MultiRep: Replication analysis of multivariate genome-wide association signal

Description

The function performs replication analysis of multivariate GWA signals.

Usage

MultiRep(training.pheno = NULL, training.phenofile = NULL,
  test.pheno = NULL, test.phenofile = NULL, pheno.names = NULL,
  training.geno, test.geno)

Arguments

training.pheno

An (optional) matrix or data frame contains the phenotype data for the discovery sample, preferrably adjusted for fixed effects and population structure before multivariate GWA analysis.

training.phenofile

An (optional) plain text file contains phenotypes for the discovery sample. If this is provided, it will serve as training.pheno.

test.pheno

An (optional) matrix or data frame contains the phenotype data for the replication sample, preferrably adjusted for fixed effects and population structure.

test.phenofile

An (optional) plain text file contains phenotypes of the replication sample. If this is provided, it will serve as test.pheno.

pheno.names

A vector (length > 1) giving the column names of the phenotypes to be analyzed.

training.geno

A matrix or data.frame that contains the discovery sample genotype dosages of the variants to replicate.

test.geno

A matrix or data.frame that contains the replication sample genotype dosages of the variants to replicate. This object should have the same column names and order as training.geno.

Value

The function returns a list of 3 matrices. $replication contains the estimate of variant effect on the corresponding compound phenotype (beta_c), standard error (s.e.), replication P-value (P), and proportion of phenotypic variance explained (R-squared). $training.coef contains the estimated coefficients in the discovery sample of each phenotype for each variant to construct the compound phenotype. $test.coef contains similar coefficients as in $training.coef but estimated in the replication sample, but these are just for the record, NOT used in the replication procedure.

References

Xia Shen, ..., Gordan Lauc, Jim Wilson, Yurii Aulchenko (2014). Multi-omic-variate analysis identified the association between 14q32.33 and compound N-Glycosylation of human Immunoglobulin G Submitted.

Examples

Run this code


## loading example discovery sample gwaa.data in GenABEL
data(ge03d2)

## running multivariate GWAS for 3 traits: height, weight, bmi
res <- Multivariate(gwaa.data = ge03d2, trait.cols = c(5, 6, 8), 
                    covariate.cols = c(2, 3))

## extracting 5 significant variants
(top <- res[order(res[,'P.F']),][2:6,])
snps <- rownames(top)
training.geno <- as.double(gtdata(ge03d2)[,snps])

## loading example test sample gwaa.data in GenABEL
data(ge03d2c)

## extracting genotypes of the 5 variants
test.geno <- as.double(gtdata(ge03d2c)[,snps])

## try replication
rep <- MultiRep(training.pheno = phdata(ge03d2), test.pheno = phdata(ge03d2c), 
                pheno.names = c('height', 'weight', 'bmi'),
                   training.geno = training.geno, test.geno = test.geno)