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首页> 外文期刊>PLoS Genetics >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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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

机译：水稻（ Oryza sativa ）的基因组选择和关联作图：性状遗传结构，训练种群组成，标记数和统计模型对优良热带水稻育种系中水稻基因组选择准确性的影响

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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. Author Summary 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.
机译：基因组选择（GS）是一种新的育种方法，其中使用全基因组标记来预测育种种群中个体的育种价值。 GS已被证明可以提高奶牛和几种农作物的育种效率，在这里我们首次评估了其在水稻自交系育种中的功效。我们对来自国际水稻研究所（IRRI）灌溉水稻育种计划的363个优良育种系的种群进行了全基因组关联研究（GWAS），并进行了五倍GS交叉验证，并在此报告了GS结果。通过测序对人群进行73,147种标记的基因分型。训练人群，用于建立GS模型的统计方法，标记数量和性状均会发生变化，以确定它们对预测准确性的影响。对于所有这三个特征，基因组预测模型的表现优于仅基于系谱记录的预测。谷物的产量和株高的预测精度范围为0.31和0.34，开花时间的预测精度范围为0.63。使用完整标记集的子集进行的分析表明，在此育种材料集中，每0.2 cM使用一个标记就足以进行基因组选择。对于谷物产量而言，RR-BLUP是表现最好的统计方法，其中GWAS未检测到大的QTL，而对于开花时间，当检测到单个非常大的QTL时，非GS多元线性回归方法优于GS模型。对于植物高度，通过GWAS确定了四个中型QTL，随机森林产生了最一致的GS模型。我们的研究结果表明，随着基因分型成本的不断降低，在GWAS对遗传结构和种群结构的解释的指导下，GS可能成为提高水稻育种效率的有效工具。作者摘要基因组选择是一种有前途的育种技术，旨在提高育种过程的效率和速度。尽管已证明它对小麦和玉米等农作物有效，但尚未应用于水稻育种。相比之下，全基因组关联研究（GWAS）用于鉴定基因或QTL，这些基因或QTL对育种具有重要意义，例如产量，开花时间或株高，并且已经在水稻中成功进行。在这里，我们尝试将基因组选择与GWAS一起应用于菲律宾国际水稻研究所的水稻育种计划，结果表明，基因组选择比单独的系谱数据可以对种系表现产生更准确的预测，并且GWAS结果可以告知GS结果。我们的结果表明，GS可能是提高水稻育种效率的有效工具。

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