|OVENDEN, BEN - Nsw Department Of Primary Industries|
|MILGATE, A - Nsw Department Of Primary Industries|
|WADE, L - Charles Sturt University|
|REBETZKE, G - Commonwealth Scientific And Industrial Research Organisation (CSIRO)|
|Holland, Jim - Jim|
Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/23/2018
Publication Date: 6/1/2018
Citation: Ovenden, B., Milgate, A., Wade, L.J., Rebetzke, G.J., Holland, J.B. 2018. Accounting for genotype–by-environment interactions and non-additive genetic variation in genomic selection for water-soluble carbohydrate concentration in wheat. Genetics. 8:1909-1919.
Interpretive Summary: We investigated the potential of improving an important abiotic stress trait (carbohydrate accumulation) by genomic selection in wheat. Our modelled additive genetic variance was smaller than non-additive genetic variance, meaning genomic estimated breeding values were not accurate predictors of genotypic values of the extant lines, although they should be reliable selection criteria to choose parents for intermating. Genomic selection could accelerate genetic gain if the breeding cycle duration can be reduced. Further, we demonstrate how genomic prediction accuracy in crop models depends on having phenotypic data from environments with strong correlations with target production environments.
Technical Abstract: Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than non-additive genetic variance for WSCC, such that genotypic values were dominated by non additive effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection.