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 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.
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DOI：
10.1371/journal.pgen.1004982
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发表时间：
2015-02
期刊：
影响因子：
4.5
通讯作者：
McCouch SR
中科院分区：
文献类型：
--
作者：
Spindel J;Begum H;Akdemir D;Virk P;Collard B;Redoña E;Atlin G;Jannink JL;McCouch SR
基因组选择是利用全基因组标记来预测育种群体中个体的育种价值的一种新的育种方法。GS已被证明能提高奶牛和几种作物的育种效率，在这里，我们首次评估了它在水稻自交系育种中的效果。我们对来自国际水稻研究所(IRRI)灌溉水稻育种计划的363个优良育种系进行了全基因组关联研究(GWAS)和五倍GS交叉验证，并在此报告了GS结果。用73,147个标记通过测序进行基因分型。不同的训练群体、建立GS模型所用的统计方法、标记数目和性状都不同，以确定它们对预测精度的影响。对于所有这三个性状，基因组预测模型的预测效果都好于仅基于系谱记录的预测。对产量和株高的预测精度分别为0.31和0.34，对开花期的预测精度为0.63。利用整个标记集的子集进行的分析表明，在这个水稻育种材料集合中，每0.2 cM使用一个标记就足以进行基因组选择。对于产量，RR-BLUP是最好的统计方法，GWAS没有检测到大效应QTL，而对于开花期，如果检测到单个非常大的QTL，非GS多元线性回归方法的效果好于GS模型。在株高方面，随机林得到了4个中等大小的QTL，其中随机林的GS模型一致性最高。我们的结果表明，随着基因分型成本的持续下降，GS可能成为提高水稻遗传结构和群体结构解释的有效工具。基因组选择是一种很有前途的育种技术，旨在提高育种过程的效率和速度。虽然它已经被证明在小麦和玉米等作物上有效，但它还没有被应用于水稻育种。相比之下，全基因组关联研究被用来确定构成育种重要性状(如产量、开花时间或株高)的基因或QTL，并已在水稻中成功实施。在这里，我们在菲律宾国际水稻研究所的一个水稻育种项目中进行了将基因组选择与GWAS相结合的实验，结果表明，与仅使用系谱数据相比，基因组选择可以产生更准确的育种系表现预测，并且Gwas结果可以为GS的结果提供信息。我们的结果表明，GS可能是提高水稻育种效率的有效工具。
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. Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.
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