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Genome-wide association study and genomic prediction for resistance to brown planthopper in rice

文献类型： 外文期刊

作者： Zhou, Cong 1 ; Jiang, Weihua 2 ; Guo, Jianping 2 ; Zhu, Lili 2 ; Liu, Lijiang 1 ; Liu, Shengyi 1 ; Chen, Rongzhi 2 ; Du, Bo 2 ; Huang, Jin 3 ;

作者机构： 1.Chinese Acad Agr Sci, Key Lab Biol & Genet Improvement Oil Crops, Minist Agr & Rural Affairs, Oil Crops Res Inst, Wuhan, Peoples R China

2.Wuhan Univ, Coll Life Sci, State Key Lab Hybrid Rice, Wuhan, Peoples R China

3.Hubei Acad Agr Sci, Cash Crops Res Inst, Wuhan, Peoples R China

关键词： rice; brown planthopper; GWAS; candidate genes; genomic prediction

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： The brown planthopper (BPH) is the most destructive insect pest that threatens rice production globally. Developing rice varieties incorporating BPH-resistant genes has proven to be an effective control measure against BPH. In this study, we assessed the resistance of a core collection consisting of 502 rice germplasms by evaluating resistance scores, weight gain rates and honeydew excretions. A total of 117 rice varieties (23.31%) exhibited resistance to BPH. Genome-wide association studies (GWAS) were performed on both the entire panel of 502 rice varieties and its subspecies, and 6 loci were significantly associated with resistance scores (P value < 1.0e(-8)). Within these loci, we identified eight candidate genes encoding receptor-like protein kinase (RLK), nucleotide-binding and leucine-rich repeat (NB-LRR), or LRR proteins. Two loci had not been detected in previous study and were entirely novel. Furthermore, we evaluated the predictive ability of genomic selection for resistance to BPH. The results revealed that the highest prediction accuracy for BPH resistance reached 0.633. As expected, the prediction accuracy increased progressively with an increasing number of SNPs, and a total of 6.7K SNPs displayed comparable accuracy to 268K SNPs. Among various statistical models tested, the random forest model exhibited superior predictive accuracy. Moreover, increasing the size of training population improved prediction accuracy; however, there was no significant difference in prediction accuracy between a training population size of 737 and 1179. Additionally, when there existed close genetic relatedness between the training and validation populations, higher prediction accuracies were observed compared to scenarios when they were genetically distant. These findings provide valuable resistance candidate genes and germplasm resources and are crucial for the application of genomic selection for breeding durable BPH-resistant rice varieties.

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