MultiLoad: Load individual-level data for multivariate GWA analysis

Description

The function imports GenABEL (gwaa.data class) or DatABEL (.fv*) data formats to perform multivariate test for each genetic variant.

Usage

MultiLoad(gwaa.data = NULL, phenofile = NULL, genofile = NULL, trait.cols,
  covariate.cols = NULL, cuts = 20, impute = TRUE, gaussianize = TRUE,
  ...)

Arguments

gwaa.data

An (optional) object of {gwaa.data-class}.

phenofile

An (optional) plain text file contains phenotypic outcomes and covariates.

genofile

An (optional) object of {databel-class} containing genotype data.

trait.cols

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

covariate.cols

An (optional) vector giving the column indices of the covariates to be included.

cuts

An integer telling how many pieces the genotype data matrix will be splitted for analyze. The value can be set depending on the memory size. The smaller the value is, potentially the faster the analysis will be.

impute

An (optional) logical argument telling whether missing genotypes should be imputed.

gaussianize

An (optional) logical argument telling whether the phenotypes should be gaussianized via inverse-Gaussian transformation.

...

not used.

Value

The function returns a list of cleaned statistics for subsequent, with class multi.loaded.

References

Xia Shen, ..., Jim Wilson, Gordan Lauc, Yurii Aulchenko (2015). Multi-omic-variate analysis identified novel loci associated with compound N-Glycosylation of human Immunoglobulin G. Submitted.

Examples

Run this code


## loading example gwaa.data in GenABEL
require(GenABEL)
data(ge03d2ex.clean)

## running multivariate GWAS for 3 traits: height, weight, bmi
loaded <- MultiLoad(gwaa.data = ge03d2ex.clean, trait.cols = c(5, 6, 8), 
                    covariate.cols = c(2, 3))


## converting the same dataset into DatABEL format files
require(DatABEL)
write.table(phdata(ge03d2ex.clean), 'pheno.txt', col.names = TRUE, row.names = TRUE, 
            quote = FALSE, sep = '\t')
geno <- as.double(ge03d2ex.clean)
matrix2databel(geno, 'geno')

## running the multivariate GWAS again
loaded <- MultiLoad(phenofile = 'pheno.txt', genofile = 'geno', trait.cols = c(5, 6, 8), 
                    covariate.cols = c(2, 3))