The Power of Protein Interaction Networks for Associating Genes with Diseases

Saket Navlakha & Carl Kingsford
University of Maryland College Park

Abstract

Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene-products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the benefits and drawbacks underlying the proposed techniques.

We assess the utility of physical interactions for determining disease-gene associations by examining the performance of seven recently developed computational methods (plus several of their variants). We find that random-walk based approaches individually outperform clustering-and neighborhood-based approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantify how a diffuse topological distribution of proteins negatively affects the quality of predictions and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required.

Predictions

The disease-gene associations outputted by each algorithm tested are available for download.

Each file is named according to the following format: [METHOD]-[NETWORK]-[LOCUS] For example, oti1-HPRD-LOC includes all predictions made by the oti1 algorithm on the HPRD interaction network using linkage intervals.

We only included higher-confidence NOLOC predictions that had a score above a threshold. The full set of predictions are available upon request.

There are 5 tab-delimited columns in each file corresponding to: [METHOD] [GENE] [DISEASE] [GENE] [CORRECT/INCORRECT] [A(p,d) SCORE].

Data

Download the HPRD and OPHID PPI networks, and the Gene-Disease OMIM associations, used in our paper.


Last modified: October 14, 2009