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Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.)
Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase in the economic portion of the plant. Our objective was to identify genetic markers associated with harvest index traits using 203 O. sativa accessions. The phenotyping...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264563/ https://www.ncbi.nlm.nih.gov/pubmed/22291889 http://dx.doi.org/10.1371/journal.pone.0029350 |
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author | Li, Xiaobai Yan, Wengui Agrama, Hesham Jia, Limeng Jackson, Aaron Moldenhauer, Karen Yeater, Kathleen McClung, Anna Wu, Dianxing |
author_facet | Li, Xiaobai Yan, Wengui Agrama, Hesham Jia, Limeng Jackson, Aaron Moldenhauer, Karen Yeater, Kathleen McClung, Anna Wu, Dianxing |
author_sort | Li, Xiaobai |
collection | PubMed |
description | Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase in the economic portion of the plant. Our objective was to identify genetic markers associated with harvest index traits using 203 O. sativa accessions. The phenotyping for 14 traits was conducted in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used 154 SSRs and an indel marker. Heading, plant height and weight, and panicle length had negative correlations, while seed set and grain weight/panicle had positive correlations with harvest index across both locations. Subsequent genetic diversity and population structure analyses identified five groups in this collection, which corresponded to their geographic origins. Model comparisons revealed that different dimensions of principal components analysis (PCA) affected harvest index traits for mapping accuracy, and kinship did not help. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were constitutively identified at both locations, while 32 and 24 markers were identified specifically in Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele 253 bp of RM431 had significantly greater effect on decreasing plant height, and 390 bp of RM24011 had the greatest effect on decreasing panicle length across both locations. Many of these identified markers are located either nearby or flanking the regions where the QTLs for harvest index have been reported. Thus, the results from this association mapping study complement and enrich the information from linkage-based QTL studies and will be the basis for improving harvest index directly and indirectly in rice. |
format | Online Article Text |
id | pubmed-3264563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-32645632012-01-30 Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) Li, Xiaobai Yan, Wengui Agrama, Hesham Jia, Limeng Jackson, Aaron Moldenhauer, Karen Yeater, Kathleen McClung, Anna Wu, Dianxing PLoS One Research Article Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase in the economic portion of the plant. Our objective was to identify genetic markers associated with harvest index traits using 203 O. sativa accessions. The phenotyping for 14 traits was conducted in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used 154 SSRs and an indel marker. Heading, plant height and weight, and panicle length had negative correlations, while seed set and grain weight/panicle had positive correlations with harvest index across both locations. Subsequent genetic diversity and population structure analyses identified five groups in this collection, which corresponded to their geographic origins. Model comparisons revealed that different dimensions of principal components analysis (PCA) affected harvest index traits for mapping accuracy, and kinship did not help. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were constitutively identified at both locations, while 32 and 24 markers were identified specifically in Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele 253 bp of RM431 had significantly greater effect on decreasing plant height, and 390 bp of RM24011 had the greatest effect on decreasing panicle length across both locations. Many of these identified markers are located either nearby or flanking the regions where the QTLs for harvest index have been reported. Thus, the results from this association mapping study complement and enrich the information from linkage-based QTL studies and will be the basis for improving harvest index directly and indirectly in rice. Public Library of Science 2012-01-23 /pmc/articles/PMC3264563/ /pubmed/22291889 http://dx.doi.org/10.1371/journal.pone.0029350 Text en This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Li, Xiaobai Yan, Wengui Agrama, Hesham Jia, Limeng Jackson, Aaron Moldenhauer, Karen Yeater, Kathleen McClung, Anna Wu, Dianxing Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) |
title | Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) |
title_full | Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) |
title_fullStr | Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) |
title_full_unstemmed | Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) |
title_short | Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.) |
title_sort | unraveling the complex trait of harvest index with association mapping in rice (oryza sativa l.) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264563/ https://www.ncbi.nlm.nih.gov/pubmed/22291889 http://dx.doi.org/10.1371/journal.pone.0029350 |
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