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Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population
Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic...
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/PMC5473752/ https://www.ncbi.nlm.nih.gov/pubmed/28592653 http://dx.doi.org/10.1534/g3.117.042101 |
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author | Raghavan, Chitra Mauleon, Ramil Lacorte, Vanica Jubay, Monalisa Zaw, Hein Bonifacio, Justine Singh, Rakesh Kumar Huang, B. Emma Leung, Hei |
author_facet | Raghavan, Chitra Mauleon, Ramil Lacorte, Vanica Jubay, Monalisa Zaw, Hein Bonifacio, Justine Singh, Rakesh Kumar Huang, B. Emma Leung, Hei |
author_sort | Raghavan, Chitra |
collection | PubMed |
description | Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance), and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations. |
format | Online Article Text |
id | pubmed-5473752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-54737522017-06-27 Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population Raghavan, Chitra Mauleon, Ramil Lacorte, Vanica Jubay, Monalisa Zaw, Hein Bonifacio, Justine Singh, Rakesh Kumar Huang, B. Emma Leung, Hei G3 (Bethesda) Multiparental Population Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance), and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations. Genetics Society of America 2017-06-05 /pmc/articles/PMC5473752/ /pubmed/28592653 http://dx.doi.org/10.1534/g3.117.042101 Text en Copyright © 2017 Raghavan 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 | Multiparental Population Raghavan, Chitra Mauleon, Ramil Lacorte, Vanica Jubay, Monalisa Zaw, Hein Bonifacio, Justine Singh, Rakesh Kumar Huang, B. Emma Leung, Hei Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population |
title | Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population |
title_full | Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population |
title_fullStr | Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population |
title_full_unstemmed | Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population |
title_short | Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population |
title_sort | approaches in characterizing genetic structure and mapping in a rice multiparental population |
topic | Multiparental Population |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473752/ https://www.ncbi.nlm.nih.gov/pubmed/28592653 http://dx.doi.org/10.1534/g3.117.042101 |
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