Rapid epistatic mixed-model association studies by controlling multiple polygenic effects
Dan Wang, Hui Tang, Jianfeng Liu, Shizhong Xu, Qin Zhang, Chao Ning
Abstract
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 the 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 h for the pairwise analysis of 5000 individuals genotyped with roughly 350 000 SNPs with five threads on Intel Xeon E5 2.6 GHz CPU. AVAILABILITY AND IMPLEMENTATION: Source codes are freely available at https://github.com/chaoning/GMAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.