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

Publication TypeJournal Article
Year of Publication2014
AuthorsHoffman GE, Mezey JG, Schadt EE
Date Published2014 Nov 01
KeywordsGenome-Wide Association Study, Genotype, Linear Models, Software

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

Alternate JournalBioinformatics
PubMed ID25035399
PubMed Central IDPMC4201153
Grant ListR01 MH095034 / MH / NIMH NIH HHS / United States
U01 AG046170 / AG / NIA NIH HHS / United States
R01AG046170 / AG / NIA NIH HHS / United States
R01MH095034 / MH / NIMH NIH HHS / United States