Evaluation of Genomic Selection Training Population Designs and Genotyping Strategies in Plant Breeding Programs Using Simulation
Evaluation of Genomic Selection Training Population Designs and Genotyping Strategies in Plant Breeding Programs Using Simulation
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DOI：
10.2135/cropsci2013.03.0195
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发表时间：
2014-07-01
期刊：
影响因子：
2.3
通讯作者：
Gorjanc, Gregor
中科院分区：
文献类型：
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作者：
Hickey, John M.;Dreisigacker, Susanne;Gorjanc, Gregor
基因组选择为提高植物育种的遗传改良率提供了巨大的潜力。这项研究使用模拟来评估不同的基因分型和表型策略的有效性，以使基因组选择在早代个体（例如，F-2）在涉及双亲或类似的育种计划（e。.,回交或顶交）群体。通过使用先前在其他双亲群体中收集的表型，可以做出选择决定，而无需等待与待收集的选择候选物直接相关的表型，这一过程将需要至少三个生长季节。如果在与选择候选物密切相关的双亲群体中收集这些表型，则仅少量标记（例如，200-500）和少量表型（例如，1000），以达到有效的准确性估计育种值。如果这些表型收集在与选择候选物不密切相关的双亲群体中，则需要多达10，000个标记和5000至20，000个表型。增加标记物密度超过10，000个标记物没有显示出益处，并且在某些情况下降低了预测的准确性。这项研究提供了一个指导，使资源分配之间的基因分型和表型投资依赖于人口发展进行优化。
Genomic selection offers great potential for increasing the rate of genetic improvement in plant breeding programs. This research used simulation to evaluate the effectiveness of different strategies for genotyping and phenotyping to enable genomic selection in early generation individuals (e.g., F-2) in breeding programs involving biparental or similar (e. ., backcross or top cross) populations. By using phenotypes that were previously collected in other biparental populations, selection decisions could be made without waiting for phenotypes that pertain directly to the selection candidate to be collected, a process that would take at least three growing seasons. If these phenotypes were collected in biparental populations that were closely related to the selection candidates, only a small number of markers (e.g., 200-500) and a small number of phenotypes (e.g., 1000) were needed to achieve effective accuracy of estimated breeding values. If these phenotypes were collected in biparental populations that were not closely related to the selection candidates, as many as 10,000 markers and 5000 to 20,000 phenotypes were needed. Increasing marker density beyond 10,000 markers did not show benefit and in some scenarios reduced the accuracy of prediction. This study provides a guide, enabling resource allocation to be optimized between genotyping and phenotyping investment dependent on the population under development.