GWAF (version 2.0)

geepack.quant.batch: function to test genetic associations between a continuous trait and a batch of genotyped SNPs in families using Generalized Estimation Equation model

Description

Fit Generalized Estimation Equation (GEE) model to test association between a continuous phenotype and all genotyped SNPs in a genotype file in family data with user specified genetic model. Each pedigree 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 genotyped 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(phenfile,genfile,pedfile,phen,model="a",covars=NULL,outfile,
col.names=T,sep.ped=",",sep.phe=",",sep.gen=",")

Value

No value is returned. Instead, results are written to outfile. When the genetic model is 'a', 'd' or 'r', the result includes the following columns. When the genetic model is 'g', beta and se are replaced with beta10, beta20, beta21, se10, se20, se21 .phenphenotype namesnpSNP namen0the number of individuals with 0 copy of coded allelesn1the number of individuals with 1 copy of coded allelesn2the number of individuals with 2 copies of coded allelesbetaregression coefficient of SNP covariatesestandard error of betachisqChi-square statistic for testing beta not equal to zerodfdegree of freedom of the Chi-square statisticmodelmodel actually used in the analysispvalp-value of the chi-square statisticbeta10regression coefficient of genotype with 1 copy of coded allele vs. that with 0 copybeta20regression coefficient of genotype with 2 copy of coded allele vs. that with 0 copybeta21regression coefficient of genotype with 2 copy of coded allele vs. that with 1 copyse10standard error of beta10se20standard error of beta20se21standard error of beta21

Details

For a continuous trait, the geepack.quant.batch function first reads in and merges phenotype-covariates, genotype and pedigree files, then tests the association of phen against all SNPs in genfile. genfile contains unique individual id and genotype data, with the column names being "id" and SNP names. For each SNP, the genotype data should be coded as 0, 1, 2 indicating the numbers of the coded alleles. The SNP name in genotype file should not have any dash, '-' and other special characters(dots and underscores are OK). phenfile contains unique individual id, phenotype and covariates data, with the column names being "id" and phenotype and covaraite names. pedfile contains pedigree informaion, with the column names being "famid","id","fa","mo","sex". In all files, missing value should be an empty space, except missing parental id in pedfile. SNPs with low genotype counts (especially minor allele homozygote) may be omitted or analyzed with dominant model. The geepack.quant.batch function fits GEE model using each pedigree as a cluster with geese function from geepack package.

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.