Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat
Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat
复制标题
多性状整体基因组预测和循环选择模拟强调了复杂性状遗传结构对于小麦长期遗传增益的重要性
DOI：
10.1093/insilicoplants/diad002
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发表时间：
2023
期刊：
影响因子：
3.1
通讯作者：
Fradgley N
中科院分区：
文献类型：
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作者：
Fradgley N
谷类作物育种者在保持遗传多样性的同时，在谷物产量等复杂遗传性状上取得了相当大的遗传收益。然而，对产量选择的关注也对其他重要性状产生了负面影响。为了更好地了解育种环境中的多性状选择，以及如何进行优化，我们分析了一个遗传多样性的16个创始人小麦多亲高世代杂交群体的基因型态和表型数据。与单性状模型相比，多性状集成基因组预测模型对近90%的性状提高了预测精度，粮食产量预测精度提高了3-52%。对于复杂的性状，非参数模型(随机森林)也优于简化的加性模型(套索)，将粮食产量预测精度提高了10-36%。然后，对循环基因组选择的模拟表明，持续更高的向前预测精度优化了长期遗传收益。对粮食产量选择的模拟发现了相关性状的间接反应，涉及到优化的拮抗性状关系。我们发现，多性状选择指数可以有效地优化不良关系，如谷物产量和蛋白质含量之间的权衡，或结合感兴趣的性状，如产量和杂草竞争力。表型选择的模拟发现，包括随机森林而不是套索遗传模型，以及多性状而不是单性状模型作为真正的遗传模型，在保持遗传多样性的同时加速和延长了长期的遗传收益。这些结果(I)表明多效性和上位性在更广泛的小麦育种计划中的重要作用，以及(Ii)为在有限的遗传库中继续获得遗传收益和优化作物改良的多个性状的机制提供了洞察。
Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.