Maize Epistasis Detection by Multi-class Quantitative Multifactor Dimensionality Reduction

Maize (Zea mays ssp. mays) is one of the most important food crops in the world, it is critical to explore the genetic architecture for improving yield in Maize. Existing methods focusing on the associations of single loci and phenotype may cause the missing heritablity. Furthermore, it is common that we only have partial phenotype information. In this paper, we transform maize epistasis detection into a correlation feature selection problem on multi-class data, and propose a Multi-class Quantitative Multifactor Dimensionality Reduction method called Epi-MQMDR to detect high order SNP interactions related to maize phenotype. First, we use semi-supervised learning to predict the trait values of samples with unknown phenotypes to enrich the genetic information. Then, we introduce an MDR-based algorithm for multi-class samples to detect SNP interactions associated with quantitative trait. We further discretize continuous phenotypic values of samples as labels and construct contingency tables from the classification results of SNP combination genotypes and sample labels to detect epistasis. Experiments on simulated models and real Zea mays datasets prove the efficacy of Epi-MQMDR on epistasis detection.