MUSE: A MULTI-LOCUS SAMPLING-BASED EPISTASIS ALGORITHM FOR QUANTITATIVE GENETIC TRAIT PREDICTION.

Abstract

Quantitative genetic trait prediction based on high-density genotyping arrays plays an important role for plant and animal breeding, as well as genetic epidemiology such as complex diseases. The prediction can be very helpful to develop breeding strategies and is crucial to translate the findings in genetics to precision medicine. Epistasis, the phenomena where the SNPs interact with each other, has been studied extensively in Genome Wide Association Studies (GWAS) but received relatively less attention for quantitative genetic trait prediction. As the number of possible interactions is generally extremely large, even pairwise interactions is very challenging. To our knowledge, there is no solid solution yet to utilize epistasis to improve genetic trait prediction. In this work, we studied the multi-locus epistasis problem where the interactions with more than two SNPs are considered. We developed an effcient algorithm MUSE to improve the genetic trait prediction with the help of multi-locus epistasis. MUSE is sampling-based and we proposed a few different sampling strategies. Our experiments on real data showed that MUSE is not only effcient but also effective to improve the genetic trait prediction. MUSE also achieved very significant improvements on a real plant data set as well as a real human data set.