Genomic Prediction of Gene Bank Wheat Landraces.
Genomic Prediction of Gene Bank Wheat Landraces.
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DOI：
10.1534/g3.116.029637
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发表时间：
2016-07-07
期刊：
影响因子：
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通讯作者：
Singh S
中科院分区：
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作者：
Crossa J;Jarquín D;Franco J;Pérez-Rodríguez P;Burgueño J;Saint-Pierre C;Vikram P;Sansaloni C;Petroli C;Akdemir D;Sneller C;Reynolds M;Tattaris M;Payne T;Guzman C;Peña RJ;Wenzl P;Singh S
本研究探讨基因组预测8416墨西哥地方品种加入和2403伊朗地方品种加入基因库中存储。墨西哥和伊朗的集合进行了评估，在单独的田间试验，包括几个性状的最佳环境，并在两个独立的环境（干旱，D和热，H）的高度遗传性状，抽穗天数（DTH），和成熟天数（DTM）。进行了考虑和不考虑群体结构的分析。基因组预测模型包括基因型×环境互作（G × E）。研究了两种替代的预测策略：（1）在20%训练（TRN）和80%测试（TST）（TRN 20-TST 80）集中对数据进行随机交叉验证，以及（2）两种类型的核心集，“多样性”和“预测”，分别包括总集合的10%和20%。与不考虑人口结构时获得的预测准确度相比，考虑人口结构后的预测准确度降低了15-20%。占人口结构的预测精度TRN 20-TST 80的范围从0.407到0.677的墨西哥地方品种，从0.166到0.662的伊朗地方品种在一个环境中评估的性状。20%的多样性核心集的预测精度是类似的TRN 20-TST 80，范围从0.412到0.654墨西哥地方品种，从0.182到0.647伊朗地方品种获得的准确性。预测核心集给出了类似的预测精度的多样性核心集墨西哥收藏，但略低于伊朗收藏。在TRN 20-TST 80中，将DTH的G × E和墨西哥地方品种的DTM合并时的预测准确度约为0.60，高于没有G × E项的预测准确度。对于伊朗地方品种，TRN 20-TST 80的G × E模型的精度为0.55。结果表明，有前途的预测精度的种质创新和快速渗入到优良材料的外来种质的潜在用途。
This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.