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.
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.
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
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.
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.
Cited 
Cited 