The exploitation and utilization of genetic variation in crop germplasm benefit genomics-informed breeding programs. The integration of genome-wide association study (GWAS) and genomic prediction (GP) holds promise for illustrating genetic basis of phenotypic variation. Currently, the use of this integrative approach for dissecting grain protein and amino acid traits remains limited in crop species. The research objectives were to identify significant single nucleotide polymorphisms (SNPs) associated with seed protein and amino acids and to assess the genomic predictive ability for nutritional traits in natural population of foxtail millet. In this study, the concentrations of seed protein and 17 amino acids were assessed in a training population consisting of 238 diverse accessions of foxtail millet. The significant SNPs associated with these phenotypes were identified by GWAS with a mix-linear model. Extensive cross-validation was performed to evaluate the accuracy of genomic prediction of seed nutritional traits. Furthermore, the genomic prediction model was optimized and externally validated in 211 untested materials with the most significant SNPs obtained from GWAS. A significant genotype variance was detected for seed protein and amino acids and broad-sense heritability () ranged from 0.45 to 0.87 ( < 0.05). The GWAS identified 39 significant SNPs associated with 15 amino acids ( < 1e-06), explaining an average of 12.2% phenotypic variation per locus. The number of significant SNPs ranged from 1 (Asp, Ile, Glu) to 23 for Phe amino acid, including six SNPs associated with one or multiple amino acids. In the training population, the prediction accuracies of protein and amino acids, tested using the 10-fold cross-validation model with whole genome-wide SNPs, ranged from 0.23 (Glu) to 0.60 (Lys) with an average value of 0.44%. Population structure had little effect on the prediction accuracy. In an empirical validation experiment involving 211 untested accessions, the averaged prediction accuracy increased by 12.3% across all traits when using the 1000 top SNPs obtained from GWAS, compared with the same number of randomly selected SNPs from the entire genome. Large variations of seed protein and amino acids were found in a natural population of foxtail millet. GWAS identified 39 significant SNPs associated with 15 amino acids, and six SNPs were linked to more than one trait. The 10-fold cross-validation model produced the relatively higher prediction accuracies in seed quality traits of diverse accessions. By applying the significant SNPs from GWAS to the untested accessions, the use of 1000 GWAS-top SNPs resulted in more powerful predictions for complex nutritional traits than those using the same number of randomly selected SNPs across the genome. The research highlights the importance and effectiveness of integrative GWAS and genomic prediction in revealing the genetic architecture of seed protein and amino acid variation in foxtail millet. The results provide valuable insights into assessment and improvement of germplasm resources for advancing precision crop phenotyping and enhancing breeding programs for crop nutritional traits.

中文翻译：

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整合全基因组关联和基因组预测来解剖谷子的种子蛋白和氨基酸

作物种质遗传变异的开发和利用有利于基因组学育种计划。全基因组关联研究（GWAS）和基因组预测（GP）的整合有望阐明表型变异的遗传基础。目前，这种用于剖析谷物蛋白质和氨基酸性状的综合方法在作物物种中的应用仍然有限。研究目标是鉴定与种子蛋白和氨基酸相关的显着单核苷酸多态性 (SNP)，并评估谷子自然群体营养性状的基因组预测能力。在这项研究中，评估了由 238 个不同的谷子品种组成的训练群体中种子蛋白和 17 种氨基酸的浓度。 GWAS 通过混合线性模型鉴定了与这些表型相关的重要 SNP。进行了广泛的交叉验证，以评估种子营养性状基因组预测的准确性。此外，基因组预测模型在 211 种未经测试的材料中进行了优化和外部验证，其中最重要的 SNP 从 GWAS 中获得。检测到种子蛋白质和氨基酸存在显着的基因型变异，广义遗传力 () 范围为 0.45 至 0.87 ( < 0.05)。 GWAS 鉴定了 39 个与 15 个氨基酸 (< 1e-06) 相关的显着 SNP，解释了每个位点平均 12.2% 的表型变异。对于 Phe 氨基酸，显着 SNP 的数量范围为 1 个（Asp、Ile、Glu）到 23 个，其中包括与一个或多个氨基酸相关的 6 个 SNP。在训练人群中，使用全基因组 SNP 的 10 倍交叉验证模型测试的蛋白质和氨基酸的预测准确度范围为 0.23 (Glu) 至 0.60 (Lys)，平均值为 0.44%。人口结构对预测精度影响不大。在涉及 211 个未经测试的种质的实证验证实验中，与从整个基因组中随机选择的相同数量的 SNP 相比，使用从 GWAS 获得的 1000 个顶级 SNP 时，所有性状的平均预测准确度提高了 12.3%。在谷子的自然种群中发现了种子蛋白质和氨基酸的巨大变化。 GWAS 鉴定出 39 个与 15 个氨基酸相关的显着 SNP，其中 6 个 SNP 与多个性状相关。 10倍交叉验证模型对不同种质的种子品质性状产生了相对较高的预测精度。通过将 GWAS 中的重要 SNP 应用于未经测试的种质，使用 1000 个 GWAS 顶级 SNP 可以比使用相同数量的在基因组中随机选择的 SNP 进行更有效的复杂营养性状预测。该研究强调了综合 GWAS 和基因组预测在揭示谷子种子蛋白质和氨基酸变异的遗传结构方面的重要性和有效性。研究结果为评估和改进种质资源提供了宝贵的见解，以推进精准作物表型分析和加强作物营养性状育种计划。