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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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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. |
format | Online Article Text |
id | pubmed-8842866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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