Abstract
Key message We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best.Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to 50% when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait.
Highlights
Releasing new varieties usually requires evaluation of progenies in a large number of environments
All traits were genetically correlated with biomass yield (Fig. 2)
Marker-based prediction relies on good phenotyping, and prediction accuracy generally increases with heritability (Combs and Bernardo 2013)
Summary
Releasing new varieties usually requires evaluation of progenies in a large number of environments. The application of best linear unbiased prediction (BLUP) using pedigree information Henderson (1975) and more recently using molecular markers (GBLUP) (VanRaden 2008; Hayes et al 2009b) are examples of efforts to meet those goals. When GBLUP or other GS models are applied, selection is made on genomic estimated breeding values (GEBVs) calculated from molecular markers and using phenotypic information of a training population. GS has been successfully applied in many animal (Vallée et al 2014; de los Campos et al 2013) and plant (Heffner et al 2011; Heslot et al 2012) breeding programs, and prediction accuracy (r) generally shows a positive correlation with heritability (h2) (Hayes et al 2009a). Benefits of multi-trait GS over single-trait GS have been reported in simulated (Calus and Veerkamp 2011) and real data (Jia and Jannink 2012; Schulthess et al 2016)
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