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Benchmarking network-based gene prioritization methods for cerebral small vessel disease.

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  • 1Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.

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Summary

Benchmarking gene prioritization algorithms for cerebral small vessel disease (cSVD) revealed that random walk with restart on heterogeneous networks (RWRH) performed best. Algorithm choice is crucial for effective disease gene discovery.

Keywords:
benchmarkingcerebral small vessel diseasedisease gene associationnetwork-based gene prioritizationprotein–protein interaction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Network-based gene prioritization algorithms leverage biological networks to identify disease-associated genes.
  • Optimal algorithm selection for specific diseases remains a challenge.
  • Cerebral small vessel disease (cSVD) requires effective gene prioritization methods.

Purpose of the Study:

  • To benchmark multiple network-based gene prioritization algorithms for their efficacy in cSVD.
  • To identify the optimal algorithm for prioritizing genes associated with cSVD.
  • To provide a generalized framework for algorithm benchmarking in other diseases.

Main Methods:

  • Curated protein-gene interactions (PGIs) and gene-disease associations (GDAs) from public databases.
  • Constructed protein-interaction (PGI) networks and disease-gene heterogeneous networks.
  • Screened and benchmarked seven representative algorithms using leave-one-out cross-validation (LOOCV) and external genome-wide association study (GWAS) validation (MEGASTROKE).

Main Results:

  • Random walk with restart on the heterogeneous network (RWRH) demonstrated superior LOOCV performance (median rank 185.5).
  • GenePanda identified the most GWAS-confirmable genes within the top 200 predictions.
  • RWRH achieved the best ranks for cSVD-associated genes confirmed by GWAS, indicating strong performance for this specific disease.

Conclusions:

  • RWRH exhibits overall superior performance for cSVD gene prioritization, despite potential bias from degree centrality.
  • The selection of gene prioritization algorithms should be disease-specific.
  • Current network-based methods may not discover entirely novel disease-associated genes; their utility is primarily in prioritizing known associations.
  • Developed tools are available for implementing and generalizing algorithm benchmarking for other diseases.