多性状集合基因组预测和循环选择模拟强调了复杂性状遗传结构对小麦长期遗传增益的重要性

IF 2.6 Q1 AGRONOMY in silico Plants Pub Date : 2023-01-01 DOI:10.1093/insilicoplants/diad002
Nick Fradgley, Keith A Gardner, Alison R Bentley, Phil Howell, Ian J Mackay, Michael F Scott, Richard Mott, James Cockram
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引用次数: 2

摘要

谷类作物育种者在保持遗传多样性的同时,在谷物产量等遗传复杂性状上取得了可观的遗传增益。然而,对产量选择的关注对其他重要性状产生了负面影响。为了更好地理解育种背景下的多性状选择,以及如何优化多性状选择,我们分析了来自一个遗传多样化的16个始祖小麦多亲本先进代杂交群体的基因型和表型数据。与单性状集合基因组预测模型相比,多性状集合基因组预测模型对近90%的性状的预测精度提高了,对粮食产量的预测精度提高了3 - 52%。对于复杂性状,非参数模型(随机森林)也优于简化的加性模型(LASSO),将粮食产量预测精度提高了10 - 36%。循环基因组选择的模拟表明,持续较高的前向预测准确性优化了长期遗传收益。对籽粒产量的选择模拟发现了相关性状的间接响应,包括优化的拮抗性状关系。研究发现,多性状选择指标可以有效地优化籽粒产量和蛋白质含量之间的权衡关系,或将籽粒产量和杂草竞争能力等感兴趣的性状组合在一起。对表型选择的模拟发现,将随机森林而非LASSO遗传模型和多性状而非单性状模型作为真正的遗传模型可以加速和延长长期遗传增益,同时保持遗传多样性。这些结果(1)表明了多效性和上位性在小麦育种计划的更广泛背景下的重要作用,(2)提供了在有限基因库中持续遗传增益的机制和优化作物改良的多种性状的见解。
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Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat
Abstract 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.
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来源期刊
in silico Plants
in silico Plants Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
4.70
自引率
9.70%
发文量
21
审稿时长
10 weeks
期刊最新文献
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