Recent approaches to the prioritization of candidate disease genes

Advanced Review

Nadezhda T. Doncheva, Tim Kacprowski, Mario Albrecht

Published Online: Jun 11 2012

DOI: 10.1002/wsbm.1177

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Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High‐throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time‐consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow‐up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene–disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods. WIREs Syst Biol Med 2012. doi: 10.1002/wsbm.1177

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Figure 1.

Exemplary molecular network of candidate genes and known disease genes. Red nodes represent known disease genes, and green nodes correspond to candidate genes. For candidate genes C1 and C2, the table lists the node degree, the 1N and 2N indices, and the average network distance to disease genes (see also Box 1).

[ Normal View 48K | Magnified View 85K ]

Figure 2.

Integrative approaches to disease gene prioritization. The typical workflow of integrative prioritization approaches based on multiple data sources consists of three major steps. The first step involves preparing the input data consisting of two different sets of genes, the known disease genes and the candidate genes. For each gene, further biomedical knowledge is retrieved from various data sources such as functional annotations from the Gene Ontology and molecular pathways from the KEGG database. In the second step, the collected information is integrated using a network representation (top) or evaluated individually for each data source, resulting in different ranking lists (bottom). The third step computes a final ranking list of candidate genes based on network measures or rank aggregation. The candidate genes are thus prioritized by their relevance to the disease of interest.

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