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IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.

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Title: IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.
Authors: Wang D; Department of Mathematics & Statistics, University of Calgary, Calgary, Alberta, Canada., Kossinna P; Department of Biochemistry & Molecular Biology, University of Calgary, Calgary, Alberta, Canada., Ardila K; Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada., Kumarapeli S; Department of Mathematics & Statistics, University of Calgary, Calgary, Alberta, Canada.; Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada., MacDonald ME; Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada., Wu J; Department of Mathematics & Statistics, University of Calgary, Calgary, Alberta, Canada., Zhang Q; Department of Mathematics & Statistics, University of Calgary, Calgary, Alberta, Canada.; Department of Biochemistry & Molecular Biology, University of Calgary, Calgary, Alberta, Canada.; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
Source: PLoS computational biology [PLoS Comput Biol] 2026 Aug 17; Vol. 22 (8), pp. e1014640. Date of Electronic Publication: 2026 Aug 17 (Print Publication: 2026).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Language: English
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms: Genetic Association Studies*/methods , Genome-Wide Association Study*/methods , Multifactorial Inheritance*/genetics, Polymorphism, Single Nucleotide/genetics ; Computational Biology/methods ; Gene Regulatory Networks/genetics ; Quantitative Trait Loci/genetics ; Humans ; Phenotype ; Computer Simulation ; Models, Genetic ; Reproducibility of Results ; Epistasis, Genetic
Abstract: Genetic contributions to complex traits are often mediated through coordinated gene-gene interaction networks, yet most existing association frameworks focus on marginal single-gene effects and overlook higher-order dependency structures. Direct modeling of interactions remains challenging due to combinatorial complexity and statistical instability. We introduce Interaction-Bridged Association Study (IBAS), a general framework that incorporates pathway-level interaction patterns into genotype-phenotype association analysis without explicitly enumerating interactions. IBAS leverages transcriptomic reference data to construct low-dimensional representations of pathway activity, which guide SNP-weighting and gene-level association testing within a kernel-based framework. In perturbation-based simulations, IBAS demonstrates improved stability and reproducibility compared to conventional TWAS and gene-based methods, while maintaining well-calibrated Type I error under phenotype permutation. Application to the WTCCC datasets identifies both known and novel genes across multiple complex diseases, including candidates with modest marginal effects missed by standard approaches. These findings are supported by replication in an independent cohort, and analyses across multiple reference tissues revealing both shared and tissue-specific signals. Overall, IBAS provides a statistically robust and computationally tractable framework for incorporating interaction effects into association mapping, extending beyond the single-gene paradigm and enabling more comprehensive characterization of complex trait. IBAS is available on GitHub at: https://github.com/QingrunZhangLab/IBAS.
(Copyright: © 2026 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Competing Interests: The authors have declared that no competing interests exist.
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Grant Information: NSERC Discovery; Alberta Innovates Graduate Student Scholarship
Entry Date(s): Date Created: 20260817 Date Completed: 20260817 Latest Revision: 20260917
Update Code: 20260917
PubMed Central ID: PMC13480648
DOI: 10.1371/journal.pcbi.1014640
PMID: 42607076
Database: MEDLINE
Description
ISSN:1553-7358
DOI:10.1371/journal.pcbi.1014640