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

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/IBASAuthor summary: Understanding how genes influence complex diseases remains a major challenge in genetics. While many studies focus on individual genes, biological systems operate through coordinated interactions among groups of genes. However, directly modeling these interactions is difficult due to their complexity and instability. In this study, we introduce Interaction-Bridged Association Studies (IBAS), a new framework that captures gene–gene interaction patterns without explicitly testing every possible interaction. Instead, IBAS uses gene expression data to summarize pathway-level activity and leverages this information to guide genetic association testing. We show that IBAS produces more stable and reproducible results compared to existing methods, particularly when data are noisy or variable. Applying IBAS to multiple disease datasets, we identify both well-known and previously unreported genes, including candidates with subtle effects that are often missed by traditional approaches. Many findings are replicated in independent cohorts and across different tissues, supporting their robustness. Overall, IBAS provides a practical and reliable way to uncover how coordinated gene activity contributes to complex traits, offering new insights into disease mechanisms.