Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

Himmelstein, Daniel S.; Baranzini, Sergio E.

doi:10.1371/journal.pcbi.1004259

Cited by 176Bioinformatics and Genomic NetworksGenetic Associations and EpidemiologyBiomedical Text Mining and Ontologies

Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

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Himmelstein, Daniel S., Baranzini, Sergio E.·2015·PLOS Computational Biology

DOI 10.1371/journal.pcbi.1004259PMC PMC4497619

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Abstract

Abstract The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which ( JAK2 , REL , RUNX3 ) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online ( http://het.io ). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains. Author Summary For complex human diseases, identifying the genes harboring susceptibility variants has taken on medical importance. Disease-associated genes provide clues for elucidating disease etiology, predicting disease risk, and highlighting therapeutic targets. Here, we develop a method to predict whether a given gene and disease are associated. To capture the multitude of biological entities underlying pathogenesis, we constructed a heterogeneous network, containing multiple node and edge types. We built on a technique developed for social network analysis, which embraces disparate sources of data to make predictions from heterogeneous networks. Using the compendium of associations from genome-wide studies, we learned the influential mechanisms underlying pathogenesis. Our findings provide a novel perspective about the existence of pervasive pleiotropy across complex diseases. Furthermore, we suggest transcriptional signatures of perturbations are an underutilized resource amongst prioritization approaches. For multiple sclerosis, we demonstrated our ability to prioritize future studies and discover novel susceptibility genes. Researchers can use these predictions to increase the statistical power of their studies, to suggest the causal genes from a set of candidates, or to generate evidence-based experimental hypothesis.

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SPOKE's reliability has been demonstrated through its successful application in various biomedical contexts.
“Thus, while SPOKE’s reliability has been demonstrated through its successful application in various biomedical contexts (Supplementary S1 Text; Himmelstein and Baranzini, 2015, Himmelstein et al. 2017, Nelson et al. 2019, Nelson et al. 2021a,b, Baranzini et al. 2022, Morris et al. 2023, Soman et al. 2023a,b, Tang et al. 2024)”
From Biomedical knowledge graph-optimized prompt generation for large language models (2024) · cites this paper as Himmelstein and Baranzini, 2015
The Discussion reports that this paper’s heterogeneous-network predictions successfully prioritized disease–gene associations across 29 diseases and helped identify three multiple-sclerosis susceptibility genes. That supports a successful biomedical application of a related network approach, but our copy does not identify the network as SPOKE or demonstrate SPOKE’s reliability across various contexts. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Developments in heterogeneous relationship predictions introduced and formalized a new framework that takes into account network heterogeneity by defining type-specific node-edge paths or meta-paths.
“Developments in heterogeneous (54) relationship predictions (55) introduced and formalized a new framework that takes into account network heterogeneity by defining typespecific node-edge paths or meta-paths (56).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [56]
Of the top 251 predicted novel genes, 23% were associated with ATG based on literature queries, whereas 193 were not.
“Of the top 251 predicted novel genes, 23% were associated with ATG based on literature queries, whereas 193 were not.”
From QSAR without borders (2020) · cites this paper as [130]

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RESEARCH ARTICLE Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes
Himmelstein, D. S., & Baranzini, S. E. (2016)
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A computational method based on the integration of heterogeneous networks for predicting disease–gene associations. PLoS One 6
Guo, X., Gao, L., Wei, C., Yang, X., Zhao, Y., & Dong, A. (2011)

Authors: Himmelstein, D. S., & Baranzini, S. E.
Affiliations: University of California, San Francisco (US)
Corresponding author: Baranzini, Sergio E.
ORCID: Himmelstein, Daniel S.
Baranzini, Sergio E.
Venue: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2015-07-09
Volume: 11
Issue: 7
Pages: e1004259
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1004259
PMC: PMC4497619
OpenAlex: W2187413858
Cited by: 172
References: 87
Citations by year: 2026: 10 · 2025: 14 · 2024: 23 · 2023: 22 · 2022: 25 · 2021: 17 · 2020: 19 · 2019: 21 · 2018: 9 · 2017: 5 · 2016: 7
FWCI: 3.04
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
eScholarship (California Digital Library) (submittedVersion)
Figshare (submittedVersion, cc-by)
Funders: National Science Foundation (1144247)
Multiple Sclerosis Society
National Institutes of Health
National Institute of General Medical Sciences (T32GM067547)
National Institute of Neurological Disorders and Stroke (R01NS088155)
National Institute of Biomedical Imaging and Bioengineering (T32EB009383)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords: complex diseases, genome-wide association study, pleiotropy, multiple sclerosis, gene-disease association, data integration, JAK2, REL, RUNX3, gene expression signatures, protein-protein interaction, pathogenesis
MeSH terms: Algorithms, Animals, Chromosome Mapping, Humans, Signal Transduction, Systems Integration, Genetic Predisposition to Disease, Proteome, Protein Interaction Mapping, Databases, Genetic, Genome-Wide Association Study, Data Mining

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Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

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