PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

FaST linear mixed models for genome-wide association studies
Christoph Lippert, Jennifer Listgarten, Ying Liu, Carl M. Kadie, Robert I. Davidson and David Heckerman
Nature Methods Volume 8, Number 10, pp. 833-835, 2011. ISSN 1548-7091

Abstract

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8774
Deposited By:Christoph Lippert
Deposited On:21 February 2012