Open Access
Translator Disclaimer
March 2013 Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies
Matti Pirinen, Peter Donnelly, Chris C. A. Spencer
Ann. Appl. Stat. 7(1): 369-390 (March 2013). DOI: 10.1214/12-AOAS586

Abstract

Motivated by genome-wide association studies, we consider a standard linear model with one additional random effect in situations where many predictors have been collected on the same subjects and each predictor is analyzed separately. Three novel contributions are (1) a transformation between the linear and log-odds scales which is accurate for the important genetic case of small effect sizes; (2) a likelihood-maximization algorithm that is an order of magnitude faster than the previously published approaches; and (3) efficient methods for computing marginal likelihoods which allow Bayesian model comparison. The methodology has been successfully applied to a large-scale association study of multiple sclerosis including over 20,000 individuals and 500,000 genetic variants.

Citation

Download Citation

Matti Pirinen. Peter Donnelly. Chris C. A. Spencer. "Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies." Ann. Appl. Stat. 7 (1) 369 - 390, March 2013. https://doi.org/10.1214/12-AOAS586

Information

Published: March 2013
First available in Project Euclid: 9 April 2013

zbMATH: 06171276
MathSciNet: MR3086423
Digital Object Identifier: 10.1214/12-AOAS586

Keywords: case-control study , genetic association study , linear mixed model

Rights: Copyright © 2013 Institute of Mathematical Statistics

JOURNAL ARTICLE
22 PAGES


SHARE
Vol.7 • No. 1 • March 2013
Back to Top