In:
PLOS Genetics, Public Library of Science (PLoS), Vol. 17, No. 11 ( 2021-11-4), p. e1009879-
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.
Type of Medium:
Online Resource
ISSN:
1553-7404
DOI:
10.1371/journal.pgen.1009879
DOI:
10.1371/journal.pgen.1009879.g001
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10.1371/journal.pgen.1009879.g002
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10.1371/journal.pgen.1009879.g003
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10.1371/journal.pgen.1009879.g004
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10.1371/journal.pgen.1009879.g005
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10.1371/journal.pgen.1009879.g006
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10.1371/journal.pgen.1009879.t001
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10.1371/journal.pgen.1009879.t002
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10.1371/journal.pgen.1009879.s001
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10.1371/journal.pgen.1009879.s002
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10.1371/journal.pgen.1009879.s003
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10.1371/journal.pgen.1009879.s004
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10.1371/journal.pgen.1009879.s005
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10.1371/journal.pgen.1009879.s023
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10.1371/journal.pgen.1009879.s026
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10.1371/journal.pgen.1009879.s028
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10.1371/journal.pgen.1009879.s029
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10.1371/journal.pgen.1009879.s030
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10.1371/journal.pgen.1009879.s031
DOI:
10.1371/journal.pgen.1009879.r001
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10.1371/journal.pgen.1009879.r002
DOI:
10.1371/journal.pgen.1009879.r003
DOI:
10.1371/journal.pgen.1009879.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2186725-2
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