MultiSummary: Multivariate genome-wide association scan using summary statistics

Description

This function performs multivariate GWA analysis using meta-GWAS summary statistics

Usage

MultiSummary(x, index = NULL, type = "direct", vars = NULL)

Arguments

A data object of class multi.summary loaded by the function load.summary.

index

A numeric vector that gives the indices of the traits to be analyzed jointly.

type

A string gives the type of analysis. Default is "outbred", referring to general outbred populations, following Hardy-Weinberg equilibrium. "inbred" refers to inbred populations, where no heterzygotes exists, namely, allele frequency = genotype frequency. "precise" refers to precise test statistics, especially when the individual-level data are available, for which the argument vars has to be given. "direct" refers to test statistics directly constructed from the T-statistics in univariate GWAS, this provides a scale-invariant test most similar to the direct MANOVA, but may be less powerful in some scenarios.

vars

A numeric vector gives the variance of the genotypes at each SNP, e.g. coded as 0, 1 and 2. Only used when type = "precise".

Value

The function returns a data frame containing the multi-trait GWAS results, where the row names are the variants names. The column names are: variant name (Marker), allele frequency (Freq), the smallest sample size of the traits (N), effect on the phenotype score (Beta.S, see reference), standard error (SE), p-value (P), and the rest the coefficients to construct the phenotype score (see reference).

References

Xia Shen, Zheng Ning, Yakov Tsepilov, Peter K. Joshi, James F. Wilson, Yudi Pawitan, Chris S. Haley, Yurii S. Aulchenko (2016). Fast pleiotropic meta-analysis for genetic studies. Submitted.

Examples

Run this code


## download the six example files from:
## https://www.dropbox.com/sh/hhta45cewvvea2s/AADfj4OXlbroToZAwIii2Buha?dl=0
## the summary statistics from Randall et al. (2013) PLoS Genet
## for males only
## bmi: body mass index
## hip: hip circumference
## wc: waist circumference
## whr: waist-hip ratio

## load the prepared set of independent SNPs
indep.snps <- as.character(read.table('indep.snps')$V1)

## load summary statistics of the six traits
stats.male <- load.summary(files = c('bmi.txt', 'height.txt', 
                           'weight.txt', 'hip.txt', 'wc.txt', 
                           'whr.txt'), indep.snps = indep.snps)

## perform multi-trait meta-GWAS
result <- MultiSummary(stats.male)
head(result)