Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

基因组选择（Genomic Selection, GS）是一种新型育种方法，通过全基因组标记预测育种群体内个体的育种值。已有研究证实，GS可提升奶牛及多种作物的育种效率，本研究首次评估其在水稻自交系育种中的应用效能。我们针对国际水稻研究所（IRRI）灌溉水稻育种项目的363份优良育种系群体，结合全基因组关联分析（Genome-Wide Association Study, GWAS）与五折GS交叉验证开展实验，本文报告了GS的研究结果。该群体通过测序分型（genotyping-by-sequencing, GBS）技术完成了73147个分子标记的基因型鉴定。我们通过调整训练群体、构建GS模型所用的统计方法、标记数量及目标性状，探究各因素对预测精度的影响。针对全部三类目标性状，基因组预测模型的表现均优于仅基于系谱记录的预测方法。预测精度区间为：籽粒产量与株高的0.31至0.34，抽穗期可达0.63。基于全标记集子集的分析显示，每0.2厘摩（cM）选取一个标记，即可满足本水稻育种材料集合的基因组选择需求。对于未通过GWAS检测到大效应数量性状位点（Quantitative Trait Locus, QTL）的籽粒产量性状，RR-BLUP是表现最优的统计方法；而对于检测到单个超大效应QTL的抽穗期，非GS的多元线性回归方法的预测效能优于GS模型。对于GWAS鉴定出4个中等效应QTL的株高性状，随机森林可构建出一致性最佳的精准GS模型。本研究结果表明，结合GWAS对遗传架构与群体结构的解析，随着基因分型成本的持续降低，基因组选择有望成为提升水稻育种效率的有效工具。