Review

Linking genes to diseases: it's all in the data

Nicki Tiffin ¹*, Miguel A Andrade-Navarro ² and Carolina Perez-Iratxeta ³

• * Corresponding author: Nicki Tiffin nickitiffin@imaginet.co.za

Author Affiliations

¹ MRC/UWC/SANBI Bioinformatics Capacity Development Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville 7535, South Africa

² Max-Delbrück Center for Molecular Medicine, Robert Rössle Strasse 10, 13125 Berlin, Germany

³ Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada

For all author emails, please log on.

Genome Medicine 2009, 1:77doi:10.1186/gm77

Published: 7 August 2009

Abstract

Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches.