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
作者： Sun, Bo 1 ; Guo, Rui 1 ; Liu, Zhi 1 ; Shi, Xiaolei 1 ; Yang, Qing 1 ; Shi, Jiayao 1 ; Zhang, Mengchen 1 ; Yang, Chunyan 1 ; Zhao, Shugang 2 ; Zhang, Jie 2 ; He, Jianhan 1 ; Zhang, Jiaoping 3 ; Su, Jianhui 5 ; Song, Qijian 6 ; Yan, Long 1 ;
作者机构： 1.Hebei Acad Agr & Forestry Sci, Inst Cereal & Oil Crops, Key Lab Crop Genet & Breeding, Shijiazhuang Branch Ctr,Natl Ctr Soybean Improveme, Shijiazhuang, Peoples R China
2.Hebei Agr Univ, Coll Life Sci, Baoding, Peoples R China
3.Natl Ctr Soybean Improvement, State Key Lab Crop Genet & Germplasm Enhancement, Nanjing, Peoples R China
4.Nanjing Agr Univ, Key Lab Biol & Genet Improvement Soybean, Gen, Minist Agr, Nanjing, Peoples R China
5.Agr Regionalizat Workstn Shijiazhuangs Gaocheng Di, Shijiazhuang, Peoples R China
6.ARS, Soybean Genom & Improvement Lab, USDA, Beltsville, MD 20705 USA
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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