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Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding

文献类型： 外文期刊

作者： Yang, Wenyu 1 ; Guo, Tingting 3 ; Luo, Jingyun 1 ; Zhang, Ruyang 4 ; Zhao, Jiuran 4 ; Warburton, Marilyn L. 5 ; Xiao, Yingjie 1 ; Yan, Jianbing 1 ;

作者机构： 1.Huazhong Agr Univ, Natl Key Lab Crop Genet Improvement, Wuhan 430070, Peoples R China

2.Huazhong Agr Univ, Coll Sci, Wuhan 430070, Peoples R China

3.Hubei Hongshan Lab, Wuhan 430070, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Beijing Key Lab Maize DNA Fingerprinting & Mol Br, Beijing 100097, Peoples R China

5.USDA ARS, Corn Host Plant Resistance Res Unit, Box 9555, Mississippi State, MS 39762 USA

关键词： Crop breeding; Multiple traits; Genomic prediction; Omics; Machine learning

期刊名称：GENOME BIOLOGY （ 影响因子：17.906； 五年影响因子：20.367 ）

ISSN： 1474-760X

年卷期： 2022 年 23 卷 1 期

页码：

收录情况： SCI

摘要： Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.

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