Sex-aware Post-impute Quality Control Pipeline Using GXwasR
Banabithi Bose
Northwestern UniversityUniversity of Colorado Anschutz Medical Campusbanabithi.bose@gmail.com
8 August 2025
Source:vignettes/postimputationQC.Rmd
postimputationQC.RmdThis document outlines the sex-aware post-imputation quality control (QC) steps, which represent our recommended best practices. Users have the flexibility to create their own pipelines using the GXwasR functions based on their specific needs.
Sex-aware Post-imputation QC pipeline:
We will run the post-imputation QC pipeline in this vignette utilizing the functions from GXwasR package.
Example Datasets
The PLINK bed, bim, and fam files are the three mandatory files representing a genotype dataset to run this pipeline. To know about these file extensions, please check https://www.cog-genomics.org/plink/1.9/formats.
GXwasR_example:
GXwasR_example.bed
GXwasR_example.bim
GXwasR_example.fam
These plink files contain genotypes for 276 individuals (males and females) simulated from 1000Genome from European decent with 26515 variants across twelve chromosomes (1-10,23,24). This dataset contains 125 males, 151 females, 108 cases and 168 controls. We will utilize this set of plink files as proxy of pre-imputated genotype data.
Post-imputation QC steps
-(A) Sex-combined QC: 1.Remove SNPs with IMPUTE2 info score < 0.6 and certainty < 0.8 This part should be done in the imputation server with the VCF files.
-(B) Sex-specific autosomal QC at the variant level: 1.Filter PAR, XTR, Ampliconic regions from the X-chromosome and combine with autosomes for autosomal QC. 2.Any SNPs in the controls with a sex difference in MAF should be carefully examined and flagged for further examination, since the differences may be due to technical confounding or sampling biases for the study cohorts 3.Prepare separate male and female subsets of the genotypes 4.Remove SNPs with MAF < 0.05 from each sex
-(C) Sex-specific X-chromosomal QC at the variant level: 1.Separate PAR, XTR, Ampliconic regions from the X-chromosome 2.Prepare separate male and female subsets of the genotypes 3.Remove SNPs with a missingness difference in cases vs controls, separately in each sex 4.In each sex, filter out SNPs based on 1% MAF, 10% missingness per individual, and 5% missingness per genotype. We recommend setting the threshold filters to be the same in males and females for analyses of common variants. 5.Identify the set of SNPs found in both of the post-QC male and female datasets and create a combined dataset with only these SNPs 6.Testing for HWE across the X in females (cases and controls combined), and later removing these regions from analysis in all samples. If assuming equal allele frequency between males and females, HWE for chrX can be considered in females only as described in Khramatsova et. al., 2023. 7.Test for significantly different MAF (p < 0.05/#SNPs) between sexes in control samples only for binary traits. ChrX variants with MAF sex differences in controls may be biologically meaningful, one could consider flagging these variants rather than removing them (Khramatsova et. al., 2023.)
-(D) Sex-specific QC at sample level: Filter samples if: 1.Remove individual with missing genotype rate > 0.1 2.Remove individual with the absolute value of the heterozygosity F statistic > 0.20 3.Remove pair of individuals with IBD statistics pihat > 0.2.
Learn about all the GXwasR functions
Before beginning the pre-imputation quality control (QC), it is recommended that users familiarize themselves with all the functions in the GXwasR package. This knowledge will help them understand the required input arguments, how to execute the functions, and what outputs to expect.
To explore all available functions, users should first load the
GXwasR library and then use the browseVignettes()
function:
library(GXwasR)
browseVignettes("GXwasR")Example Dataset Summary
Dataset: GXwasR_example
DataDir <- GXwasR:::GXwasR_data()
ResultDir <- tempdir()
finput <- "GXwasR_example"
x <- PlinkSummary(DataDir, ResultDir, finput)
#> ℹ Dataset: GXwasR_example
#> ℹ Number of missing phenotypes: 0
#> ℹ Number of males: 125
#> ℹ Number of females: 151
#> ℹ This is case-control data
#> ℹ Number of cases: 108
#> ℹ Number of controls: 168
#> ℹ Number of cases in males: 53
#> ℹ Number of controls in males: 72
#> ℹ Number of cases in females: 55
#> ℹ Number of controls in females: 96
#> ℹ Number of chromosomes: 12
#> - Chr: 1
#> - Chr: 2
#> - Chr: 3
#> - Chr: 4
#> - Chr: 5
#> - Chr: 6
#> - Chr: 7
#> - Chr: 8
#> - Chr: 9
#> - Chr: 10
#> - Chr: 23
#> - Chr: 24
#> ℹ Total number of SNPs: 26527
#> ℹ Total number of samples: 276Step1: Filtering multi-allelic variants
GXwasR cannot work with multi-allelic variants
foutput <- "PostimputeEX_QC"
x <- FilterAllele(DataDir, ResultDir, finput, foutput)
#> ℹ There is no multi-allelic SNP present in the input dataset.Since there is no multi-allelic SNPs, proceed with the same input dataset.
Step 2: Filtering Ambiguous SNPs, indels and SNPs failed for HWE
For this we will use QCsnp() blocking all other
filtering. The function will implicitly perform filtering based on
ambiguous SNPs.
finput <- "GXwasR_example"
foutput <- "PostimputeEX_QC1"
geno <- NULL
maf <- NULL
casecontrol <- FALSE
hweCase <- NULL
hweCase <- 1e-10
hweControl <- 1e-06
monomorphicSNPs <- FALSE
caldiffmiss <- FALSE
ld_prunning <- FALSE
x <- QCsnp(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, geno = geno, maf = maf, hweCase = hweCase, hweControl = hweControl, ld_prunning = ld_prunning, casecontrol = casecontrol, monomorphicSNPs = monomorphicSNPs, caldiffmiss = caldiffmiss)
#> ℹ 4214 Ambiguous SNPs (A-T/G-C), indels etc. were removed.
#> ✔ Thresholds for maf, geno and hwe worked.
#> ℹ There are no monomorphic SNPs.
#> ℹ No filter based on differential missingness will be applied.
#> ✔ Output PLINK files prefixed as ,PostimputeEX_QC1, with passed SNPs are saved in ResultDir.
#> ℹ Input file has 26527 SNPs.
#> ℹ Output file has 22313 SNPs after filtering.Copying the plink files from ResultDir to DataDir.
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC1")
file.copy(paste0(ResultDir, "/", ftemp), DataDir)
#> [1] TRUE TRUE TRUE TRUEStep 3: Flag PAR, XTR and Ampliconic Regions
Now, we will filter Pseudo-Autosomal Region (PAR), X-transposed region (XTR) and Ampliconic regions from the X-chromosome and will combine these regions with autosomes for autosomal QC.
## Running
finput <- "PostimputeEX_QC1"
x <- PlinkSummary(DataDir, ResultDir, finput)
#> ℹ Dataset: PostimputeEX_QC1
#> ℹ Number of missing phenotypes: 0
#> ℹ Number of males: 125
#> ℹ Number of females: 151
#> ℹ This is case-control data
#> ℹ Number of cases: 108
#> ℹ Number of controls: 168
#> ℹ Number of cases in males: 53
#> ℹ Number of controls in males: 72
#> ℹ Number of cases in females: 55
#> ℹ Number of controls in females: 96
#> ℹ Number of chromosomes: 12
#> - Chr: 1
#> - Chr: 2
#> - Chr: 3
#> - Chr: 4
#> - Chr: 5
#> - Chr: 6
#> - Chr: 7
#> - Chr: 8
#> - Chr: 9
#> - Chr: 10
#> - Chr: 23
#> - Chr: 24
#> ℹ Total number of SNPs: 22313
#> ℹ Total number of samples: 276
foutput <- "PostimputeEX_QC2_Dummy"
x <- FilterRegion(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, CHRX = TRUE, CHRY = FALSE, filterPAR = TRUE, filterXTR = TRUE, filterAmpliconic = TRUE, regionfile = FALSE, filterCHR = NULL, Hg = "38", exclude = FALSE)
#> ℹ chrX
#> ✖ There is no PAR region in the input data. Argument filterPAR cannot set to be TRUE.
#> ℹ Changing filterPAR to FALSE
#> ✖ There is no XTR region in the input data. Argument filterXTR cannot set to be TRUE.
#> ℹ Changing filterXTR to FALSE
#> ℹ Ampliconic SNPs:8
#> ℹ SNPs are only flagged for the desired region.
XTR_SNPs <- x$XTR
Ampli_SNPs <- x$Ampliconic
PAR_SNPs <- x$PAR
save(XTR_SNPs, file = paste0(ResultDir, "/Postimpute_XTR_SNPs.Rda"))
save(Ampli_SNPs, file = paste0(ResultDir, "/Postimpute_Ampli_SNPs.Rda"))
save(PAR_SNPs, file = paste0(ResultDir, "/Postimpute_PAR_SNPs.Rda"))
## Output
## SNPs in PAR
PAR_SNPs <- x$PAR ## No PAR SNP
## SNPs in XTR
XTR_SNPs <- x$XTR ## 42 SNPs
knitr::kable(XTR_SNPs, caption = "SNPs in XTR.")Table: SNPs in XTR.
## SNPs in Ampliconic region
Ampliconic_SNPs <- x$Ampliconic # 7 SNPs
knitr::kable(Ampliconic_SNPs, caption = "SNPs in Ampliconic region.")| CHR | SNP | START | END | A1 | A2 |
|---|---|---|---|---|---|
| 23 | rs62602496 | 0 | 48349540 | G | A |
| 23 | rs6612314 | 0 | 55442087 | A | C |
| 23 | rs151231489 | 0 | 55445831 | C | A |
| 23 | rs73498395 | 0 | 71740842 | T | C |
| 23 | rs6527 | 0 | 73066891 | A | C |
| 23 | rs4907822 | 0 | 102264099 | A | G |
| 23 | rs142219143 | 0 | 102354248 | G | A |
| 23 | rs139353379 | 0 | 103970756 | G | A |
Note: We will not perform step 4, since we do not have any PAR regions. The steps are provided for the users in the case if they have PAR regions in their dataset.
Step 4: Filter only PAR Regions from X-chromosome
finput <- "PostimputeEX_QC1"
x <- PlinkSummary(DataDir, ResultDir, finput)
foutput <- "PostimputeEX_QC2"
x <- FilterRegion(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, CHRX = TRUE, CHRY = FALSE, filterPAR = TRUE, filterXTR = FALSE, filterAmpliconic = FALSE, regionfile = FALSE, filterCHR = NULL, Hg = "38", exclude = TRUE)Now, users need to move the QC-ed files (i.e., “PostimputeEX_QC2” and “PostimputeEX_QC2_snps_extracted”) to DataDir to be used as input datasets for the next step of the pipeline.
Copying the plink files from ResultDir to DataDir.
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC2")
file.copy(paste0(ResultDir, "/", ftemp), DataDir)
#> [1] FALSEStep 5: Preparing autosomal and X-chromosomal plink binary files
Now, we will prepare two sets of plink binary files from
“PostimputeEX_QC1” containing autosomes and X-chromosomes, respectively.
For this purpose, we will again use FilterRegion().
finput <- "PostimputeEX_QC1"
foutput <- "PostimputeEX_QC3"
y <- FilterRegion(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, CHRX = FALSE, CHRY = FALSE, filterPAR = FALSE, filterXTR = FALSE, filterAmpliconic = FALSE, regionfile = FALSE, filterCHR = 23, Hg = "38", exclude = TRUE)“PostimputeEX_QC3_snps_extracted” contains X-chromosome, while “PostimputeEX_QC3” contains autosomal SNPs.
# Now, copying these files to DataDir.
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC3")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))Note: We will not perform step 6, since we do not have any PAR regions. The steps are provided for the users in the case if they have PAR regions in their dataset.
Step 6: Merging the autosomal regions with PAR regions
We will now merge the autosomal files with PAR regions using
MergeRegion().
finput1 <- "PostimputeEX_QC3"
finput2 <- "PostimputeEX_QC2_snps_extracted"
foutput <- "PostimputeEX_QC3_autopar"
y <- MergeRegion(DataDir, ResultDir, finput1, finput2, foutput, use_common_snps = FALSE)
## Copying the files to DataDir
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC3_autopar")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))With “PostimputeEX_QC3”, we will flag the SNPs in the controls with a sex difference in MAF, since the differences may be due to technical confounding or sampling biases for the study cohorts.
Step 7: Flag the SNPs in the controls with a sex difference in MAF
finput <- "PostimputeEX_QC3"
foutput <- "Test_output"
x <- MAFdiffSexControl(DataDir, ResultDir, finput, foutput, filterSNP = FALSE)
#> ℹ No SNP to be flagged or excluded.Since no SNP is having MAF difference in sexes in controls, we will proceed to the next steps of the pipeline.
Step 8: Prepare separate male and female subsets of the genotypes.
finput <- "PostimputeEX_QC3"
foutput <- "PostimputeEX_QC4_Female"
sex <- "females"
x <- GetMFPlink(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, sex = sex, xplink = FALSE, autoplink = FALSE)
#> ✔ Output PLINK files, prefixed as PostimputeEX_QC4_Female, are in
#> /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
## Making male plink files
foutput <- "PostimputeEX_QC4_Male"
sex <- "males"
x <- GetMFPlink(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, sex = sex, xplink = FALSE, autoplink = FALSE)
#> ✔ Output PLINK files, prefixed as PostimputeEX_QC4_Male, are in
#> /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZRemoving the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
## Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC1")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
## Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC2")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
## Copying new plink files to DataDir
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC4")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))Step 9: Remove SNPs with MAF < 0.05 from each sex from the sex-specific autosomal plink files
Here, we will perform QC of SNPs using MAF, call rate, monomorphism and differential missingness in cases vs controls from each sex from the sex-specific autosomal datasets.
We recommend setting the threshold filters to be the same in males and females for analyses of common variants.
For this, we will use QCsnp() function.
## Applying filter to female-specific plink files
finput <- "PostimputeEX_QC4_Female"
foutput <- "PostimputeEX_QC5_Female"
geno <- 0.05
maf <- 0.05
casecontrol <- TRUE
hweCase <- NULL
hweControl <- NULL
hweCase <- NULL
monomorphicSNPs <- TRUE
caldiffmiss <- TRUE
ld_prunning <- FALSE
x <- QCsnp(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, geno = geno, maf = maf, hweCase = hweCase, hweControl = hweControl, ld_prunning = ld_prunning, casecontrol = casecontrol, monomorphicSNPs = monomorphicSNPs, caldiffmiss = caldiffmiss)
#> ℹ 0 Ambiguous SNPs (A-T/G-C), indels etc. were removed.
#> ✔ Thresholds for maf, geno and hwe worked.
#> 11 variants removed due to missing genotype data (--geno).
#> 5363 variants removed due to minor allele threshold(s)
#> • In cases,
#> • In controls,
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as filtered_temp2
#> ℹ There are no monomorphic SNPs.
#> ℹ Filtering for differential missingness between cases and controls is turned off.
#> ℹ No SNP with differential missingness between cases and controls.
#> ✔ Output PLINK files prefixed as ,PostimputeEX_QC5_Female, with passed SNPs are saved in ResultDir.
#> ℹ Input file has 21330 SNPs.
#> ℹ Output file has 15956 SNPs after filtering.
## Applying filter to male-specific plink files
finput <- "PostimputeEX_QC4_Male"
foutput <- "PostimputeEX_QC5_Male"
x <- QCsnp(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, geno = geno, maf = maf, hweCase = hweCase, hweControl = hweControl, ld_prunning = ld_prunning, casecontrol = casecontrol, monomorphicSNPs = monomorphicSNPs, caldiffmiss = caldiffmiss)
#> ℹ 0 Ambiguous SNPs (A-T/G-C), indels etc. were removed.
#> ✔ Thresholds for maf, geno and hwe worked.
#> 11 variants removed due to missing genotype data (--geno).
#> 5309 variants removed due to minor allele threshold(s)
#> • In cases,
#> • In controls,
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as filtered_temp2
#> ℹ There are no monomorphic SNPs.
#> ℹ Filtering for differential missingness between cases and controls is turned off.
#> ℹ No SNP with differential missingness between cases and controls.
#> ✔ Output PLINK files prefixed as ,PostimputeEX_QC5_Male, with passed SNPs are saved in ResultDir.
#> ℹ Input file has 21330 SNPs.
#> ℹ Output file has 16010 SNPs after filtering.Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC4")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC4")
invisible(file.remove(paste0(ResultDir, "/", ftemp)))
## Copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC5")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))Step 10: Combine sex-specific autosomal QC-ed files using common SNPs:
finput1 <- "PostimputeEX_QC5_Female"
finput2 <- "PostimputeEX_QC5_Male"
foutput <- "PostimputeEX_Auto"
y <- MergeRegion(DataDir, ResultDir, finput1, finput2, foutput, use_common_snps = TRUE)
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as PostimputeEX_AutoRemoving the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC5")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_Auto")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC5")
invisible(file.remove(paste0(ResultDir, "/", ftemp)))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_Auto")
invisible(file.remove(paste0(ResultDir, "/", ftemp)))Step 11: Get sex-specific X-chromosomal plink files
For this, we will use plink files prefixed as “PostimputeEX_QC3_snps_extracted”. As a reminder, these plink files contain X chromosome.
We will utilize GetMFPlink() function to get male and
female plink binary files with X chromosomes. We will move these files
from ResultDir to DataDir and will run the sex-specific QC steps.
finput <- "PostimputeEX_QC3_snps_extracted"
foutput <- "PostimputeEX_QC3_XFemale"
## Making female-specific plink files X chromosome
sex <- "females"
x <- GetMFPlink(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, sex = sex, xplink = FALSE, autoplink = FALSE)
#> ✔ Output PLINK files, prefixed as PostimputeEX_QC3_XFemale, are in
#> /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
## Making male-specific plink files X chromosome
foutput <- "PostimputeEX_QC3_XMale"
sex <- "males"
x <- GetMFPlink(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, sex = sex, xplink = FALSE, autoplink = FALSE)
#> ✔ Output PLINK files, prefixed as PostimputeEX_QC3_XMale, are in
#> /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
## Copying sex-specific plink files
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC3_X")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))Step 12: QC of sex-specific X-chromosomal SNPs
Here, we will remove SNPs with a missingness difference in cases vs controls, separately in each sex, and will filter out SNPs based on 1% MAF, 5% missingness per genotype, and monomorphic SNPs. We recommend setting the threshold filters to be the same in males and females for analyses of common variants.
For this, we will use QCsnp() function.
## Female-specific QC
finput <- "PostimputeEX_QC3_XFemale"
foutput <- "PostimputeEX_QC4_XFemale"
geno <- 0.05
maf <- 0.01
casecontrol <- TRUE
hweControl <- NULL
hweCase <- NULL
monomorphicSNPs <- FALSE
caldiffmiss <- TRUE
diffmissFilter <- TRUE
dmissX <- TRUE # Since our input has X chromosome, setting this as TRUE wouldn’t change the result.
dmissAutoY <- FALSE # Since our input has X chromosome, setting this as TRUE wouldn’t change the result.
x <- QCsnp(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, geno = geno, maf = maf, hweCase = hweCase, hweControl = hweControl, ld_prunning = ld_prunning, casecontrol = casecontrol, monomorphicSNPs = monomorphicSNPs, caldiffmiss = caldiffmiss, diffmissFilter = diffmissFilter, dmissX = dmissX, dmissAutoY = dmissAutoY)
#> ℹ 0 Ambiguous SNPs (A-T/G-C), indels etc. were removed.
#> ✔ Thresholds for maf, geno and hwe worked.
#> 0 variants removed due to missing genotype data (--geno).
#> 110 variants removed due to minor allele threshold(s)
#> • In cases,
#> • In controls,
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as filtered_temp2
#> ℹ No SNP with differential missingness between cases and controls.
#> ✔ Output PLINK files prefixed as ,PostimputeEX_QC4_XFemale, with passed SNPs are saved in ResultDir.
#> ℹ Input file has 983 SNPs.
#> ℹ Output file has 873 SNPs after filtering.
## For male-specific QC:
finput <- "PostimputeEX_QC3_XMale"
foutput <- "PostimputeEX_QC4_XMale"
x <- QCsnp(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, geno = geno, maf = maf, hweCase = hweCase, hweControl = hweControl, ld_prunning = ld_prunning, casecontrol = casecontrol, monomorphicSNPs = monomorphicSNPs, caldiffmiss = caldiffmiss, diffmissFilter = diffmissFilter, dmissX = dmissX, dmissAutoY = dmissAutoY)
#> ℹ 0 Ambiguous SNPs (A-T/G-C), indels etc. were removed.
#> ✔ Thresholds for maf, geno and hwe worked.
#> 0 variants removed due to missing genotype data (--geno).
#> 131 variants removed due to minor allele threshold(s)
#> • In cases,
#> • In controls,
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as filtered_temp2
#> ℹ No SNP with differential missingness between cases and controls.
#> ✔ Output PLINK files prefixed as ,PostimputeEX_QC4_XMale, with passed SNPs are saved in ResultDir.
#> ℹ Input file has 983 SNPs.
#> ℹ Output file has 852 SNPs after filtering.
## Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC3")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
## Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC3")
invisible(file.remove(paste0(ResultDir, "/", ftemp)))
## Copying sex-specific plink files
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC4_X")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC4")
invisible(file.remove(paste0(ResultDir, "/", ftemp)))Step 13: Combine sex-specific QC-ed X chromosomal dataset using common SNPs
finput1 <- "PostimputeEX_QC4_XFemale"
finput2 <- "PostimputeEX_QC4_XMale"
foutput <- "PostimputeEX_QC5_Xchr"
y <- MergeRegion(DataDir, ResultDir, finput1, finput2, foutput, use_common_snps = TRUE)
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as PostimputeEX_QC5_Xchr
## Copying merged plink files to DataDir
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_QC5_Xchr")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
invisible(file.remove(paste0(ResultDir, "/", ftemp)))
## Removing other QC-ed files from DataDir
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC4")
invisible(file.remove(paste0(DataDir, "/", ftemp)))Step 14: Test for HWE across the X chromosomes in females
Test for HWE across the X in females (cases and controls combined) and remove these regions from analysis for all samples.
finput <- "PostimputeEX_QC5_Xchr"
foutput <- "PostimputeEX_Xchr"
x <- Xhwe(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, filterSNP = TRUE)
#> ✔ Output PLINK files, prefixed as female, are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
#> This test is running on a case-control dataset with female samples.
#> ℹ Failed SNPs are excluded from the output PLINK files prefixed as PostimputeEX_Xchr is in
#> /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
## No. of the SNPs failed the test.
length(x) # 2 SNPs in X chr in females failed HWE test.
#> [1] 3
## The failed SNPs
x
#> [1] "rs56053951" "rs12353847" "rs5940058"
## Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX_QC5_Xchr")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
## Copying new QC-ed plink files to DataDir
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeEX_Xchr")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))Step 15: Checking for X-chromosomal SNPs having MAF difference in sexes in controls
This test should be performed only for binary traits. We will flag the failed SNPs if any.
finput <- "PostimputeEX_Xchr"
foutput <- "Test_output"
x <- MAFdiffSexControl(DataDir, ResultDir, finput, filterSNP = FALSE, foutput = foutput)
#> ℹ No SNP to be flagged or excluded.
x
#> NULLStep 16: Combining autosomal and X-chromosomal QC-ed datasets
finput1 <- "PostimputeEX_Auto"
finput2 <- "PostimputeEX_Xchr"
foutput <- "PostimputeQC"
use_common_snps <- FALSE
y <- MergeRegion(DataDir, ResultDir, finput1, finput2, foutput, use_common_snps = FALSE)
#> ✔ Merging is done with all the SNPs i.e., union of the SNPs.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as PostimputeQC
## Removing the previous QC-ed file from DataDir and copying the new QC-ed file to DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "PostimputeEX")
invisible(file.remove(paste0(DataDir, "/", ftemp)))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "PostimputeQC")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
invisible(file.remove(paste0(ResultDir, "/", ftemp)))Step 17: Preparing separate male and female genotype dataset for sample level QC
## Making female-specific plink files
finput <- "PostimputeQC"
foutput <- "Postimpute_Female"
sex <- "females"
x <- GetMFPlink(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, sex = sex, xplink = FALSE, autoplink = FALSE)
#> ✔ Output PLINK files, prefixed as Postimpute_Female, are in
#> /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
## Making male-specific plink files
finput <- "PostimputeQC"
foutput <- "Postimpute_Male"
sex <- "males"
x <- GetMFPlink(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, sex = sex, xplink = FALSE, autoplink = FALSE)
#> ✔ Output PLINK files, prefixed as Postimpute_Male, are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
## Copying sex-specific plink files to DataDir
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "Postimpute_Female")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
invisible(file.remove(paste0(ResultDir, "/", ftemp)))
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "Postimpute_Male")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
invisible(file.remove(paste0(ResultDir, "/", ftemp)))Step 18: Sex-specific sample level QC
From each sex-specific plink files, remove individuals with missing genotype rate > 0.1, with absolute value of the heterozygosity F statistic > 0.20 and IBD statistics pihat > 0.2.
For this, we will use QCsample().
## Running the function female-specific QC
finput <- "Postimpute_Female"
foutput <- "Postimpute_Female1"
imiss <- 0.02
## Since het = 3 was removing a lot of samples (185), I decided to increase the threshold after looking at the plot.
het <- 3
small_sample_mod <- TRUE
IBD <- 0.2
x <- QCsample(DataDir = DataDir, ResultDir = ResultDir, finput = finput, foutput = foutput, imiss = imiss, het = het, small_sample_mod = small_sample_mod, IBD = IBD)
#> • Plots are initiated.
#> ℹ No. of samples filtered/flagged for missingness: 0
#> ℹ No. of samples filtered/flagged for heterozygosity threshold: 2
#> ℹ No. of samples filtered/flagged for missingness and heterozygosity: 2
#> ℹ No sample is filtered out for IDB after missingness and heterozygosity filter.
#> ℹ No. of samples in input PLINK files: 151
#> ℹ No. of samples in output PLINK files: 149
#> ✔ Output PLINK files, Postimpute_Female1 with final samples are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ.
## Running the function male-specific QC
finput <- "Postimpute_Male"
foutput <- "Postimpute_Male1"
imiss <- 0.02
## Since het = 3 was removing a lot of samples (185), I decided to increase the threshold after looking at the plot.
het <- 5
small_sample_mod <- TRUE
IBD <- 0.2
x <- QCsample(DataDir = DataDir, ResultDir = ResultDir, small_sample_mod = small_sample_mod, finput = finput, foutput = foutput, imiss = imiss, het = het, IBD = IBD)
#> • Plots are initiated.
#> ℹ No. of samples filtered/flagged for missingness: 0
#> ℹ No. of samples filtered/flagged for heterozygosity: 0
#> ℹ No. of samples filtered for missingness and heterozygosity: 0
#> ℹ No sample is filtered out for IDB after missingness and heterozygosity filter.
#> ℹ No. of samples in input PLINK files: 125
#> ℹ No. of samples in output PLINK files: 125
#> ✔ Output PLINK files, Postimpute_Male1 with final samples are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ.
## Copying filtered plink files to DataDir
ftemp <- list.files(paste0(ResultDir, "/"), pattern = "Postimpute_")
invisible(file.copy(paste0(ResultDir, "/", ftemp), DataDir))
invisible(file.remove(paste0(ResultDir, "/", ftemp)))Step 19: Combine male and female-specific Qc-ed genotype datasets
## Combine male and female-specific files
## Running the function
finput1 <- "Postimpute_Female1"
finput2 <- "Postimpute_Male1"
foutput <- "PostimputeFinal"
y <- MergeRegion(DataDir, ResultDir, finput1, finput2, foutput, use_common_snps = TRUE)
#> ✔ Merging is done using the common SNPs between the input genotype files.
#> ✔ Plink files with merged regions are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ prefixed as PostimputeFinal
## Removing the not needed files from DataDir.
ftemp <- list.files(paste0(DataDir, "/"), pattern = "Postimpute_")
invisible(file.remove(paste0(DataDir, "/", ftemp)))Final Summary of the final QC-ed genotype dataset
finput <- "PostimputeFinal"
x <- PlinkSummary(ResultDir, ResultDir, finput)
#> ℹ Dataset: PostimputeFinal
#> ℹ Number of missing phenotypes: 0
#> ℹ Number of males: 125
#> ℹ Number of females: 149
#> ℹ This is case-control data
#> ℹ Number of cases: 107
#> ℹ Number of controls: 167
#> ℹ Number of cases in males: 53
#> ℹ Number of controls in males: 72
#> ℹ Number of cases in females: 54
#> ℹ Number of controls in females: 95
#> ℹ Number of chromosomes: 11
#> - Chr: 1
#> - Chr: 2
#> - Chr: 3
#> - Chr: 4
#> - Chr: 5
#> - Chr: 6
#> - Chr: 7
#> - Chr: 8
#> - Chr: 9
#> - Chr: 10
#> - Chr: 23
#> ℹ Total number of SNPs: 16208
#> ℹ Total number of samples: 274Citing GXwasR
We hope that GXwasR will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("GXwasR")
#> To cite package 'GXwasR' in publications use:
#>
#> Bose B, Blostein F, Kim J, Winters J, Actkins KV, Mayer D, Congivaram H, Niarchou M, Edwards DV, Davis
#> LK, Stranger BE (2025). "GXwasR: A Toolkit for Investigating Sex-Differentiated Genetic Effects on
#> Complex Traits." _medRxiv 2025.06.10.25329327_. doi:10.1101/2025.06.10.25329327
#> <https://doi.org/10.1101/2025.06.10.25329327>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {GXwasR: A Toolkit for Investigating Sex-Differentiated Genetic Effects on Complex Traits},
#> author = {Banabithi Bose and Freida Blostein and Jeewoo Kim and Jessica Winters and Ky’Era V. Actkins and David Mayer and Harrsha Congivaram and Maria Niarchou and Digna Velez Edwards and Lea K. Davis and Barbara E. Stranger},
#> journal = {medRxiv 2025.06.10.25329327},
#> year = {2025},
#> doi = {10.1101/2025.06.10.25329327},
#> }Reproducibility
The GXwasR package (Bose, Blostein, Kim et al., 2025) was made possible thanks to:
- R (R Core Team, 2025)
- BiocStyle (Oleś, 2025)
- knitr (Xie, 2025)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux et al., 2024)
- sessioninfo (Wickham, Chang, Flight et al., 2025)
- testthat (Wickham, 2011)
This package was developed using biocthis.
R session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
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#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2025-08-08
#> pandoc 3.6.3 @ /Applications/Positron.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
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This vignette was generated using BiocStyle (Oleś, 2025) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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[4] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.36.0. 2025. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.
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