For continuous traits, GWAF package provides two sets of functions for each of genome-wide association/interaction analyses with observed/imputed SNP genotypes for family data. One fits Linear Mixed Effects (LME) model and the other fits Generalized Estimation Equation (GEE) model to accounting for within pedigree correlation. While for dichotomous trait, GWAF package provides functions to fit GEE model for genome-wide association/interaction analyses. For rare variant analysis, GWAF fits LME and generalized linear mixed effects (GLMM) model for continuous traits and dichotomous traits, respectively. In addition, GWAF package also provides functions for making genome-wide p-values plot and QQ plot that contains genomic control parameter estimate and generating scripts for genome-wide association analysis. Please read UsingGWAF.pdf for more information including examples and description to output files.
geepack.lgst.int.batch
(Package: GWAF) :
function to test gene-environment or gene-gene interactions between a dichotomous trait and a batch of genotyped SNPs in families using
Fit logistic regression via Generalized Estimation Equation (GEE) to test gene-environment or gene-gene interactions for a dichotomous phenotype and all genotyped SNPs in a genotype file in family data under additive genetic model. The interaction term is the product of SNP genotype and a covariate for interaction (cov.int). The covariate for interaction (cov.int) can be SNP genotype (gene-gene interaction) or an environmental factor (gene-environment interaction). Only one interaction term is allowed. When cov.int is dichotomous, stratified analyses can be requested by specifying sub="Y". The covariance between the main effect (SNP) and the interaction effect is provided in the output when stratified analysis is not requested. Each pedigree is treated as a cluster with independence working correlation matrix used in the robust variance estimator. This function applies the same interaction test to all SNPs in a genotype file. The interaction test is carried out by geepack.lgst.int function from GWAF where the the geese function from package geepack is used.
geepack.lgst.int.imputed
(Package: GWAF) :
function for testing gene-environment or gene-gene interaction between a dichotomous trait and an imputed SNP in family data using Generalized Estimation Equation model
Fit logistic regression via Generalized Estimation Equation (GEE) to test gene-environment or gene-gene interaction between a dichotomous phenotype and one imputed SNP in a genotype file under additive genetic model. The interaction term is the product of SNP genotype and a covariate for interaction (cov.int). The covariate for interaction (cov.int) can be SNP genotype (gene-gene interaction) or an environmental factor (gene-environment interaction). Only one interaction term is allowed. When cov.int is dichotomous, stratified analyses can be requested by specifying sub="Y". The covariance between the main effect (SNP) and the interaction effect is provided in the output when stratified analysis is not requested. Each family is treated as a cluster, with independence working correlation matrix used in the robust variance estimator. This function is called in geepack.lgst.int.batch.imputed function to apply interaction test to all imputed SNPs in a genotype file. The interaction test is carried out by the geese function from package geepack.
geepack.lgst.imputed
(Package: GWAF) :
function for testing association between a dichotomous trait and an imputed SNP in family data using Generalized Estimation Equation model
Fit logistic regression via Generalized Estimation Equation (GEE) to test association between a dichotomous phenotype and one imputed SNP 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 trait-SNP association test is carried out by the geese function from package geepack. This function is called in geepack.lgst.batch.imputed function to apply association test to all imputed SNPs in a genotype file.
lmeVpack.batch.imputed
(Package: GWAF) :
function to efficiently test associations between a continuous trait and a batch of imputed SNPs in families using
A faster version of function lmepack.batch.imputed. Unlike lmepack.batch.imputed that estimates polygenic variation for every SNP in a batch of imputed SNPs, lmeVpack.batch.imputed only estimates once for a batch of imputed SNPs. Particularly recommended for analyzing 1000G imputed genotype data.
glmm.lgst.batch
(Package: GWAF) :
function to test genetic association between a dichotomous trait and a batch of genotyped SNPs in families using
Fit Generalized Linear Mixed Effects model (GLMM) with logistic link and a normal distributed random intercept for each cluster to test associations between a dichotomous phenotype and all genotyped SNPs in a genotype file in family data with user specified genetic model. Each pedigree is treated as a cluster. This function applies the same trait-SNP association test to all SNPs in the genotype data. When analyzing rare variants for dichotomous traits, this GLMM, as implemeted by this function, is recommended over other methods such as GEE. The trait-SNP association test is carried out by glmm.lgst function where the the lmer function from package lme4 is used.
lmepack.batch.imputed
(Package: GWAF) :
function to test associations between a continuous trait and a batch of imputed SNPs in families using
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.
geepack.quant.batch
(Package: GWAF) :
function to test genetic associations between a continuous trait and a batch of genotyped SNPs in families using
Fit Generalized Estimation Equation (GEE) model to test associations 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.
auto
(Package: GWAF) :
function to generate scripts for genome-wide association/interaction analysis
Given a path/directory (genopath) that keeps geotype files, phenotype file, pedigree file, phenotype of interest, covariates, analysis of interest (can be 'lmepack', 'lmepack.imputed', 'lmeVpack.imputed', 'glmm', 'geepack', 'geepack.imputed', 'geepack.quant', 'geepack.quant.imputed', 'lmepack.int', 'lmepack.int.imputed', 'geepack.int', 'geepack.int.imputed', 'geepack.quant.int', 'geepack.quant.int.imputed') and other arguments, auto function generates one R script, one shell script that can excute R script, and one list file that can excute all shell scripts in batch mode, for each genotype file. Once the list file (XXXX.lst) is generated, user can use ksh XXXX.lst to submit all jobs to test all SNPs in genopath.
● Data Source:
CranContrib
● Keywords:
● Alias: auto
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