Table4_Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction.xls

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction’s potential to a set of 482 pea (Pisum sativum L.) accessions—genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components—for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

种质资源库的表型鉴定与高效利用往往耗时费力、成本高昂。然而，随着下一代测序成本的大幅下降，以及基因组选择被纳入植物育种工具箱，如今我们能够更高效地挖掘大型种质资源库中的遗传多样性。本研究针对美国农业部（USDA）豌豆种质资源库中的482份豌豆（Pisum sativum L.）种质材料开展基因组预测潜力的应用与评估：该批材料通过30600个单核苷酸多态性（SNP）标记进行基因分型，并针对种子产量及产量相关性状进行了表型鉴定，以优化种质材料的选择流程。本研究通过一系列针对复杂性状的交叉验证方案，对基因组预测模型及若干影响预测性能的因素进行了评估。不同基因组预测模型的结果表现相近，各性状的预测性能介于0.23至0.60之间，且并无一款模型能在所有性状上均取得最优表现。增大训练群体规模可提升多数性状（包括种子产量）的预测性能。随着标记数量增加，预测性能先上升后趋于平稳，这一现象推测与豌豆基因组中广泛存在的连锁不平衡有关。考虑群体结构效应并未显著提升整体预测性能，但在种子产量性状上观察到了小幅改善。通过选用最优基因组预测模型（如RR-BLUP），本研究进一步分析了已完成基因分型但未进行表型鉴定的种质材料的分布情况，以及基因组估计育种值（GEBV）的可靠性。基因组估计育种值的分布结果显示，所有未进行表型鉴定的种质材料的预期表现均未超出已表型鉴定种质的表现范围。可靠性较高的优良育种值可用于识别与筛选优良种质材料。扩大训练群体规模，并将转录组学、代谢组学、生理性状等额外正交信息整合至基因组预测框架中，可进一步提升预测精度。