skatMeta (version 1.4.3)

burdenMeta: Combine burden tests from multiple cohorts

Description

Takes as input `skatCohort` objects (from the skatCohort function), and meta-analyzes the corresponding burden test.

Usage

burdenMeta(..., SNPInfo=NULL, wts = 1, snpNames = "Name", aggregateBy = "gene", 
	mafRange = c(0,0.5), verbose=FALSE)

Arguments

...

skatCohort objects

SNPInfo

the SNP Info file. This should contain 'Name' and 'gene' fields, which match the 'Name' and 'gene' fields of the SNP Info file used in each cohort. Only SNPs and genes in this table will be meta analyzed, so this may be used to restrict the analysis.

wts

weights for the burden test, as a function of maf, or a character string specifying weights in the SNP Info file.

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'. For single snps which are intended only for single variant analyses, it is reccomended that they have a unique identifier in this field.

mafRange

Range of MAF's to include in the analysis (endpoints included). Default is all SNPs (0

verbose

logical. whether progress bars should be printed.

Value

• a data frame with the following columns:
• gene
  the name of the gene or unit of aggregation being meta analyzed
• p
  the p-value from the burden tests
• beta
  approximate coefficient for the effect of genotype
• se
  approximate standard error for the effect of genotype
• cmafTotal
  the cumulative minor allele frequency of the gene
• cmafUsed
  the cumulative minor allele frequency of snps used in the analysis
• nsnpsTotal
  the number of snps in the gene
• nsnpsUsed
  the number of snps used in the analysis
• nmiss
  The number of `missing` SNPs. For a gene with a single SNP this is the number of individuals which do not contribute to the analysis, due to cohorts that did not report results for that SNP. For a gene with multiple SNPs, is totalled over the gene.

Details

This function uses the scores and their variances available in a skatCohort to perform burden tests. Though coefficients are reported, the tests are formally score tests, and the coefficients can be thought of as one-step approximations to those reported in a Wald test.

See Also

skatMeta skatOMeta skatCohort

Examples

###load example data for two cohorts	
data(skatExample)

####run on each cohort:
cohort1 <- skatCohort(Z=Z1, y~1, SNPInfo = SNPInfo, data =pheno1)
cohort2 <- skatCohort(Z=Z2, y~1, SNPInfo = SNPInfo, data =pheno2)

#### combine results:
out <- burdenMeta(cohort1, cohort2, SNPInfo = SNPInfo, mafRange=c(0,.01))
head(out)

##### Compare with analysis on full data set:
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")])
burden.p <- c(by(SNPInfo$Name, SNPInfo$gene,function(snp.names){
		inds <- match(snp.names,colnames(bigZ))
		burden <- rowSums(bigZ[,na.omit(inds)],na.rm=TRUE)
		mod <- lm(y~burden + gl(2,nrow(pheno1)),data=pheno)
		summary(mod)$coef[2,4]
	}))

head(cbind(out$p,burden.p))

#will be slightly different:
plot(y=out$p,x=burden.p, ylab = "burden meta p-values", xlab = "complete data p-values")