Login | Request Account (DAF staff only)

Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis

Velazco, J. G., Jordan, D. R., Mace, E. S., Hunt, C. H., Malosetti, M. and van Eeuwijk, F. A. (2019) Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis. Frontiers in Plant Science, 10 . p. 997. ISSN 1664-462X

[img]
Preview
PDF
1MB

Article Link(s): https://doi.org/0.3389/fpls.2019.00997

Publisher URL: https://www.frontiersin.org/articles/10.3389/fpls.2019.00997/full

Abstract

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH) and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.

Item Type:Article
Business groups:Crop and Food Science
Keywords:Genomic prediction, Multi-trait analysis, Sorghum, Auxiliary trait, Grain yeild, Stay-green, blended kinship matrix, BLUP
Subjects:Science > Botany > Genetics
Agriculture > Agriculture (General) > Methods and systems of culture. Cropping systems
Plant culture > Field crops > Sorghum
Deposited On:04 Feb 2020 06:38
Last Modified:27 Sep 2020 23:07

Repository Staff Only: item control page

Downloads

Downloads per month over past year

View more statistics