MINED: An Efficient Mutual Information Based Epistasis Detection Method to Improve Quantitative Genetic Trait Prediction

Abstract

Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a great deal of attention, as it is very relevant to plant and animal breeding. More effective breeding strategies can be developed based on a more accurate prediction. Most of the existing work considers an additive model on single markers, or genotypes only. In this work, we studied the problem of epistasis detection for genetic trait prediction, where different alleles, or genes, can interact with each other. We have developed a novel method MINED to detect significant pairwise epistasis effects that contribute most to prediction performance. A dynamic thresholding and a sampling strategy allow very efficient detection, and it is generally 20 to 30 times faster than an exhaustive search. In our experiments on real plant data sets, MINED is able to capture the pairwise epistasis effects that improve the prediction. We show it achieves better prediction accuracy than the state-of-the-art methods. To our knowledge, MINED is the first algorithm to detect epistasis in the genetic trait prediction problem. We further proposed a constrained version of MINED that converts the epistasis detection problem into a Weighted Maximum Independent Set problem. We show that Constrained-MINED is able to improve the prediction accuracy even more.