Further improvements to linear mixed models for genome-wide association studies.
We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.
eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA, 90024, United States.
Christian
C
Widmer
eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA, 90024, United States.
Christoph
C
Lippert
Computer Science Department, Technion - Israel Institute of Technology, Haifa 32000, Israel.
Omer
O
Weissbrod
eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA, 90024, United States.
Nicolo
N
Fusi
eScience Group, Microsoft Research, One Microsoft Way, Redmond, WA, 98052, United States.
Carl
C
Kadie
eScience Group, Microsoft Research, One Microsoft Way, Redmond, WA, 98052, United States.
Robert
R
Davidson
eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA, 90024, United States.
Jennifer
J
Listgarten
eScience Group, Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles, CA, 90024, United States.
David
D
Heckerman
D000465:Algorithms; D000818:Animals; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D005838:Genotype; D006801:Humans; D016014:Linear Models; D051379:Mice; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012984:Software