Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects.

SUMMARY We have developed a rapid mixed model algorithm for exhaustive genome-wide epistatic association analysis by controlling multiple polygenic effects. Our model can simultaneously handle additive by additive epistasis, dominance by dominance epistasis and additive by dominance epistasis, and account for intrasubject fluctuations due to individuals with repeated records. Furthermore, we suggest a simple but efficient approximate algorithm, which allows examination of all pairwise interactions in a remarkably fast manner of linear with population size. Simulation studies are performed to investigate the properties of REMMAX. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model on computational efficiency. It took less than 40 hours for the pairwise analysis of 5,000 individuals genotyped with roughly 350,000 SNPs with five threads on Intel Xeon E5 2.6GHz CPU. AVAILABILITY AND IMPLEMENTATION Source codes are freely available at https://github.com/chaoning/GMAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  R. Fisher.  XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[2]  D. Garrick,et al.  Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. , 2009, Journal of dairy science.

[3]  Jason H. Moore,et al.  Why epistasis is important for tackling complex human disease genetics , 2014, Genome Medicine.

[4]  Erika Cule,et al.  Significance testing in ridge regression for genetic data , 2011, BMC Bioinformatics.

[5]  Dan Wang,et al.  Efficient multivariate analysis algorithms for longitudinal genome-wide association studies , 2019, Bioinform..

[6]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[7]  Leonid Kruglyak,et al.  Genetic interactions contribute less than additive effects to quantitative trait variation in yeast , 2015, Nature Communications.

[8]  Moudud Alam,et al.  A Novel Generalized Ridge Regression Method for Quantitative Genetics , 2013, Genetics.

[9]  Jan Bocianowski,et al.  Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect , 2013, Genetics and molecular biology.

[10]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[11]  D. Heckerman,et al.  Efficient Control of Population Structure in Model Organism Association Mapping , 2008, Genetics.

[12]  P. Visscher,et al.  Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model , 2015, PLoS genetics.

[13]  P. Phillips.  Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems , 2008, Nature Reviews Genetics.

[14]  Oswaldo Trelles,et al.  Review: High-performance computing to detect epistasis in genome scale data sets , 2016, Briefings Bioinform..

[15]  Dan Wang,et al.  A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values , 2018, Bioinform..

[16]  Jan Bocianowski,et al.  Mixed linear model approaches in mapping QTLs with epistatic effects by a simulation study , 2014, Euphytica.

[17]  M. McMullen,et al.  A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.

[18]  Qiang Yang,et al.  BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies , 2010, American journal of human genetics.

[19]  David Heckerman,et al.  CORRIGENDUM: An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data , 2013, Scientific Reports.