Efficient multivariate linear mixed model algorithms for genome-wide association studies
Xiang Zhou,Matthew Stephens +1 more
TLDR
Efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests are presented, which offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.Abstract:
Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.read more
Citations
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Methods of integrating data to uncover genotype–phenotype interactions
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TL;DR: A model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty is empirically derived by analyzing imputed data for a large number of human traits.
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Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer.
TL;DR: Sommer as discussed by the authors is an open-source R package to facilitate the use of mixed models for genomic selection and hybrid prediction purposes using more than one variance component and allowing specification of covariance structures.
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Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations
Henry R. Kranzler,Henry R. Kranzler,Hang Zhou,Hang Zhou,Rachel L. Kember,Rachel L. Kember,Rachel Vickers Smith,Rachel Vickers Smith,Amy C. Justice,Amy C. Justice,Scott M. Damrauer,Scott M. Damrauer,Philip S. Tsao,Philip S. Tsao,Derek Klarin,Aris Baras,Jeffrey S. Reid,John D. Overton,Daniel J. Rader,Zhongshan Cheng,Zhongshan Cheng,Janet P. Tate,Janet P. Tate,William C. Becker,William C. Becker,John Concato,John Concato,Ke Xu,Ke Xu,Renato Polimanti,Renato Polimanti,Hongyu Zhao,Joel Gelernter,Joel Gelernter +33 more
TL;DR: It is concluded that, although heavy drinking is a key risk factor for AUD, it is not a sufficient cause of the disorder and a total of 18 associated loci are identified.
References
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Journal ArticleDOI
PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses
Shaun Purcell,Shaun Purcell,Benjamin M. Neale,Benjamin M. Neale,Kathe Todd-Brown,Lori Thomas,Manuel A. R. Ferreira,David Bender,David Bender,Julian Maller,Julian Maller,Pamela Sklar,Pamela Sklar,Paul I.W. de Bakker,Paul I.W. de Bakker,Mark J. Daly,Mark J. Daly,Pak C. Sham +17 more
TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Journal ArticleDOI
GCTA: a tool for genome-wide complex trait analysis.
TL;DR: The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets and focuses on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation.
Journal ArticleDOI
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
Jianming Yu,Gaël Pressoir,William H. Briggs,Irie Vroh Bi,Masanori Yamasaki,John Doebley,Michael D. McMullen,Michael D. McMullen,Brandon S. Gaut,Dahlia M. Nielsen,James B. Holland,James B. Holland,Stephen Kresovich,Edward S. Buckler,Edward S. Buckler +14 more
TL;DR: A unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers is developed and provides a powerful complement to currently available methods for association mapping.
Journal ArticleDOI
Genome-wide efficient mixed-model analysis for association studies.
Xiang Zhou,Matthew Stephens +1 more
TL;DR: 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.
Journal ArticleDOI
Variance component model to account for sample structure in genome-wide association studies
Hyun Min Kang,Jae Hoon Sul,Noah Zaitlen,Sit Yee Kong,Nelson B. Freimer,Chiara Sabatti,Eleazar Eskin +6 more
TL;DR: 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 is reported.
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Genome-wide efficient mixed-model analysis for association studies.
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