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Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR

MOTIVATION: The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Mo...

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Autores principales: Bourke, Peter M, Voorrips, Roeland E, Hackett, Christine A, van Geest, Geert, Willemsen, Johan H, Arens, Paul, Smulders, Marinus J M, Visser, Richard G F, Maliepaard, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570814/
https://www.ncbi.nlm.nih.gov/pubmed/34358315
http://dx.doi.org/10.1093/bioinformatics/btab574
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author Bourke, Peter M
Voorrips, Roeland E
Hackett, Christine A
van Geest, Geert
Willemsen, Johan H
Arens, Paul
Smulders, Marinus J M
Visser, Richard G F
Maliepaard, Chris
author_facet Bourke, Peter M
Voorrips, Roeland E
Hackett, Christine A
van Geest, Geert
Willemsen, Johan H
Arens, Paul
Smulders, Marinus J M
Visser, Richard G F
Maliepaard, Chris
author_sort Bourke, Peter M
collection PubMed
description MOTIVATION: The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. RESULTS: Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F(1) populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. AVAILABILITYAND IMPLEMENTATION: polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-85708142021-11-08 Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR Bourke, Peter M Voorrips, Roeland E Hackett, Christine A van Geest, Geert Willemsen, Johan H Arens, Paul Smulders, Marinus J M Visser, Richard G F Maliepaard, Chris Bioinformatics Original Papers MOTIVATION: The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. RESULTS: Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F(1) populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. AVAILABILITYAND IMPLEMENTATION: polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-08-06 /pmc/articles/PMC8570814/ /pubmed/34358315 http://dx.doi.org/10.1093/bioinformatics/btab574 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Bourke, Peter M
Voorrips, Roeland E
Hackett, Christine A
van Geest, Geert
Willemsen, Johan H
Arens, Paul
Smulders, Marinus J M
Visser, Richard G F
Maliepaard, Chris
Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR
title Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR
title_full Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR
title_fullStr Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR
title_full_unstemmed Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR
title_short Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR
title_sort detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlr
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570814/
https://www.ncbi.nlm.nih.gov/pubmed/34358315
http://dx.doi.org/10.1093/bioinformatics/btab574
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