Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes
Open accessAbstract
Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.
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GBA approaches are widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease.
“GBA approaches are also widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease [26, 121–126] (Figure 1).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [124]
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- Authors
- Franke, L., Bakel, H. V., Fokkens, L., de Jong, E. D., Egmont-Petersen, M., & Wijmenga, C.
- Affiliations
- University Medical Center Utrecht (NL)
- Radboud University Medical Center (NL)
- Corresponding author
- Wijmenga, Cisca
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2006-06
- Volume
- 78
- Issue
- 6
- Pages
- 1011-1025
- ISSN
- 0002-9297 (ISSN-L)
- OpenAlex
- W2070412833
- Cited by
- 476
- References
- 59
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1 · 2023: 6 · 2022: 5 · 2021: 11 · 2020: 10 · 2019: 16 · 2018: 14 · 2017: 14 · 2016: 17 · 2015: 22 · 2014: 38 · 2013: 32 · 2012: 44
- FWCI
- 16.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed (publishedVersion)
- UCLA - Biblioteca de Ciencias de la Salud (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (901-04-219)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- gene networks, positional candidate genes, disease gene prioritization, complex genetic disorders, genetic susceptibility loci, pathway integration, protein-protein interaction, gene co-expression, Gene Ontology, OMIM, genetic disorders
- MeSH terms
- Humans, Genome, Human, Computational Biology, Genetic Predisposition to Disease, Genetic Diseases, Inborn, Databases, Genetic
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