Prediction of Subgenome Additive and Interaction Effects in Allohexaploid Wheat

Abstract

Whole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset from CIMMYT previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.

Keywords: Allopolyploidy; Epistasis; Genomic prediction; Heterosis.

Figure 1

Estimates and standard errors of variance components from the full model (red) compared to the sampling distribution of variance component estimates from the cross-validation scheme (black violins). Two traits from the CNLM population, (A) PH and (B) HD, with contrasting genetic architectures are shown. G$\times$G (5) and ABD$\times$ABD (7) models are shown to the left and right of the dotted line, respectively. The sum of the additive $\left({\sum }_{\text{Add}}\right)$ and epitstatic $\left({\text{∑}}_{\text{Epi}}\right)$ variance components is also shown for the ABD$\times$ABD model.

Figure 2

Plot of whole genome additive effects (GEBV) by subgenome additive effects (SGEBV) for GY in the CNLM populations. The dotted line indicates the 95% quantiles for whole or subgenome effects. Blue squares, triangles and diamonds indicate the line with the highest SGEBV for each of the A, B and D subgenomes, respectively.

Figure 3

Subgenome additive and interaction variance parameter estimates from the ABD$\times$ABD (7) model correcting for population structure with $k\in \left\{0,1,...,10\right\}$ principal components as fixed effects. Models were fit with four traits for the CNLM population.

Figure A1

Distribution of minor allele frequencies for 11,604 GBS markers in the CNLM population.

Figure A2

Distribution of 11,604 GBS markers on the 21 wheat chromosomes comprised of 7 homeologs of three subgenomes, A, B and D, for the CNLM population.