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Multiallelic models for QTL mapping in diverse polyploid populations

ABSTRACT: Quantitative trait locus (QTL) analysis allows to identify regions responsible for a trait and to associate alleles with their effect on phenotypes. When using biallelic markers to find these QTL regions, two alleles per QTL are modelled. This assumption might be close to reality in specif...

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Autores principales: Thérèse Navarro, Alejandro, Tumino, Giorgio, Voorrips, Roeland E., Arens, Paul, Smulders, Marinus J. M., van de Weg, Eric, Maliepaard, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842866/
https://www.ncbi.nlm.nih.gov/pubmed/35164669
http://dx.doi.org/10.1186/s12859-022-04607-z
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author Thérèse Navarro, Alejandro
Tumino, Giorgio
Voorrips, Roeland E.
Arens, Paul
Smulders, Marinus J. M.
van de Weg, Eric
Maliepaard, Chris
author_facet Thérèse Navarro, Alejandro
Tumino, Giorgio
Voorrips, Roeland E.
Arens, Paul
Smulders, Marinus J. M.
van de Weg, Eric
Maliepaard, Chris
author_sort Thérèse Navarro, Alejandro
collection PubMed
description ABSTRACT: Quantitative trait locus (QTL) analysis allows to identify regions responsible for a trait and to associate alleles with their effect on phenotypes. When using biallelic markers to find these QTL regions, two alleles per QTL are modelled. This assumption might be close to reality in specific biparental crosses but is unrealistic in situations where broader genetic diversity is studied. Diversity panels used in genome-wide association studies or multi-parental populations can easily harbour multiple QTL alleles at each locus, more so in the case of polyploids that carry more than two alleles per individual. In such situations a multiallelic model would be closer to reality, allowing for different genetic effects for each potential allele in the population. To obtain such multiallelic markers we propose the usage of haplotypes, concatenations of nearby SNPs. We developed “mpQTL” an R package that can perform a QTL analysis at any ploidy level under biallelic and multiallelic models, depending on the marker type given. We tested the effect of genetic diversity on the power and accuracy difference between bi-allelic and multiallelic models using a set of simulated multiparental autotetraploid, outbreeding populations. Multiallelic models had higher detection power and were more precise than biallelic, SNP-based models, particularly when genetic diversity was higher. This confirms that moving to multi-allelic QTL models can lead to improved detection and characterization of QTLs. KEY MESSAGE: QTL detection in populations with more than two functional QTL alleles (which is likely in multiparental and/or polyploid populations) is more powerful when using multiallelic models, rather than biallelic models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04607-z.
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spelling pubmed-88428662022-02-16 Multiallelic models for QTL mapping in diverse polyploid populations Thérèse Navarro, Alejandro Tumino, Giorgio Voorrips, Roeland E. Arens, Paul Smulders, Marinus J. M. van de Weg, Eric Maliepaard, Chris BMC Bioinformatics Research ABSTRACT: Quantitative trait locus (QTL) analysis allows to identify regions responsible for a trait and to associate alleles with their effect on phenotypes. When using biallelic markers to find these QTL regions, two alleles per QTL are modelled. This assumption might be close to reality in specific biparental crosses but is unrealistic in situations where broader genetic diversity is studied. Diversity panels used in genome-wide association studies or multi-parental populations can easily harbour multiple QTL alleles at each locus, more so in the case of polyploids that carry more than two alleles per individual. In such situations a multiallelic model would be closer to reality, allowing for different genetic effects for each potential allele in the population. To obtain such multiallelic markers we propose the usage of haplotypes, concatenations of nearby SNPs. We developed “mpQTL” an R package that can perform a QTL analysis at any ploidy level under biallelic and multiallelic models, depending on the marker type given. We tested the effect of genetic diversity on the power and accuracy difference between bi-allelic and multiallelic models using a set of simulated multiparental autotetraploid, outbreeding populations. Multiallelic models had higher detection power and were more precise than biallelic, SNP-based models, particularly when genetic diversity was higher. This confirms that moving to multi-allelic QTL models can lead to improved detection and characterization of QTLs. KEY MESSAGE: QTL detection in populations with more than two functional QTL alleles (which is likely in multiparental and/or polyploid populations) is more powerful when using multiallelic models, rather than biallelic models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04607-z. BioMed Central 2022-02-14 /pmc/articles/PMC8842866/ /pubmed/35164669 http://dx.doi.org/10.1186/s12859-022-04607-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Thérèse Navarro, Alejandro
Tumino, Giorgio
Voorrips, Roeland E.
Arens, Paul
Smulders, Marinus J. M.
van de Weg, Eric
Maliepaard, Chris
Multiallelic models for QTL mapping in diverse polyploid populations
title Multiallelic models for QTL mapping in diverse polyploid populations
title_full Multiallelic models for QTL mapping in diverse polyploid populations
title_fullStr Multiallelic models for QTL mapping in diverse polyploid populations
title_full_unstemmed Multiallelic models for QTL mapping in diverse polyploid populations
title_short Multiallelic models for QTL mapping in diverse polyploid populations
title_sort multiallelic models for qtl mapping in diverse polyploid populations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842866/
https://www.ncbi.nlm.nih.gov/pubmed/35164669
http://dx.doi.org/10.1186/s12859-022-04607-z
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