Computational selection and prioritization of candidate genes for Fetal Alcohol Syndrome
1 Division of Human Genetics, National Health Laboratory Service & School of Pathology, University of the Witwatersrand, Johannesburg, 2001, South Africa
2 South African National Bioinformatics Institute (SANBI) Research Group, University of the Western Cape, Bellville, 7530, South Africa
3 Division of Human Genetics, University of Cape Town, Cape Town, 8001, South Africa
BMC Genomics 2007, 8:389 doi:10.1186/1471-2164-8-389Published: 25 October 2007
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.
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.
This analysis highlighted a list of strong candidate genes from the TGF-β, 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.