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 METHOD corresponds to the algorithm (and variant) used: neighborhood, oti1 (+ our variants: oti2 and oti3 each embedded in the 'oti' file), gs1 (+ our variants gs2, gs3, and gs-all, each embedded in the 'gs' file), mcl, vicut, rw, or prop.
• The NETWORK corresponds to the protein interaction network used: HPRD (The Human Protein Reference Database) or OPHID (The Online Predicted Human Interaction Database).
• The LOCUS corresponds to whether linkage intervals were taken into account when making a prediction: LOC (assuming linkage intervals are known) or NOLOC (unknown).

Data