Hi R-help folks,
I have been doing some single SNP association work using snpMatrix. This works
well, but produces a lot of false positives, because of population structure in
my data. I would like to correct the p-values (which snpMatrix gives me) for
population structure, possibly using principle component analysis (PCA).
My data is complicated, so here's a simple example of what I'd like to do:
# 3x8 matrix of example snp data, 1 = allele A, -1 = allele B, 0 = hetero
snp.data = matrix(
c(0,1,0,-1,-1,1,1,-1,0,1,1,0,0,1,0,-1,-1,NA,0,-1,0,0,1,0),
nrow=3,
dimnames = list(
c("bob", "frita", "trudy"),
c("snp1", "snp2", "snp3", "snp4", "snp5","snp6", "snp7",
"snp8")
)
)
# phenotype data - resistant or susceptible to zombie infection
phenotype.data = matrix(
c("bob", "frita", "trudy",
"resistant", "susceptible", "resistant"),
nrow=3,
dimnames = list(
c("bob", "frita", "trudy"),
c("rowNames", "cc")
)
)
library("snpMatrix")
# add one in the following line so genotypes are 0,1 or 2, so
single.snp.tests()
method doesn't complain
snp.matrix <- as(snp.data + 1,'snp.matrix')
single.snp.assoc <- single.snp.tests(cc, data=as.data.frame(phenotype.data),
snp.data=snp.matrix )
Okay, so now I have p-values for the association between SNPs and resistance to
zombie infection.
I do this for PCA:
snp.data = replace( snp.data, is.na(snp.data), 0) # workaround, b/c I can't get
prcomp to ignore NAs no matter what I do
pca = prcomp(snp.data)
So, the question is, can I use the PCA data to correct my p-values (in
single.snp.association) for population structure? In my real data, population
structure is causing a lot of type I errors (false positive SNPs).
I have read of some standalone software that does this sort of thing, for
example EIGENSTRAT:
http://www.biostat.jhsph.edu/~iruczins/teaching/misc/gwas/papers/price2006.pdf
But I'd like to stick to R if possible.
Any advice/comments welcome.
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