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Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets

Nature Genetics volume 56, pages 1310–1318 (2024)Cite this article

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Abstract

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

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Fig. 1: Selected results from simulations.
Fig. 2: Main GSA-MiXeR results for schizophrenia.
Fig. 3: Exploratory GSA-MiXeR results for schizophrenia.
Fig. 4: Replication analysis for schizophrenia.

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Data availability

The datasets analyzed in this study are available for download from the following URLs: Schizophrenia GWAS58 from PGC, https://pgc.unc.edu/for-researchers/download-results/; Molecular Signatures Database v7.5, https://www.gsea-msigdb.org/gsea/msigdb/; Synaptic Gene Ontologies 20210225 release, https://syngoportal.org/; functional categories (baselineLD_v2.2), https://alkesgroup.broadinstitute.org/LDSCORE/; 1000 Genomes Phase3 data, http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. UKB, https://ams.ukbiobank.ac.uk/ams/; HRC Release 1.1, https://ega-archive.org/datasets/EGAD00001002729; TOP cohort, https://app.cristin.no/projects/show.jsf?id=2505365, and country-level GWAS summary statistics for PGC SCZ, https://pgc.unc.edu/for-researchers/data-access-committee/data-access-information/, have controlled data access. These data are not publicly available due to national data privacy regulations as they contain information that could compromise research participant privacy and/or consent. Statistical source data are provided for Figs. 1–4. The minimum dataset59 of this study is made publicly available and contains reference data formatted to GSA-MiXeR format, including definitions of genes, gene sets, functional categories and sharable nonsensitive data derived from UKB and HRC references. Source data are provided with this paper.

Code availability

The GSA-MiXeR software (v1.0.0), user tutorial and all codes required to process input data and generate the major results of this study are available from https://github.com/precimed/gsa-mixer60. Additionally, the following software packages were involved in performing data analysis: PLINK 1.90 build 20200616, https://www.cog-genomics.org/plink/1.9/; MAGMA v1.09b, https://ctg.cncr.nl/software/magma; LDAK v5.2, https://dougspeed.com/ldak-gbat/; KING v2.2.5, https://www.kingrelatedness.com/; flashpca 2.0, https://github.com/gabraham/flashpca; cleansumstats v1.6.0, https://github.com/BioPsyk/cleansumstats/; modified sLDSC v2023.05.15, https://github.com/ofrei/ldsc/tree/disable_jackknife; simu v0.9.3 pipeline to simulate synthetic GWAS from genotypes, https://github.com/precimed/simu; GWAS pipeline for UK Biobank v2, https://github.com/Nealelab/UK_Biobank_GWAS.

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Acknowledgements

Funding was provided by the Research Council of Norway (RCN) #334920 to K.S.O.; Norwegian Health Association #22731 to S.B.; European Union’s Horizon 2020 (EU H2020) under the Marie Skłodowska-Curie Actions (Scientia fellowship) #801133 to N.P.; RCN #223273, #273291, #300309, #324252, #324499, #326813, South-Eastern Norway Regional Health Authority (HSØ) #2022073, The Kristian Gerhard Jebsen Stiftelsen #SKGJ-MED-021, EU H2020 grant #847776 (CoMorMent) and #964874 (RealMent), EEA and Norway grant #EEA-RO-NO-2018-0573, American National Institutes of Health (NIH) grant 5R01MH124839-02 (PGC4) to O.A.A.; NIH grants U24DA041123; R01AG076838; U24DA055330, OT2 HL161847 to A.M.D. This research has been conducted using the UK Biobank Resource under application number 27412. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), including Expanse and OASIS resources provided by San Diego Supercomputer Center at UC San Diego through allocation IBN200001. This work also used the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT, [email protected]), using resources provided by UNINETT Sigma2 – the National Infrastructure for High Performance Computing and Data Storage in Norway. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. A full list of members and their affiliations appears in Supplementary Information.

Authors and Affiliations

  1. Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Oleksandr Frei, Guy Hindley, Alexey A. Shadrin, Dennis van der Meer, Bayram C. Akdeniz, Espen Hagen, Weiqiu Cheng, Kevin S. O’Connell, Shahram Bahrami, Nadine Parker, Olav B. Smeland & Ole A. Andreassen

  2. Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway

    Oleksandr Frei & Bayram C. Akdeniz

  3. School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands

    Dennis van der Meer

  4. Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA

    Dominic Holland & Anders M. Dale

  5. Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands

    Christiaan de Leeuw & Danielle Posthuma

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  1. Oleksandr Frei
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Consortia

Schizophrenia Working Group of the Psychiatric Genomics Consortium

  • Oleksandr Frei
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Contributions

O.F. conceived the study; O.F., A.A.S., N.P., S.B. and K.S.O. preprocessed the data; O.F., D.v.d.M, B.C.A. and E.H. performed all analyses, with conceptual input from G.H., A.A.S., C.d.L., D.P., W.C., D.H., O.B.S., O.A.A. and A.M.D.; O.F. and G.H. drafted the manuscript; all authors contributed to and approved the final manuscript.

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Correspondence to Oleksandr Frei.

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Competing interests

A.M.D. was a Founder of and holds equity in CorTechs Labs, Inc., and serves on its Scientific Advisory Board. He is also a member of the Scientific Advisory Board of Human Longevity, Inc. (HLI), and the Mohn Medical Imaging and Visualization Centre in Bergen, Norway. He receives funding through a research agreement with General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. O.A.A. has received speaker fees from Lundbeck, Janssen, Otsuka and Sunovion and is a consultant to Cortechs.ai unrelated to the topic of this study. C.d.L. is funded by Hoffman-La Roche. The remaining authors have no competing interest.

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Frei, O., Hindley, G., Shadrin, A.A. et al. Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets. Nat Genet 56, 1310–1318 (2024). https://doi.org/10.1038/s41588-024-01771-1

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