Work

Fast computation for genome-wide association studies using boosted one-step statistics

Year: 2012

Type: article

Abstract: MOTIVATION: Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical. RESULTS: In this article, we present simple methods that capitalize on the structure inherent in GWAS studies to dramatically speed up computation for a wide variety of problems, with a special focus on methods for correlated phenotypes. AVAILABILITY: The R package 'boss' is available on the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/web/packages/boss/

Source: Bioinformatics

DOI: 10.1093/bioinformatics/bts291

Authors: Arend Voorman, Ken Rice, Thomas Lumley

Institutions: University of Auckland, University of Washington

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Heart, Lung, and Blood Institute

Awards: G277817719, G702969726