Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 482765
Title Prioritization of candidate genes in QTL regions based on associations between traits and biological processes
Author(s) Bargsten, J.W.; Nap, J.P.H.; Sanchez Perez, G.F.; Dijk, A.D.J. van
Source BMC Plant Biology 14 (2014). - ISSN 1471-2229
DOI https://doi.org/10.1186/s12870-014-0330-3
Department(s) Plant Breeding
BIOS Applied Bioinformatics
Mathematical and Statistical Methods - Biometris
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) genome-wide association - protein function prediction - arabidopsis-thaliana - nucleotide polymorphisms - enrichment analysis - flowering time - complex traits - oryza-sativa - rice - architecture
Abstract 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.
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