geepack.quant.batch.imputed: function to test associations between a continuous trait and...

In GWAF: Genome-Wide Association/Interaction Analysis and Rare Variant Analysis with Family Data

Description

Fit Generalized Estimation Equation (GEE) model to test associations between a continuous phenotype and all imputed SNPs in a genotype file in family data under additive genetic model. Each family is treated as a cluster, with independence working correlation matrix used in the robust variance estimator. The proportion of phenotype variation explained by the tested SNP is not provided. This function applies the same trait-SNP association test to all imputed SNPs in the genotype data. The trait-SNP association test is carried out by using the geese function from package geepack.

Usage

geepack.quant.batch.imputed(phenfile,genfile,pedfile,phen,
covars=NULL,outfile,col.names=T,sep.ped=",",sep.phe=",",sep.gen=",")

Arguments

phenfile	a character string naming the phenotype file for reading (see format requirement in details)
genfile	a character string naming the genotype file for reading (see format requirement in details)
pedfile	a character string naming the pedigree file for reading (see format requirement in details)
phen	a character string for a phenotype name in phenfile
covars	a character vector for covariates in phenfile
outfile	a character string naming the result file for writing
col.names	a logical value indicating whether the output file should contain column names
sep.ped	the field separator character for pedigree file
sep.phe	the field separator character for phenotype file
sep.gen	the field separator character for genotype file

Details

Similar to the details for geepack.quant.batch function but here the SNP data contains imputed genotypes (allele dosages) that are continuous and range from 0 to 2. In addition, the user specified genetic model argument is not available.

Value

No value is returned. Instead, results are written to outfile.

phen	phenotype name
snp	SNP name
N	the number of individuals in analysis
AF	imputed allele frequency of coded allele
beta	regression coefficient of SNP covariate
se	standard error of beta
pval	p-value of testing beta not equal to zero

Author(s)

Qiong Yang <[e-mail]> and Ming-Huei Chen <[e-mail]>

References

Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73 13–22.

Zeger, S.L. and Liang, K.Y. (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42 121–130.

Yan, J and Fine, J. (2004) Estimating equations for association structures. Stat Med, 23 859–874.

Examples

## Not run: 
geepack.quant.batch.imputed(phenfile="simphen.csv",genfile="simgen.csv",
pedfile="simped.csv",phen="SIMQT",outfile="simout.csv",col.names=T,covars="sex",
sep.ped=",",sep.phe=",",sep.gen=",")

## End(Not run)

GWAF documentation built on May 2, 2019, 2:47 p.m.