Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat-猫眼课题宝

DOI：

10.1101/2022.11.08.515457

发表时间：

2022-11

期刊：

bioRxiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

N. Fradgley;Keith A. Gardner;A. Bentley;P. Howell;I. Mackay;M. F. Scott;R. Mott;J. Cockram

谷类作物育种者在保持遗传多样性的同时，在遗传复杂性状（如粮食产量）方面取得了相当大的遗传增益。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选择，以及如何优化选择，我们分析了来自16个不同的小麦多亲本高级代杂交（MAGIC）群体的基因型和表型数据。与单性状预测模型相比，多性状集合基因组预测模型提高了近90%的性状预测精度，提高了3-52%的产量预测精度。对于复杂性状，非参数模型（Random Forest）也优于简化的加性模型（LASSO），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，相关性状的间接响应涉及拮抗性状关系的优化。我们发现，多性状选择指数可以用来优化不理想的关系，如粮食产量和蛋白质含量之间的权衡，或组合感兴趣的性状，如产量和杂草竞争能力。对表型选择的模拟发现，采用随机森林而不是LASSO遗传模型，采用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遗传增益。这些结果表明，多效性和上位性在小麦育种计划的更广泛背景下发挥着重要作用，并为在有限的基因库中持续遗传增益的机制和优化作物改良的多性状提供了见解。

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 selection within a breeding context, and how it might be optimised, we analysed genotypic and phenotypic data from a diverse, 16-founder wheat multi-parent advanced generation inter-cross (MAGIC) 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 optimised long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, which involved optimisation of antagonistic trait relationships. We found multi-trait selection indices could be used to optimise 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 suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes and provide insights into mechanisms for continued genetic gain in a limited genepool and optimisation of multiple traits for crop improvement.

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat

文献摘要

相似文献