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An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize

BACKGROUND: To safeguard the food supply for the growing human population, it is important to understand and exploit the genetic basis of quantitative traits. Next-generation sequencing technology performs advantageously and effectively in genetic mapping and genome analysis of diverse genetic resou...

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Autores principales: Liu, Hongjun, Niu, Yongchao, Gonzalez-Portilla, Pedro J., Zhou, Huangkai, Wang, Liya, Zuo, Tao, Qin, Cheng, Tai, Shuaishuai, Jansen, Constantin, Shen, Yaou, Lin, Haijian, Lee, Michael, Ware, Doreen, Zhang, Zhiming, Lübberstedt, Thomas, Pan, Guangtang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687334/
https://www.ncbi.nlm.nih.gov/pubmed/26691201
http://dx.doi.org/10.1186/s12864-015-2242-5
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author Liu, Hongjun
Niu, Yongchao
Gonzalez-Portilla, Pedro J.
Zhou, Huangkai
Wang, Liya
Zuo, Tao
Qin, Cheng
Tai, Shuaishuai
Jansen, Constantin
Shen, Yaou
Lin, Haijian
Lee, Michael
Ware, Doreen
Zhang, Zhiming
Lübberstedt, Thomas
Pan, Guangtang
author_facet Liu, Hongjun
Niu, Yongchao
Gonzalez-Portilla, Pedro J.
Zhou, Huangkai
Wang, Liya
Zuo, Tao
Qin, Cheng
Tai, Shuaishuai
Jansen, Constantin
Shen, Yaou
Lin, Haijian
Lee, Michael
Ware, Doreen
Zhang, Zhiming
Lübberstedt, Thomas
Pan, Guangtang
author_sort Liu, Hongjun
collection PubMed
description BACKGROUND: To safeguard the food supply for the growing human population, it is important to understand and exploit the genetic basis of quantitative traits. Next-generation sequencing technology performs advantageously and effectively in genetic mapping and genome analysis of diverse genetic resources. Hence, we combined re-sequencing technology and a bin map strategy to construct an ultra-high-density bin map with thousands of bin markers to precisely map a quantitative trait locus. RESULTS: In this study, we generated a linkage map containing 1,151,856 high quality SNPs between Mo17 and B73, which were verified in the maize intermated B73 × Mo17 (IBM) Syn10 population. This resource is an excellent complement to existing maize genetic maps available in an online database  (iPlant, http://data.maizecode.org/maize/qtl/syn10/). Moreover, in this population combined with the IBM Syn4 RIL population, we detected 135 QTLs for flowering time and plant height traits across the two populations. Eighteen known functional genes and twenty-five candidate genes for flowering time and plant height trait were fine-mapped into a 2.21–4.96 Mb interval. Map expansion and segregation distortion were also analyzed, and evidence for inadvertent selection of early flowering time in the process of mapping population development was observed. Furthermore, an updated integrated map with 1,151,856 high-quality SNPs, 2,916 traditional markers and 6,618 bin markers was constructed. The data were deposited into the iPlant Discovery Environment (DE), which provides a fundamental resource of genetic data for the maize genetic research community. CONCLUSIONS: Our findings provide basic essential genetic data for the maize genetic research community. An updated IBM Syn10 population and a reliable, verified high-quality SNP set between Mo17 and B73 will aid in future molecular breeding efforts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-2242-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-46873342015-12-23 An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize Liu, Hongjun Niu, Yongchao Gonzalez-Portilla, Pedro J. Zhou, Huangkai Wang, Liya Zuo, Tao Qin, Cheng Tai, Shuaishuai Jansen, Constantin Shen, Yaou Lin, Haijian Lee, Michael Ware, Doreen Zhang, Zhiming Lübberstedt, Thomas Pan, Guangtang BMC Genomics Research Article BACKGROUND: To safeguard the food supply for the growing human population, it is important to understand and exploit the genetic basis of quantitative traits. Next-generation sequencing technology performs advantageously and effectively in genetic mapping and genome analysis of diverse genetic resources. Hence, we combined re-sequencing technology and a bin map strategy to construct an ultra-high-density bin map with thousands of bin markers to precisely map a quantitative trait locus. RESULTS: In this study, we generated a linkage map containing 1,151,856 high quality SNPs between Mo17 and B73, which were verified in the maize intermated B73 × Mo17 (IBM) Syn10 population. This resource is an excellent complement to existing maize genetic maps available in an online database  (iPlant, http://data.maizecode.org/maize/qtl/syn10/). Moreover, in this population combined with the IBM Syn4 RIL population, we detected 135 QTLs for flowering time and plant height traits across the two populations. Eighteen known functional genes and twenty-five candidate genes for flowering time and plant height trait were fine-mapped into a 2.21–4.96 Mb interval. Map expansion and segregation distortion were also analyzed, and evidence for inadvertent selection of early flowering time in the process of mapping population development was observed. Furthermore, an updated integrated map with 1,151,856 high-quality SNPs, 2,916 traditional markers and 6,618 bin markers was constructed. The data were deposited into the iPlant Discovery Environment (DE), which provides a fundamental resource of genetic data for the maize genetic research community. CONCLUSIONS: Our findings provide basic essential genetic data for the maize genetic research community. An updated IBM Syn10 population and a reliable, verified high-quality SNP set between Mo17 and B73 will aid in future molecular breeding efforts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-2242-5) contains supplementary material, which is available to authorized users. BioMed Central 2015-12-21 /pmc/articles/PMC4687334/ /pubmed/26691201 http://dx.doi.org/10.1186/s12864-015-2242-5 Text en © Liu et al. 2015 Open AccessThis article is 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 you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Liu, Hongjun
Niu, Yongchao
Gonzalez-Portilla, Pedro J.
Zhou, Huangkai
Wang, Liya
Zuo, Tao
Qin, Cheng
Tai, Shuaishuai
Jansen, Constantin
Shen, Yaou
Lin, Haijian
Lee, Michael
Ware, Doreen
Zhang, Zhiming
Lübberstedt, Thomas
Pan, Guangtang
An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
title An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
title_full An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
title_fullStr An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
title_full_unstemmed An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
title_short An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
title_sort ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687334/
https://www.ncbi.nlm.nih.gov/pubmed/26691201
http://dx.doi.org/10.1186/s12864-015-2242-5
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