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Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population

High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 sin...

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Autores principales: Serin, Elise A. R., Snoek, L. B., Nijveen, Harm, Willems, Leo A. J., Jiménez-Gómez, Jose M., Hilhorst, Henk W. M., Ligterink, Wilco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5723289/
https://www.ncbi.nlm.nih.gov/pubmed/29259624
http://dx.doi.org/10.3389/fgene.2017.00201
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author Serin, Elise A. R.
Snoek, L. B.
Nijveen, Harm
Willems, Leo A. J.
Jiménez-Gómez, Jose M.
Hilhorst, Henk W. M.
Ligterink, Wilco
author_facet Serin, Elise A. R.
Snoek, L. B.
Nijveen, Harm
Willems, Leo A. J.
Jiménez-Gómez, Jose M.
Hilhorst, Henk W. M.
Ligterink, Wilco
author_sort Serin, Elise A. R.
collection PubMed
description High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 single-nucleotide polymorphisms (SNPs) covering the whole genome. Based on a 100-kbp window SNP binning method, 1059 bin-markers were identified, physically anchored on the genome. The total length of the RNA-seq genetic map spans 471.70 centimorgans (cM) with an average marker distance of 0.45 cM and a maximum marker distance of 4.81 cM. This high resolution genotyping revealed new recombination breakpoints in the population. To highlight the advantages of such high-density map, we compared it to two publicly available genetic maps for the same population, comprising 69 PCR-based markers and 497 gene expression markers derived from microarray data, respectively. In this study, we show that SNP markers can effectively be derived from RNA-seq data. The new RNA-seq map closes many existing gaps in marker coverage, saturating the previously available genetic maps. Quantitative trait locus (QTL) analysis for published phenotypes using the available genetic maps showed increased QTL mapping resolution and reduced QTL confidence interval using the RNA-seq map. The new high-density map is a valuable resource that facilitates the identification of candidate genes and map-based cloning approaches.
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spelling pubmed-57232892017-12-19 Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population Serin, Elise A. R. Snoek, L. B. Nijveen, Harm Willems, Leo A. J. Jiménez-Gómez, Jose M. Hilhorst, Henk W. M. Ligterink, Wilco Front Genet Genetics High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 single-nucleotide polymorphisms (SNPs) covering the whole genome. Based on a 100-kbp window SNP binning method, 1059 bin-markers were identified, physically anchored on the genome. The total length of the RNA-seq genetic map spans 471.70 centimorgans (cM) with an average marker distance of 0.45 cM and a maximum marker distance of 4.81 cM. This high resolution genotyping revealed new recombination breakpoints in the population. To highlight the advantages of such high-density map, we compared it to two publicly available genetic maps for the same population, comprising 69 PCR-based markers and 497 gene expression markers derived from microarray data, respectively. In this study, we show that SNP markers can effectively be derived from RNA-seq data. The new RNA-seq map closes many existing gaps in marker coverage, saturating the previously available genetic maps. Quantitative trait locus (QTL) analysis for published phenotypes using the available genetic maps showed increased QTL mapping resolution and reduced QTL confidence interval using the RNA-seq map. The new high-density map is a valuable resource that facilitates the identification of candidate genes and map-based cloning approaches. Frontiers Media S.A. 2017-12-05 /pmc/articles/PMC5723289/ /pubmed/29259624 http://dx.doi.org/10.3389/fgene.2017.00201 Text en Copyright © 2017 Serin, Snoek, Nijveen, Willems, Jiménez-Gómez, Hilhorst and Ligterink. 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 Genetics
Serin, Elise A. R.
Snoek, L. B.
Nijveen, Harm
Willems, Leo A. J.
Jiménez-Gómez, Jose M.
Hilhorst, Henk W. M.
Ligterink, Wilco
Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population
title Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population
title_full Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population
title_fullStr Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population
title_full_unstemmed Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population
title_short Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population
title_sort construction of a high-density genetic map from rna-seq data for an arabidopsis bay-0 × shahdara ril population
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5723289/
https://www.ncbi.nlm.nih.gov/pubmed/29259624
http://dx.doi.org/10.3389/fgene.2017.00201
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