数字农科院2.0

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.)

文献类型： 外文期刊

作者： Yuanyuan Zhang;Mengchen Zhang;Junhua Ye;Qun Xu;Yue Feng;Siliang Xu;Dongxiu Hu;Xinghua Wei;Peisong Hu;Yaolong Yang

作者机构：

关键词： Genome-wide association study;Genotype-to-phenotype;Predictive ability;Statistical models

期刊名称： Molecular Breeding

ISSN： 1380-3743

年卷期： 2023 年 43 卷 11 期

页码：

收录情况： SCIE（2023版）

摘要： Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice.

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