Abstract
Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.
Key points
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This protocol describes a method for discovering interactions between biological pathways in genome-wide association study data by evaluating variant-level interactions connecting between and within biological pathways.
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The technique differs from approaches that perform interaction tests for every pair of variants with a phenotype of interest as it specifically assesses the impact of combinations of interacting loci at the pathway level, which affords Bridging Gene sets with Epistasis (BridGE) greater statistical power for identifying genetic interactions.
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Data availability
The datasets used in this protocol included (1) a sample 1000 Genomes Project GWAS dataset with simulated binary phenotypes, which is available at the Zenodo repository (https://doi.org/10.5281/zenodo.8067407); (2) a copy of 1000 Genomes Project data, which is available at the Zenodo repository (https://doi.org/10.5281/zenodo.8067407); and (3) a Parkinson’s disease GWAS dataset from the IPDGC (dbGaP study accession: phs000918.v1.p1), which is available at the dbGaP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000918.v1.p1).
Code availability
BridGE-Python can be obtained from the GitHub repository (https://github.com/csbio/BridGE-Python). It can be freely used for educational and research purposes by nonprofit institutions and US government agencies. A license for commercial use of this software is available from the University of Minnesota’s Office for Technology Commercialization.
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Acknowledgements
This work was partially supported by a grant from the Alzheimer’s Association, Alzheimer’s Research UK, The Michael J. Fox Foundation for Parkinson’s Research and the Weston Brain Institute (BAND-19-615151), and grants from the NIH (R21CA235352, R01HG005084 and R01HG005853). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. This study makes use of genome-wide association datasets provided by dbGaP (study accession numbers: phs000089.v3.p2, phs000126.v2.p1, phs000918.v1.p1). We acknowledge the contributing investigators who submitted data from their original study to dbGaP, the primary funding organization that supported the contributing investigators and the NIH data repository. Computing resources and data storage services were partially provided by the Minnesota Supercomputing Institute and the University of Minnesota’s Office of Information Technology, respectively.
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Authors and Affiliations
Contributions
M.H., W.W. and C.L.M. created the software design based on the original BridGE method. M.H., W.W. and M.A. developed the software. M.H., M.F. and W.W. tested the protocol and provided feedback on improvements. M.H. and W.W. wrote an initial draft of the protocol manuscript, which was then reviewed and revised by all co-authors. W.W. and C.L.M. supervised the software development process, manuscript writing and testing of the protocol, and secured funding to support the project.
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Nature Protocols thanks Marylyn Ritchie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol
Wang, W. et al. PLoS Genet. 13, e1006973 (2017): https://doi.org/10.1371/journal.pgen.1006973
Fang, G. et al. Nat. Commun. 10, 4274 (2019): https://doi.org/10.1038/s41467-019-12131-7
Supplementary information
Supplementary Data 1
The output of the original BridGE and BridGE 2.0 are compared for the two Parkinson’s disease GWAS cohorts, which were also analyzed in the original BridGE paper. The Excel files include summary tables and lists of BPM, WPM and PATH discovered at the FDR cutoff shown in the summary tables.
Supplementary Data 2
BridGE output files from the simulated 1000 Genomes GWAS data based on the combined disease model. The Excel files include BridGE results and interaction lists for BPMs and WPMs. The PDF file provides the visualization of pathway–pathway interactions.
Supplementary Data 3
A complete set of BridGE output files from the Parkinson’s disease IPDGC cohort (dbGaP study accession: phs000918.v1.p1). The Excel files include information about significant pathway-level interactions (FDR <0.25) discovered under the corresponding disease model (RR, DD, RD and combined) and pairwise SNP interaction lists and associated statistics (DD disease model only), one for BPM and one for WPM. The PDF file provides a network visualization.
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Hajiaghabozorgi, M., Fischbach, M., Albrecht, M. et al. BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00954-8
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DOI: https://doi.org/10.1038/s41596-024-00954-8
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