FaST linear mixed models for genome-wide association studies

Christoph Lippert; Jennifer Listgarten; Ying Liu; Carl M Kadie; Robert I Davidson; David Heckerman

doi:10.1038/nmeth.1681

FaST linear mixed models for genome-wide association studies

Nat Methods. 2011 Sep 4;8(10):833-5. doi: 10.1038/nmeth.1681.

Authors

Christoph Lippert¹, Jennifer Listgarten, Ying Liu, Carl M Kadie, Robert I Davidson, David Heckerman

Affiliation

¹ Microsoft Research, Los Angeles, California, USA. christoph.lippert@tuebingen.mpg.de

PMID: 21892150
DOI: 10.1038/nmeth.1681

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/).

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computer Simulation
Genome-Wide Association Study*
Models, Genetic*
Software

Abstract

Publication types

MeSH terms

Grants and funding