Epistasis Detection and Modeling for Genomic Selection in Cowpea (Vigna unguiculata L. Walp.).

Marcus O Olatoye,Zhenbin Hu,Peter O Aikpokpodion

doi:10.3389/fgene.2019.00677

Abstract

Genetic architecture reflects the pattern of effects and interaction of genes underlying phenotypic variation. Most mapping and breeding approaches generally consider the additive part of variation but offer limited knowledge on the benefits of epistasis which explains in part the variation observed in traits. In this study, the cowpea multiparent advanced generation inter-cross (MAGIC) population was used to characterize the epistatic genetic architecture of flowering time, maturity, and seed size. In addition, consideration for epistatic genetic architecture in genomic-enabled breeding (GEB) was investigated using parametric, semi-parametric, and non-parametric genomic selection (GS) models. Our results showed that large and moderate effect–sized two-way epistatic interactions underlie the traits examined. Flowering time QTL colocalized with cowpea putative orthologs of Arabidopsis thaliana and Glycine max genes like PHYTOCLOCK1 (PCL1 [Vigun11g157600]) and PHYTOCHROME A (PHY A [Vigun01g205500]). Flowering time adaptation to long and short photoperiod was found to be controlled by distinct and common main and epistatic loci. Parametric and semi-parametric GS models outperformed non-parametric GS model, while using known quantitative trait nucleotide(s) (QTNs) as fixed effects improved prediction accuracy when traits were controlled by large effect loci. In general, our study demonstrated that prior understanding of the genetic architecture of a trait can help make informed decisions in GEB.

Highlights

Asymmetric transgressive variation in quantitative traits is usually controlled by non-additive gene interaction known as epistasis (Rieseberg et al, 1999)
Using the cowpea multiparent advanced generation inter-cross (MAGIC) population, this study showed that both additive main quantitative trait loci (QTL) and additive × additive epistatic QTL with large and moderate effects underlie flowering time (FLT), MAT, and seed size (SS) in cowpea
We identified two-way epistatic interactions, results showed that some loci were involved in interactions with more than one independent loci (Figures 4 and 5 and Figures S4–11)

Summary

INTRODUCTION

Asymmetric transgressive variation in quantitative traits is usually controlled by non-additive gene interaction known as epistasis (Rieseberg et al, 1999). Common quantitative traits mapping approaches are often single-locus analysis techniques These techniques focus on the additive contribution of genomic loci (Barton and Keightley, 2002), which only explains a fraction of the genetic variation which can lead to missing heritability. Parametric GS models are known to capture additive genetic effects but are not efficient with epistatic effects due to the computational burden of highorder interactions (Moore and Williams, 2009; Howard et al, 2014). We used the cowpea MAGIC population to first characterize the genetic architecture (main effect and epistatic effect loci) of flowering time, maturity, and seed size, and second, to evaluate considerations for genetic architecture in genomicenabled breeding using parametric, semi-parametric, and non-parametric GS models and MAS. Accounting for large effect loci as fixed effects in parametric GS model improved prediction accuracy

EXPERIMENTAL PROCEDURES

RESULTS

DISCUSSION