lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R.
Bioinformatics
; 30(21): 3134-5, 2014 Nov 01.
Article
en En
| MEDLINE
| ID: mdl-25035399
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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Estudio de Asociación del Genoma Completo
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2014
Tipo del documento:
Article