HIBAG – an R Package for HLA Genotype Imputation with Attribute Bagging
Xiuwen Zheng
May 3, 2018
- Overview
- Features
- Examples
- Resources
- Session Info
Overview
The human leukocyte antigen (HLA) system, located in the major histocompatibility complex (MHC) on chromosome 6p21.3, is highly polymorphic. This region has been shown to be important in human disease, adverse drug reactions and organ transplantation (Shiina et al. 2009). HLA genes play a role in the immune system and autoimmunity as they are central to the presentation of antigens for recognition by T cells. Since they have to provide defense against a great diversity of environmental microbes, HLA genes must be able to present a wide range of peptides. Evolutionary pressure at these loci have given rise to a great deal of functional diversity. For example, the HLA–B locus has 1,898 four-digit alleles listed in the April 2012 release of the IMGT-HLA Database (Robinson et al. 2013) (http://www.ebi.ac.uk/imgt/hla/).
Classical HLA genotyping methodologies have been predominantly developed for tissue typing purposes, with sequence based typing (SBT) approaches currently considered the gold standard. While there is widespread availability of vendors offering HLA genotyping services, the complexities involved in performing this to the standard required for diagnostic purposes make using a SBT approach time-consuming and cost-prohibitive for most research studies wishing to look in detail at the involvement of classical HLA genes in disease.
Here we introduce a new prediction method for HLA Imputation using attribute BAGging, HIBAG, that is highly accurate, computationally tractable, and can be used with published parameter estimates, eliminating the need to access large training samples (Zheng et al. 2013). It relies on a training set with known HLA and SNP genotypes, and combines the concepts of attribute bagging with haplotype inference from unphased SNPs and HLA types. Attribute bagging is a technique for improving the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection (Breiman 1996, 2001; Bryll, Gutierrez-Osuna, and Quek 2003). In this case, individual classifiers are created which utilize a subset of SNPs to predict HLA types and haplotype frequencies estimated from a training data set of SNPs and HLA types. Each of the classifiers employs a variable selection algorithm with a random component to select a subset of the SNPs. HLA type predictions are determined by maximizing the average posterior probabilities from all classifiers.
Features
- HIBAG can be used by researchers with published parameter estimates ([http://www.biostat.washington.edu/~bsweir/HIBAG/](http://www.biostat.washington.edu/~bsweir/HIBAG/)) instead of requiring access to large training sample datasets.
- A typical HIBAG parameter file contains only haplotype frequencies at different SNP subsets rather than individual training genotypes.
- SNPs within the xMHC region (chromosome 6) are used for imputation.
- HIBAG employs unphased genotypes of unrelated individuals as a training set.
- HIBAG supports parallel computing with R.
Examples
## HIBAG (HLA Genotype Imputation with Attribute Bagging)## Kernel Version: v1.4## Supported by Streaming SIMD Extensions (SSE2) [64-bit]Pre-fit HIBAG Models for HLA Imputation
# load the published parameter estimates from European ancestry
# e.g., filename <- "HumanOmniExpressExome-European-HLA4-hg19.RData"
# here, we use example data in the package
filename <- system.file("extdata", "ModelList.RData", package="HIBAG")
model.list <- get(load(filename))
# HLA imputation at HLA-A
hla.id <- "A"
model <- hlaModelFromObj(model.list[[hla.id]])
summary(model)## Gene: A
## Training dataset: 60 samples X 266 SNPs
## # of HLA alleles: 14
## # of individual classifiers: 100
## total # of SNPs used: 245
## avg. # of SNPs in an individual classifier: 15.92
## (sd: 2.43, min: 10, max: 24, median: 16.00)
## avg. # of haplotypes in an individual classifier: 39.89
## (sd: 14.28, min: 18, max: 87, median: 37.00)
## avg. out-of-bag accuracy: 93.77%
## (sd: 4.56%, min: 78.95%, max: 100.00%, median: 94.74%)
## Genome assembly: hg19## [1] "rs2523442" "rs9257863" "rs2107191" "rs4713226" "rs2746150" "rs1233492"## [1] 29417816 29425583 29434294 29434413 29442700 29458476#########################################################################
# Import your PLINK BED file
#
yourgeno <- hlaBED2Geno(bed.fn=".bed", fam.fn=".fam", bim.fn=".bim")
summary(yourgeno)
# best-guess genotypes and all posterior probabilities
pred.guess <- predict(model, yourgeno, type="response+prob")
summary(pred.guess)
pred.guess$value
pred.guess$postprobBuild a HIBAG Model for HLA Genotype Imputation
library(HIBAG)
# load HLA types and SNP genotypes in the package
data(HLA_Type_Table, package="HIBAG")
data(HapMap_CEU_Geno, package="HIBAG")
# a list of HLA genotypes
# e.g., train.HLA <- hlaAllele(sample.id, H1=c("01:02", "05:01", ...),
# H2=c("02:01", "03:01", ...), locus="A")
# the HLA type of the first individual is 01:02/02:01,
# the second is 05:01/03:01, ...
hla.id <- "A"
train.HLA <- hlaAllele(HLA_Type_Table$sample.id,
H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
locus=hla.id, assembly="hg19")
# selected SNPs:
# the flanking region of 500kb on each side,
# or an appropriate flanking size without sacrificing predictive accuracy
region <- 500 # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19")
length(snpid)
# training genotypes
# import your PLINK BED file,
# e.g., train.geno <- hlaBED2Geno(bed.fn=".bed", fam.fn=".fam", bim.fn=".bim")
# or,
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
snp.sel = match(snpid, HapMap_CEU_Geno$snp.id))
summary(train.geno)
# train a HIBAG model
set.seed(100)
model <- hlaAttrBagging(train.HLA, train.geno, nclassifier=100)## Gene: A
## Training dataset: 60 samples X 266 SNPs
## # of HLA alleles: 14
## # of individual classifiers: 100
## total # of SNPs used: 245
## avg. # of SNPs in an individual classifier: 15.92
## (sd: 2.43, min: 10, max: 24, median: 16.00)
## avg. # of haplotypes in an individual classifier: 39.89
## (sd: 14.28, min: 18, max: 87, median: 37.00)
## avg. out-of-bag accuracy: 93.77%
## (sd: 4.56%, min: 78.95%, max: 100.00%, median: 94.74%)
## Genome assembly: hg19Build and Predict in Parallel
library(HIBAG)
library(parallel)
# load HLA types and SNP genotypes in the package
data(HLA_Type_Table, package="HIBAG")
data(HapMap_CEU_Geno, package="HIBAG")
# a list of HLA genotypes
# e.g., train.HLA <- hlaAllele(sample.id, H1=c("01:02", "05:01", ...),
# H2=c("02:01", "03:01", ...), locus="A")
# the HLA type of the first individual is 01:02/02:01,
# the second is 05:01/03:01, ...
hla.id <- "A"
train.HLA <- hlaAllele(HLA_Type_Table$sample.id,
H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
locus=hla.id, assembly="hg19")
# selected SNPs:
# the flanking region of 500kb on each side,
# or an appropriate flanking size without sacrificing predictive accuracy
region <- 500 # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19")
length(snpid)
# training genotypes
# import your PLINK BED file,
# e.g., train.geno <- hlaBED2Geno(bed.fn=".bed", fam.fn=".fam", bim.fn=".bim")
# or,
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
snp.sel = match(snpid, HapMap_CEU_Geno$snp.id))
summary(train.geno)
# Create an environment with an appropriate cluster size,
# e.g., 2 -- # of (CPU) cores
cl <- makeCluster(2)
# Building ...
set.seed(1000)
hlaParallelAttrBagging(cl, train.HLA, train.geno, nclassifier=100,
auto.save="AutoSaveModel.RData", stop.cluster=TRUE)
model.obj <- get(load("AutoSaveModel.RData"))
model <- hlaModelFromObj(model.obj)
summary(model)Evaluate Overall Accuracy, Sensitivity, Specificity, etc
The function hlaReport() can be used to automatically generate a tex or HTML report when a validation dataset is available.
library(HIBAG)
# load HLA types and SNP genotypes in the package
data(HLA_Type_Table, package="HIBAG")
data(HapMap_CEU_Geno, package="HIBAG")
# make a list of HLA types
hla.id <- "A"
hla <- hlaAllele(HLA_Type_Table$sample.id,
H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
locus=hla.id, assembly="hg19")
# divide HLA types randomly
set.seed(100)
hlatab <- hlaSplitAllele(hla, train.prop=0.5)
names(hlatab)## [1] "training" "validation"## Gene: A
## Range: [29910247bp, 29913661bp] on hg19
## # of samples: 34
## # of unique HLA alleles: 14
## # of unique HLA genotypes: 23## Gene: A
## Range: [29910247bp, 29913661bp] on hg19
## # of samples: 26
## # of unique HLA alleles: 12
## # of unique HLA genotypes: 14# SNP predictors within the flanking region on each side
region <- 500 # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19")
length(snpid)## [1] 275# training and validation genotypes
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
snp.sel = match(snpid, HapMap_CEU_Geno$snp.id),
samp.sel = match(hlatab$training$value$sample.id,
HapMap_CEU_Geno$sample.id))
summary(train.geno)## SNP genotypes:
## 34 samples X 275 SNPs
## SNPs range from 29417816bp to 30410205bp on hg19
## Missing rate per SNP:
## min: 0, max: 0.0882353, mean: 0.0863102, median: 0.0882353, sd: 0.0124152
## Missing rate per sample:
## min: 0, max: 0.974545, mean: 0.0863102, median: 0, sd: 0.280474
## Minor allele frequency:
## min: 0, max: 0.5, mean: 0.218176, median: 0.193548, sd: 0.136552
## Allelic information:
## C/T A/G G/T A/C
## 125 97 32 21test.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$training, train.geno, nclassifier=100)## Gene: A
## Training dataset: 34 samples X 266 SNPs
## # of HLA alleles: 14
## # of individual classifiers: 100
## total # of SNPs used: 244
## avg. # of SNPs in an individual classifier: 14.60
## (sd: 2.76, min: 10, max: 21, median: 14.00)
## avg. # of haplotypes in an individual classifier: 39.68
## (sd: 16.31, min: 15, max: 84, median: 37.50)
## avg. out-of-bag accuracy: 83.80%
## (sd: 8.69%, min: 62.50%, max: 100.00%, median: 84.62%)
## Matching proportion:
## Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. Max. Mean
## 0.0009366995 0.0009474879 0.0010445837 0.0054325672 0.0100158851 0.0158869387 0.4092814785 0.0388578948
## SD
## 0.0926409732
## Genome assembly: hg19## HIBAG model: 100 individual classifiers, 266 SNPs, 14 unique HLA alleles.
## Predicting based on the averaged posterior probabilities from all individual classifiers.
## Model assembly: hg19, SNP assembly: hg19
## No allelic strand orders are switched.
## Number of samples: 26
## Predicting (2019-05-02 21:19:24) 0%
## Predicting (2019-05-02 21:19:24) 100%## Gene: A
## Range: [29910247bp, 29913661bp] on hg19
## # of samples: 26
## # of unique HLA alleles: 12
## # of unique HLA genotypes: 14
## Posterior probability:
## [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
## 2 (7.7%) 2 (7.7%) 2 (7.7%) 20 (76.9%)
## Matching proportion of SNP haplotype:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0004095 0.0053788 0.0085626 0.0291253 0.0271491 0.4092815# compare
comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0)
comp$overall## total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold n.call call.rate
## 1 26 24 50 0.9230769 0.9615385 0 26 1Evaluation in Figures
The distance matrix is calculated based on the haplotype similarity carrying HLA alleles:
# hierarchical cluster analysis
d <- hlaDistance(model)
p <- hclust(as.dist(d))
plot(p, xlab="HLA alleles")
## Loading required namespace: ggplot2## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
Matching proportion is a measure or proportion describing how the SNP profile matches the SNP haplotypes observed in the training set, i.e., the likelihood of SNP profile in a random-mating population consisting of training haplotypes. It is not directly related to confidence score, but a very low value of matching indicates that it is underrepresented in the training set.
Report in Text
Output to plain text format:
## Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall
## Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
## ----
## Overall accuracy: 96.2%, Call rate: 100.0%
## 01:01 14 0.2059 12 0.2308 100.0 98.1 100.0 97.5 92.3 100.0 --
## 02:01 23 0.3382 22 0.4231 100.0 98.1 95.5 100.0 100.0 96.8 29:02 (100)
## 02:06 1 0.0147 0 0 -- -- -- -- -- -- --
## 03:01 4 0.0588 5 0.0962 100.0 100.0 100.0 100.0 100.0 100.0 --
## 11:01 3 0.0441 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
## 23:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
## 24:02 6 0.0882 3 0.0577 100.0 98.1 66.7 100.0 100.0 98.0 01:01 (100)
## 24:03 1 0.0147 0 0 -- -- -- -- -- -- --
## 25:01 3 0.0441 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 98.1 100.0 98.0 50.0 100.0 --
## 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 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
## 68:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --Report in Markdown
Output to a markdown file:
# report overall accuracy, per-allele sensitivity, specificity, etc
hlaReport(comp, type="markdown")Overall accuracy: 96.2%, Call rate: 100.0%
| Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 01:01 | 14 | 0.2059 | 12 | 0.2308 | 100.0 | 98.1 | 100.0 | 97.5 | 92.3 | 100.0 | – |
| 02:01 | 23 | 0.3382 | 22 | 0.4231 | 100.0 | 98.1 | 95.5 | 100.0 | 100.0 | 96.8 | 29:02 (100) |
| 02:06 | 1 | 0.0147 | 0 | 0 | – | – | – | – | – | – | – |
| 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | – |
| 11:01 | 3 | 0.0441 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | – |
| 23:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | – |
| 24:02 | 6 | 0.0882 | 3 | 0.0577 | 100.0 | 98.1 | 66.7 | 100.0 | 100.0 | 98.0 | 01:01 (100) |
| 24:03 | 1 | 0.0147 | 0 | 0 | – | – | – | – | – | – | – |
| 25:01 | 3 | 0.0441 | 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 | 100.0 | 98.0 | 50.0 | 100.0 | – |
| 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 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | – |
| 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | – |
Report in LaTeX
Output to a tex file, and please add \usepackage{longtable} to your tex file:
# report overall accuracy, per-allele sensitivity, specificity, etc
hlaReport(comp, type="tex", header=FALSE)## % -------- 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: 96.2\%, Call rate: 100.0\%} \\
## 01:01 & 14 & 0.2059 & 12 & 0.2308 & 100.0 & 98.1 & 100.0 & 97.5 & 92.3 & 100.0 & -- \\
## 02:01 & 23 & 0.3382 & 22 & 0.4231 & 100.0 & 98.1 & 95.5 & 100.0 & 100.0 & 96.8 & 29:02 (100) \\
## 02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
## 03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
## 11:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
## 23:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
## 24:02 & 6 & 0.0882 & 3 & 0.0577 & 100.0 & 98.1 & 66.7 & 100.0 & 100.0 & 98.0 & 01:01 (100) \\
## 24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
## 25:01 & 3 & 0.0441 & 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 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
## 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 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
## 68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
## \end{longtable}
## % -------- END TABLE --------Release HIBAG Models without Confidential Information
library(HIBAG)
# make a list of HLA types
hla.id <- "DQA1"
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")
# training genotypes
region <- 500 # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position,
hla.id, region*1000, assembly="hg19")
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
snp.sel = match(snpid, HapMap_CEU_Geno$snp.id),
samp.sel = match(hla$value$sample.id, HapMap_CEU_Geno$sample.id))
set.seed(1000)
model <- hlaAttrBagging(hla, train.geno, nclassifier=100)
summary(model)
# remove unused SNPs and sample IDs from the model
mobj <- hlaPublish(model,
platform = "Illumina 1M Duo",
information = "Training set -- HapMap Phase II",
warning = NULL,
rm.unused.snp=TRUE, anonymize=TRUE)
save(mobj, file="Your_HIBAG_Model.RData")Release a Collection of HIBAG Models
Association Tests
In the association tests of HLA allele, four models (dominant, additive, recessive and genotype) are allowed, and linear/logistic regressions can be conducted according to the dependent variable.
| Model | Description (given a specific HLA allele h) |
|---|---|
| dominant | [-/-] vs. [-/h,h/h] (0 vs. 1 in design matrix) |
| additive | [-] vs. [h] in Chi-squared and Fisher’s exact test, the allele dosage in regressions (0: -/-, 1: -/h, 2: h/h in design matrix) |
| recessive | [-/-,-/h] vs. [h/h] (0 vs. 1 in design matrix) |
| genotype | three categories: [-/-], [-/h], [h/h] |
# prepare data
fn <- "case_control.txt"
# or fn <- system.file("doc", "case_control.txt", package="HIBAG")The example text file: case_control.txt.
## sample.id disease A A.1 trait pc1 pc2 prob
## 1 N0001 0 68:01 11:01 0.59 -0.7768 1.9784 0.66486
## 2 N0002 1 03:01 24:02 -0.11 -0.2013 1.4537 0.80609
## 3 N0003 0 31:01 11:01 0.58 -0.1655 1.8288 0.70520
## 4 N0004 1 01:01 29:02 0.91 0.5545 1.3598 0.90045
## 5 N0005 1 03:01 03:01 -0.36 0.4781 1.6197 0.89971
## 6 N0006 0 31:01 24:02 0.60 -0.5116 -0.6259 0.72470# make an object for hlaAssocTest
hla <- hlaAllele(dat$sample.id, H1=dat$A, H2=dat$A.1, locus="A", assembly="hg19", prob=dat$prob)
summary(hla)## Gene: A
## Range: [29910247bp, 29913661bp] on hg19
## # of samples: 500
## # of unique HLA alleles: 18
## # of unique HLA genotypes: 132
## Posterior probability:
## [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
## 0 (0.0%) 20 (4.0%) 262 (52.4%) 218 (43.6%)Or the best-guess HLA genotypes from predict() or hlaPredict():
Allelic Association
h in the formula is denoted for HLA genotypes. Pearson’s Chi-squared and Fisher’s exact test will be performed if the dependent variable is categorial, while two-sample t test or ANOVA F test will be used for continuous dependent variables.
## Logistic regression (dominant model) with 500 individuals:
## glm(disease ~ h, family = binomial, data = data)
## [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est h.2.5% h.97.5% h.pval
## 26:01 468 32 54.1 15.6 1.621e+01 <0.001* <0.001* -1.849150 -2.8205 -0.8778 <0.001*
## -----
## 01:01 427 73 51.3 53.4 4.446e-02 0.833 0.800 0.085667 -0.4118 0.5832 0.736
## 02:01 474 26 52.3 38.5 1.381e+00 0.240 0.226 -0.562897 -1.3733 0.2475 0.173
## 02:05 441 59 52.8 42.4 1.881e+00 0.170 0.165 -0.420985 -0.9702 0.1282 0.133
## 03:01 383 117 50.1 56.4 1.175e+00 0.278 0.246 0.252607 -0.1641 0.6693 0.235
## 11:01 442 58 51.8 50.0 1.431e-02 0.905 0.889 -0.072430 -0.6199 0.4751 0.795
## 23:01 470 30 51.1 60.0 5.794e-01 0.447 0.354 0.362905 -0.3895 1.1153 0.345
## 24:02 387 113 50.4 55.8 8.045e-01 0.370 0.337 0.215608 -0.2057 0.6369 0.316
## 25:01 481 19 50.9 68.4 1.592e+00 0.207 0.163 0.735763 -0.2480 1.7195 0.143
## 26:08 455 45 52.3 44.4 7.234e-01 0.395 0.350 -0.315517 -0.9316 0.3006 0.315
## 29:01 472 28 52.5 35.7 2.361e+00 0.124 0.118 -0.689569 -1.4834 0.1043 0.089
## 29:02 440 60 51.6 51.7 3.369e-30 1.000 1.000 0.003034 -0.5367 0.5428 0.991
## 31:01 445 55 51.9 49.1 6.335e-02 0.801 0.775 -0.112809 -0.6732 0.4476 0.693
## 32:01 482 18 51.7 50.0 9.437e-31 1.000 1.000 -0.066414 -1.0075 0.8746 0.890
## 32:02 389 111 50.4 55.9 8.273e-01 0.363 0.333 0.219890 -0.2042 0.6440 0.310
## 33:01 498 2 51.4 100.0 4.403e-01 0.507 0.500 14.509828 -1208.8876 1237.9073 0.981
## 68:01 448 52 50.4 61.5 1.873e+00 0.171 0.144 0.452146 -0.1364 1.0407 0.132
## 68:06 433 67 51.0 55.2 2.565e-01 0.613 0.600 0.168144 -0.3489 0.6852 0.524## [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est h.2.5%
## 01:01 427 73 51.3 53.4 4.446014e-02 8.329998e-01 8.002913e-01 0.085667471 -0.4118182
## 02:01 474 26 52.3 38.5 1.381333e+00 2.398743e-01 2.262590e-01 -0.562897376 -1.3732827
## 02:05 441 59 52.8 42.4 1.880794e+00 1.702439e-01 1.650743e-01 -0.420985064 -0.9701514
## 03:01 383 117 50.1 56.4 1.174861e+00 2.784048e-01 2.464423e-01 0.252607162 -0.1640977
## 11:01 442 58 51.8 50.0 1.430580e-02 9.047946e-01 8.890155e-01 -0.072429838 -0.6199133
## 23:01 470 30 51.1 60.0 5.793734e-01 4.465580e-01 3.544810e-01 0.362905438 -0.3895338
## 24:02 387 113 50.4 55.8 8.045038e-01 3.697502e-01 3.367351e-01 0.215607533 -0.2057114
## 25:01 481 19 50.9 68.4 1.592268e+00 2.070025e-01 1.631167e-01 0.735763483 -0.2479520
## 26:01 468 32 54.1 15.6 1.621106e+01 5.666222e-05 2.764124e-05 -1.849150412 -2.8205279
## 26:08 455 45 52.3 44.4 7.234172e-01 3.950253e-01 3.500228e-01 -0.315516870 -0.9316086
## 29:01 472 28 52.5 35.7 2.361181e+00 1.243880e-01 1.181831e-01 -0.689569359 -1.4834203
## 29:02 440 60 51.6 51.7 3.369257e-30 1.000000e+00 1.000000e+00 0.003033523 -0.5367255
## 31:01 445 55 51.9 49.1 6.334576e-02 8.012839e-01 7.752251e-01 -0.112809340 -0.6732124
## 32:01 482 18 51.7 50.0 9.436715e-31 1.000000e+00 1.000000e+00 -0.066414443 -1.0074628
## 32:02 389 111 50.4 55.9 8.272755e-01 3.630615e-01 3.334643e-01 0.219889615 -0.2042055
## 33:01 498 2 51.4 100.0 4.402581e-01 5.069979e-01 4.995110e-01 14.509828058 -1208.8876041
## 68:01 448 52 50.4 61.5 1.872644e+00 1.711725e-01 1.440661e-01 0.452146012 -0.1364285
## 68:06 433 67 51.0 55.2 2.565230e-01 6.125190e-01 5.996497e-01 0.168144103 -0.3489354
## h.97.5% h.pval
## 01:01 0.5831531 0.7357343407
## 02:01 0.2474880 0.1733873224
## 02:05 0.1281813 0.1329712474
## 03:01 0.6693120 0.2347808891
## 11:01 0.4750536 0.7954074246
## 23:01 1.1153446 0.3445051873
## 24:02 0.6369265 0.3158607252
## 25:01 1.7194790 0.1426640661
## 26:01 -0.8777729 0.0001906754
## 26:08 0.3005749 0.3154994339
## 29:01 0.1042816 0.0886617025
## 29:02 0.5427926 0.9912112585
## 31:01 0.4475938 0.6931813422
## 32:01 0.8746339 0.8899840700
## 32:02 0.6439847 0.3095226073
## 33:01 1237.9072602 0.9814542781
## 68:01 1.0407206 0.1321558948
## 68:06 0.6852236 0.5239022516## Exclude 20 individuals from the study due to the call threshold (0.5)
## Logistic regression (dominant model) with 480 individuals:
## glm(disease ~ h, family = binomial, data = data)
## [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est h.2.5% h.97.5% h.pval
## 26:01 451 29 53.7 17.2 1.305e+01 <0.001* <0.001* -1.71522 -2.6963 -0.7342 <0.001*
## -----
## 01:01 411 69 50.6 56.5 6.073e-01 0.436 0.435 0.23803 -0.2757 0.7518 0.364
## 02:01 456 24 52.0 41.7 6.009e-01 0.438 0.403 -0.41546 -1.2475 0.4165 0.328
## 02:05 422 58 52.8 41.4 2.244e+00 0.134 0.123 -0.46217 -1.0185 0.0942 0.103
## 03:01 367 113 50.1 55.8 8.776e-01 0.349 0.333 0.22566 -0.1982 0.6495 0.297
## 11:01 424 56 51.9 48.2 1.403e-01 0.708 0.670 -0.14697 -0.7047 0.4107 0.606
## 23:01 452 28 50.9 60.7 6.643e-01 0.415 0.336 0.39992 -0.3805 1.1804 0.315
## 24:02 370 110 50.5 54.5 3.959e-01 0.529 0.515 0.16070 -0.2664 0.5878 0.461
## 25:01 463 17 51.0 64.7 7.495e-01 0.387 0.327 0.56725 -0.4440 1.5785 0.272
## 26:08 436 44 52.1 45.5 4.594e-01 0.498 0.432 -0.26494 -0.8874 0.3575 0.404
## 29:01 452 28 52.4 35.7 2.319e+00 0.128 0.118 -0.68521 -1.4800 0.1095 0.091
## 29:02 423 57 51.5 50.9 1.075e-30 1.000 1.000 -0.02639 -0.5796 0.5268 0.925
## 31:01 427 53 52.0 47.2 2.669e-01 0.605 0.561 -0.19300 -0.7647 0.3788 0.508
## 32:01 462 18 51.5 50.0 2.527e-29 1.000 1.000 -0.06062 -1.0024 0.8812 0.900
## 32:02 373 107 50.1 56.1 9.490e-01 0.330 0.323 0.23884 -0.1935 0.6712 0.279
## 33:01 479 1 51.4 100.0 1.477e-30 1.000 1.000 13.51177 -1035.8748 1062.8984 0.980
## 68:01 429 51 50.3 60.8 1.591e+00 0.207 0.183 0.42427 -0.1689 1.0174 0.161
## 68:06 417 63 50.6 57.1 6.945e-01 0.405 0.347 0.26370 -0.2709 0.7983 0.334## Logistic regression (dominant model) with 500 individuals:
## glm(disease ~ h, family = binomial, data = data)
## [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR h.2.5%_OR h.97.5%_OR h.pval
## 26:01 468 32 54.1 15.6 1.621e+01 <0.001* <0.001* 1.574e-01 0.05957 0.4157 <0.001*
## -----
## 01:01 427 73 51.3 53.4 4.446e-02 0.833 0.800 1.089e+00 0.66244 1.7917 0.736
## 02:01 474 26 52.3 38.5 1.381e+00 0.240 0.226 5.696e-01 0.25327 1.2808 0.173
## 02:05 441 59 52.8 42.4 1.881e+00 0.170 0.165 6.564e-01 0.37903 1.1368 0.133
## 03:01 383 117 50.1 56.4 1.175e+00 0.278 0.246 1.287e+00 0.84866 1.9529 0.235
## 11:01 442 58 51.8 50.0 1.431e-02 0.905 0.889 9.301e-01 0.53799 1.6081 0.795
## 23:01 470 30 51.1 60.0 5.794e-01 0.447 0.354 1.437e+00 0.67737 3.0506 0.345
## 24:02 387 113 50.4 55.8 8.045e-01 0.370 0.337 1.241e+00 0.81407 1.8907 0.316
## 25:01 481 19 50.9 68.4 1.592e+00 0.207 0.163 2.087e+00 0.78040 5.5816 0.143
## 26:08 455 45 52.3 44.4 7.234e-01 0.395 0.350 7.294e-01 0.39392 1.3506 0.315
## 29:01 472 28 52.5 35.7 2.361e+00 0.124 0.118 5.018e-01 0.22686 1.1099 0.089
## 29:02 440 60 51.6 51.7 3.369e-30 1.000 1.000 1.003e+00 0.58466 1.7208 0.991
## 31:01 445 55 51.9 49.1 6.335e-02 0.801 0.775 8.933e-01 0.51007 1.5645 0.693
## 32:01 482 18 51.7 50.0 9.437e-31 1.000 1.000 9.357e-01 0.36514 2.3980 0.890
## 32:02 389 111 50.4 55.9 8.273e-01 0.363 0.333 1.246e+00 0.81529 1.9041 0.310
## 33:01 498 2 51.4 100.0 4.403e-01 0.507 0.500 2.002e+06 0.00000 Inf 0.981
## 68:01 448 52 50.4 61.5 1.873e+00 0.171 0.144 1.572e+00 0.87247 2.8313 0.132
## 68:06 433 67 51.0 55.2 2.565e-01 0.613 0.600 1.183e+00 0.70544 1.9842 0.524## Logistic regression (dominant model) with 500 individuals:
## glm(disease ~ h + pc1, family = binomial, data = data)
## [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est h.2.5% h.97.5% h.pval
## 26:01 468 32 54.1 15.6 1.621e+01 <0.001* <0.001* -1.853857 -2.8256 -0.8821 <0.001*
## -----
## 01:01 427 73 51.3 53.4 4.446e-02 0.833 0.800 0.082045 -0.4158 0.5799 0.747
## 02:01 474 26 52.3 38.5 1.381e+00 0.240 0.226 -0.579229 -1.3919 0.2335 0.162
## 02:05 441 59 52.8 42.4 1.881e+00 0.170 0.165 -0.435378 -0.9869 0.1162 0.122
## 03:01 383 117 50.1 56.4 1.175e+00 0.278 0.246 0.250189 -0.1667 0.6671 0.240
## 11:01 442 58 51.8 50.0 1.431e-02 0.905 0.889 -0.069363 -0.6171 0.4784 0.804
## 23:01 470 30 51.1 60.0 5.794e-01 0.447 0.354 0.370754 -0.3825 1.1240 0.335
## 24:02 387 113 50.4 55.8 8.045e-01 0.370 0.337 0.219475 -0.2023 0.6412 0.308
## 25:01 481 19 50.9 68.4 1.592e+00 0.207 0.163 0.750118 -0.2352 1.7355 0.136
## 26:08 455 45 52.3 44.4 7.234e-01 0.395 0.350 -0.315579 -0.9318 0.3006 0.316
## 29:01 472 28 52.5 35.7 2.361e+00 0.124 0.118 -0.684869 -1.4791 0.1094 0.091
## 29:02 440 60 51.6 51.7 3.369e-30 1.000 1.000 0.008454 -0.5319 0.5488 0.976
## 31:01 445 55 51.9 49.1 6.335e-02 0.801 0.775 -0.107611 -0.6686 0.4534 0.707
## 32:01 482 18 51.7 50.0 9.437e-31 1.000 1.000 -0.074015 -1.0159 0.8679 0.878
## 32:02 389 111 50.4 55.9 8.273e-01 0.363 0.333 0.216892 -0.2075 0.6413 0.316
## 33:01 498 2 51.4 100.0 4.403e-01 0.507 0.500 14.514988 -1208.0722 1237.1022 0.981
## 68:01 448 52 50.4 61.5 1.873e+00 0.171 0.144 0.470353 -0.1215 1.0622 0.119
## 68:06 433 67 51.0 55.2 2.565e-01 0.613 0.600 0.165689 -0.3516 0.6830 0.530
## pc1.est pc1.2.5% pc1.97.5% pc1.pval
## 26:01 0.04909 -0.1283 0.2265 0.588
## -----
## 01:01 0.03941 -0.1349 0.2138 0.658
## 02:01 0.04951 -0.1255 0.2246 0.579
## 02:05 0.05251 -0.1228 0.2278 0.557
## 03:01 0.03749 -0.1370 0.2120 0.674
## 11:01 0.03980 -0.1345 0.2141 0.655
## 23:01 0.04397 -0.1306 0.2185 0.622
## 24:02 0.04368 -0.1309 0.2183 0.624
## 25:01 0.04803 -0.1268 0.2228 0.590
## 26:08 0.04038 -0.1341 0.2148 0.650
## 29:01 0.03575 -0.1391 0.2106 0.689
## 29:02 0.04045 -0.1340 0.2149 0.649
## 31:01 0.03895 -0.1355 0.2134 0.662
## 32:01 0.04081 -0.1336 0.2152 0.646
## 32:02 0.03738 -0.1371 0.2119 0.675
## 33:01 0.04154 -0.1333 0.2163 0.641
## 68:01 0.05452 -0.1211 0.2301 0.543
## 68:06 0.03915 -0.1352 0.2135 0.660## Logistic regression (additive model) with 500 individuals:
## glm(disease ~ h, family = binomial, data = data)
## [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5% h.97.5% h.pval
## 26:01 967 33 52.8 15.2 1.668e+01 <0.001* <0.001* -1.82587 -2.7913 -0.8604 <0.001*
## -----
## 01:01 925 75 51.4 54.7 1.870e-01 0.665 0.632 0.13636 -0.3420 0.6147 0.576
## 02:01 972 28 52.0 39.3 1.279e+00 0.258 0.250 -0.46222 -1.1962 0.2718 0.217
## 02:05 941 59 52.2 42.4 1.763e+00 0.184 0.179 -0.42099 -0.9702 0.1282 0.133
## 03:01 876 124 51.0 55.6 7.518e-01 0.386 0.339 0.18821 -0.1929 0.5693 0.333
## 11:01 940 60 51.8 48.3 1.513e-01 0.697 0.690 -0.13812 -0.6588 0.3825 0.603
## 23:01 970 30 51.3 60.0 5.615e-01 0.454 0.362 0.36291 -0.3895 1.1153 0.345
## 24:02 878 122 50.8 57.4 1.603e+00 0.206 0.177 0.25841 -0.1197 0.6366 0.180
## 25:01 981 19 51.3 68.4 1.561e+00 0.211 0.167 0.73576 -0.2480 1.7195 0.143
## 26:08 953 47 52.0 42.6 1.258e+00 0.262 0.233 -0.36825 -0.9510 0.2145 0.216
## 29:01 972 28 52.1 35.7 2.293e+00 0.130 0.124 -0.68957 -1.4834 0.1043 0.089
## 29:02 938 62 51.7 50.0 1.667e-02 0.897 0.795 -0.06808 -0.5815 0.4453 0.795
## 31:01 943 57 51.9 47.4 2.723e-01 0.602 0.586 -0.17724 -0.7093 0.3548 0.514
## 32:01 982 18 51.6 50.0 4.632e-31 1.000 1.000 -0.06641 -1.0075 0.8746 0.890
## 32:02 886 114 51.0 56.1 8.668e-01 0.352 0.321 0.22164 -0.1856 0.6289 0.286
## 33:01 998 2 51.5 100.0 4.394e-01 0.507 0.500 14.50983 -1208.8876 1237.9073 0.981
## 68:01 948 52 51.1 61.5 1.770e+00 0.183 0.155 0.45215 -0.1364 1.0407 0.132
## 68:06 930 70 51.2 57.1 7.027e-01 0.402 0.386 0.23664 -0.2506 0.7239 0.341## Linear regression (dominant model) with 500 individuals:
## glm(trait ~ h, data = data)
## [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5% h.pval
## 33:01 498 2 -0.015763 -0.3650000 0.006* -0.34924 -1.6842 0.9857 0.608
## -----
## 01:01 427 73 -0.030445 0.0605479 0.461 0.09099 -0.1476 0.3295 0.455
## 02:01 474 26 -0.023312 0.0950000 0.558 0.11831 -0.2611 0.4978 0.541
## 02:05 441 59 -0.021701 0.0167797 0.779 0.03848 -0.2227 0.2997 0.773
## 03:01 383 117 -0.019817 -0.0084615 0.905 0.01136 -0.1877 0.2104 0.911
## 11:01 442 58 -0.014050 -0.0408621 0.842 -0.02681 -0.2900 0.2364 0.842
## 23:01 470 30 -0.006404 -0.1856667 0.295 -0.17926 -0.5338 0.1753 0.322
## 24:02 387 113 -0.021059 -0.0038053 0.862 0.01725 -0.1843 0.2188 0.867
## 25:01 481 19 -0.024844 0.1773684 0.452 0.20221 -0.2382 0.6427 0.369
## 26:01 468 32 -0.010107 -0.1203125 0.577 -0.11021 -0.4544 0.2340 0.531
## 26:08 455 45 -0.009187 -0.0977778 0.569 -0.08859 -0.3830 0.2058 0.556
## 29:01 472 28 -0.013242 -0.0832143 0.737 -0.06997 -0.4365 0.2965 0.708
## 29:02 440 60 -0.026705 0.0528333 0.563 0.07954 -0.1797 0.3388 0.548
## 31:01 445 55 -0.009843 -0.0763636 0.644 -0.06652 -0.3358 0.2028 0.629
## 32:01 482 18 -0.009606 -0.2194444 0.265 -0.20984 -0.6619 0.2422 0.363
## 32:02 389 111 -0.022159 0.0003604 0.835 0.02252 -0.1803 0.2253 0.828
## 68:01 448 52 -0.010804 -0.0719231 0.678 -0.06112 -0.3372 0.2149 0.665
## 68:06 433 67 -0.015658 -0.0268657 0.929 -0.01121 -0.2586 0.2362 0.929Amino Acid Association
We convert P-coded alleles to amino acid sequences. hlaConvSequence(..., code="P.code.merge") returns the protein sequence in the ‘antigen binding domains’ (exons 2 and 3 for HLA Class I genes, exon 2 for HLA Class II genes).
## Allelic ambiguity: 68:01, 03:01, 31:01, 01:01, 29:01, 02:01, 29:02, 32:01, 25:01, 02:05, 24:02, 26:01, 11:01, 33:01, 23:01- the sequence is displayed as a hyphen (-) where it is identical to the reference.
- an insertion or deletion is represented by a period (.).
- an unknown or ambiguous position in the alignment is represented by an asterisk (*).
- a capital X is used for the ‘stop’ codons in protein alignments.
## [1] "*-------Y----------------------------------R-----------------RN---V--Q-----VD------------A------M-------S---------------------K--------------T--H----A-V---W-A----T--EW---------------*"
## [2] "*------------------------------------------R----------------------V--Q-----VD------------A--------------S--------------------------------------------A-E---L-A--D-T--EW---------------*"
## [3] "*-------T----------------------------------R-----------R----------V-----I--VD------------A------M-------S--------Q-----------------------------Q-----ARV---L-A----T--EW---------------*"
## [4] "*-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*"
## [5] "*------------------------------------------R----------------------V--Q-----VD------------A--------------S--------------------------------------------A-E---L-A--D-T--EW---------------*"
## [6] "*-------T----------------------------------R-----------R----------V-----I--VD------------A------M-------S--------Q-----------------------------Q-----ARV---L-A----T--EW---------------*"## Pos Num * - A D E F G H I K L M N Q R S T V W Y
## 1 1000 839 161 . . . . . . . . . . . . . . . . . .
## 9 1000 . 359 . . . . . . . . . . . . . 152 149 . . 340
## 43 1000 . 941 . . . . . . . . . . . . 59 . . . . .
## 44 1000 . 75 . . . . . . . . . . . . 925 . . . . .
## 56 1000 . 943 . . . . . . . . . . . . 57 . . . . .
## 62 1000 . 448 . . 152 . 87 . . . 90 . . . 223 . . . . .
## 63 1000 . 687 . . . . . . . . . . 223 90 . . . . . .
## 65 1000 . 848 . . . . 152 . . . . . . . . . . . . .
## 66 1000 . 761 . . . . . . . 239 . . . . . . . . . .
## 67 1000 . 75 . . . . . . . . . . . . . . . 925 . .
## 70 1000 . 604 . . . . . . . . . . . 396 . . . . . .
## 73 1000 . 941 . . . . . . 59 . . . . . . . . . . .
## 74 1000 . 913 . . . . . 87 . . . . . . . . . . . .
## 76 1000 . 245 . . 303 . . . . . . . . . . . . 452 . .
## 77 1000 . 397 . 452 . . . . . . . . . . . 151 . . . .
## 79 1000 . 697 . . . . . . . . . . . . 303 . . . . .
## 80 1000 . 697 . . . . . . 303 . . . . . . . . . . .
## 81 1000 . 697 303 . . . . . . . . . . . . . . . . .
## 82 1000 . 697 . . . . . . . . 303 . . . . . . . . .
## 83 1000 . 697 . . . . . . . . . . . . 303 . . . . .
## 90 1000 . 234 766 . . . . . . . . . . . . . . . . .
## 95 1000 . 761 . . . . . . . . 211 . . . . . . 28 . .
## 97 1000 . 259 . . . . . . . . . 555 . . 186 . . . . .
## 99 1000 . 848 . . . 152 . . . . . . . . . . . . . .
## 102 1000 . 972 . . . . . 28 . . . . . . . . . . . .
## 105 1000 . 366 . . . . . . . . . . . . . 634 . . . .
## 107 1000 . 913 . . . . . . . . . . . . . . . . 87 .
## 109 1000 . 868 . . . . . . . . 132 . . . . . . . . .
## 114 1000 . 401 . . 70 . . 239 . . . . . 290 . . . . . .
## 116 1000 . 691 . . . . . 70 . . . . . . . . . . . 239
## 127 1000 . 639 . . . . . . . 361 . . . . . . . . . .
## 142 1000 . 791 . . . . . . . . . . . . . . 209 . . .
## 144 1000 . 590 . . . . . . . . . . . 410 . . . . . .
## 145 1000 . 791 . . . . . 209 . . . . . . . . . . . .
## 149 1000 . 901 . . . . . . . . . . . . . . 99 . . .
## 150 1000 . 75 925 . . . . . . . . . . . . . . . . .
## 151 1000 . 803 . . . . . . . . . . . . 197 . . . . .
## 152 1000 . 135 . . 223 . . . . . . . . . . . . 642 . .
## 156 1000 . 75 . . . . . . . . 349 . . 343 . . . . 233 .
## 158 1000 . 75 925 . . . . . . . . . . . . . . . . .
## 161 1000 . 876 . 124 . . . . . . . . . . . . . . . .
## 163 1000 . 234 . . . . . . . . . . . . . . 766 . . .
## 166 1000 . 227 . . 773 . . . . . . . . . . . . . . .
## 167 1000 . 227 . . . . . . . . . . . . . . . . 773 .
## 171 1000 . 998 . . . . . 2 . . . . . . . . . . . .
## 183 1000 839 161 . . . . . . . . . . . . . . . . . .## Logistic regression (dominant model) with 500 individuals:
## pos num ref poly fisher.p amino.acid h.est h.2.5% h.97.5% h.pval
## 70 62 1000 Q -,E,G,L,R 0.089 -,E,L,R vs G -0.48863 -9.682e-01 -9.098e-03 0.046*
## 91 74 1000 D -,H 0.056 - vs H -0.48863 -9.682e-01 -9.098e-03 0.046*
## 92 74 1000 D -,H 0.056 - vs H -0.48863 -9.682e-01 -9.098e-03 0.046*
## 94 76 1000 A -,E,V 0.006* - vs E,V 0.71784 -8.666e-03 1.444e+00 0.053
## 95 76 1000 A -,E,V 0.006* -,V vs E 0.38177 2.926e-02 7.343e-01 0.034*
## 96 76 1000 A -,E,V 0.006* -,E vs V 0.11153 -2.677e-01 4.907e-01 0.564
## 101 79 1000 G -,R 0.016* - vs R 0.38177 2.926e-02 7.343e-01 0.034*
## 102 79 1000 G -,R 0.016* - vs R 0.38177 2.926e-02 7.343e-01 0.034*
## 103 80 1000 T -,I 0.016* - vs I 0.38177 2.926e-02 7.343e-01 0.034*
## 104 80 1000 T -,I 0.016* - vs I 0.38177 2.926e-02 7.343e-01 0.034*
## 105 81 1000 L -,A 0.016* - vs A 0.38177 2.926e-02 7.343e-01 0.034*
## 106 81 1000 L -,A 0.016* - vs A 0.38177 2.926e-02 7.343e-01 0.034*
## 107 82 1000 R -,L 0.016* - vs L 0.38177 2.926e-02 7.343e-01 0.034*
## 108 82 1000 R -,L 0.016* - vs L 0.38177 2.926e-02 7.343e-01 0.034*
## 109 83 1000 G -,R 0.016* - vs R 0.38177 2.926e-02 7.343e-01 0.034*
## 110 83 1000 G -,R 0.016* - vs R 0.38177 2.926e-02 7.343e-01 0.034*
## 127 97 1000 I -,M,R 0.002* - vs M,R 0.06697 -7.878e-01 9.217e-01 0.878
## 128 97 1000 I -,M,R 0.002* -,R vs M 0.53138 5.976e-02 1.003e+00 0.027*
## 129 97 1000 I -,M,R 0.002* -,M vs R -0.65698 -1.033e+00 -2.809e-01 <0.001*
## 142 107 1000 G -,W 0.056 - vs W -0.48863 -9.682e-01 -9.098e-03 0.046*
## 143 107 1000 G -,W 0.056 - vs W -0.48863 -9.682e-01 -9.098e-03 0.046*
## 195 149 1000 A -,T 0.006* - vs T -0.55565 -1.012e+00 -9.979e-02 0.017*
## 196 149 1000 A -,T 0.006* - vs T -0.55565 -1.012e+00 -9.979e-02 0.017*## Exclude 20 individuals from the study due to the call threshold (0.5)
## Logistic regression (dominant model) with 480 individuals:
## pos num ref poly fisher.p amino.acid h.est h.2.5% h.97.5% h.pval
## 94 76 960 A -,E,V 0.026* - vs E,V 0.66262 -7.054e-02 1.39577 0.076
## 95 76 960 A -,E,V 0.026* -,V vs E 0.34761 -1.188e-02 0.70710 0.058
## 96 76 960 A -,E,V 0.026* -,E vs V 0.06517 -3.216e-01 0.45198 0.741
## 101 79 960 G -,R 0.035* - vs R 0.34761 -1.188e-02 0.70710 0.058
## 102 79 960 G -,R 0.035* - vs R 0.34761 -1.188e-02 0.70710 0.058
## 103 80 960 T -,I 0.035* - vs I 0.34761 -1.188e-02 0.70710 0.058
## 104 80 960 T -,I 0.035* - vs I 0.34761 -1.188e-02 0.70710 0.058
## 105 81 960 L -,A 0.035* - vs A 0.34761 -1.188e-02 0.70710 0.058
## 106 81 960 L -,A 0.035* - vs A 0.34761 -1.188e-02 0.70710 0.058
## 107 82 960 R -,L 0.035* - vs L 0.34761 -1.188e-02 0.70710 0.058
## 108 82 960 R -,L 0.035* - vs L 0.34761 -1.188e-02 0.70710 0.058
## 109 83 960 G -,R 0.035* - vs R 0.34761 -1.188e-02 0.70710 0.058
## 110 83 960 G -,R 0.035* - vs R 0.34761 -1.188e-02 0.70710 0.058
## 127 97 960 I -,M,R 0.004* - vs M,R 0.06089 -8.345e-01 0.95628 0.894
## 128 97 960 I -,M,R 0.004* -,R vs M 0.55575 6.717e-02 1.04432 0.026*
## 129 97 960 I -,M,R 0.004* -,M vs R -0.63547 -1.020e+00 -0.25115 0.001*
## 195 149 960 A -,T 0.012* - vs T -0.52109 -9.901e-01 -0.05203 0.029*
## 196 149 960 A -,T 0.012* - vs T -0.52109 -9.901e-01 -0.05203 0.029*## Logistic regression (recessive model) with 500 individuals:
## pos num ref poly fisher.p amino.acid h.est h.2.5% h.97.5% h.pval
## 10 9 1000 F -,S,T,Y 0.108 -,T,Y vs S 1.67600 1.590e-01 3.19299 0.030*
## 68 62 1000 Q -,E,G,L,R 0.089 -,G,L,R vs E 1.67600 1.590e-01 3.19299 0.030*
## 76 65 1000 R -,G 0.095 - vs G 1.67600 1.590e-01 3.19299 0.030*
## 77 65 1000 R -,G 0.095 - vs G 1.67600 1.590e-01 3.19299 0.030*
## 93 76 1000 A -,E,V 0.006* - vs E,V 0.45718 9.996e-02 0.81440 0.012*
## 94 76 1000 A -,E,V 0.006* -,V vs E 0.58152 -5.592e-02 1.21896 0.074
## 95 76 1000 A -,E,V 0.006* -,E vs V -0.05768 -4.852e-01 0.36984 0.791
## 100 79 1000 G -,R 0.016* - vs R 0.58152 -5.592e-02 1.21896 0.074
## 101 79 1000 G -,R 0.016* - vs R 0.58152 -5.592e-02 1.21896 0.074
## 102 80 1000 T -,I 0.016* - vs I 0.58152 -5.592e-02 1.21896 0.074
## 103 80 1000 T -,I 0.016* - vs I 0.58152 -5.592e-02 1.21896 0.074
## 104 81 1000 L -,A 0.016* - vs A 0.58152 -5.592e-02 1.21896 0.074
## 105 81 1000 L -,A 0.016* - vs A 0.58152 -5.592e-02 1.21896 0.074
## 106 82 1000 R -,L 0.016* - vs L 0.58152 -5.592e-02 1.21896 0.074
## 107 82 1000 R -,L 0.016* - vs L 0.58152 -5.592e-02 1.21896 0.074
## 108 83 1000 G -,R 0.016* - vs R 0.58152 -5.592e-02 1.21896 0.074
## 109 83 1000 G -,R 0.016* - vs R 0.58152 -5.592e-02 1.21896 0.074
## 126 97 1000 I -,M,R 0.002* - vs M,R -0.18350 -5.352e-01 0.16819 0.306
## 127 97 1000 I -,M,R 0.002* -,R vs M 0.28787 -1.043e-01 0.68003 0.150
## 128 97 1000 I -,M,R 0.002* -,M vs R -0.97078 -2.029e+00 0.08766 0.072
## 130 99 1000 Y -,F 0.095 - vs F 1.67600 1.590e-01 3.19299 0.030*
## 131 99 1000 Y -,F 0.095 - vs F 1.67600 1.590e-01 3.19299 0.030*
## 186 144 1000 K -,Q 0.108 - vs Q -0.52294 -1.006e+00 -0.03991 0.034*
## 187 144 1000 K -,Q 0.108 - vs Q -0.52294 -1.006e+00 -0.03991 0.034*
## 193 149 1000 A -,T 0.006* - vs T -15.65097 -1.291e+03 1260.03779 0.981
## 194 149 1000 A -,T 0.006* - vs T -15.65097 -1.291e+03 1260.03779 0.981Resources
- Allele Frequency Net Database (AFND): http://www.allelefrequencies.net.
- IMGT/HLA Database: http://www.ebi.ac.uk/imgt/hla.
- HLA Nomenclature: G Codes and P Codes for reporting of ambiguous allele typings.
Session Info
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
## [4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] HIBAG_1.20.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 knitr_1.22 magrittr_1.5 tidyselect_0.2.5 munsell_0.5.0 colorspace_1.4-1
## [7] R6_2.4.0 rlang_0.3.4 dplyr_0.8.0.1 stringr_1.4.0 plyr_1.8.4 tools_3.6.0
## [13] grid_3.6.0 gtable_0.3.0 xfun_0.6 withr_2.1.2 htmltools_0.3.6 assertthat_0.2.1
## [19] yaml_2.2.0 lazyeval_0.2.2 digest_0.6.18 tibble_2.1.1 crayon_1.3.4 purrr_0.3.2
## [25] ggplot2_3.1.1 glue_1.3.1 evaluate_0.13 rmarkdown_1.12 labeling_0.3 stringi_1.4.3
## [31] compiler_3.6.0 pillar_1.3.1 scales_1.0.0 pkgconfig_2.0.2Breiman, Leo. 1996. “Bagging Predictors.” Mach. Learn. 24 (2):123–40. https://doi.org/10.1023/A:1018054314350.
———. 2001. “Random Forests.” Mach. Learn. 45 (1):5–32. https://doi.org/10.1023/A:1010933404324.
Bryll, Robert, Ricardo Gutierrez-Osuna, and Francis Quek. 2003. “Attribute Bagging: Improving Accuracy of Classifier Ensembles by Using Random Feature Subsets.” Pattern Recognition 36 (6):1291–1302. https://doi.org/10.1016/S0031-3203(02)00121-8.
Robinson, James, Jason A. Halliwell, Hamish McWilliam, Rodrigo Lopez, Peter Parham, and Steven G.E. Marsh. 2013. “The IMGT/HLA Database.” Nucleic Acids Res 41 (Database issue):1222–7. https://doi.org/10.1093/nar/gks949.
Shiina, Takashi, Kazuyoshi Hosomichi, Hidetoshi Inoko, and Jerzy Kulski. 2009. “The HLA Genomic Loci Map: Expression, Interaction, Diversity and Disease.” Journal of Human Genetics 54 (1):15–39. https://doi.org/10.1038/jhg.2008.5.
Zheng, Xiuwen, Judong Shen, Charles Cox, Jonathan C. Wakefield, Margaret G. Ehm, Matthew R. Nelson, and Bruce S. Weir. 2013. “HIBAG – HLA Genotype Imputation with Attribute Bagging.” Pharmacogenomics J, May. https://doi.org/10.1038/tpj.2013.18.