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bioRxiv · 10.1101/2020.03.05.976498

Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

Wang, D.·Tang, H.·Liu, J.-F.·Xu, S.·Zhang, Q.·Ning, C.
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Abstract

SummaryWe 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. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model-based Plink 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 implementationSource codes are freely available at https://github.com/chaoning/GMAT.

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Wang, D., Tang, H., Liu, J.-F., Xu, S., Zhang, Q., Ning, C.. 2020-03-05. Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects. https://doi.org/10.1101/2020.03.05.976498

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