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Computational selection and prioritization of candidate genes for fetal alcohol syndrome.

Lombard Z, Tiffin N, Hofmann O, Bajic VB, Hide W, Ramsay M - BMC Genomics (2007)

Bottom Line: A group of 87 genes was prioritized as candidates and for future experimental validation.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Human Genetics, National Health Laboratory Service & School of Pathology, University of the Witwatersrand, Johannesburg, 2001, South Africa. zane.lombard@gmail.com

ABSTRACT

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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The method of integrated literature- and data mining to identify an initial list of putative candidate genes.
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Figure 2: The method of integrated literature- and data mining to identify an initial list of putative candidate genes.

Mentions: The method previously described by Tiffin et al. [31] was used to extract candidate genes based on the information obtained from the literature mining. Figure 2 illustrates the process of literature- and data-mining used to select candidate genes. Briefly, this method ranks the extracted eVOC terms by calculating a ranking score for each associated eVOC term, according to the frequency of association and the frequency of annotation of the eVOC term. The four top-scoring eVOC terms were selected from the ranked list, and compared with eVOC terms annotated to genes within the Ensembl database (Ensembl v33, September 2005) to select candidates. The system allows for one mismatch, such that candidates selected are those annotated with at least three of the four top-scoring eVOC terms. This approach was tested by the authors on a subset of genes representative of those that might be selected by a linkage analysis study, and not the full complement of genes in the Ensembl database, as in the current study.


Computational selection and prioritization of candidate genes for fetal alcohol syndrome.

Lombard Z, Tiffin N, Hofmann O, Bajic VB, Hide W, Ramsay M - BMC Genomics (2007)

The method of integrated literature- and data mining to identify an initial list of putative candidate genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194724&req=5

Figure 2: The method of integrated literature- and data mining to identify an initial list of putative candidate genes.
Mentions: The method previously described by Tiffin et al. [31] was used to extract candidate genes based on the information obtained from the literature mining. Figure 2 illustrates the process of literature- and data-mining used to select candidate genes. Briefly, this method ranks the extracted eVOC terms by calculating a ranking score for each associated eVOC term, according to the frequency of association and the frequency of annotation of the eVOC term. The four top-scoring eVOC terms were selected from the ranked list, and compared with eVOC terms annotated to genes within the Ensembl database (Ensembl v33, September 2005) to select candidates. The system allows for one mismatch, such that candidates selected are those annotated with at least three of the four top-scoring eVOC terms. This approach was tested by the authors on a subset of genes representative of those that might be selected by a linkage analysis study, and not the full complement of genes in the Ensembl database, as in the current study.

Bottom Line: A group of 87 genes was prioritized as candidates and for future experimental validation.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Human Genetics, National Health Laboratory Service & School of Pathology, University of the Witwatersrand, Johannesburg, 2001, South Africa. zane.lombard@gmail.com

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus