Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program
Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program
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DOI：
10.3835/plantgenome2016.01.0005
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发表时间：
2016-07-01
期刊：
影响因子：
4.2
通讯作者：
Poland, Jesse A.
中科院分区：
文献类型：
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作者：
Battenfield, Sarah D.;Guzman, Carlos;Poland, Jesse A.
小麦（Triticum aestivum L.）栽培品种必须具有合适的最终使用质量，以供放行和消费者接受。然而，品质性状的育种通常被认为是相对于产量的次要目标，主要是因为所需的种子量和费用。没有测试和选择，许多不受欢迎的材料被提出，消耗额外的资源。在这里，我们开发和验证CIMMYT面包小麦育种计划中最终使用质量表型的全基因组预测模型。使用对2009年至2015年在墨西哥索诺拉的奥巴桑乔城进行的不平衡产量试验中测试的育种品系（n = 5520）的正向预测来测试模型准确性。质量参数包括测试重量，1000粒重，硬度，谷物和面粉蛋白质，面粉产量，十二烷基硫酸钠沉淀，混合器和气泡仪性能，和面包体积。一般来说，随着时间的推移，随着更多数据可用于训练模型，预测准确性大幅提高。为了反映育种计划中基因组选择（GS）的实际实施，2015年评估了品质参数的前向预测精度（r），范围为0.32（籽粒硬度）至0.62（混合时间）。增加选择强度是可能的GS，因为更多的条目可以基因型比表型和预期的遗传增益是1.4至2.7倍，在所有性状比表型选择。考虑到测量许多品系的质量的局限性，我们得出结论，GS是一个强大的工具，以促进小麦最终使用质量的早代选择，留下更大的群体在先进的测试过程中选择产量，并导致更好的获得面包小麦育种计划的质量和产量。
Wheat (Triticum aestivum L.) cultivars must possess suitable end-use quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs.