Document Type
Article
Abstract
The utilization of heterosis is a successful strategy in increasing yield for many crops. However, it consumes tremendous manpower to test the combining ability of the parents in fields. Here, we applied the genomic-selection (GS) strategy and developed models that significantly increase the predictability of heterosis by introducing the concept of a regional parental genetic-similarity index (PGSI) and reducing dimension in the calculation matrix in a machine-learning approach. Overall, PGSI negatively affected grain yield and several other traits but positively influenced the thousand-seed weight of the hybrids. It was found that the C subgenome of rapeseed had a greater impact on heterosis than the A subgenome. We drew maps with overviews of quantitative-trait loci that were responsible for the heterosis (h-QTLs) of various agronomic traits. Identifications and annotations of genes underlying high impacting h-QTLs were provided. Using models that we elaborated, combining abilities between an Ogu-CMS-pool member and a potential restorer can be simulated in silico, sidestepping laborious work, such as testing crosses in fields. The achievements here provide a case of heterosis prediction in polyploid genomes with relatively large genome sizes.
Digital Object Identifier (DOI)
Publication Info
Published in PLoS Genetics, Volume 17, Issue 11, 2021, pages e1009879-.
APA Citation
Wang, Q., Yan, T., Long, Z., Huang, L. Y., Zhu, Y., Xu, Y., Chen, X., Pak, H., Li, J., Wu, D., Xu, Y., Hua, S., & Jiang, L. (2021). Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences. PLoS Genetics, 17(11), e1009879.https://doi.org/10.1371/journal.pgen.1009879
Rights
© 2021 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.