Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding
Wenyu Yang, Tingting Guo, Jingyun Luo, Ruyang Zhang, Jiuran Zhao, Marilyn L. Warburton, Yingjie Xiao, Jianbing Yan
Abstract
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.