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Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways

Nature Genetics volume 54, pages 1125–1132 (2022)Cite this article

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Abstract

Insomnia is a heritable, highly prevalent sleep disorder for which no sufficient treatment currently exists. Previous genome-wide association studies with up to 1.3 million subjects identified over 200 associated loci. This extreme polygenicity suggested that many more loci remain to be discovered. The current study almost doubled the sample size to 593,724 cases and 1,771,286 controls, thereby increasing statistical power, and identified 554 risk loci (including 364 novel loci). To capitalize on this large number of loci, we propose a novel strategy to prioritize genes using external biological resources and functional interactions between genes across risk loci. Of all 3,898 genes naively implicated from the risk loci, we prioritize 289 and find brain-tissue expression specificity and enrichment in specific gene sets of synaptic signaling functions and neuronal differentiation. We show that this novel gene prioritization strategy yields specific hypotheses on underlying mechanisms of insomnia that would have been missed by traditional approaches.

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Fig. 1: GWAS meta-analysis of insomnia in 2,365,010 individuals.
Fig. 2: Tissues, brain regions, cell types and gene sets associated with insomnia based on MAGMA analyses.
Fig. 3: Schematic overview of gene prioritization strategy from risk loci.
Fig. 4: Tissues and gene sets associated with the HCP genes.

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Data availability

The full GWAS summary statistics for UKB and the top 10,000 SNPs for 23andMe are available at https://ctg.cncr.nl/software/summary_statistics/. The full GWAS summary statistics for the 23andMe dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of 23andMe participants. Please visit https://research.23andme.com/collaborate/#publication for more information and to apply to access the data. The following publicly available datasets were used in this manuscript: GTEx v.8 (https://gtexportal.org/home/datasets), Allen Human Brain Atlas (http://human.brain-map.org/static/download), scRNA-seq from Linnerson’s laboratory (http://linnarssonlab.org/data/; GSE60361, GSE74672, GSE75330, GSE76381, GSE97478), DropViz (http://dropviz.org/), MsigDB v.6.2 (http://software.broadinstitute.org/gsea/msigdb/index.jsp), InWeb protein–protein interaction (https://inbio-discover.com/download), eQTLGen (https://www.eqtlgen.org/) and PsychEncode (http://resource.psychencode.org/).

Code availability

The R script used to perform gene prioritization approach proposed in this manuscript is available at https://doi.org/10.5281/zenodo.6598552 (ref. 77). The following software and packages were used for data analysis: PLINK 2.0 (https://www.cog-genomics.org/plink/2.0/), METAL (http://csg.sph.umich.edu/abecasis/Metal/download/), MAGMA v.1.07 (https://ctg.cncr.nl/software/magma), FUMA (https://fuma.ctglab.nl/), LDscore (https://github.com/bulik/ldsc), LDstore v.1.1 (http://www.christianbenner.com/#), FINEMAP v.1.3.1 (http://www.christianbenner.com/#), PRSice v.2.2.1 (https://www.prsice.info/), Eagle2 (https://alkesgroup.broadinstitute.org/Eagle/downloads/), Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3), REGENIE v.2.0.1 (https://rgcgithub.github.io/regenie/), MiXeR (https://github.com/precimed/mixer), BUHMBOX (https://software.broadinstitute.org/mpg/buhmbox/) and R v.3.6.0 (https://www.r-project.org/) with packages data.table v.1.12.2, GenomicRegion v.1.36.0, stats v.3.6.3, fpc v.2.2-3, coloc v.3.2-1, Rtsne v.0.15 and ggplot2 v.3.2.0.

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Acknowledgements

We thank both UKB and 23andMe participants who consented to participate in research, and researchers who collected and contributed the data. D.P. was funded by The Netherlands Organization for Scientific Research (no. NWO VICI 453-14-005), NWO Gravitation: BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology (grant no. 024.004.012) and a European Research Council advanced grant (no. ERC-2018-AdG GWAS2FUNC 834057). E.J.W.V.S. was funded by the European Research Council (no. ERC-ADG-2014-671084 INSOMNIA) and P.R.J. was funded by the Netherlands Organization for Scientific Research (no. ZonMW VENI-09150162010138). The research was conducted using the UK Biobank Resource (application no. 16406). Analyses were carried out on the Genetic Cluster Computer hosted by the Dutch National Computing and Networking Services, SurfSARA. We additionally thank the GTEx Portal for providing RNA-seq data. The research was based in part on data from the Million Veteran Program – Office of Research and Development, Veterans Health Administration, supported by award nos. CSP 575B and Merit 1I01CX001849.e.

Author information

Authors and Affiliations

  1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands

    Kyoko Watanabe, Philip R. Jansen, Jeanne E. Savage & Danielle Posthuma

  2. Department of Human Genetics, Section Clinical Genetics, Amsterdam University Medical Centers, Amsterdam, the Netherlands

    Philip R. Jansen

  3. 23andMe, Inc., Sunnyvale, CA, USA

    Priyanka Nandakumar, Xin Wang, Michelle Agee, Stella Aslibekyan, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah K. Clark, Sarah L. Elson, Kipper Fletez-Brant, Pierre Fontanillas, Nicholas A. Furlotte, Pooja M. Gandhi, Karl Heilbron, Barry Hicks, Karen E. Huber, Ethan M. Jewett, Yunxuan Jiang, Aaron Kleinman, Keng-Han Lin, Nadia K. Litterman, Jennifer C. McCreight, Matthew H. McIntyre, Kimberly F. McManus, Joanna L. Mountain, Sahar V. Mozaffari, Elizabeth S. Noblin, Carrie A. M. Northover, Jared O’Connell, Steven J. Pitts, G. David Poznik, J. Fah Sathirapongsasuti, Janie F. Shelton, Jing Shi, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Robert J. Tunney, Vladimir Vacic, Wei Wang & David A. Hinds

  4. Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA

    Joel Gelernter, Daniel F. Levey & Renato Polimanti

  5. Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA

    Joel Gelernter, Daniel F. Levey & Renato Polimanti

  6. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Murray B. Stein

  7. Psychiatry Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

    Murray B. Stein

  8. Departments of Integrative Neurophysiology and Psychiatry InGeest, Amsterdam Neuroscience, VU University and Medical Center, Amsterdam, the Netherlands

    Eus J. W. Van Someren

  9. Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands

    Eus J. W. Van Someren

  10. Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands

    August B. Smit

  11. Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Centers, Amsterdam, the Netherlands

    Danielle Posthuma

Authors
  1. Kyoko Watanabe
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  2. Philip R. Jansen
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  3. Jeanne E. Savage
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  4. Priyanka Nandakumar
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  5. Xin Wang
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  6. David A. Hinds
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  7. Joel Gelernter
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  8. Daniel F. Levey
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  9. Renato Polimanti
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  10. Murray B. Stein
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  11. Eus J. W. Van Someren
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  13. Danielle Posthuma
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Consortia

23andMe Research Team

  • Michelle Agee
  • , Stella Aslibekyan
  • , Adam Auton
  • , Robert K. Bell
  • , Katarzyna Bryc
  • , Sarah K. Clark
  • , Sarah L. Elson
  • , Kipper Fletez-Brant
  • , Pierre Fontanillas
  • , Nicholas A. Furlotte
  • , Pooja M. Gandhi
  • , Karl Heilbron
  • , Barry Hicks
  • , Karen E. Huber
  • , Ethan M. Jewett
  • , Yunxuan Jiang
  • , Aaron Kleinman
  • , Keng-Han Lin
  • , Nadia K. Litterman
  • , Jennifer C. McCreight
  • , Matthew H. McIntyre
  • , Kimberly F. McManus
  • , Joanna L. Mountain
  • , Sahar V. Mozaffari
  • , Elizabeth S. Noblin
  • , Carrie A. M. Northover
  • , Jared O’Connell
  • , Steven J. Pitts
  • , G. David Poznik
  • , J. Fah Sathirapongsasuti
  • , Janie F. Shelton
  • , Jing Shi
  • , Suyash Shringarpure
  • , Chao Tian
  • , Joyce Y. Tung
  • , Robert J. Tunney
  • , Vladimir Vacic
  •  & Wei Wang

Contributions

D.P. conceived the study. K.W. performed analyses. J.F.S. performed quality control on the UKB data and wrote the analysis pipeline. P.N., D.A.H., X.W. and the 23andMe Research Team contributed and analyzed the 23andMe cohort data. J.G., D.F.L., R.P. and M.B.S. performed PGS analysis for the MVP cohort. E.J.W.V.S and A.B.S provided valuable discussions. K.W., P.R.J. and D.P. wrote the paper.

Corresponding author

Correspondence to Danielle Posthuma.

Ethics declarations

Competing interests

P.N., X.W., D.A.H. and members of the 23andMe research team are employees of 23andMe, Inc. and hold stock or stock options in 23andMe, Inc. K.W. is a current employee of Regeneron Pharmaceuticals and holds stock and stock options in Regeneron Pharmaceuticals. The remaining authors declare no competing interests.

Peer review

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Phenotypic variance explained by polygenic risk scoring.

Bars are colored by P-value threshold of SNPs used to compute the polygenic risk score.

Extended Data Fig. 2 Genetic overlap between insomnia and 350 traits.

Significant genetic correlations of insomnia with 350 traits after Bonferroni correction (p < 9.07e-5). P-values were based on two-sided Z-test. Each data point represents a trait and is colored by the domain category.

Extended Data Fig. 3 Distribution of PRS based on metabolic and psychiatric loci.

A single star represents nominal significant (p<0.05) and double star represents significant after Bonferroni correction (p<0.05/9) of two-sided Mann-Whitney U test (see Supplementary Table 21 for full results). The boxes indicate 25% (Q1) and 75% (Q3) quantiles and horizontal black lines indelicate median. The minimum and maximum of the whisker are Q1-1.5*IQR and Q3+1.5*IQR where IQR is Q3-Q1. Data points which do not fall within the whisker’s interval are displayed as dots. Number of data points (individuals) are: for column 1 (based on metabolic loci) 300 top and 299 bottom 1%, 1495 top and bottom 5%, 2986 top and 2984 bottom 10% for overall health rating, 297 top and bottom 1%, 1475 top and 1471 bottom 5%, 2950 top and 2948 bottom 10% for body fat percentage, 281 top and 283 bottom 1%, 1405 top and 1402 bottom 5%, 2812 top and 2815 bottom 10% for depressive symptoms, for column 2 (based on psychiatric loci) 299 top and 298 bottom 1%, 1490 top and 1493 bottom 5%, 2986 top and 2990 bottom 10% for overall health rating, 297 top and 294 bottom 1%, 1470 top and 1477 bottom 5%, 2941 top and 2959 bottom 10% for body fat percentage, 287 top and 285 bottom 1%, 1403 top and 1418 bottom 5%, 2809 top and 2844 bottom 10% for depressive symptoms.

Extended Data Fig. 4 Additional conditional analyses for MAGMA tissue and brain region association analyses.

P-values were computed by MAGMA gene analysis based on one-sided T-test for the regression coefficient of the gene expression. (a) P-values of brain regions from GTEx, with (Conditional) and without (Marginal) conditioning on the average expression across 13 brain regions. (b) Comparison of AHBA (low resolution) and DropViz datasets with MAGMA gene-property analysis. P-values (top) and standardized effect size (Beta, bottom) of brain regions from the AHBA low dataset and cell types from the DropViz dataset. The most left bar indicates the marginal association statistics for each item. The middle bar indicates the association statistics based only on genes present in both datasets (~11,000 genes). The most right bar indicates the association statistics based only on genes that are not available in the other dataset (~2,000 for AHBA low and ~4,000 for DropViz). The horizontal dashed line indicates the Bonferroni corrected threshold for statistical significance (p=0.05/5974).

Extended Data Fig. 5 MAGMA gene-property and gene-set analyses conditioning on sets of genes from insomnia risk loci.

The top (most significantly associated) 5 brain regions/cell types/gene-sets (referred to as gene-sets hereafter) were selected for each dataset, except for DropViz where 4 independently associated cell types were selected. For each gene-set, MAGMA was performed while conditioning on 3 sets of genes; high-confidence prioritized (HCP), unsolved and excluded genes.

Extended Data Fig. 6 Heatmap of the overlap of genes across significantly enriched gene-sets.

The displayed 18 gene-sets showed significant enrichment with 289 HCP genes. The heatmap is asymmetric. A cell of row i and column j represents the proportion of the prioritized genes in the gene-set i relative to the number of prioritized genes in the gene-set j.

Supplementary information

Supplementary Information

Supplementary note and Figs. 1–8.

Reporting Summary

Peer Review File

Supplementary Data 1

Locus Zoom plots for 554 loci.

Supplementary Tables

Supplementary Tables 1–49.

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Watanabe, K., Jansen, P.R., Savage, J.E. et al. Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. Nat Genet 54, 1125–1132 (2022). https://doi.org/10.1038/s41588-022-01124-w

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