Detecting Epistatic Effects Under a Qualitative Context: Importance and Quantification

Abstract

The objectives of this work were to simulate and quantify epistatic effects on oligogenic traits and to verify the efficiency of Artificial Neural Networks (ANN) and Ridge Regression Best Linear Unbiased Predictor (RRBLUP) in the prediction of genetic values of oligogenic traits controlled by epistatic genes. We simulated 10 F2 populations in Hardy-Weinberg equilibrium with 800 individuals, each. The individuals were genotyped with 105 codominant markers equidistantly distributed along 10 chromosomes. Genotypic values were simulated for each individual considering five oligogenic traits controlled by two biallelic loci, according to five different epistatic models: duplicate recessive genes, dominant and recessive interaction, duplicate dominant genes, recessive epistasis, and dominant epistasis. RRBLUP, as well as an ANN, were used to perform genomic selection. The coefficient of determination of the regression model revealed a mean epistatic effect ranging from 13.3% in the duplicate recessive genes to 62.5% in the duplicate dominant genes. The identification of epistatic genes was superior in the ANN model compared with the RRBLUP approach. Our result reinforces the potential of ANN in predicting genetic values in situations where other than linear or quadratic relationships are present. The results presented in this work offer important insights about the exploration of epistasis in the qualitative context. In situations where epistatic effects are completely ignored, they can play a role on genetic values, as seen for the duplicative dominant genes.