BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS

Chad L. Myers, Mehrad Hajiaghabozorgi, Mathew Fischbach, Michael Albrecht, Wen Wang

Published: 2024-03-21 DOI: 10.1038/s41596-024-00954-8

GWAS

Genetic interactions

BridGE

Pathway-based analysis

Epistasis

AI 解读

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Sections

Figures

References

Introduction
Materials
Procedure 1: running BridGE on a single server
Procedure 2: running BridGE on a computing cluster or cloud computing platform
Procedure 3: running BridGE on user-defined, variant-level interactions
Troubleshooting
Timing
Anticipated results
- Reporting summary
Acknowledgements

Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).
Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008).
Phillips, P. C. Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genet. 9, 855–867 (2008).
Cordell, H. J. Detecting gene–gene interactions that underlie human diseases. Nat. Rev. Genet. 10, 392–404 (2009).
Mackay, T. F. & Moore, J. H. Why epistasis is important for tackling complex human disease genetics. Genome Med. 6, 42 (2014).
Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012).
Hu, X. et al. SHEsisEpi, a GPU-enhanced genome-wide SNP–SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder. Cell Res. 20, 854–857 (2010).
Schüpbach, T., Xenarios, I., Bergmann, S. & Kapur, K. FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics 26, 1468–1469 (2010).
Wan, X. et al. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet. 87, 325–340 (2010).
Yung, L. S., Yang, C., Wan, X. & Yu, W. GBOOST: a GPU-based tool for detecting gene–gene interactions in genome-wide case control studies. Bioinformatics 27, 1309–1310 (2011).
Goudey, B. et al. GWIS-model-free, fast and exhaustive search for epistatic interactions in case-control GWAS. BMC Genom. 14, 1–18 (2013).
Wang, X. et al. ELSSI: parallel SNP–SNP interactions detection by ensemble multi-type detectors. Brief. Bioinform. 23, bbac213 (2022).
Chatelain, C., Durand, G., Thuillier, V. & Augé, F. Performance of epistasis detection methods in semi-simulated GWAS. BMC Bioinform. 19, 1–17 (2018).
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).
Kuzmin, E. et al. Systematic analysis of complex genetic interactions. Science 360, eaao1729 (2018).
Wang, W. et al. Pathway-based discovery of genetic interactions in breast cancer. PLoS Genet. 13, e1006973 (2017).
Fang, G. et al. Discovering genetic interactions bridging pathways in genome-wide association studies. Nat. Commun. 10, 4274 (2019).
Ueki, M. & Cordell, H. J. Improved statistics for genome-wide interaction analysis. PLoS Genet. 8, e1002625 (2012).
Sollis, E. et al. The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977–D985 (2023).
Hallacli, E. et al. The Parkinson’s disease protein alpha-synuclein is a modulator of processing bodies and mRNA stability. Cell 185, 2035–2056.e33 (2022).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Wang, K., Li, M. & Bucan, M. Pathway-based approaches for analysis of genome-wide association studies. Am. J. Hum. Genet. 81, 1278–1283 (2007).
Kim, N. C. et al. Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS. BioData Min. 5, 9 (2012).
Pandey, A. et al. Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder. Transl. Psychiatry 2, e154–e154 (2012).
Ma, L. et al. Knowledge-driven analysis identifies a gene–gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations. PLoS Genet. 8, e1002714 (2012).
Ma, L., Clark, A. G. & Keinan, A. Gene-based testing of interactions in association studies of quantitative traits. PLoS Genet. 9, e1003321 (2013).
Sun, X. et al. Analysis pipeline for the epistasis search–statistical versus biological filtering. Front. Genet. 5, 106 (2014).
Brossard, M. et al. Integrated pathway and epistasis analysis reveals interactive effect of genetic variants at TERF1 and AFAP1L2 loci on melanoma risk. Int. J. Cancer 137, 1901–1909 (2015).
Mitra, I. et al. Reverse pathway genetic approach identifies epistasis in autism spectrum disorders. PLoS Genet. 13, e1006516 (2017).
Chen, L. S. et al. Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data. Am. J. Hum. Genet. 86, 860–871 (2010).
Zhao, J., Gupta, S., Seielstad, M., Liu, J. & Thalamuthu, A. Pathway-based analysis using reduced gene subsets in genome-wide association studies. BMC Bioinform. 12, 1–14 (2011).
Huang, A., Martin, E. R., Vance, J. M. & Cai, X. Detecting genetic interactions in pathway‐based genome‐wide association studies. Genet. Epidemiol. 38, 300–309 (2014).
Ritchie, M. D. Large-scale analysis of genetic and clinical patient data. Annu. Rev. Biomed. Data Sci. 1, 263–274 (2018).
Silberstein, M., Nesbit, N., Cai, J. & Lee, P. H. Pathway analysis for genome-wide genetic variation data: analytic principles, latest developments, and new opportunities. J. Genet. Genom. 48, 173–183 (2021).
Cui, T. et al. Gene–gene interaction detection with deep learning. Commun. Biol. 5, 1238 (2022).
Liu, L. et al. Using machine learning to identify gene interaction networks associated with breast cancer. BMC Cancer 22, 1070 (2022).
Consortium, G. P. A global reference for human genetic variation. Nature 526, 68 (2015).
Consortium, I. P. D. G. Imputation of sequence variants for identification of genetic risks for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet 377, 641–649 (2011).
Lewontin, R. C. & Kojima, K.-I. The evolutionary dynamics of complex polymorphisms. Evolution 14, 458–472 (1960).

BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS

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