Created
October 7, 2019 18:49
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# LD and kinship coeff | |
ld <- .2 | |
kin <- .1 | |
snpgdsBED2GDS(bed.fn = "convertGDS.bed", bim.fn = "convertGDS.bim", | |
fam.fn = "convertGDS.fam", out.gdsfn = "myGDS", | |
cvt.chr = "char") | |
genofile <- snpgdsOpen("myGDS", readonly = F) | |
gds.ids <- read.gdsn(index.gdsn(genofile, "sample.id")) | |
gds.ids <- sub("-1", "", gds.ids) | |
add.gdsn(genofile, "sample.id", gds.ids, replace = T) | |
geno.sample.ids <- rownames(genData$SNP) | |
# First filter for LD | |
snpSUB <- snpgdsLDpruning(genofile, ld.threshold = ld, | |
sample.id = geno.sample.ids, | |
snp.id = colnames(genData$SNP)) | |
snpset.ibd <- unlist(snpSUB, use.names = F) | |
# And now filter for MoM | |
ibd <- snpgdsIBDMoM(genofile, kinship = T, | |
sample.id = geno.sample.ids, | |
snp.id = snpset.ibd, | |
num.thread = 1) | |
ibdcoef <- snpgdsIBDSelection(ibd) | |
ibdcoef <- ibdcoef[ibdcoef$kinship >= kin,] | |
# Filter samples out | |
related.samples <- NULL | |
while (nrow(ibdcoef) > 0) { | |
# count the number of occurrences of each and take the top one | |
sample.counts <- sort(table(c(ibdcoef$ID1, ibdcoef$ID2)), decreasing = T) | |
rm.sample <- names(sample.counts)[1] | |
cat("Removing sample", rm.sample, "too closely related to", | |
sample.counts[1], "other samples.\n") | |
# remove from ibdcoef and add to list | |
ibdcoef <- ibdcoef[ibdcoef$ID1 != rm.sample & ibdcoef$ID2 != rm.sample,] | |
related.samples <- c(as.character(rm.sample), related.samples) | |
} | |
genData$SNP <- genData$SNP[!(rownames(genData$SNP) %in% related.samples),] | |
genData$LIP <- genData$LIP[!(rownames(genData$LIP) %in% related.samples),] |
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