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Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections

Detecting QTLs (quantitative trait loci) that enhance cotton yield and fiber quality traits and accelerate breeding has been the focus of many cotton breeders. In the present study, 359 SSR (simple sequence repeat) markers were used for the association mapping of 241 Upland cotton collections. A tot...

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Autores principales: Qin, Hongde, Chen, Min, Yi, Xianda, Bie, Shu, Zhang, Cheng, Zhang, Youchang, Lan, Jiayang, Meng, Yanyan, Yuan, Youlu, Jiao, Chunhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311988/
https://www.ncbi.nlm.nih.gov/pubmed/25635680
http://dx.doi.org/10.1371/journal.pone.0118073
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author Qin, Hongde
Chen, Min
Yi, Xianda
Bie, Shu
Zhang, Cheng
Zhang, Youchang
Lan, Jiayang
Meng, Yanyan
Yuan, Youlu
Jiao, Chunhai
author_facet Qin, Hongde
Chen, Min
Yi, Xianda
Bie, Shu
Zhang, Cheng
Zhang, Youchang
Lan, Jiayang
Meng, Yanyan
Yuan, Youlu
Jiao, Chunhai
author_sort Qin, Hongde
collection PubMed
description Detecting QTLs (quantitative trait loci) that enhance cotton yield and fiber quality traits and accelerate breeding has been the focus of many cotton breeders. In the present study, 359 SSR (simple sequence repeat) markers were used for the association mapping of 241 Upland cotton collections. A total of 333 markers, representing 733 polymorphic loci, were detected. The average linkage disequilibrium (LD) decay distances were 8.58 cM (r(2) > 0.1) and 5.76 cM (r(2) > 0.2). 241 collections were arranged into two subgroups using STRUCTURE software. Mixed linear modeling (MLM) methods (with population structure (Q) and relative kinship matrix (K)) were applied to analyze four phenotypic datasets obtained from four environments (two different locations and two years). Forty-six markers associated with the number of bolls per plant (NB), boll weight (BW), lint percentage (LP), fiber length (FL), fiber strength (FS) and fiber micornaire value (FM) were repeatedly detected in at least two environments. Of 46 associated markers, 32 were identified as new association markers, and 14 had been previously reported in the literature. Nine association markers were near QTLs (at a distance of less than 1–2 LD decay on the reference map) that had been previously described. These results provide new useful markers for marker-assisted selection in breeding programs and new insights for understanding the genetic basis of Upland cotton yields and fiber quality traits at the whole-genome level.
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spelling pubmed-43119882015-02-13 Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections Qin, Hongde Chen, Min Yi, Xianda Bie, Shu Zhang, Cheng Zhang, Youchang Lan, Jiayang Meng, Yanyan Yuan, Youlu Jiao, Chunhai PLoS One Research Article Detecting QTLs (quantitative trait loci) that enhance cotton yield and fiber quality traits and accelerate breeding has been the focus of many cotton breeders. In the present study, 359 SSR (simple sequence repeat) markers were used for the association mapping of 241 Upland cotton collections. A total of 333 markers, representing 733 polymorphic loci, were detected. The average linkage disequilibrium (LD) decay distances were 8.58 cM (r(2) > 0.1) and 5.76 cM (r(2) > 0.2). 241 collections were arranged into two subgroups using STRUCTURE software. Mixed linear modeling (MLM) methods (with population structure (Q) and relative kinship matrix (K)) were applied to analyze four phenotypic datasets obtained from four environments (two different locations and two years). Forty-six markers associated with the number of bolls per plant (NB), boll weight (BW), lint percentage (LP), fiber length (FL), fiber strength (FS) and fiber micornaire value (FM) were repeatedly detected in at least two environments. Of 46 associated markers, 32 were identified as new association markers, and 14 had been previously reported in the literature. Nine association markers were near QTLs (at a distance of less than 1–2 LD decay on the reference map) that had been previously described. These results provide new useful markers for marker-assisted selection in breeding programs and new insights for understanding the genetic basis of Upland cotton yields and fiber quality traits at the whole-genome level. Public Library of Science 2015-01-30 /pmc/articles/PMC4311988/ /pubmed/25635680 http://dx.doi.org/10.1371/journal.pone.0118073 Text en © 2015 Qin et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Qin, Hongde
Chen, Min
Yi, Xianda
Bie, Shu
Zhang, Cheng
Zhang, Youchang
Lan, Jiayang
Meng, Yanyan
Yuan, Youlu
Jiao, Chunhai
Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections
title Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections
title_full Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections
title_fullStr Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections
title_full_unstemmed Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections
title_short Identification of Associated SSR Markers for Yield Component and Fiber Quality Traits Based on Frame Map and Upland Cotton Collections
title_sort identification of associated ssr markers for yield component and fiber quality traits based on frame map and upland cotton collections
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311988/
https://www.ncbi.nlm.nih.gov/pubmed/25635680
http://dx.doi.org/10.1371/journal.pone.0118073
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