生物
可预测性
基因组选择
现象
物候学
特质
最佳线性无偏预测
组学
选择(遗传算法)
生物技术
混合的
表型
计算生物学
基因组学
基因组
生物信息学
遗传学
基因
计算机科学
机器学习
农学
统计
数学
基因型
单核苷酸多态性
程序设计语言
作者
Yongsheng Xu,Yue Zhao,Xin Wang,Ying Ma,Pengcheng Li,Zefeng Yang,Xuecai Zhang,Chenwu Xu,Shizhong Xu
摘要
Summary Hybrid breeding has been shown to effectively increase rice productivity. However, identifying desirable hybrids out of numerous potential combinations is a daunting challenge. Genomic selection holds great promise for accelerating hybrid breeding by enabling early selection before phenotypes are measured. With the recent advances in multi‐omic technologies, hybrid prediction based on transcriptomic and metabolomic data has received increasing attention. However, the current omic‐based hybrid prediction has ignored parental phenotypic information, which is of fundamental importance in plant breeding. In this study, we integrated parental phenotypic information into various multi‐omic prediction models applied in hybrid breeding of rice and compared the predictabilities of 15 combinations from four sets of predictors from the parents, that is genome, transcriptome, metabolome and phenome. The predictability for each combination was evaluated using the best linear unbiased prediction and a modified fast HAT method. We found significant interactions between predictors and traits in predictability, but joint prediction with various combinations of the predictors significantly improved predictability relative to prediction of any single source omic data for each trait investigated. Incorporation of parental phenotypic data into various omic predictors increased the predictability, averagely by 13.6%, 54.5%, 19.9% and 8.3%, for grain yield, number of tillers per plant, number of grains per panicle and 1000 grain weight, respectively. Among nine models of incorporating parental traits, the AD‐All model was the most effective one. This novel strategy of incorporating parental phenotypic data into multi‐omic prediction is expected to improve hybrid breeding progress, especially with the development of high‐throughput phenotyping technologies.
科研通智能强力驱动
Strongly Powered by AbleSci AI