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Mendeley readers
| Title |
Prioritization of candidate genes in QTL regions based on associations between traits and biological processes
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|---|---|
| Published in |
BMC Plant Biology, December 2014
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| DOI | 10.1186/s12870-014-0330-3 |
| Pubmed ID | |
| Authors |
Joachim W Bargsten, Jan-Peter Nap, Gabino F Sanchez-Perez, Aalt DJ van Dijk |
| Abstract |
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. |
Mendeley readers
The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Israel | 1 | <1% |
| Sweden | 1 | <1% |
| Benin | 1 | <1% |
| Unknown | 99 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 33% |
| Student > Ph. D. Student | 25 | 24% |
| Student > Master | 7 | 7% |
| Student > Doctoral Student | 5 | 5% |
| Student > Postgraduate | 3 | 3% |
| Other | 8 | 8% |
| Unknown | 22 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 66 | 63% |
| Biochemistry, Genetics and Molecular Biology | 8 | 8% |
| Computer Science | 3 | 3% |
| Environmental Science | 2 | 2% |
| Business, Management and Accounting | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 25 | 24% |