Variance component model to account for sample structure in genome-wide association studies

By Hyun Min Kang, J. Sul, S. Service, Noah A. Zaitlen, Sit-yee Kong, N. Freimer, Chiara Sabatti, Eleazar Eskin. Nature Genetics, 2010

2,667 citations recorded by the source corpus.

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/6241d9ac2a24852e592a63cc7023017ffe73390c

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Citations in the corpus

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies (2016, methodology): “Among MLMmethods, the CMLM and FaST-LMMmethods work best with large sample sizes; the P3D/EMMAX and GRAMMAR-Gamma (Software: GenABEL R package, v 1.8–0) methods work best with high marker densities.”
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, neutral): “In recent years, there has been a growing interest in using LMMs for this task because of improved algorithms (Kang et al., 2010; Lippert et al., 2011; Tucker et al., 2014; Loh et al., 2015; Furlotte and Eskin, 2015).”
GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction (2016, methodology): “…of MLMs, including efficient mixed-model association (EMMA) (Kang et al., 2008), EMMA expedited and population parameter previously determined (Kang et al., 2010; Zhang et al., 2010), genome-wide EMMA (Zhou and Stephens, 2012), FaST-LMM (Lippert et al., 2011), and GenABEL (Svishcheva et al.,…”
Correlation and Causation in the Study of Personality (2012, background): “partialing out all loci in the genome, then, we are in effect partialing out the ancestral events confounding the gene and the trait (Kang et al., 2010; Patterson et al., 2006).”
BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions (2018, neutral): “Thus, population parameters for the testing markers can be approximated by the estimates from a reduced model without fitting each marker [20, 21].”
Multiple Causal Inference with Latent Confounding (2018, background): “, 2006) which closely relates to LMM (Kang et al., 2010; Lippert et al., 2011).”
Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, neutral): “GCTA-LOCO, EMMAX) memory usage exceeded the 96GB available at N =15K (resp.”
Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, methodology): “…and altitude; 2) EMMAX, a genomewide statistical test for association between SNP frequency and altitude that accounts for population substructure (Kang et al. 2010); 3) the Population Branch Statistic (PBS) which identifies SNPs with unusually high FST values between high- and low-altitude…”