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Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs
Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel o...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182660/ https://www.ncbi.nlm.nih.gov/pubmed/35684219 http://dx.doi.org/10.3390/plants11111446 |
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author | Billings, Grant T. Jones, Michael A. Rustgi, Sachin Bridges, William C. Holland, James B. Hulse-Kemp, Amanda M. Campbell, B. Todd |
author_facet | Billings, Grant T. Jones, Michael A. Rustgi, Sachin Bridges, William C. Holland, James B. Hulse-Kemp, Amanda M. Campbell, B. Todd |
author_sort | Billings, Grant T. |
collection | PubMed |
description | Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era. |
format | Online Article Text |
id | pubmed-9182660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91826602022-06-10 Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs Billings, Grant T. Jones, Michael A. Rustgi, Sachin Bridges, William C. Holland, James B. Hulse-Kemp, Amanda M. Campbell, B. Todd Plants (Basel) Article Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era. MDPI 2022-05-29 /pmc/articles/PMC9182660/ /pubmed/35684219 http://dx.doi.org/10.3390/plants11111446 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Billings, Grant T. Jones, Michael A. Rustgi, Sachin Bridges, William C. Holland, James B. Hulse-Kemp, Amanda M. Campbell, B. Todd Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs |
title | Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs |
title_full | Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs |
title_fullStr | Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs |
title_full_unstemmed | Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs |
title_short | Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs |
title_sort | outlook for implementation of genomics-based selection in public cotton breeding programs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182660/ https://www.ncbi.nlm.nih.gov/pubmed/35684219 http://dx.doi.org/10.3390/plants11111446 |
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