Mining for disease genes

Genomic data continue to accumulate at an ever-increasing rate, and the question of how best to exploit this for human disease research and therapy is critical.  Nicki Tiffin and colleagues discuss computational methods for efficient identification of disease-gene associations in “Linking genes to disease: it’s all in the data”, a Review article recently published in Genome Medicine.

Genome-wide association studies, public genome databases and the dissemination of data on gene expression, variation and regulation have led to the identification of staggering numbers of candidate genes for many complex diseases. Tiffin and colleagues consider computational approaches which can bring together these sources of data and streamline subsequent translational research.

Many bioinformatic methods for assessing candidate genes, including examination of the properties of the gene itself (structure, expression, regulation) or comparison with known disease genes, are available.  Recently, research on diseases such as skeletal dysplasia and asthma has shown the validity of including information about clinical phenotype to prioritize disease candidates. However, routine standardization of disease phenotype descriptions will be necessary to realize the potential of this exciting approach.

Genome Medicine, BioMed Central’s premier medical journal, stands at the forefront of research and clinical practice in the post-genomic era. The journal is led by six Section Editors and is supported by a world renowned Editorial Board.

We welcome cutting-edge genomic and post-genomic research reporting findings that significantly advance our understanding and management of human health and disease.Keep abreast of recent developments in these exciting times: register for article updates and submit your next outstanding research manuscript to Genome Medicine.

Rebecca Furlong

Assistant Editor, Genome Medicine

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