skatMeta (version 1.4.3)

singlesnpMeta: Meta analyze single snp effects from multiple cohorts

Description

Takes as input `skatCohort` objects (from the skatCohort function), and meta analyzes them.

Usage

singlesnpMeta(..., SNPInfo=NULL, snpNames = "Name", aggregateBy = "gene", 
	cohortBetas = TRUE, verbose = FALSE)

Arguments

...

skatCohort objects

SNPInfo

The SNP Info file. This should contain the fields listed in snpNames and aggregateBy. Only SNPs in this table will be meta analyzed, so this may be used to restrict the analysis.

snpNames

The field of SNPInfo where the SNP identifiers are found. Default is 'Name'

aggregateBy

The field of SNPInfo on which the skat results were aggregated. Default is 'gene'. Though gene groupings are not explicitely required for single snp analysis, it is required to find where single snp information is stored in the skatCohort objects.

cohortBetas

Whether or not to include cohort-level effects in the output.

verbose

logical. Whether progress bars should be printed.

Value

• a data frame with the gene, snp name, meta analysis.

Details

This function meta analyzes score tests for single variant effects. Though the test is formally a score test, coefficients and standard errors are also returned, which can be interpreted as a `one-step` approximation to the maximum likelihood estimates.

References

Lin, DY and Zeng, D. On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika. 2010.

See Also

skatCohort skatMeta skatOMeta

Examples

data(skatExample)

####run on each cohort:
cohort1 <- skatCohort(Z=Z1, y~sex+bmi, SNPInfo = SNPInfo, data =pheno1)
cohort2 <- skatCohort(Z=Z2, y~sex+bmi, SNPInfo = SNPInfo, data =pheno2)

#### combine results:
out <- singlesnpMeta(cohort1, cohort2, SNPInfo = SNPInfo)
head(out)

##compare
bigZ <- matrix(NA,2*n,nrow(SNPInfo))
colnames(bigZ) <- SNPInfo$Name
for(gene in unique(SNPInfo$gene)){
	snp.names <- SNPInfo$Name[SNPInfo$gene == gene]
	bigZ[1:n,SNPInfo$gene == gene][,
		snp.names %in% colnames(Z1)] <- Z1[,
		na.omit(match(snp.names,colnames(Z1)))]
	bigZ[(n+1):(2*n),SNPInfo$gene == gene][,
		snp.names %in% colnames(Z2)] <- Z2[,
		na.omit(match(snp.names,colnames(Z2)))]
}
pheno <- rbind(pheno1[,c("y","sex","bmi")],pheno2[,c("y","sex","bmi")])
out3 <- apply(bigZ,2,function(z){
	if(any(!is.na(z))){
		z[is.na(z)] <- mean(z,na.rm=TRUE)
		mod <- lm(y ~ sex+bmi+c(rep(1,n),rep(0,n))+z,data=pheno)
		beta <- mod$coef["z"]
		se <- sqrt(vcov(mod)["z","z"])
		p <- pchisq( (beta/se)^2,df=1,lower=F)
		return(c(beta,se,p))
	} else {
		return(c(0,Inf,1))
	}
})
out3 <- t(out3[,match(out$Name,colnames(out3))])

##plot
par(mfrow=c(2,2))
plot(x=out3[,1],y=out$beta, xlab = "complete data (Wald)", 
	ylab = "meta-analysis (Score)", main = "coefficients");abline(0,1)
plot(x=out3[,2],y=out$se, xlab = "complete data (Wald)", y
	lab = "meta-analysis (Score)", main = "standard errors");abline(0,1)
plot(x=out3[,3],y=out$p, xlab = "complete data (Wald)", 
	ylab = "meta-analysis (Score)", main = "p-values");abline(0,1)