Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum

• Published in: Theoretical and applied genetics 2018-03, Vol.131 (3), p.747-755
• Main Authors: Fernandes, Samuel B., Dias, Kaio O. G., Ferreira, Daniel F., Brown, Patrick J.
• Format: Article
• Language: English
• Subjects: Accuracy; Agriculture; BASIC BIOLOGICAL SCIENCES; Biochemistry; Biomass; Biomedical and Life Sciences; Biotechnology; Breeding; Genetic aspects; Genomics; Genotype; Genotypes; Life Sciences; Observations; Original; Original Article; Phenotype; Plant Biochemistry; Plant Breeding; Plant Breeding/Biotechnology; Plant Genetics and Genomics; Quantitative trait loci; Selection, Genetic; Sorghum; Sorghum - genetics; Sorghum - growth & development; Yield
• ISSN: 0040-5752; 1432-2242
• DOI: 10.1007/s00122-017-3033-y

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.