基因组选择
遗传增益
生物
选择(遗传算法)
背景(考古学)
植物育种
育种计划
最佳线性无偏预测
生物技术
标记辅助选择
扎梅斯
农学
数量性状位点
栽培
遗传变异
遗传学
计算机科学
基因型
机器学习
单核苷酸多态性
古生物学
基因
作者
Elliot L. Heffner,Aaron J. Lorenz,Jean‐Luc Jannink,Mark E. Sorrells
出处
期刊:Crop Science
[Wiley]
日期:2010-09-01
卷期号:50 (5): 1681-1690
被引量:526
标识
DOI:10.2135/cropsci2009.11.0662
摘要
ABSTRACT Advancements in genotyping are rapidly decreasing marker costs and increasing genome coverage. This is facilitating the use of marker‐assisted selection (MAS) in plant breeding. Commonly employed MAS strategies, however, are not well suited for agronomically important complex traits, requiring extra time for field‐based phenotyping to identify agronomically superior lines. Genomic selection (GS) is an emerging alternative to MAS that uses all marker information to calculate genomic estimated breeding values (GEBVs) for complex traits. Selections are made directly on GEBV without further phenotyping. We developed an analytical framework to (i) compare gains from MAS and GS for complex traits and (ii) provide a plant breeding context for interpreting results from studies on GEBV accuracy. We designed MAS and GS breeding strategies with equal budgets for a high‐investment maize ( Zea mays L.) program and a low‐investment winter wheat ( Triticum aestivum L.) program. Results indicate that GS can outperform MAS on a per‐year basis even at low GEBV accuracies. Using a previously reported GEBV accuracy of 0.53 for net merit in dairy cattle, expected annual gain from GS exceeded that of MAS by about threefold for maize and twofold for winter wheat. We conclude that if moderate selection accuracies can be achieved, GS could dramatically accelerate genetic gain through its shorter breeding cycle.
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