Computational selection and prioritization of candidate genes for Fetal Alcohol Syndrome
AuthorLombard, Z; Tiffin, N; Hofmann, O; Bajic, VB; Hide, W; Ramsay, M
Source TitleBMC Genomics
University of Melbourne Author/sHofmann, Oliver
Document TypeJournal Article
CitationsLombard, Z., Tiffin, N., Hofmann, O., Bajic, V. B., Hide, W. & Ramsay, M. (2007). Computational selection and prioritization of candidate genes for Fetal Alcohol Syndrome. BMC GENOMICS, 8 (1), https://doi.org/10.1186/1471-2164-8-389.
Access StatusOpen Access
BACKGROUND: Fetal alcohol syndrome (FAS) is a serious global health problem and is observed at high frequencies in certain South African communities. Although in utero alcohol exposure is the primary trigger, there is evidence for genetic- and other susceptibility factors in FAS development. No genome-wide association or linkage studies have been performed for FAS, making computational selection and -prioritization of candidate disease genes an attractive approach. RESULTS: 10174 Candidate genes were initially selected from the whole genome using a previously described method, which selects candidate genes according to their expression in disease-affected tissues. Hereafter candidates were prioritized for experimental investigation by investigating criteria pertinent to FAS and binary filtering. 29 Criteria were assessed by mining various database sources to populate criteria-specific gene lists. Candidate genes were then prioritized for experimental investigation using a binary system that assessed the criteria gene lists against the candidate list, and candidate genes were scored accordingly. A group of 87 genes was prioritized as candidates and for future experimental validation. The validity of the binary prioritization method was assessed by investigating the protein-protein interactions, functional enrichment and common promoter element binding sites of the top-ranked genes. CONCLUSION: This analysis highlighted a list of strong candidate genes from the TGF-beta, MAPK and Hedgehog signalling pathways, which are all integral to fetal development and potential targets for alcohol's teratogenic effect. We conclude that this novel bioinformatics approach effectively prioritizes credible candidate genes for further experimental analysis.
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