Prioritization of candidate genes in QTL regions based on associations between traits and biological processes

BMC Plant Biology

Methodology Article

Aalt DJ van Dijk¹  Gabino F Sanchez-Perez²  Jan-Peter Nap³  Joachim W Bargsten⁴ 

[1] Applied Bioinformatics, Bioscience, Plant Sciences Group, Wageningen University and Research Centre, Wageningen, The Netherlands;Biometris, Wageningen University and Research Centre, Wageningen, The Netherlands;Applied Bioinformatics, Bioscience, Plant Sciences Group, Wageningen University and Research Centre, Wageningen, The Netherlands;Laboratory of Bioinformatics, Plant Sciences Group, Wageningen University and Research Centre, Wageningen, The Netherlands;Applied Bioinformatics, Bioscience, Plant Sciences Group, Wageningen University and Research Centre, Wageningen, The Netherlands;Netherlands Bioinformatics Centre (NBIC), Nijmegen, The Netherlands;Applied Bioinformatics, Bioscience, Plant Sciences Group, Wageningen University and Research Centre, Wageningen, The Netherlands;Netherlands Bioinformatics Centre (NBIC), Nijmegen, The Netherlands;Laboratory for Plant Breeding, Plant Sciences Group, Wageningen University and Research Centre, Wageningen, The Netherlands;

关键词: Quantitative trait locus;    Candidate gene prioritization;    Gene function prediction;   

DOI  :  10.1186/s12870-014-0330-3

 received in 2014-09-15, accepted in 2014-11-10,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundElucidation 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.ResultsThe 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Bargsten et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. 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.