Efficient control of population structure in model organism association mapping.

TitleEfficient control of population structure in model organism association mapping.
Publication TypeJournal Article
Year of Publication2008
AuthorsKang H M, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E
JournalGenetics
Volume178
Issue3
Pagination1709-23
Date Published2008 Mar
ISSN0016-6731
KeywordsAnimals, Arabidopsis, Body Weight, Chromosome Mapping, Flowers, Genome, Inbreeding, Mice, Mice, Inbred Strains, Models, Biological, Models, Genetic, Organ Size, Phenotype, Polymorphism, Single Nucleotide, Population Dynamics, Quantitative Trait, Heritable, Saccharin, Software, Zea mays
Abstract

Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.

DOI10.1534/genetics.107.080101
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/18385116?dopt=Abstract
PMCPMC2278096
Alternate TitleGenetics
PubMed ID18385116