Incorporation of parental phenotypic data into multi‐omic models improves prediction of yield‐related traits in hybrid rice

dc.creatorYang Xu
dc.creatorYue Zhao
dc.creatorXin Wang
dc.creatorYing Ma
dc.creatorPengcheng Li
dc.creatorZefeng Yang
dc.creatorZhang, X.
dc.creatorChenwu Xu
dc.creatorShizhong Xu
dc.date2020-12-09T01:05:17Z
dc.date2020-12-09T01:05:17Z
dc.date2021
dc.date.accessioned2023-07-17T20:06:28Z
dc.date.available2023-07-17T20:06:28Z
dc.identifierhttps://hdl.handle.net/10883/21060
dc.identifier10.1111/pbi.13458
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512846
dc.descriptionHybrid 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.
dc.description261-272
dc.languageEnglish
dc.publisherWiley
dc.relationhttps://onlinelibrary.wiley.com/doi/10.1111/pbi.13458#pbi13458-sec-0017-title
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source2
dc.source19
dc.source1467-7652
dc.sourcePlant Biotechnology Journal
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenomic Selection
dc.subjectMulti-Omics Data
dc.subjectParental Traits
dc.subjectBest Linear Unbiased Prediction
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectRICE
dc.subjectHYBRIDS
dc.subjectBEST LINEAR UNBIASED PREDICTOR
dc.titleIncorporation of parental phenotypic data into multi‐omic models improves prediction of yield‐related traits in hybrid rice
dc.typeArticle
dc.typePublished Version
dc.coverageUnited Kingdom


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