snpgdsIBDSelection {SNPRelate} | R Documentation |
Return a data frame with IBD coefficients.
snpgdsIBDSelection(ibdobj, kinship.cutoff=NaN, samp.sel=NULL)
ibdobj |
an object of |
kinship.cutoff |
select the individual pairs with kinship coefficients
>= kinship.cutoff; no filter if |
samp.sel |
a logical vector or integer vector to specify selection of samples |
Return a data.frame:
ID1 |
the id of the first individual |
ID2 |
the id of the second individual |
k0 |
the probability of sharing ZERO alleles |
k1 |
the probability of sharing ONE alleles |
kinship |
kinship coefficient |
Xiuwen Zheng
snpgdsIBDMLE
, snpgdsIBDMoM
,
snpgdsIBDKING
# open an example dataset (HapMap) genofile <- snpgdsOpen(snpgdsExampleFileName()) # YRI population YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[ read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"] pibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id) flag <- lower.tri(pibd$k0) plot(NaN, xlim=c(0,1), ylim=c(0,1), xlab="k0", ylab="k1") lines(c(0,1), c(1,0), col="red", lty=3) points(pibd$k0[flag], pibd$k1[flag]) # close the genotype file snpgdsClose(genofile) # IBD coefficients dat <- snpgdsIBDSelection(pibd, 1/32) head(dat) # ID1 ID2 k0 k1 kinship # 1 NA19152 NA19154 0.010749154 0.9892508 0.24731271 # 2 NA19152 NA19093 0.848207777 0.1517922 0.03794806 # 3 NA19139 NA19138 0.010788047 0.9770181 0.25035144 # 4 NA19139 NA19137 0.012900661 0.9870993 0.24677483 # 5 NA18912 NA18914 0.008633077 0.9913669 0.24784173 # 6 NA19160 NA19161 0.008635754 0.9847777 0.24948770