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Prioritization of candidate genes in QTL regions based on associations between traits and biological processes
BACKGROUND: Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically con...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4274756/ https://www.ncbi.nlm.nih.gov/pubmed/25492368 http://dx.doi.org/10.1186/s12870-014-0330-3 |
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author | Bargsten, Joachim W Nap, Jan-Peter Sanchez-Perez, Gabino F van Dijk, Aalt DJ |
author_facet | Bargsten, Joachim W Nap, Jan-Peter Sanchez-Perez, Gabino F van Dijk, Aalt DJ |
author_sort | Bargsten, Joachim W |
collection | PubMed |
description | BACKGROUND: Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait. RESULTS: The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait. CONCLUSIONS: Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12870-014-0330-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4274756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42747562014-12-24 Prioritization of candidate genes in QTL regions based on associations between traits and biological processes Bargsten, Joachim W Nap, Jan-Peter Sanchez-Perez, Gabino F van Dijk, Aalt DJ BMC Plant Biol Methodology Article BACKGROUND: Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait. RESULTS: The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait. CONCLUSIONS: Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12870-014-0330-3) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-10 /pmc/articles/PMC4274756/ /pubmed/25492368 http://dx.doi.org/10.1186/s12870-014-0330-3 Text en © Bargsten et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Bargsten, Joachim W Nap, Jan-Peter Sanchez-Perez, Gabino F van Dijk, Aalt DJ Prioritization of candidate genes in QTL regions based on associations between traits and biological processes |
title | Prioritization of candidate genes in QTL regions based on associations between traits and biological processes |
title_full | Prioritization of candidate genes in QTL regions based on associations between traits and biological processes |
title_fullStr | Prioritization of candidate genes in QTL regions based on associations between traits and biological processes |
title_full_unstemmed | Prioritization of candidate genes in QTL regions based on associations between traits and biological processes |
title_short | Prioritization of candidate genes in QTL regions based on associations between traits and biological processes |
title_sort | prioritization of candidate genes in qtl regions based on associations between traits and biological processes |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4274756/ https://www.ncbi.nlm.nih.gov/pubmed/25492368 http://dx.doi.org/10.1186/s12870-014-0330-3 |
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