Enhancing genomic prediction with genome-wide association studies in multiparental maize populations

Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the who...利用高密度标记集开展的全基因组关联作图（Genome-wide association mapping）已成功鉴定出部分影响复杂性状的核苷酸变异，并通过精细定位（fine-mapping）与功能分析（functional analysis）完成验证。然而，玉米中与复杂性状相关的诸多序列变异往往效应微弱且重复性较低。与全基因组关联研究（Genome-wide Association Study, GWAS）不同，基因组预测（Genomic Prediction, GP）通常基于整合了所有可用标记信息的模型，而非针对单个基因座的效应进行建模。本研究探讨了在多个相互关联的家系背景下，将GWAS结果整合至GP模型中的方法。我们比较了两种关联检验策略：一种基于双等位基因加性模型（biallelic additive model），将单核苷酸多态性（Single-nucleotide polymorphism, SNP）在其分离的所有家系中的效应设为恒定；另一种则允许SNP效应在不同家系间存在差异。随后，将经关联分析筛选得到的SNP作为固定效应纳入GP模型，该模型同时包含全……