Genomic prediction of regional-scale performance in switchgrass ( Panicum virgatum ) by accounting for genotype-by-environment variation and yield surrogate traits

作者：Neal W. Tilhou, Jason Bonnette, Arvid R. Boe, Philip A. Fay, Felix Fritschi, Robert B. Mitchell, F. M. Rouquette, Yanqi Wu, Julie Jastrow, Michael P. Ricketts, Shelley D Maher, Thomas Juenger, David B. Lowry · 发表于：G3 Genes Genomes Genetics · 年份：2024 · DOI：10.1093/g3journal/jkae159 · 被引用次数：4 · 研究领域：Bioenergy crop production and management、Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north-south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of -0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resour...