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Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

Teber, Erdahl T., Liu, Jason Y., Ballouz, Sara, Fatkin, Diane and Wouters, Merridee A. 2009, Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies, BMC bioinformatics, vol. 10, no. Supplement 1:S69, pp. 1-10, doi: 10.1186/1471-2105-10-S1-S69.

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Title Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
Author(s) Teber, Erdahl T.
Liu, Jason Y.
Ballouz, Sara
Fatkin, Diane
Wouters, Merridee A.
Journal name BMC bioinformatics
Volume number 10
Issue number Supplement 1:S69
Start page 1
End page 10
Total pages 10
Publisher BioMed Central
Place of publication London, England
Publication date 2009
ISSN 1471-2105
Summary Background
Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.

Results

Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.

Conclusion
The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.
Notes This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Language eng
DOI 10.1186/1471-2105-10-S1-S69
Field of Research 060199 Biochemistry and Cell Biology not elsewhere classified
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2009, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30038987

Document type: Journal Article
Collections: School of Life and Environmental Sciences
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.