Multiple platform build/check report for BioC 3.20 - CHECK results for MungeSumstats on merida1

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo2	Linux (Ubuntu 24.04.1 LTS)	x86_64	4.4.2 (2024-10-31) -- "Pile of Leaves"	4744
palomino8	Windows Server 2022 Datacenter	x64	4.4.2 (2024-10-31 ucrt) -- "Pile of Leaves"	4487
merida1	macOS 12.7.5 Monterey	x86_64	4.4.2 (2024-10-31) -- "Pile of Leaves"	4515
kjohnson1	macOS 13.6.6 Ventura	arm64	4.4.2 (2024-10-31) -- "Pile of Leaves"	4467
Click on any hostname to see more info about the system (e.g. compilers) () as reported by 'uname -p', except on Windows and Mac OS X*

CHECK results for MungeSumstats on merida1

To the developers/maintainers of the MungeSumstats package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/MungeSumstats.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results

Summary

Package: MungeSumstats

Version: 1.14.1

Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MungeSumstats.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MungeSumstats_1.14.1.tar.gz

StartedAt: 2024-12-20 06:44:25 -0500 (Fri, 20 Dec 2024)

EndedAt: 2024-12-20 07:24:26 -0500 (Fri, 20 Dec 2024)

EllapsedTime: 2400.8 seconds

RetCode: None

Status: TIMEOUT

CheckDir: MungeSumstats.Rcheck

Warnings: NA

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MungeSumstats.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MungeSumstats_1.14.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/MungeSumstats.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘MungeSumstats/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MungeSumstats’ version ‘1.14.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... NOTE
Found the following hidden files and directories:
  .BBSoptions
These were most likely included in error. See section ‘Package
structure’ in the ‘Writing R Extensions’ manual.
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘MungeSumstats’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) check_no_chr_bp.Rd:56-57: Lost braces
    56 | \item \code{sumstats_dt}{
       |                         ^
checkRd: (-1) check_no_chr_bp.Rd:58-59: Lost braces
    58 | \item \code{rsids}{
       |                   ^
checkRd: (-1) check_no_chr_bp.Rd:60-61: Lost braces
    60 | \item \code{log_files}{
       |                       ^
checkRd: (-1) check_on_ref_genome.Rd:65-66: Lost braces
    65 | \item \code{sumstats_dt}{
       |                         ^
checkRd: (-1) check_on_ref_genome.Rd:67-68: Lost braces
    67 | \item \code{rsids}{
       |                   ^
checkRd: (-1) check_on_ref_genome.Rd:69-70: Lost braces
    69 | \item \code{log_files}{
       |                       ^
checkRd: (-1) compute_nsize.Rd:32: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:33-36: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:37-38: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:39-40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:41-42: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:43-44: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:21-28: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:30-34: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:36-40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:42-46: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:48-52: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:16-23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:25-29: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:31-35: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:37-41: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:43-47: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:21-28: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:30-34: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:36-40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:42-46: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:48-52: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_sumstats.Rd:29: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_sumstats.Rd:30: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_sumstats.Rd:31-32: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf.Rd:64: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf.Rd:65: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf.Rd:66-67: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf_parallel.Rd:40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf_parallel.Rd:41: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf_parallel.Rd:42-43: Lost braces in \itemize; meant \describe ?
checkRd: (-1) select_vcf_fields.Rd:27: Lost braces in \itemize; meant \describe ?
checkRd: (-1) select_vcf_fields.Rd:28: Lost braces in \itemize; meant \describe ?
checkRd: (-1) select_vcf_fields.Rd:29-30: Lost braces in \itemize; meant \describe ?
checkRd: (-1) sort_coords.Rd:19-21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) sort_coords.Rd:22-24: Lost braces in \itemize; meant \describe ?
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                       user system elapsed
get_genome_builds   136.543  9.100 148.539
format_sumstats     135.860  7.481 152.735
liftover             13.118  0.096  15.307
compute_nsize        12.315  0.419  13.568
infer_effect_column  12.109  0.094  12.487
formatted_example    10.911  0.289  11.344
index_tabular        10.832  0.364  11.323
standardise_header   10.996  0.046  11.428
index_vcf            10.813  0.040  10.940
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’

Installation output

MungeSumstats.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL MungeSumstats
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘MungeSumstats’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (MungeSumstats)

Tests output

MungeSumstats.Rcheck/tests/testthat.Rout


R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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.

> library(testthat)
> library(MungeSumstats)
> 
> test_check("MungeSumstats")


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90172b327dde.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90174be31bbe
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A0	A1	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A0	A1	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90172b327dde.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.229 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901764b3b419.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90174be31bbe
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901764b3b419.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.191 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90171e4d580.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017172cdb1b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 77 seconds.
1 SNPs are non-biallelic. These will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/snp_bi_allelic.tsv.gz
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90171e4d580.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.494 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90177deb6581.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017172cdb1b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 42 seconds.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90177deb6581.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.923 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017b9be01.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Found 1 Indels. These will be removed from the sumstats. 
WARNING If you want to keep Indels, set the drop_indel param to FALSE & rerun MungeSumstats::format_sumstats()
Writing in tabular format ==> /tmp/RtmpDh3pAA/indel.tsv.gz
Loading required namespace: GenomicFiles
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
Dropping 1 duplicate column(s).
1 sample detected: EBI-a-GCST005647
Constructing ScanVcfParam object.
VCF contains: 39,630,630 variant(s) x 1 sample(s)
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Dropping 1 duplicate column(s).
Checking for empty columns.
Unlisting 3 columns.
Dropped 314 duplicate rows.
Time difference of 0.2 secs
VCF data.table contains: 101 rows x 11 columns.
Time difference of 1.4 secs
Renaming ID as SNP.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017d5ba603.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017602a9bd0
Checking for empty columns.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017d5ba603.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.194 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ    BETA       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
       SE          P      N
    <num>      <num>  <int>
1: 0.0393 0.42730011 293723
2: 0.0353 0.74669974 293723
3: 0.0370 0.05464998 293723
4: 0.0830 0.77249913 293723
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90176c199ccc.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	Beta	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	Beta	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90176c199ccc.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.199 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ      ES       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
       SE          P      N    BETA
    <num>      <num>  <int>   <num>
1: 0.0393 0.42730011 293723  0.0312
2: 0.0353 0.74669974 293723 -0.0114
3: 0.0370 0.05464998 293723  0.0711
4: 0.0830 0.77249913 293723 -0.0240
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173bcb6204.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017602a9bd0
Checking for empty columns.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	P	N	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	P	N	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
The sumstats SE column is not present...Deriving SE from Beta and P
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90173bcb6204.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.219 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ    BETA       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
            P      N         SE IMPUTATION_SE
        <num>  <int>      <num>        <lgcl>
1: 0.42730011 293723 0.03930361          TRUE
2: 0.74669974 293723 0.03529477          TRUE
3: 0.05464998 293723 0.03699948          TRUE
4: 0.77249913 293723 0.08301411          TRUE
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90177541c9ca.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017602a9bd0
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	Z	SE	P	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	Z	SE	P	N	
Summary statistics report:
   - 25 rows
   - 25 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
The sumstats BETA column is not present...Deriving BETA from Z and SE
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
13 SNPs (52%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90177541c9ca.tsv.gz
Summary statistics report:
   - 25 rows (100% of original 25 rows)
   - 25 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.189 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR     BP     A1     A2       FRQ      Z          SE      P
        <char> <int>  <int> <char> <char>     <num>  <num>       <num>  <num>
1:  rs12184267     1 715265      C      T 0.9591931 -0.916 0.007518884 0.3598
2:  rs12184277     1 715367      A      G 0.9589313 -0.656 0.007491601 0.5116
3:  rs12184279     1 717485      C      A 0.9594241 -1.050 0.007534860 0.2938
4: rs116801199     1 720381      G      T 0.9578380 -0.300 0.007391344 0.7644
        N         BETA IMPUTATION_BETA
    <int>        <num>          <lgcl>
1: 225955 -0.006887298            TRUE
2: 226215 -0.004914490            TRUE
3: 226224 -0.007911603            TRUE
4: 226626 -0.002217403            TRUE
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
Filtering SNPs based on INFO score.
46 SNPs are below the INFO threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/info_filter.tsv.gz
INFO_filter==0. Skipping INFO score filtering step.
Filtering SNPs based on INFO score.
All rows have INFO>=0.9
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
3 p-values are >1 which LDSC/MAGMA may not be able to handle. These will be converted to 1.
5 p-values are <0 which LDSC/MAGMA may not be able to handle. These will be converted to 0.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
8 p-values are <=5e-324 which LDSC/MAGMA may not be able to handle. These will be converted to 0.
Reading header.
Tabular format detected.
Reading header.
Tabular format detected.
Reading header.
Tabular format detected.
Reading header.
VCF format detected.This will be converted to a standardised table format.
Importing tabular file: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library/MungeSumstats/extdata/eduAttainOkbay.txt
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Computing Z-score from P using formula: `sign(BETA)*sqrt(stats::qchisq(P,1,lower=FALSE)`
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	Z	newZ	
Computing Z-score from BETA ans SE using formula: `BETA/SE`


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017709ccaff.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90172168f0db
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Standardising column headers.
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP_A2_A1 has been separated into the columns CHR, BP, A2, A1
If this is the incorrect format for the column, update the column name to the correct format e.g.`CHR:BP:A2:A1` and format_sumstats().
Standardising column headers.
First line of summary statistics file: 
SNP	FRQ	BETA	SE	P	CHR	BP	A2	A1	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017709ccaff.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.382 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017732bf677.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90172168f0db
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017732bf677.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.207 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901722d6b4b2.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017424e4dd0
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Standardising column headers.
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP_A2_A1 has been separated into the columns CHR, BP, A2, A1
If this is the incorrect format for the column, update the column name to the correct format e.g.`CHR:BP:A2:A1` and format_sumstats().
Standardising column headers.
First line of summary statistics file: 
SNP	FRQ	BETA	SE	P	CHR	BP	A2	A1	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901722d6b4b2.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.387 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173280c5ec.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017424e4dd0
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90173280c5ec.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.193 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017704e97ef.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017750a31d8
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	EAF	Beta	SE	Pval	alleles	allele	
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	EAF	Beta	SE	Pval	alleles	allele	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Warning: Multiple columns in the sumstats file seem to relate to alleles A1>A2.
The column ALLELES will be kept whereas the column(s) ALLELE will be removed.
If this is not the correct column to keep, please remove all incorrect columns from those listed here before 
running `format_sumstats()`.
Column ALLELES has been separated into the columns A1, A2
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017704e97ef.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.187 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017718a532d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017750a31d8
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017718a532d.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.193 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017602dcf14.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017930c272
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	CHR	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017602dcf14.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.378 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017176e23b4.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017930c272
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017176e23b4.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.189 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90174885df33.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017229a6d6c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	CHR_BP_2	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	CHR_BP_2	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Warning: Multiple columns in the sumstats file seem to relate to Chromosome:Base Pair position.
The column CHR_BP_2 will be kept whereas the column(s) CHR_BP will be removed.
If this is not the correct column to keep, please remove all incorrect columns from those listed here before 
running `format_sumstats()`.
Column CHR_BP_2 has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	CHR	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90174885df33.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.365 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901742b09a8.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017229a6d6c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901742b09a8.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.182 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901732462216.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901763b5c3be
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901732462216.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.186 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90175c97ac20.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90172e06f01
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90175c97ac20.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.187 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Setting sorted=FALSE (required when formatted=FALSE).


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90171ed8d0a9.tsv.gz
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Assigning N=1000 for all SNPs.
N already exists within sumstats_dt.
[1] "Testing: compute_n='ldsc'"
Computing effective sample size using the LDSC method:
 Neff = (N_CAS+N_CON) * (N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))[(N_CAS+N_CON)==max(N_CAS+N_CON)]))
[1] "Testing: compute_n='giant'"
Computing effective sample size using the GIANT method:
 Neff = 2 / (1/N_CAS + 1/N_CON)
[1] "Testing: compute_n='metal'"
Computing effective sample size using the METAL method:
 Neff = 4 / (1/N_CAS + 1/N_CON)
[1] "Testing: compute_n='sum'"
Computing sample size using the sum method:
 N = N_CAS + N_CON


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901714c98f67.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017731d7c44
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901714c98f67.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.191 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173338dc33.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Saving output messages to:
/tmp/RtmpDh3pAA/file90173338dc33_log_msg.txt
Any runtime errors will be saved to:
/tmp/RtmpDh3pAA/file90173338dc33_log_output.txt
Messages will not be printed to terminal.
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90176f87d569.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901716a5217a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90176f87d569.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.188 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901759a25290.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017691cad01
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 186 rows
   - 93 unique variants
   - 140 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
93 sumstat rows are duplicated. These duplicates will be removed.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901759a25290.tsv.gz
Summary statistics report:
   - 93 rows (50% of original 186 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.186 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901752bb1918.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017691cad01
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901752bb1918.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.182 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90171b9e6dc0.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017691cad01
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 94 rows
   - 94 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
1 base-pair positions are duplicated in the sumstats file. These duplicates will be removed.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 40 seconds.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90171b9e6dc0.tsv.gz
Summary statistics report:
   - 93 rows (98.9% of original 94 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.875 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173052b02b.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017712eb0b2
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Filtering effect columns, ensuring none equal 0.
5 SNPs have effect values = 0 and will be removed
Ensuring all SNPs have N<5 std dev above mean.
44 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90173052b02b.tsv.gz
Summary statistics report:
   - 88 rows (94.6% of original 93 rows)
   - 88 unique variants
   - 65 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.195 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90176583fd3e.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90173361fa7c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs based on FRQ.
38 SNPs are below the FRQ threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/frq_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
55 SNPs (100%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90176583fd3e.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.186 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
        FRQ
      <num>
1: 1.863269
2: 1.169733
3: 1.401423
4: 1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901747d9356e.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90173361fa7c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs based on FRQ.
38 SNPs are below the FRQ threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/frq_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
55 SNPs (100%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=FALSE, the FRQ column will be renamed MAJOR_ALLELE_FRQ to differentiate the values from 
minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901747d9356e.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.186 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
   MAJOR_ALLELE_FRQ
              <num>
1:         1.863269
2:         1.169733
3:         1.401423
4:         1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901735a2979.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017413512a1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901735a2979.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.187 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017457a07d.tsv
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.
Reading header.
Reading entire file.
Sorting coordinates with 'GenomicRanges'.
Converting summary statistics to GenomicRanges.
Sorting coordinates with 'data.table'.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901761b7342b.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017549c4dca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	INFO	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	INFO	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
Filtering SNPs based on INFO score.
38 SNPs are below the INFO threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/info_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
28 SNPs (50.9%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901761b7342b.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.185 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
       INFO
      <num>
1: 1.863269
2: 1.169733
3: 1.401423
4: 1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90172ee1ab07.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901764ad51fc
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90172ee1ab07.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.19 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901761a03325.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901761a03325.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.186 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
[1] "/tmp/RtmpDh3pAA/data/file1/file901755c281c3.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file2/file901754d55531.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file3/file90171cc2f7a.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file4/file9017440f382.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file5/file90174432ddfc.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file6/file9017678bee5e.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file7/file901713ce926e.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file8/file90175eef7dea.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file9/file90173d33be57.tsv.gz"
[1] "/tmp/RtmpDh3pAA/data/file10/file901710156125.tsv.gz"
10 file(s) found.
Parsing info from 10 log file(s).


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017517a237a.tsv.gz
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017517a237a.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.205 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE  PVAL_ORG
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
          LP         P
       <num>     <num>
1:  7.746178 1.794e-08
2:  9.627272 2.359e-10
3: 13.424581 3.762e-14
4:  7.745452 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90177654208b.tsv.gz
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90177654208b.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.182 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE  PVAL_ORG
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
           LP         P
        <num>     <num>
1:  -7.746178 1.794e-08
2:  -9.627272 2.359e-10
3: -13.424581 3.762e-14
4:  -7.745452 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901750505ea5.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90177b486e86
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
WARNING: 1 rows in sumstats file are missing data and will be removed.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901750505ea5.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.184 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901758599c34.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90177b486e86
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901758599c34.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.184 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901751306a5b.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 3 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901751306a5b.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.247 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901794950a5.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 3 seconds.
Checking for missing data.
WARNING: 93 rows in sumstats file are missing data and will be removed.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901715710243.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 4 seconds.
Checking for missing data.
WARNING: None of the inputted columns:
CHRA APOS
To be checked for missing data were found in the sumstats. Sumstats columns:
SNP CHR BP A1 A2 FRQ BETA SE P EXTRA
This check will not be run.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901715710243.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.275 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P  EXTRA
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num> <lgcl>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09     NA
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08     NA
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10     NA
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11     NA
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90171d52bec7.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90175603f32f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
chromosome	rs_id	markername	position_hg18	Effect_allele	Other_allele	EAF_HapMapCEU	N_SMK	Effect_SMK	StdErr_SMK	P_value_SMK	N_NONSMK	Effect_NonSMK	StdErr_NonSMK	P_value_NonSMK	
Standardising column headers.
First line of summary statistics file: 
chromosome	rs_id	markername	position_hg18	Effect_allele	Other_allele	EAF_HapMapCEU	N_SMK	Effect_SMK	StdErr_SMK	P_value_SMK	N_NONSMK	Effect_NonSMK	StdErr_NonSMK	P_value_NonSMK	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 1 chromosomes
Checking for multi-GWAS.
WARNING: Multiple traits found in sumstats file only one of which can be analysed: 
SMK, NONSMK
Standardising column headers.
First line of summary statistics file: 
CHR	SNP	MARKERNAME	POSITION_HG18	A2	A1	EAF_HAPMAPCEU	N	EFFECT	STDERR	P_VALUE	N_NONSMK	EFFECT_NONSMK	STDERR_NONSMK	P_VALUE_NONSMK	
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted and will be removed.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column MARKERNAME has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
CHR	SNP	POSITION_HG18	A2	A1	EAF_HAPMAPCEU	N	BETA	SE	P	N_NONSMK	EFFECT_NONSMK	STDERR_NONSMK	P_VALUE_NONSMK	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Ensuring that the N column is all integers.
The sumstats N column is not all integers, this could effect downstream analysis. These will be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90171d52bec7.tsv.gz
Summary statistics report:
   - 4 rows (80% of original 5 rows)
   - 4 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.565 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
         SNP    CHR        BP     A1     A2 POSITION_HG18 EAF_HAPMAPCEU     N
      <char> <char>     <int> <char> <char>         <int>         <num> <int>
1: rs1000050   chr1 161003087      C      T     161003087        0.9000 36257
2: rs1000073   chr1 155522020      G      A     155522020        0.3136 36335
3: rs1000075   chr1  94939420      C      T      94939420        0.3583 38959
4: rs1000085   chr1  66630503      G      C      66630503        0.1667 38761
      BETA     SE      P N_NONSMK EFFECT_NONSMK STDERR_NONSMK P_VALUE_NONSMK
     <num>  <num>  <num>    <int>         <num>         <num>          <num>
1:  0.0001 0.0109 0.9931   127514        0.0058        0.0059         0.3307
2:  0.0046 0.0083 0.5812   126780        0.0038        0.0045         0.3979
3: -0.0013 0.0082 0.8687   147567       -0.0043        0.0044         0.3259
4:  0.0053 0.0095 0.5746   147259       -0.0034        0.0052         0.5157
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901770c2fe38.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017627ec52c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	N_fixed	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	N_fixed	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Ensuring that the N column is all integers.
The sumstats N column is not all integers, this could effect downstream analysis. These will be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901770c2fe38.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.191 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     5
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     1
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     1
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     7
   N_FIXED
     <int>
1:       5
2:       1
3:       1
4:       7
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90177f24539c.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017488e6f2a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpDh3pAA/n_large.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90177f24539c.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.206 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017d4d7bea.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017488e6f2a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpDh3pAA/n_large.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017d4d7bea.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.199 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90171c4e8f1e.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017488e6f2a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpDh3pAA/n_large.tsv.gz
Removing rows where is.na(N)
0 SNPs have N values that are NA and will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/n_null.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90171c4e8f1e.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.188 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173e8da446.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017412853ad
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	BP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 43 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90173e8da446.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.107 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90172735f281.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90174c4487e5
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 41 seconds.
Writing in tabular format ==> /tmp/RtmpDh3pAA/chr_bp_not_found_from_snp.tsv.gz
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90172735f281.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.091 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173d1078e5.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901722130c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90173d1078e5.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.202 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90174e0750cb.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901722130c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90174e0750cb.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.192 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90174038e8ed.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90175b71931c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs appear to be made up of chr:bp, these will be replaced by their SNP ID from the reference genome
Loading SNPlocs data.
1 SNP IDs are not correctly formatted and will be removed.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data.
There is no Chromosome or Base Pair Position column found within the data. It must be inferred from other column information.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 92 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 43 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90174038e8ed.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.131 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90175e419779.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90177d71422a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data.
1 SNP IDs appear to be made up of chr:bp, these will be replaced by their SNP ID from the reference genome
Loading SNPlocs data.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90175e419779.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.215 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017392916f1.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901716af13bd
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901763dc00c3.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90175b71931c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901763dc00c3.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.19 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173e865bd7.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901727acc052
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data.
There is no SNP column found within the data. It must be inferred from other column information.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90173e865bd7.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.279 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017400b6072.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90173aff1250
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 23 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
1 SNPs have been removed as their BP column is not in the range of 1 to the length of the chromosome
Writing in tabular format ==> /tmp/RtmpDh3pAA/bad_bp.tsv.gz
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs are on chromosomes X, Y, MT and will be removed.
Writing in tabular format ==> /tmp/RtmpDh3pAA/chr_excl.tsv.gz
45 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017400b6072.tsv.gz
Summary statistics report:
   - 90 rows (96.8% of original 93 rows)
   - 90 unique variants
   - 67 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.197 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90175053c7b7.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90173aff1250
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90175053c7b7.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.191 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Reading header.
Reading entire file.
Reading header.
Reading header.
Reading header.
Reading header.
Reading header.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017133fc8b8
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90175eddd5cf
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901748cbaf29.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpDh3pAA/file901748cbaf29.vcf.bgz
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.2 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 1 secs
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
ID	chr	BP	end	REF	ALT	SNP	FRQ	BETA	SE	P	
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
Dropping 1 duplicate column(s).
1 sample detected: EBI-a-GCST005647
Constructing ScanVcfParam object.
VCF contains: 39,630,630 variant(s) x 1 sample(s)
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Dropping 1 duplicate column(s).
Checking for empty columns.
Unlisting 3 columns.
Dropped 314 duplicate rows.
Time difference of 0.2 secs
VCF data.table contains: 101 rows x 11 columns.
Time difference of 1.2 secs
Renaming ID as SNP.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90174c3e3dff.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpDh3pAA/file90174c3e3dff.vcf.bgz
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.4 secs
VCF data.table contains: 101 rows x 13 columns.
Time difference of 1.8 secs
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
ID	chr	BP	end	REF	SNP	END	FILTER	FRQ	BETA	LP	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901728ccb8f8.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Infer Effect Column
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	
Standardising column headers.
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
5 SNP IDs contain other information in the same column. These will be separated.
Checking for merged allele column.
Column SNP_INFO has been separated into the columns A1, A2
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
SE is not present but can be imputed with BETA & P. Set impute_se=TRUE and rerun to do this.
Ensuring all SNPs have N<5 std dev above mean.
3 SNPs (60%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901728ccb8f8.tsv.gz
Summary statistics report:
   - 5 rows (100% of original 5 rows)
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.336 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2           P       FRQ      BETA
        <char> <int> <int> <char> <char>       <num>     <num>     <num>
1: rs140052487     1 54353      C      A 0.037219838 0.3000548 0.8797957
2: rs558796213     1 54564      G      T 0.004382482 0.5848666 0.7068747
3: rs561234294     1 54591      A      G 0.070968402 0.3334671 0.7319726
4:   rs2462492     1 54676      C      T 0.065769040 0.6220120 0.9316344
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90176f5031a5.tsv.gz
Log data to be saved to ==>  /tmp/RtmpDh3pAA
Infer Effect Column
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	A1	A2	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	A1	A2	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
SE is not present but can be imputed with BETA & P. Set impute_se=TRUE and rerun to do this.
Ensuring all SNPs have N<5 std dev above mean.
3 SNPs (60%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90176f5031a5.tsv.gz
Summary statistics report:
   - 5 rows (100% of original 5 rows)
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.373 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2           P       FRQ      BETA
        <char> <int> <int> <char> <char>       <num>     <num>     <num>
1: rs140052487     1 54353      C      A 0.037219838 0.3000548 0.8797957
2: rs558796213     1 54564      G      T 0.004382482 0.5848666 0.7068747
3: rs561234294     1 54591      A      G 0.070968402 0.3334671 0.7319726
4:   rs2462492     1 54676      C      T 0.065769040 0.6220120 0.9316344
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90173456a5e2.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017657a1e3a
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file901720358f6c.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90176c7f66ca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file901720358f6c.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.342 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017684d862e.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90176c7f66ca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017684d862e.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.349 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90174e5fcd6a.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file90172070cfe1
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90174e5fcd6a.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.221 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file9017324d24b3.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file901747880245
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file9017324d24b3.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.189 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpDh3pAA/file90172fc0b7ab.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpDh3pAA/file9017b981d9f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
5 SNPs have SE values <= 0 and will be removed
Ensuring all SNPs have N<5 std dev above mean.
44 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpDh3pAA/file90172fc0b7ab.tsv.gz
Summary statistics report:
   - 88 rows (94.6% of original 93 rows)
   - 88 unique variants
   - 65 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.191 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	Support	
Returning unmapped column names without making them uppercase.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	Support

Example timings

MungeSumstats.Rcheck/MungeSumstats-Ex.timings

name	user	system	elapsed
compute_nsize	12.315	0.419	13.568
download_vcf	0.001	0.001	0.002
find_sumstats	0.002	0.001	0.003
format_sumstats	135.860	7.481	152.735
formatted_example	10.911	0.289	11.344
get_genome_builds	136.543	9.100	148.539
import_sumstats	0.002	0.001	0.003
index_tabular	10.832	0.364	11.323
index_vcf	10.813	0.040	10.940
infer_effect_column	12.109	0.094	12.487
liftover	13.118	0.096	15.307
list_sumstats	0.003	0.002	0.005
load_snp_loc_data	0.001	0.001	0.003
parse_logs	0.017	0.001	0.018
read_header	0.005	0.002	0.007
read_sumstats	0.011	0.002	0.012
read_vcf	3.890	0.029	3.947
standardise_header	10.996	0.046	11.428
vcf2df	1.238	0.010	1.255
write_sumstats	0.012	0.002	0.015