Data from: Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement

To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains.

为应对全球气候变化、人口增长与收入提升所带来的多重粮食安全挑战，植物育种工作者正研发可同时提升农业生产力与环境可持续性的新型作物品种。然而，当前的育种实践已无法匹配需求增速。基因组选择（Genomic Selection, GS）是一项新兴技术，通过全基因组数据预测子代育种价值，可加速育种中的遗传增益进程。本文介绍一种新型GS模型：将RR-BLUP与从针对RR-BLUP训练数据开展的全基因组关联研究（Genome-Wide Association Study, GWAS）结果中筛选出的固定效应标记相结合。我们将该模型命名为GS + 从头全基因组关联分析（de novo GWAS）。在热带水稻育种群体中，GS + 从头全基因组关联分析模型在多种性状及多个环境下的表现均优于其余六种模型。基于上述结果，我们提出一种拓展的两阶段育种设计，可高效将新遗传变异整合至优良育种群体中，从而扩大遗传多样性并提升可持续生产力增益的潜力。