lme.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 linear mixed effects (LME) model to test associations between a continuous phenotype and all imputed SNPs in a genotype file in family data under additive genetic model. The SNP genotype is treated as a fixed effect, and a random effect correlated according to degree of relatedness within a family is also fitted. In each trait-SNP assocaition test, the lmekin function from package coxme is used.

Usage

lmepack.batch.imputed(phenfile, genfile, pedfile, phen, kinmat, 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
kinmat	a character string naming the file where kinship coefficient matrix is kept
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 lmepack.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
h2q	the portion of phenotypic variation explained by the SNP
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

coxme package: mixed-effects Cox models, sparse matrices, and modeling data from large pedigrees. Beth Atkinson ([e-mail]) for pedigree functions.Terry Therneau ([e-mail]) for all other functions. 2007. Ref Type: Computer Program http://cran.r-project.org/web/packages/coxme/.

Abecasis, G. R., Cardon, L. R., Cookson, W. O., Sham, P. C., & Cherny, S. S. Association analysis in a variance components framework. Genet Epidemiol, 21 Suppl 1, S341-S346 (2001).

Examples

## Not run: 
lmepack.batch.imputed(phenfile="simphen.csv",genfile="simgen.csv",pedfile="simped.csv",
phen="SIMQT",kinmat="simkmat.Rdata",outfile="simout.csv",covars=c("age","sex"),
sep.ped=",",sep.phe=",",sep.gen=",")

## End(Not run)

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