Genome-wide Efficient Mixed Model Analysis for Association Studies

By Xiaoping Zhou, Matthew Stephens. Nature Genetics, 2012

3,207 citations recorded by the source corpus.

Linear mixed models have attracted considerable attention recently as a powerful and effective tool for accounting for population stratification and relatedness in genetic association tests. However, existing methods for exact computation of standard test statistics are computationally impractical for even moderate-sized genome-wide association studies. To address this issue, several approximate methods have been proposed. Here, we present an efficient exact method, which we refer to as genome-wide efficient mixed-model association (GEMMA), that makes approximations unnecessary in many contexts. This method is approximately n times faster than the widely used exact method known as efficient mixed-model association (EMMA), where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/1a5af849470fc77578847c64d8087e25c5062c07

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In Genetics

1. Alkes L Price
2. Zhiwu Zhang
3. Matthew Stephens
4. Jian Yang
5. Peter M. Visscher
6. Edward S. Buckler
7. Po-Ru Loh
8. Olivier Francois

Citations in the corpus

• Genetic mapping of quantitative trait loci in crops (2017, methodology): “[65] X. Zhou, P. Carbonetto, M. Stephens, Polygenic modeling with Bayesian sparse linear mixed models, PLoS Genet.”
• Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, methodology): “In contrast, EMMAX and GEMMA had deflated test statistics (Supplementary Fig.”
• SpatialDE: identification of spatially variable genes (2018, positive): “We adapt algebraic reformulations that have been proposed in statistical genetics11,22, coupled with efficient pre-computations of all terms possible, to improve scalability of the model (Supp.”
• Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models (2012, methodology): “2011), standard linear mixed models (Zhou and Stephens 2012), and Bayesian mixed models (Coop et al.”
• Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015, methodology): “Owing to efficient implementations of mixed models in applications to GWAS 29,49 , these correlation tests are extremely efficient ( Supplementary Notes ).”
• Computationally efficient whole-genome regression for quantitative and binary traits (2020, neutral): “7).”
• Robust Identification of Local Adaptation from Allele Frequencies (2012, background): “…is conceptually similar to linear mixed model (LMM) approaches that account for kinship structure in genome-wide association studies (GWAS) (e.g. Yu et al., 2006; Kang et al., 2008, 2010; Zhou and Stephens, 2012, who use an observed relatedness matrix as the covariance matrix of the random effect).”
• Natural variations and genome-wide association studies in crop plants. (2014, methodology): “Some advances have substantially decreased the computation time, including the Efficient Mixed-Model Association eXpedited (EMMAX) program and the compressed mixed linear model method (53, 66, 67, 134, 135, 139).”
• 1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana (2016, methodology): “We examined correlations of genetic variants with six climate variables that capture variability in current temperature and precipitation using amixedmodel that controls for genome-wide relatedness across samples (Zhou and Stephens, 2012).”
• Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, methodology): “In referring to u as the “random effects” we are following standard terminology from LMMs. Standard terminology also refers to the coefficients ˜ β as “fixed effects”, but this phrase has a range of potential meanings [55] and so we avoid it here.”
• BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions (2018, positive): “InspiredbyEMMA, EMMAx, and P3D, an exact algorithm, genome-wide efficient mixed-model association (GEMMA) [22], was developed to derive estimates of population parameters for each testing marker with the same computing speed as P3D and EMMAX.”