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Published on: August 20, 2019
Predicting disease genes using protein-protein interactions
Background:
The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes.
Objective:
To investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases.
Methods:
72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes.
Results:
Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci.
Conclusions:
Exploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.
Insights
Protein-protein interactions help identify disease genes for genetically heterogeneous diseases. This approach significantly increases the discovery of positional candidate genes, offering a 10-fold enrichment over traditional methods.
Area of Science:
- Genomics
- Proteomics
- Systems Biology
Background:
- Identifying causative genes for genetically mapped disease loci remains a challenge.
- Physically interacting proteins often participate in shared cellular pathways, suggesting their genes may contribute to similar disease phenotypes.
Discussion:
- This study explored the utility of protein-protein interaction networks in predicting candidate disease genes for genetically heterogeneous disorders.
- A large-scale analysis integrated 72,940 interactions among 10,894 human proteins to identify potential disease genes at 432 loci linked to 383 hereditary diseases.
Key Insights:
- The protein-protein interaction approach yielded nearly 300 candidate disease gene predictions, with some subsequently confirmed.
- Predictions showed a 10-fold enrichment compared to using positional information alone, with an expected 10% genuine disease gene rate.
- Notable candidates include AKAP6 for arrhythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy.
Outlook:
- Leveraging protein-protein interactions substantially enhances the discovery of positional candidate disease genes.
- Large-scale application of this method promises novel candidate gene identification for complex hereditary diseases.
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