This article appears in the following Briefings in Bioinformatics issue: Special Issue: Plant Genomics [View the issue table of contents]
Software engineering the mixed model for genome-wide association studies on large samples
Corresponding author. Zhiwu Zhang, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. Tel: +1-607-255-3270; Fax: +1-607-255-6249; E-mail: zz19{at}cornell.edu
Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software–user interaction. The available software packages are evaluated, and suggestions made for future software development.
Keywords: mixed model, association study, quantitative trait loci, genome-wide, kinship, population structure
Submitted: May 6, 2009. Received (in revised form): September 25, 2009.