 64 . . . . . . . . . . . 1 . . 66 112 . 97 . 15 . . . . . . . . . . . . . . 67 112 . 97 . . . . . 15 . . . . . . . . . . 70 112 3 50 . . 3 . . . . . . . . . 56 . . . 71 112 3 14 . 3 . . . . 12 . . . . . . . 80 . 72 112 3 109 . . . . . . . . . . . . . . . . 73 112 3 109 . . . . . . . . . . . . . . . . 74 112 3 17 12 . 80 . . . . . . . . . . . . . 75 112 3 29 . . . . . . . 80 . . . . . . . . 76 112 3 109 . . . . . . . . . . . . . . . . 77 112 3 26 . . . . . . . . . . . . . . 83 . 78 112 3 109 . . . . . . . . . . . . . . . . 79 112 3 109 . . . . . . . . . . . . . . . . 80 112 3 109 . . . . . . . . . . . . . . . . 81 112 3 109 . . . . . . . . . . . . . . . . 82 112 3 109 . . . . . . . . . . . . . . . . 83 112 3 109 . . . . . . . . . . . . . . . . 84 112 3 51 . . . . . . . . . . . 58 . . . . 85 112 3 51 . . . . . . . 58 . . . . . . . . 86 112 3 50 . . 58 . 1 . . . . . . . . . . . 87 112 3 15 . . . 36 . . . 58 . . . . . . . . 88 112 3 109 . . . . . . . . . . . . . . . . 89 112 3 51 . . . . . . . . . . . . . . 58 . 90 112 3 51 . . . . . . . . . . . . . . 58 . 91 112 3 109 . . . . . . . . . . . . . . . . 92 112 3 109 . . . . . . . . . . . . . . . . 93 112 3 109 . . . . . . . . . . . . . . . . 94 112 17 95 . . . . . . . . . . . . . . . . 95 112 112 . . . . . . . . . . . . . . . . . 96 112 112 . . . . . . . . . . . . . . . . . 97 112 112 . . . . . . . . . . . . . . . . . 98 112 112 . . . . . . . . . . . . . . . . . 99 112 112 . . . . . . . . . . . . . . . . . 100 112 112 . . . . . . . . . . . . . . . . . 101 112 112 . . . . . . . . . . . . . . . . . 102 112 112 . . . . . . . . . . . . . . . . . 103 112 112 . . . . . . . . . . . . . . . . . 104 112 112 . . . . . . . . . . . . . . . . . 105 112 112 . . . . . . . . . . . . . . . . . 106 112 112 . . . . . . . . . . . . . . . . . 107 112 112 . . . . . . . . . . . . . . . . . 108 112 112 . . . . . . . . . . . . . . . . . 109 112 112 . . . . . . . . . . . . . . . . . 110 112 112 . . . . . . . . . . . . . . . . . 111 112 112 . . . . . . . . . . . . . . . . . 112 112 112 . . . . . . . . . . . . . . . . . 113 112 112 . . . . . . . . . . . . . . . . . 114 112 112 . . . . . . . . . . . . . . . . . 115 112 112 . . . . . . . . . . . . . . . . . 116 112 112 . . . . . . . . . . . . . . . . . 117 112 112 . . . . . . . . . . . . . . . . . 118 112 112 . . . . . . . . . . . . . . . . . 119 112 112 . . . . . . . . . . . . . . . . . 120 112 112 . . . . . . . . . . . . . . . . . 121 112 112 . . . . . . . . . . . . . . . . . 122 112 112 . . . . . . . . . . . . . . . . . 123 112 112 . . . . . . . . . . . . . . . . . 124 112 112 . . . . . . . . . . . . . . . . . 125 112 112 . . . . . . . . . . . . . . . . . 126 112 112 . . . . . . . . . . . . . . . . . 127 112 112 . . . . . . . . . . . . . . . . . 128 112 112 . . . . . . . . . . . . . . . . . 129 112 112 . . . . . . . . . . . . . . . . . 130 112 112 . . . . . . . . . . . . . . . . . 131 112 112 . . . . . . . . . . . . . . . . . 132 112 112 . . . . . . . . . . . . . . . . . 133 112 112 . . . . . . . . . . . . . . . . . 134 112 112 . . . . . . . . . . . . . . . . . 135 112 112 . . . . . . . . . . . . . . . . . 136 112 112 . . . . . . . . . . . . . . . . . 137 112 112 . . . . . . . . . . . . . . . . . 138 112 112 . . . . . . . . . . . . . . . . . 139 112 112 . . . . . . . . . . . . . . . . . 140 112 112 . . . . . . . . . . . . . . . . . 141 112 112 . . . . . . . . . . . . . . . . . 142 112 112 . . . . . . . . . . . . . . . . . 143 112 112 . . . . . . . . . . . . . . . . . 144 112 112 . . . . . . . . . . . . . . . . . 145 112 112 . . . . . . . . . . . . . . . . . 146 112 112 . . . . . . . . . . . . . . . . . 147 112 112 . . . . . . . . . . . . . . . . . 148 112 112 . . . . . . . . . . . . . . . . . 149 112 112 . . . . . . . . . . . . . . . . . 150 112 112 . . . . . . . . . . . . . . . . . 151 112 112 . . . . . . . . . . . . . . . . . 152 112 112 . . . . . . . . . . . . . . . . . 153 112 112 . . . . . . . . . . . . . . . . . 154 112 112 . . . . . . . . . . . . . . . . . 155 112 112 . . . . . . . . . . . . . . . . . 156 112 112 . . . . . . . . . . . . . . . . . 157 112 112 . . . . . . . . . . . . . . . . . 158 112 112 . . . . . . . . . . . . . . . . . 159 112 112 . . . . . . . . . . . . . . . . . 160 112 112 . . . . . . . . . . . . . . . . . 161 112 112 . . . . . . . . . . . . . . . . . 162 112 112 . . . . . . . . . . . . . . . . . 163 112 112 . . . . . . . . . . . . . . . . . 164 112 112 . . . . . . . . . . . . . . . . . 165 112 112 . . . . . . . . . . . . . . . . . 166 112 112 . . . . . . . . . . . . . . . . . 167 112 112 . . . . . . . . . . . . . . . . . 168 112 112 . . . . . . . . . . . . . . . . . 169 112 112 . . . . . . . . . . . . . . . . . 170 112 112 . . . . . . . . . . . . . . . . . 171 112 112 . . . . . . . . . . . . . . . . . 172 112 112 . . . . . . . . . . . . . . . . . 173 112 112 . . . . . . . . . . . . . . . . . 174 112 112 . . . . . . . . . . . . . . . . . 175 112 112 . . . . . . . . . . . . . . . . . 176 112 112 . . . . . . . . . . . . . . . . . 177 112 112 . . . . . . . . . . . . . . . . . 178 112 112 . . . . . . . . . . . . . . . . . 179 112 112 . . . . . . . . . . . . . . . . . 180 112 112 . . . . . . . . . . . . . . . . . 181 112 112 . . . . . . . . . . . . . . . . . 182 112 112 . . . . . . . . . . . . . . . . . 183 112 112 . . . . . . . . . . . . . . . . . 184 112 112 . . . . . . . . . . . . . . . . . 185 112 112 . . . . . . . . . . . . . . . . . 186 112 112 . . . . . . . . . . . . . . . . . 187 112 112 . . . . . . . . . . . . . . . . . 188 112 112 . . . . . . . . . . . . . . . . . 189 112 112 . . . . . . . . . . . . . . . . . 190 112 112 . . . . . . . . . . . . . . . . . 191 112 112 . . . . . . . . . . . . . . . . . 192 112 112 . . . . . . . . . . . . . . . . . 193 112 112 . . . . . . . . . . . . . . . . . 194 112 112 . . . . . . . . . . . . . . . . . 195 112 112 . . . . . . . . . . . . . . . . . 196 112 112 . . . . . . . . . . . . . . . . . 197 112 112 . . . . . . . . . . . . . . . . . 198 112 112 . . . . . . . . . . . . . . . . . 199 112 112 . . . . . . . . . . . . . . . . . 200 112 112 . . . . . . . . . . . . . . . . . 201 112 112 . . . . . . . . . . . . . . . . . 202 112 112 . . . . . . . . . . . . . . . . . 203 112 112 . . . . . . . . . . . . . . . . . 204 112 112 . . . . . . . . . . . . . . . . . 205 112 112 . . . . . . . . . . . . . . . . . 206 112 112 . . . . . . . . . . . . . . . . . 207 112 112 . . . . . . . . . . . . . . . . . 208 112 112 . . . . . . . . . . . . . . . . . 209 112 112 . . . . . . . . . . . . . . . . . 210 112 112 . . . . . . . . . . . . . . . . . 211 112 112 . . . . . . . . . . . . . . . . . 212 112 112 . . . . . . . . . . . . . . . . . 213 112 112 . . . . . . . . . . . . . . . . . 214 112 112 . . . . . . . . . . . . . . . . . 215 112 112 . . . . . . . . . . . . . . . . . 216 112 112 . . . . . . . . . . . . . . . . . 217 112 112 . . . . . . . . . . . . . . . . . 218 112 112 . . . . . . . . . . . . . . . . . 219 112 112 . . . . . . . . . . . . . . . . . 220 112 112 . . . . . . . . . . . . . . . . . 221 112 112 . . . . . . . . . . . . . . . . . 222 112 112 . . . . . . . . . . . . . . . . . 223 112 112 . . . . . . . . . . . . . . . . . 224 112 112 . . . . . . . . . . . . . . . . . 225 112 112 . . . . . . . . . . . . . . . . . 226 112 112 . . . . . . . . . . . . . . . . . 227 112 112 . . . . . . . . . . . . . . . . . 228 112 112 . . . . . . . . . . . . . . . . . 229 112 112 . . . . . . . . . . . . . . . . . 230 112 112 . . . . . . . . . . . . . . . . . 231 112 112 . . . . . . . . . . . . . . . . . 232 112 112 . . . . . . . . . . . . . . . . . 233 112 112 . . . . . . . . . . . . . . . . . 234 112 112 . . . . . . . . . . . . . . . . . 235 112 112 . . . . . . . . . . . . . . . . . 236 112 112 . . . . . . . . . . . . . . . . . 237 112 112 . . . . . . . . . . . . . . . . . SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 Exclude 9 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:16 [1] 2019-10-16 04:26:16, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32 [2] 2019-10-16 04:26:16, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40 [3] 2019-10-16 04:26:17, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21 [4] 2019-10-16 04:26:17, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25 [5] 2019-10-16 04:26:17, OOB Acc: 78.95%, # of SNPs: 14, # of Haplo: 21 [6] 2019-10-16 04:26:17, OOB Acc: 93.75%, # of SNPs: 16, # of Haplo: 22 [7] 2019-10-16 04:26:17, OOB Acc: 93.75%, # of SNPs: 24, # of Haplo: 81 [8] 2019-10-16 04:26:17, OOB Acc: 92.86%, # of SNPs: 20, # of Haplo: 45 [9] 2019-10-16 04:26:18, OOB Acc: 94.74%, # of SNPs: 16, # of Haplo: 45 [10] 2019-10-16 04:26:18, OOB Acc: 97.37%, # of SNPs: 15, # of Haplo: 40 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 Max. Mean SD 0.3770420992 0.0416903408 0.0825371577 Accuracy with training data: 98.3% Out-of-bag accuracy: 91.9% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 10 total # of SNPs used: 95 avg. # of SNPs in an individual classifier: 16.00 (sd: 3.50, min: 12, max: 24, median: 15.00) avg. # of haplotypes in an individual classifier: 37.20 (sd: 18.22, min: 21, max: 81, median: 36.00) avg. out-of-bag accuracy: 91.92% (sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 Max. Mean SD 0.3770420992 0.0416903408 0.0825371577 Genome assembly: hg19 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 Loading required namespace: gdsfmt Loading required namespace: SNPRelate Import 2348 SNPs from chromosome 6. 3 SNPs with invalid alleles have been removed. SNP genotypes: 165 samples X 2345 SNPs SNPs range from 24652946bp to 33524089bp on hg18 Missing rate per SNP: min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919 Missing rate per sample: min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105 Minor allele frequency: min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319 Allelic information: A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A 556 467 410 400 116 109 103 81 30 29 28 16 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. No allelic strand orders are switched. SNP genotypes: 150 samples X 1214 SNPs SNPs range from 28695148bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841 Missing rate per sample: min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119 Minor allele frequency: min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855 Allelic information: A/G C/T G/T A/C 505 496 109 104 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1197 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446 Missing rate per sample: min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786 Minor allele frequency: min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338 Allelic information: A/G C/T A/C G/T 511 476 105 105 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam". Open "C:/Users/biocbuild/bbs-3.9-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. SNP genotypes: 90 samples X 3932 SNPs SNPs range from 28694391bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489 Missing rate per sample: min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554 Minor allele frequency: min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144 Allelic information: A/G C/T G/T A/C C/G A/T 1567 1510 348 332 111 64 No allelic strand orders are switched. SNP genotypes: 60 samples X 1214 SNPs SNPs range from 28695148bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381 Missing rate per sample: min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373 Minor allele frequency: min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833 Allelic information: A/G C/T G/T A/C 505 496 109 104 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 MAF filter (>=0.01), excluding 9 SNP(s) using the default genome assembly (assembly="hg19") SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:20 [1] 2019-10-16 04:26:20, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02 Max. Mean SD 9.105734e-02 2.054649e-02 2.598603e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 92.0% Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:20 [1] 2019-10-16 04:26:20, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02 Max. Mean SD 9.784316e-02 1.490504e-02 1.947399e-02 Accuracy with training data: 97.5% Out-of-bag accuracy: 97.5% Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:20 [1] 2019-10-16 04:26:20, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02 Max. Mean SD 1.808372e-01 2.422083e-02 3.699146e-02 Accuracy with training data: 95.8% Out-of-bag accuracy: 88.9% Gene: C Training dataset: 60 samples X 83 SNPs # of HLA alleles: 17 # of individual classifiers: 3 total # of SNPs used: 40 avg. # of SNPs in an individual classifier: 18.67 (sd: 5.03, min: 14, max: 24, median: 18.00) avg. # of haplotypes in an individual classifier: 33.67 (sd: 4.51, min: 29, max: 38, median: 34.00) avg. out-of-bag accuracy: 92.80% (sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02 Max. Mean SD 1.210500e-01 1.989079e-02 2.507466e-02 Genome assembly: hg19 Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 9 # of SNPs: 77, # of samples: 60 # of unique HLA alleles: 12 [-] 2019-10-16 04:26:20 [1] 2019-10-16 04:26:20, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20 [2] 2019-10-16 04:26:20, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Accuracy with training data: 95.0% Out-of-bag accuracy: 94.5% Gene: DQB1 Training dataset: 60 samples X 77 SNPs # of HLA alleles: 12 # of individual classifiers: 2 total # of SNPs used: 20 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.41, min: 13, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 20.50 (sd: 0.71, min: 20, max: 21, median: 20.50) avg. out-of-bag accuracy: 94.45% (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Genome assembly: hg19 Exclude 9 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:20 [1] 2019-10-16 04:26:20, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32 [2] 2019-10-16 04:26:20, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40 [3] 2019-10-16 04:26:21, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21 [4] 2019-10-16 04:26:21, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 Max. Mean SD 0.3714064677 0.0407831671 0.0808783377 Accuracy with training data: 99.2% Out-of-bag accuracy: 92.0% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 42 avg. # of SNPs in an individual classifier: 13.75 (sd: 1.26, min: 12, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 29.50 (sd: 8.35, min: 21, max: 40, median: 28.50) avg. out-of-bag accuracy: 91.96% (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 Max. Mean SD 0.3714064677 0.0407831671 0.0808783377 Genome assembly: hg19 Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 42 avg. # of SNPs in an individual classifier: 13.75 (sd: 1.26, min: 12, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 29.50 (sd: 8.35, min: 21, max: 40, median: 28.50) avg. out-of-bag accuracy: 91.96% (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 Max. Mean SD 0.3714064677 0.0407831671 0.0808783377 Genome assembly: hg19 Wed Oct 16 04:26:21 2019, passing the 1/4 classifiers. Wed Oct 16 04:26:21 2019, passing the 2/4 classifiers. Wed Oct 16 04:26:21 2019, passing the 3/4 classifiers. Wed Oct 16 04:26:21 2019, passing the 4/4 classifiers. Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall Valid. Valid. (%) (%) (%) (%) (%) (%) (%) ---- Overall accuracy: 92.0%, Call rate: 100.0% 01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 -- 02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 -- 02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100) 03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 -- 11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 -- 23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100) 24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 -- 24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75) 25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 -- 26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83) 29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75) 31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 -- 32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 -- 68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 -- \title{Imputation Evaluation} \documentclass[12pt]{article} \usepackage{fullpage} \usepackage{longtable} \begin{document} \maketitle \setlength{\LTcapwidth}{6.5in} % -------- BEGIN TABLE -------- \begin{longtable}{rrr | rrrrrrl} \caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).} \label{tab:accuracy} \\ Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endfirsthead \multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\ Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endhead \hline \multicolumn{10}{r}{Continued on next page ...} \\ \hline \endfoot \hline\hline \endlastfoot \multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\ 01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\ 02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\ 03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\ 24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\ 24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\ 25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\ 26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\ 29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\ 31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ \end{longtable} % -------- END TABLE -------- \end{document} Imputation Evaluation

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Allele	Num. Valid.	Freq. Valid.	CR (%)	ACC (%)	SEN (%)	SPE (%)	PPV (%)	NPV (%)	Miscall (%)
Overall accuracy: 92.0%, Call rate: 100.0%
01:01	25	0.2083	100.0	100.0	100.0	100.0	100.0	100.0	--
02:01	43	0.3583	100.0	96.7	100.0	95.1	92.5	100.0	--
02:06	1	0.0083	25.0	97.7	0.0	100.0	--	97.7	02:01 (100)
03:01	9	0.0750	100.0	100.0	100.0	100.0	100.0	100.0	--
11:01	5	0.0417	100.0	100.0	100.0	100.0	100.0	100.0	--
23:01	3	0.0250	100.0	98.4	75.0	100.0	100.0	98.4	24:02 (100)
24:02	11	0.0917	100.0	97.3	100.0	97.1	76.2	100.0	--
24:03	1	0.0083	100.0	97.8	0.0	100.0	--	97.8	24:02 (75)
25:01	5	0.0417	100.0	98.4	100.0	98.3	84.7	100.0	--
26:01	3	0.0250	100.0	98.4	62.5	100.0	100.0	98.4	25:01 (83)
29:02	4	0.0333	100.0	97.8	75.0	100.0	100.0	97.8	02:01 (75)
31:01	3	0.0250	75.0	100.0	100.0	100.0	100.0	100.0	--
32:01	4	0.0333	100.0	100.0	100.0	100.0	100.0	100.0	--
68:01	3	0.0250	100.0	100.0	100.0	100.0	100.0	100.0	--

Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes: 2019-10-16 04:26:22, 1, job 2, # of SNPs: 12, # of haplo: 53, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91% 2019-10-16 04:26:22, 2, job 2, # of SNPs: 14, # of haplo: 26, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91% 2019-10-16 04:26:22, 3, job 1, # of SNPs: 14, # of haplo: 70, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91% 2019-10-16 04:26:22, 4, job 2, # of SNPs: 15, # of haplo: 51, acc: 70.8% -- avg out-of-bag acc: 85.89%, sd: 10.04%, min: 70.83%, max: 90.91% Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001081400 0.0001260082 0.0002868224 0.0024431008 0.0080031409 0.0325892348 Max. Mean SD 0.4701164424 0.0507868945 0.1188983069 Accuracy with training data: 98.5% Out-of-bag accuracy: 85.9% Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes: The model is autosaved in 'tmp_model.RData'. 2019-10-16 04:26:23, 1, job 1, # of SNPs: 11, # of haplo: 25, acc: 87.5% -- avg out-of-bag acc: 87.50%, sd: NA%, min: 87.50%, max: 87.50% 2019-10-16 04:26:23, 2, job 2, # of SNPs: 15, # of haplo: 38, acc: 83.3% -- avg out-of-bag acc: 85.42%, sd: 2.95%, min: 83.33%, max: 87.50% 2019-10-16 04:26:23, 3, job 1, # of SNPs: 19, # of haplo: 57, acc: 93.8% Stop "job 1". -- avg out-of-bag acc: 88.19%, sd: 5.24%, min: 83.33%, max: 93.75% 2019-10-16 04:26:23, 4, job 1, # of SNPs: 12, # of haplo: 54, acc: 79.2% Stop "job 1". -- avg out-of-bag acc: 85.94%, sd: 6.22%, min: 79.17%, max: 93.75% Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0006401993 0.0006512536 0.0007507428 0.0037575807 0.0077551837 0.0190314220 Max. Mean SD 0.4576709956 0.0416266235 0.1076618487 Accuracy with training data: 97.1% Out-of-bag accuracy: 85.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 47 avg. # of SNPs in an individual classifier: 14.25 (sd: 3.59, min: 11, max: 19, median: 13.50) avg. # of haplotypes in an individual classifier: 43.50 (sd: 14.89, min: 25, max: 57, median: 46.00) avg. out-of-bag accuracy: 85.94% (sd: 6.22%, min: 79.17%, max: 93.75%, median: 85.42%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0006401993 0.0006512536 0.0007507428 0.0037575807 0.0077551837 0.0190314220 Max. Mean SD 0.4576709956 0.0416266235 0.1076618487 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:23) 0% Predicting (2019-10-16 04:26:23) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 3 (11.5%) 7 (26.9%) 14 (53.8%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.001220 0.004518 0.028600 0.016672 0.457671 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Run in parallel with 2 compute nodes. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 3 (11.5%) 7 (26.9%) 14 (53.8%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.001220 0.004518 0.028600 0.016672 0.457671 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:24 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:26:24, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:26:24, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.000000e+00 4.210645e-09 4.210645e-08 2.324904e-03 8.109951e-03 1.989621e-02 Max. Mean SD 5.990492e-02 1.420983e-02 1.652472e-02 Accuracy with training data: 100.0% Out-of-bag accuracy: 94.9% Exclude 11 monomorphic SNPs Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:24 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [1] 2019-10-16 04:26:24, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [2] 2019-10-16 04:26:25, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002703521 0.0002971139 0.0005379705 0.0035684045 0.0131584084 0.0413920204 Max. Mean SD 0.5762364122 0.0439769026 0.0999476134 Accuracy with training data: 97.1% Out-of-bag accuracy: 90.8% HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting by voting from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:25) 0% Predicting (2019-10-16 04:26:25) 100% HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting by voting from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:25) 0% Predicting (2019-10-16 04:26:25) 100% Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 13 # of SNPs: 158, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:25 1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14 2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15 3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19 4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23 5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23 6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27 7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35 8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51 9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55 10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55 11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56 12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59 13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58 14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60 [1] 2019-10-16 04:26:25, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60 1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15 2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18 3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23 4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29 5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40 6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40 7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41 8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41 9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43 10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43 11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43 12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44 13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45 14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45 15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45 16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47 17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47 18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50 [2] 2019-10-16 04:26:25, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Accuracy with training data: 96.7% Out-of-bag accuracy: 93.2% Gene: A Training dataset: 60 samples X 158 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 28 avg. # of SNPs in an individual classifier: 16.00 (sd: 2.83, min: 14, max: 18, median: 16.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: 7.07, min: 50, max: 60, median: 55.00) avg. out-of-bag accuracy: 93.20% (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Genome assembly: hg19 Remove 130 unused SNPs. Gene: A Training dataset: 60 samples X 28 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 28 avg. # of SNPs in an individual classifier: 16.00 (sd: 2.83, min: 14, max: 18, median: 16.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: 7.07, min: 50, max: 60, median: 55.00) avg. out-of-bag accuracy: 93.20% (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Genome assembly: hg19 Platform: Illumina 1M Duo Information: Training set -- HapMap Phase II HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 60 Predicting (2019-10-16 04:26:25) 0% Predicting (2019-10-16 04:26:25) 100% HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 60 Predicting (2019-10-16 04:26:25) 0% Predicting (2019-10-16 04:26:25) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:25 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:26:25, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:26:26, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 04:26:26, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 04:26:26, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:26) 0% Predicting (2019-10-16 04:26:26) 100% Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%) ---- Overall accuracy: 88.5%, Call rate: 100.0% 01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 -- 02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100) 02:06 1 0.0147 0 0 -- -- -- -- -- -- -- 03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 -- 11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 -- 23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100) 24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50) 24:03 1 0.0147 0 0 -- -- -- -- -- -- -- 25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50) 31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 -- 68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 -- \title{Imputation Evaluation} \documentclass[12pt]{article} \usepackage{fullpage} \usepackage{longtable} \begin{document} \maketitle \setlength{\LTcapwidth}{6.5in} % -------- BEGIN TABLE -------- \begin{longtable}{rrrrr | rrrrrrl} \caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).} \label{tab:accuracy} \\ Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endfirsthead \multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\ Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endhead \hline \multicolumn{12}{r}{Continued on next page ...} \\ \hline \endfoot \hline\hline \endlastfoot \multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\ 01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\ 02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\ 02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\ 03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\ 11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\ 24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\ 24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\ 25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\ 31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\ \end{longtable} % -------- END TABLE -------- \end{document} Imputation Evaluation

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Allele	Num. Train	Freq. Train	Num. Valid.	Freq. Valid.	CR (%)	ACC (%)	SEN (%)	SPE (%)	PPV (%)	NPV (%)	Miscall (%)
Overall accuracy: 88.5%, Call rate: 100.0%
01:01	13	0.1912	12	0.2308	100.0	96.2	100.0	95.0	85.7	100.0	--
02:01	25	0.3676	18	0.3462	100.0	98.1	94.4	100.0	100.0	97.1	01:01 (100)
02:06	1	0.0147	0	0	--	--	--	--	--	--	--
03:01	4	0.0588	5	0.0962	100.0	98.1	100.0	97.9	83.3	100.0	--
11:01	2	0.0294	3	0.0577	100.0	100.0	100.0	100.0	100.0	100.0	--
23:01	1	0.0147	2	0.0385	100.0	96.2	0.0	100.0	--	96.2	24:02 (100)
24:02	6	0.0882	5	0.0962	100.0	92.3	60.0	95.7	60.0	95.7	01:01 (50)
24:03	1	0.0147	0	0	--	--	--	--	--	--	--
25:01	4	0.0588	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
26:01	2	0.0294	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
29:02	3	0.0441	1	0.0192	100.0	98.1	0.0	100.0	--	98.1	03:01 (50)
31:01	2	0.0294	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
32:01	2	0.0294	2	0.0385	100.0	100.0	100.0	100.0	100.0	100.0	--
68:01	2	0.0294	1	0.0192	100.0	98.1	100.0	98.0	50.0	100.0	--

**Overall accuracy: 88.5%, Call rate: 100.0%** | Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) | |:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--| | 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- | | 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) | | 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- | | 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- | | 11:01 | 2 | 0.0294 | 3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 23:01 | 1 | 0.0147 | 2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) | | 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) | | 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- | | 25:01 | 4 | 0.0588 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) | | 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 32:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- | Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:26 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:26:26, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:26:26, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 04:26:26, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 04:26:26, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:26) 0% Predicting (2019-10-16 04:26:26) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 8 # of SNPs: 51, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:27 1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18 3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19 4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20 5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20 6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23 7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25 8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29 9, SNP: 42, Loss: 47.0092, OOB Acc: 85.42%, # of Haplo: 37 10, SNP: 33, Loss: 41.5486, OOB Acc: 85.42%, # of Haplo: 41 11, SNP: 5, Loss: 39.769, OOB Acc: 85.42%, # of Haplo: 51 12, SNP: 10, Loss: 34.0977, OOB Acc: 85.42%, # of Haplo: 51 13, SNP: 37, Loss: 32.3969, OOB Acc: 85.42%, # of Haplo: 52 14, SNP: 7, Loss: 28.1492, OOB Acc: 85.42%, # of Haplo: 52 15, SNP: 15, Loss: 27.2163, OOB Acc: 85.42%, # of Haplo: 55 [1] 2019-10-16 04:26:27, OOB Acc: 85.42%, # of SNPs: 15, # of Haplo: 55 1, SNP: 18, Loss: 453.852, OOB Acc: 44.12%, # of Haplo: 17 2, SNP: 4, Loss: 358.517, OOB Acc: 50.00%, # of Haplo: 18 3, SNP: 49, Loss: 258.495, OOB Acc: 52.94%, # of Haplo: 18 4, SNP: 5, Loss: 172.555, OOB Acc: 67.65%, # of Haplo: 21 5, SNP: 42, Loss: 144.905, OOB Acc: 76.47%, # of Haplo: 21 6, SNP: 38, Loss: 98.7462, OOB Acc: 79.41%, # of Haplo: 21 7, SNP: 36, Loss: 83.4743, OOB Acc: 82.35%, # of Haplo: 24 8, SNP: 19, Loss: 60.2385, OOB Acc: 88.24%, # of Haplo: 24 9, SNP: 46, Loss: 49.1775, OOB Acc: 88.24%, # of Haplo: 24 10, SNP: 20, Loss: 42.3205, OOB Acc: 88.24%, # of Haplo: 24 11, SNP: 12, Loss: 41.1299, OOB Acc: 91.18%, # of Haplo: 25 12, SNP: 1, Loss: 33.8332, OOB Acc: 91.18%, # of Haplo: 25 13, SNP: 37, Loss: 32.8313, OOB Acc: 91.18%, # of Haplo: 26 14, SNP: 7, Loss: 38.8398, OOB Acc: 94.12%, # of Haplo: 27 15, SNP: 15, Loss: 35.0817, OOB Acc: 94.12%, # of Haplo: 32 16, SNP: 39, Loss: 33.7063, OOB Acc: 94.12%, # of Haplo: 30 [2] 2019-10-16 04:26:27, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 30 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 Max. Mean SD 9.805734e-02 2.430678e-02 2.697855e-02 Accuracy with training data: 95.8% Out-of-bag accuracy: 89.8% Gene: C Training dataset: 60 samples X 51 SNPs # of HLA alleles: 17 # of individual classifiers: 2 total # of SNPs used: 23 avg. # of SNPs in an individual classifier: 15.50 (sd: 0.71, min: 15, max: 16, median: 15.50) avg. # of haplotypes in an individual classifier: 42.50 (sd: 17.68, min: 30, max: 55, median: 42.50) avg. out-of-bag accuracy: 89.77% (sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 Max. Mean SD 9.805734e-02 2.430678e-02 2.697855e-02 Genome assembly: hg19 Gene: C Training dataset: 60 samples X 51 SNPs # of HLA alleles: 17 # of individual classifiers: 1 total # of SNPs used: 15 avg. # of SNPs in an individual classifier: 15.00 (sd: NA, min: 15, max: 15, median: 15.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: NA, min: 55, max: 55, median: 55.00) avg. out-of-bag accuracy: 85.42% (sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 Max. Mean SD 9.805734e-02 2.430678e-02 2.697855e-02 Genome assembly: hg19 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:27 1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17 3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20 4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20 5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22 6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24 7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24 8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22 9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24 10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24 11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28 12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29 13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37 14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38 15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39 16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40 17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41 18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43 19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43 [1] 2019-10-16 04:26:27, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43 1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19 2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21 3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21 4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21 5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21 6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21 7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21 8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22 9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26 10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27 11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27 12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27 13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30 14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33 15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37 16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43 17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51 18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52 [2] 2019-10-16 04:26:27, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 Max. Mean SD 7.489335e-02 1.870792e-02 2.126647e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 91.9% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 04:26:27 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 04:26:27, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 04:26:27, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 04:26:27, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 04:26:28, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 04:26:28) 0% Predicting (2019-10-16 04:26:28) 100% Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 04:26:28 1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17 3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20 4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20 5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22 6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24 7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24 8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22 9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24 10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24 11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28 12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29 13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37 14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38 15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39 16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40 17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41 18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43 19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43 [1] 2019-10-16 04:26:28, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43 1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19 2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21 3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21 4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21 5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21 6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21 7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21 8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22 9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26 10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27 11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27 12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27 13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30 14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33 15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37 16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43 17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51 18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52 [2] 2019-10-16 04:26:28, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 Max. Mean SD 7.489335e-02 1.870792e-02 2.126647e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 91.9% Gene: C Training dataset: 60 samples X 83 SNPs # of HLA alleles: 17 # of individual classifiers: 2 total # of SNPs used: 31 avg. # of SNPs in an individual classifier: 18.50 (sd: 0.71, min: 18, max: 19, median: 18.50) avg. # of haplotypes in an individual classifier: 47.50 (sd: 6.36, min: 43, max: 52, median: 47.50) avg. out-of-bag accuracy: 91.85% (sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 Max. Mean SD 7.489335e-02 1.870792e-02 2.126647e-02 Genome assembly: hg19 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 > > proc.time() user system elapsed 22.15 0.31 25.34

CHECK report for HIBAG on tokay2

Summary

Command output

Installation output

Tests output

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Example timings