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

Overview

MeSH

  • Genotype
  • Linear Models

MeSH Major

  • Genome-Wide Association Study
  • Software

abstract

  • 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. lrgpr is an R package available from lrgpr.r-forge.r-project.org. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

publication date

  • November 1, 2014

has subject area

  • Genome-Wide Association Study
  • Genotype
  • Linear Models
  • Software

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4201153

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btu435

PubMed ID

  • 25035399

Additional Document Info

start page

  • 3134

end page

  • 3135

volume

  • 30

number

  • 21