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

Zhang, H, Ferguson, A, Robertson, G, Jiang, M, Zhang, T, Sudlow, C, Smith, K ORCID: https://orcid.org/0000-0002-4615-9020, Rannikmae, K and Wu, H, 2021. Benchmarking network-based gene prioritization methods for cerebral small vessel disease. Briefings in Bioinformatics, 22 (5): bbab006. ISSN 1467-5463

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Abstract

Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene-disease associations (GDAs) and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD). We curated protein-gene interactions (PGIs) and GDAs from databases and assembled PGI networks and disease-gene heterogeneous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19 463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke-associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality.

Item Type:	Journal article
Publication Title:	Briefings in Bioinformatics
Creators:	Zhang, H., Ferguson, A., Robertson, G., Jiang, M., Zhang, T., Sudlow, C., Smith, K., Rannikmae, K. and Wu, H.
Publisher:	Oxford University Press (OUP)
Date:	September 2021
Volume:	22
Number:	5
ISSN:	1467-5463
Identifiers:	Number Type 10.1093/bib/bbab006 DOI 1502665 Other
Rights:	© The Author(s) 2021 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	10 Dec 2021 12:49
Last Modified:	10 Dec 2021 12:49
URI:	https://irep.ntu.ac.uk/id/eprint/45100

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