Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes.

KEY MESSAGE: Exploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding. ABSTRACT: Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.