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...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2014-11, Vol.4 (1), p.6874-6874, Article 6874
Main Authors: Widmer, Christian, Lippert, Christoph, Weissbrod, Omer, Fusi, Nicolo, Kadie, Carl, Davidson, Robert, Listgarten, Jennifer, Heckerman, David
Format: Article
Language:English
Subjects:
45
631/114/1305
631/114/2785
Algorithms
Animals
Genetic variance
Genome-wide association studies
Genome-Wide Association Study - statistics & numerical data
Genomes
Genotype
Genotype & phenotype
Humanities and Social Sciences
Humans
Linear Models
Mice
Models, Genetic
multidisciplinary
Phenotype
Polymorphism
Polymorphism, Single Nucleotide
Population structure
Science
Single-nucleotide polymorphism
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 .
ISSN:2045-2322
2045-2322
DOI:10.1038/srep06874