Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content

文献类型： 外文期刊

第一作者： Sun, Bo

作者： Sun, Bo;Guo, Rui;Liu, Zhi;Shi, Xiaolei;Yang, Qing;Shi, Jiayao;Zhang, Mengchen;Yang, Chunyan;He, Jianhan;Yan, Long;Sun, Bo;Zhao, Shugang;Zhang, Jie;Zhang, Jiaoping;Zhang, Jiaoping;Su, Jianhui;Song, Qijian

作者机构：

关键词： soybean; protein content; oil content; GS; prediction accuracy

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： IntroductionGenomic selection (GS) is a potential breeding approach for soybean improvement. MethodsIn this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. ResultsOur results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log(10)(P) greater than 1 were included. DiscussionThis information will help improve GS efficiency and facilitate the application of GS.

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