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

Abstract

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 resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

Keywords: climate change; genomic prediction; genotype by environment; switchgrass.

Fig. 1.

AMMI model biplot showing the relationship among 10 sites and the mean biomass performance of the 4 major subpopulations of switchgrass (Panicum virgatum). Scores near 0 indicate less of a GxE interaction. Subpopulations that occur near field sites in the biplot indicate overperformance of those subpopulations in those sites.

Fig. 2.

Plot showing the mean biomass performance across environments (grams plant⁻¹ year⁻¹; square root transformed; x-axis) and the first principal component (PC1) from an AMMI model summarizing biomass variation among 10 sites (y-axis).

Fig. 3.

Mean predictive ability across 10 sites for 3 switchgrass subpopulations based on CV of individuals that had biomass performance measured at 0, 1, 2, 3, 4, and 5 sites (# environments) included in the training data. Predictive ability is the correlations among predicted and observed performances of omitted individuals for a given site. Point and line colors indicate the subpopulations.

Fig. 4.

Predictive ability across 10 sites within 3 regions for 3 switchgrass subpopulations using models that simulate a range of the field data collection effort for the validation population. Field observations for validation individuals included the following: no observations (CV1: None), 1 site flowering time measurement (FT), 1 fall height and 1 flowering time measurement (FT/HT), 1 site fall height measurement (HT), 1 site biomass measurement (B1), 1 flowering time measurement and 1 biomass measurement (1B/FT) biomass evaluations at 5 sites (5B). Predictive ability is the correlation among the predicted and observed performances of omitted individuals in unknown sites. Bar colors indicate the different levels of field observation effort.