Dataset Information

Credibility analysis of putative disease-causing genes using bioinformatics.

ABSTRACT:

Background

Genetic studies are challenging in many complex diseases, particularly those with limited diagnostic certainty, low prevalence or of old age. The result is that genes may be reported as disease-causing with varying levels of evidence, and in some cases, the data may be so limited as to be indistinguishable from chance findings. When there are large numbers of such genes, an objective method for ranking the evidence is useful. Using the neurodegenerative and complex disease amyotrophic lateral sclerosis (ALS) as a model, and the disease-specific database ALSoD, the objective is to develop a method using publicly available data to generate a credibility score for putative disease-causing genes.

Methods

Genes with at least one publication suggesting involvement in adult onset familial ALS were collated following an exhaustive literature search. SQL was used to generate a score by extracting information from the publications and combined with a pathogenicity analysis using bioinformatics tools. The resulting score allowed us to rank genes in order of credibility. To validate the method, we compared the objective ranking with a rank generated by ALS genetics experts. Spearman's Rho was used to compare rankings generated by the different methods.

Results

The automated method ranked ALS genes in the following order: TARDBP, FUS, ANG, SPG11, NEFH, OPTN, ALS2, SETX, FIG4, VAPB, DCTN1, TAF15, VCP, DAO. This compared very well to the ranking of ALS genetics experts, with Spearman's Rho of 0.69 (P?=?0.009).

Conclusion

We have presented an automated method for scoring the level of evidence for a gene being disease-causing. In developing the method we have used the model disease ALS, but it could equally be applied to any disease in which there is genotypic uncertainty.

SUBMITTER: Abel O

PROVIDER: S-EPMC3674010 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Credibility analysis of putative disease-causing genes using bioinformatics.

Abel Olubunmi O Powell John F JF Andersen Peter M PM Al-Chalabi Ammar A

PloS one 20130605 6

<h4>Background</h4>Genetic studies are challenging in many complex diseases, particularly those with limited diagnostic certainty, low prevalence or of old age. The result is that genes may be reported as disease-causing with varying levels of evidence, and in some cases, the data may be so limited as to be indistinguishable from chance findings. When there are large numbers of such genes, an objective method for ranking the evidence is useful. Using the neurodegenerative and complex disease amy ...[more]

PMID: 23755159

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Dataset Information

Credibility analysis of putative disease-causing genes using bioinformatics.

Background

Methods

Results

Conclusion

Publications

Credibility analysis of putative disease-causing genes using bioinformatics.

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