Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Annotating genetic variants to target genes using H-MAGMA

Nature Protocols volume 18, pages 22–35 (2023)Cite this article

Subjects

Abstract

An outstanding goal in modern genomics is to systematically predict the functional outcome of noncoding variation associated with complex traits. To address this, we developed Hi-C-coupled multi-marker analysis of genomic annotation (H-MAGMA), which builds on traditional MAGMA—a gene-based analysis tool that assigns variants to their target genes—by incorporating 3D chromatin configuration in assigning variants to their putative target genes. Applying this approach, we identified key biological pathways implicated in a wide range of brain disorders and showed its utility in complementing other functional genomic resources such as expression quantitative trait loci–based variant annotation. Here, we provide a detailed protocol for generating the H-MAGMA variant-gene annotation file by using chromatin interaction data from the adult human brain. In addition, we provide an example of how H-MAGMA is run by using genome-wide association study summary statistics of Parkinson’s disease. Lastly, we generated variant-gene annotation files for 28 tissues and cell types, with the hope of contributing a resource for researchers studying a broad set of complex genetic disorders. H-MAGMA can be performed in <2 h for any cell type in which Hi-C data are available.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic of the protocol.
Fig. 2: Overview of H-MAGMA.
Fig. 3: Number of PD risk genes at different FDR thresholds.

Similar content being viewed by others

Data availability

All required data to run the protocol are publicly available. Downloadable versions of files are also provided in the Zenodo repository at https://doi.org/10.5281/zenodo.550387629. In addition to the variant-gene annotation file generated from adult brain Hi-C data6, we have uploaded variant-gene annotation files generated from 28 different cell and tissue types by using promoter-capture Hi-C data from Jung et al8. All 28 variant-gene annotation files including commands used to generate these files are also available in the Zenodo repository. All files including source code and documentation to run MAGMA are available on the MAGMA website by using the following link: https://ctg.cncr.nl/software/magma.

Code availability

An executable version of the commands used in this protocol is provided as an R Markdown (.rmd) file in the Zenodo repository at https://doi.org/10.5281/zenodo.550387629.

References

  1. Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).

    Article  CAS  Google Scholar 

  2. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  Google Scholar 

  3. Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    Article  CAS  Google Scholar 

  4. Pratt, B. M. & Won, H. Advances in profiling chromatin architecture shed light on the regulatory dynamics underlying brain disorders. Semin. Cell Dev. Biol. 121, 153–160 (2022).

    Article  CAS  Google Scholar 

  5. Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).

    Article  CAS  Google Scholar 

  6. Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).

    Article  CAS  Google Scholar 

  7. Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    Article  CAS  Google Scholar 

  8. Jung, I. et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nat. Genet. 51, 1442–1449 (2019).

    Article  CAS  Google Scholar 

  9. Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    Article  Google Scholar 

  10. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  CAS  Google Scholar 

  11. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  Google Scholar 

  12. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    Article  CAS  Google Scholar 

  13. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    Article  CAS  Google Scholar 

  14. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  Google Scholar 

  15. Andlauer, T. F. M. et al. Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation. Sci. Adv. 2, e1501678 (2016).

    Article  Google Scholar 

  16. Van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).

    Article  Google Scholar 

  17. Constantino, J. N. & Marrus, N. The early origins of autism. Child Adolesc. Psychiatr. Clin. N. Am. 26, 555–570 (2017).

    Article  Google Scholar 

  18. Yurko, R., Roeder, K., Devlin, B. & G’Sell, M. H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. Ann. Hum. Genet. 85, 97–100 (2021).

    Article  Google Scholar 

  19. de Leeuw, C., Sey, N. Y. A., Posthuma, D. & Won, H. A response to Yurko et al: H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. Preprint at https://www.biorxiv.org/content/10.1101/2020.09.25.310722v1(2020)

  20. Matoba, N. et al. Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. Transl. Psychiatry 10, 265 (2020).

    Article  CAS  Google Scholar 

  21. Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    Article  Google Scholar 

  22. Quach, B. C. et al. Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits. Nat. Commun. 11, 5562 (2020).

    Article  CAS  Google Scholar 

  23. Song, M. et al. Cell-type-specific 3D epigenomes in the developing human cortex. Nature 587, 644–649 (2020).

    Article  CAS  Google Scholar 

  24. Feleke, R. et al. Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases. Acta Neuropathol. 142, 449–474 (2021).

    Article  Google Scholar 

  25. Hu, B. et al. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat. Commun. 12, 3968 (2021).

    Article  CAS  Google Scholar 

  26. Sey, N. Y. A. et al. Chromatin architecture in addiction circuitry identifies risk genes and potential biological mechanisms underlying cigarette smoking and alcohol use traits. Mol. Psychiatry 27, 3085–3094 (2022).

    Article  Google Scholar 

  27. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    Article  CAS  Google Scholar 

  28. Fang, R. et al. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 26, 1345–1348 (2016).

    Article  CAS  Google Scholar 

  29. Sey, N., Pratt, B. & Won, H. H-MAGMA Protocol. Annotating genetic variants to target genes using H-MAGMA. Available at https://zenodo.org/record/6382668#.YwglBuzMIq0 (2021).

Download references

Acknowledgements

We thank members of the Won Lab for helpful discussions and feedback on this protocol. We also thank Drs. Danielle Posthuma and Christiaan de Leeuw from the Complex Traits Genetics Lab at VU University for developing and updating the MAGMA software. This research was supported by the National Institute of Mental Health (R00MH113823, DP2MH122403, R21DA051921 and U01MH122509 to H.W.) and the NARSAD Young Investigator Award from the Brain and Behavior Research Foundation to H.W. N.Y.A.S. was supported by a grant to the University of North Carolina at Chapel Hill from the Howard Hughes Medical Institute (HHMI) through the James H. Gilliam Fellowships for Advanced Study program and the National Science Foundation (NSF) Graduate Research Fellowship Program (DGE-1650116). B.M.P. was supported by a training grant from the UNC Pharmacology Department (5T32GM135095-02).

Author information

Authors and Affiliations

  1. UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA

    Nancy Y. A. Sey & Hyejung Won

  2. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    Nancy Y. A. Sey & Hyejung Won

  3. Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA

    Brandon M. Pratt

Authors
  1. Nancy Y. A. Sey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brandon M. Pratt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyejung Won
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.W. designed the H-MAGMA framework. N.Y.A.S. and B.M.P. wrote the codes with supervision from H.W. N.Y.A.S. wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Hyejung Won.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Protocols thanks Alexi Nott, Oliver Pain and Roddy Walsh for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Sey, N. Y. A. et al. Nat. Neurosci. 23, 583–593 (2020): https://doi.org/10.1038/s41593-020-0603-0

Hu, B. et al. Nat. Commun. 12, 3968 (2021): https://doi.org/10.1038/s41467-021-24243-0

Sey, N. Y. A. et al. Mol. Psychiatry 27, 3085–3094 (2022): https://doi.org/10.1038/s41380-022-01558-y

Key data used in this protocol

Wang, D. et al. Science 362, eaat8464 (2018): https://doi.org/10.1126/science.aat8464

Jung, I. et al. Nat. Genet. 51, 1442–1449 (2019): https://doi.org/10.1038/s41588-019-0494-8

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sey, N.Y.A., Pratt, B.M. & Won, H. Annotating genetic variants to target genes using H-MAGMA. Nat Protoc 18, 22–35 (2023). https://doi.org/10.1038/s41596-022-00745-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-022-00745-z

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics