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High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GB...

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Autores principales: Su, Chengfu, Wang, Wei, Gong, Shunliang, Zuo, Jinghui, Li, Shujiang, Xu, Shizhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420586/
https://www.ncbi.nlm.nih.gov/pubmed/28533786
http://dx.doi.org/10.3389/fpls.2017.00706
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author Su, Chengfu
Wang, Wei
Gong, Shunliang
Zuo, Jinghui
Li, Shujiang
Xu, Shizhong
author_facet Su, Chengfu
Wang, Wei
Gong, Shunliang
Zuo, Jinghui
Li, Shujiang
Xu, Shizhong
author_sort Su, Chengfu
collection PubMed
description Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GBS) for large-scale SNP discovery and simultaneous genotyping of all F(2) individuals from a cross between two varieties of maize that are in clear contrast in yield and related traits. A set of 199 F(2) progeny derived from the cross of varieties SG-5 and SG-7 were generated and genotyped by GBS. A total of 1,046,524,604 reads with an average of 5,258,918 reads per F(2) individual were generated. This number of reads represents an approximately 0.36-fold coverage of the maize reference genome Zea_mays.AGPv3.29 for each F(2) individual. A total of 68,882 raw SNPs were discovered in the F(2) population, which, after stringent filtering, led to a total of 29,927 high quality SNPs. Comparative analysis using these physically mapped marker loci revealed a higher degree of synteny with the reference genome. The SNP genotype data were utilized to construct an intra-specific genetic linkage map of maize consisting of 3,305 bins on 10 linkage groups spanning 2,236.66 cM at an average distance of 0.68 cM between consecutive markers. From this map, we identified 28 QTLs associated with yield traits (100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, corn grains per row, ear weight, and grain weight per plant) using the composite interval mapping (CIM) method and 29 QTLs using the least absolute shrinkage selection operator (LASSO) method. QTLs identified by the CIM method account for 6.4% to 19.7% of the phenotypic variation. Small intervals of three QTLs (qCGR-1, qKW-2, and qGWP-4) contain several genes, including one gene (GRMZM2G139872) encoding the F-box protein, three genes (GRMZM2G180811, GRMZM5G828139, and GRMZM5G873194) encoding the WD40-repeat protein, and one gene (GRMZM2G019183) encoding the UDP-Glycosyltransferase. The work will not only help to understand the mechanisms that control yield traits of maize, but also provide a basis for marker-assisted selection and map-based cloning in further studies.
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spelling pubmed-54205862017-05-22 High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology Su, Chengfu Wang, Wei Gong, Shunliang Zuo, Jinghui Li, Shujiang Xu, Shizhong Front Plant Sci Plant Science Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GBS) for large-scale SNP discovery and simultaneous genotyping of all F(2) individuals from a cross between two varieties of maize that are in clear contrast in yield and related traits. A set of 199 F(2) progeny derived from the cross of varieties SG-5 and SG-7 were generated and genotyped by GBS. A total of 1,046,524,604 reads with an average of 5,258,918 reads per F(2) individual were generated. This number of reads represents an approximately 0.36-fold coverage of the maize reference genome Zea_mays.AGPv3.29 for each F(2) individual. A total of 68,882 raw SNPs were discovered in the F(2) population, which, after stringent filtering, led to a total of 29,927 high quality SNPs. Comparative analysis using these physically mapped marker loci revealed a higher degree of synteny with the reference genome. The SNP genotype data were utilized to construct an intra-specific genetic linkage map of maize consisting of 3,305 bins on 10 linkage groups spanning 2,236.66 cM at an average distance of 0.68 cM between consecutive markers. From this map, we identified 28 QTLs associated with yield traits (100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, corn grains per row, ear weight, and grain weight per plant) using the composite interval mapping (CIM) method and 29 QTLs using the least absolute shrinkage selection operator (LASSO) method. QTLs identified by the CIM method account for 6.4% to 19.7% of the phenotypic variation. Small intervals of three QTLs (qCGR-1, qKW-2, and qGWP-4) contain several genes, including one gene (GRMZM2G139872) encoding the F-box protein, three genes (GRMZM2G180811, GRMZM5G828139, and GRMZM5G873194) encoding the WD40-repeat protein, and one gene (GRMZM2G019183) encoding the UDP-Glycosyltransferase. The work will not only help to understand the mechanisms that control yield traits of maize, but also provide a basis for marker-assisted selection and map-based cloning in further studies. Frontiers Media S.A. 2017-05-08 /pmc/articles/PMC5420586/ /pubmed/28533786 http://dx.doi.org/10.3389/fpls.2017.00706 Text en Copyright © 2017 Su, Wang, Gong, Zuo, Li and Xu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Su, Chengfu
Wang, Wei
Gong, Shunliang
Zuo, Jinghui
Li, Shujiang
Xu, Shizhong
High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology
title High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology
title_full High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology
title_fullStr High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology
title_full_unstemmed High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology
title_short High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology
title_sort high density linkage map construction and mapping of yield trait qtls in maize (zea mays) using the genotyping-by-sequencing (gbs) technology
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420586/
https://www.ncbi.nlm.nih.gov/pubmed/28533786
http://dx.doi.org/10.3389/fpls.2017.00706
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