Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA.
Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA.
G3 (Bethesda). 2024 Oct 7;14(10). doi: 10.1093/g3journal/jkae159.
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.
柳枝稷是生物能源或碳捕获计划的一种有潜力的作物，但需要通过有选择性的培育来进一步提高产量，以鼓励其商业化。为了确定有前途的柳枝稷种质资源，用于未来的培育工作，我们对来自 4 个柳枝稷亚种群（海湾、中西部、沿海和德克萨斯）的 630 个基因型的多样性群体进行了多点和多性状基因组预测，这些基因型在 10 个地点进行了株距植物生物量产量的测量。我们的研究侧重于利用基因组预测在性状和环境之间共享信息。具体来说，我们评估了仅使用遗传数据和训练集（交叉验证 1：CV1）、部分站点（交叉验证 2：CV2）或 2 个产量替代物（开花时间和秋季植物高度）进行交叉验证方案的预测能力。我们发现，基因型-环境互作主要归因于站点的南北分布。产量替代物与生物量产量之间的遗传相关性通常为正（平均高度 r = 0.85；平均开花时间 r = 0.45），并且不受亚种群或生长区域（北部、中部或南部）的影响。仅使用遗传数据（CV1）的个体的基因组预测模型的交叉验证预测能力为-0.02，但在训练数据中包含 1、2、3、4 和 5 个站点的生物量表现数据的个体的交叉验证预测能力分别为 0.55、0.69、0.76、0.81 和 0.84。为了模拟资源有限的育种计划，我们确定了以下模型的预测能力：1 个观测点的开花时间（0.39）；1 个观测点的开花时间和秋季高度（0.51）；1 个观测点的秋季高度（0.52）；1 个观测点的生物量（0.55）；和 5 个观测点的生物量产量（0.84）。在区域尺度上共享信息的能力非常令人鼓舞，但需要进一步的研究来准确地将株距植物生物量转化为商业规模的草丛生物量性能。
