Dataset Information

Lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R.

ABSTRACT:

Unlabelled

The linear mixed model is the state-of-the-art method to account for the confounding effects of kinship and population structure in genome-wide association studies (GWAS). Current implementations test the effect of one or more genetic markers while including prespecified covariates such as sex. Here we develop an efficient implementation of the linear mixed model that allows composite hypothesis tests to consider genotype interactions with variables such as other genotypes, environment, sex or ancestry. Our R package, lrgpr, allows interactive model fitting and examination of regression diagnostics to facilitate exploratory data analysis in the context of the linear mixed model. By leveraging parallel and out-of-core computing for datasets too large to fit in main memory, lrgpr is applicable to large GWAS datasets and next-generation sequencing data.

Availability and implementation

lrgpr is an R package available from lrgpr.r-forge.r-project.org.

SUBMITTER: Hoffman GE

PROVIDER: S-EPMC4201153 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R.

Hoffman Gabriel E GE Mezey Jason G JG Schadt Eric E EE

Bioinformatics (Oxford, England) 20140716 21

<h4>Unlabelled</h4>The linear mixed model is the state-of-the-art method to account for the confounding effects of kinship and population structure in genome-wide association studies (GWAS). Current implementations test the effect of one or more genetic markers while including prespecified covariates such as sex. Here we develop an efficient implementation of the linear mixed model that allows composite hypothesis tests to consider genotype interactions with variables such as other genotypes, en ...[more]

PMID: 25035399

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