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Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower
Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potenti...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5919744/ https://www.ncbi.nlm.nih.gov/pubmed/29255118 http://dx.doi.org/10.1534/g3.117.300199 |
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author | Thorwarth, Patrick Yousef, Eltohamy A. A. Schmid, Karl J. |
author_facet | Thorwarth, Patrick Yousef, Eltohamy A. A. Schmid, Karl J. |
author_sort | Thorwarth, Patrick |
collection | PubMed |
description | Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. |
format | Online Article Text |
id | pubmed-5919744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-59197442018-04-27 Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower Thorwarth, Patrick Yousef, Eltohamy A. A. Schmid, Karl J. G3 (Bethesda) Genomic Selection Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Genetics Society of America 2017-12-18 /pmc/articles/PMC5919744/ /pubmed/29255118 http://dx.doi.org/10.1534/g3.117.300199 Text en Copyright © 2018 Thorwarth et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Genomic Selection Thorwarth, Patrick Yousef, Eltohamy A. A. Schmid, Karl J. Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower |
title | Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower |
title_full | Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower |
title_fullStr | Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower |
title_full_unstemmed | Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower |
title_short | Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower |
title_sort | genomic prediction and association mapping of curd-related traits in gene bank accessions of cauliflower |
topic | Genomic Selection |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5919744/ https://www.ncbi.nlm.nih.gov/pubmed/29255118 http://dx.doi.org/10.1534/g3.117.300199 |
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