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Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis

Abstract

We interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. We identify four broad factors (neurodevelopmental, compulsive, psychotic and internalizing) that underlie genetic correlations among the disorders and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce stratified genomic structural equation modeling, which we use to identify gene sets that disproportionately contribute to genetic risk sharing. This includes protein-truncating variant-intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for genetic overlap across disorders with psychotic features. Multivariate association analyses detect 152 (20 new) independent loci that act on the individual factors and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate-to-high genetic correlations across all 11 disorders, we find little utility of a single dimension of genetic risk across psychiatric disorders either at the level of biobehavioral correlates or at the level of individual variants.

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Fig. 1: Multivariate genetic architecture of 11 psychiatric disorders.
Fig. 2: Model comparisons for producing Q metrics.
Fig. 3: Genetic correlations with complex traits across psychiatric factors.
Fig. 4: Genetic correlations with accelerometer data across psychiatric disorders and factors.
Fig. 5: Genetic enrichment of factors for brain cell, PI and PI × brain cell annotations.
Fig. 6: Miami plots for psychiatric factors.

Data availability

The data that support the findings of this study are all publicly available or can be requested for access. Specific download links for various datasets are directly below.

Summary statistics for data from the PGC can be downloaded or requested here:

https://www.med.unc.edu/pgc/download-results/.

Summary statistics for the anxiety phenotype in UKB (TotANX_OR) can be downloaded here:

https://drive.google.com/drive/folders/1fguHvz7l2G45sbMI9h_veQun4aXNTy1v.

23andMe summary statistics are made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of 23andMe participants. Please visit research.23andme.com/collaborate/#publication for more information.

Summary statistics for the volume-based neuroimaging phenotypes were downloaded from https://github.com/BIG-S2/GWAS.

Summary statistics for the health and well-being complex trait correlations can be downloaded from https://atlas.ctglab.nl/.

Summary statistics for the circadian rhythm correlations across 24 hours can be downloaded from https://cnsgenomics.com/software/gcta/#DataResource.

Data from gnomAD used to identify PI genes for creation of annotations can be downloaded here: https://storage.googleapis.com/gnomad-public/release/2.1.1/constraint/gnomad.v2.1.1.lof_metrics.by_gene.txt.bgz.

Gene count data per cell for creation of annotations were obtained from https://storage.googleapis.com/gtex_additional_datasets/single_cell_data/GTEx_droncseq_hip_pcf.tar.

Data that map individual cells to cell types (e.g., neuron, astrocyte) were obtained from https://static-content.springer.com/esm/art%3A10.1038%2Fnmeth.4407/MediaObjects/41592_2017_BFnmeth4407_MOESM10_ESM.xlsx.

Links to the LD scores, reference panel data and the code used to produce the current results can all be found at https://github.com/MichelNivard/GenomicSEM/wiki.

Links to the BaselineLD v2.2 annotations can be found here: https://data.broadinstitute.org/alkesgroup/LDSCORE/.

https://data.broadinstitute.org/alkesgroup/LDSCORE/

Code availability

Genomic SEM software (which now includes the stratified genomic SEM extension) is an R package that is available from GitHub at the following URL: https://github.com/GenomicSEM/GenomicSEM.

Directions for installing the genomic SEM R package can be found at https://github.com/GenomicSEM/GenomicSEM/wiki.

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Acknowledgements

This work presented here would not have been possible without the enormous efforts put forth by the investigators and participants from Psychiatric Genetics Consortium, iPSYCH, UK Biobank and 23andMe. The work from these contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donation. Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health (NIH) under Award Number R01MH120219. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. A.D.G. was additionally supported by NIH grant R01HD083613. E.M.T.-D. was additionally supported by NIH grants R01AG054628 and R01HD083613 and the Jacobs Foundation. E.M.T.-D. is a faculty associate of the Population Research Center at the University of Texas, which is supported by NIH grant P2CHD042849, and the Center on Aging and Population Sciences, which is supported by NIH grant P30AG066614. M.G.N. is additionally supported by ZonMW grants 849200011 and 531003014 from The Netherlands Organisation for Health Research and Development and a VENI grant awarded by NWO (VI.Veni.191 G.030) and is a Jacobs Foundation Fellow. W.A.A. is supported by the European Union’s Horizon 2020 Research and Innovation Programme, Marie Sklodowska Curie Actions – MSCA-ITN-2016 – Innovative Training Networks under grant agreement 721567. H.F.I. is supported by the Aggression in Children: unraveling gene-environment interplay to inform Treatment and InterventiON strategies (ACTION) project. ACTION receives funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement 602768. C.M.L. is supported by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. A.M.M. is supported by the Wellcome Trust (104036/Z/14/Z, 216767/Z/19/Z), UKRI MRC (MC_PC_17209, MR/S035818/1). K.-P.L. is supported by the Deutsche Forschungsgemeinschaft (DFG: CRU 125, CRC TRR 58 A1/A5, No. 44541416), the European Union’s Seventh Framework Programme under grant 602805 (Aggressotype), the Horizon 2020 Research and Innovation Programme under grant 728018 (Eat2beNICE) and 643051 (MiND), Fritz Thyssen Foundation (10.13.1185), ERA-Net NEURON/RESPOND 01EW1602B, ERA-Net NEURON/DECODE FKZ01EW1902 and 5-100 Russian Academic Excellence Project. G.B. is supported by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. P.H.L. is supported by NIH grants R01MH119243 and R00MH101367. The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the EU FP7 Program (grant 602805, ‘Aggressotype’) and H2020 Program (grant 667302, ‘CoCA’), NIMH (1U01MH109514-01 to ADB) and the universities and university hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing (Aarhus University, Denmark) (grant to A.D.B.).

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Contributions

A.D.G., M.G.N. and E.M.T.-D. designed the study. A.D.G., M.G.N. and E.M.T.-D. developed the methods used in this study. A.D.G., H.F.I., M.G.N. and E.M.T.-D. developed the software used in this study. A.D.G., M.G.N. and E,M.T.-D. performed the simulation studies. W.A.A., A.D.G. and M.G.N. created gene sets and annotations. A.D.G., T.T.M., M.G.N. and E.M.T.-D. contributed to genetic factor modeling, multivariate GWASs, complex trait correlations and multivariate enrichment analyses. A.D.G., M.G.N. and E.M.T.-D. wrote the manuscript, and A.D.G., T.T.M., W.A.A., H.F.I., M.J.A., C.M.L., A.M.M., J.G., S.D., K.-P.L., N.S., S.M.M., M.M., A.D.B., O.M., G.B., P.H.L., K.S.K., J.W.S., E.M.T.-D. and M.G.N. provided manuscript feedback and editing.

Corresponding author

Correspondence to Andrew D. Grotzinger.

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Competing interests

J.W.S. is an unpaid member of the Bipolar/Depression Research Community Advisory Panel of 23andMe. C.M.L. is on the SAB for Myriad Neuroscience. G.B. is a scientific advisor for COMPASS Pathways. The other authors declare no competing interests.

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Grotzinger, A.D., Mallard, T.T., Akingbuwa, W.A. et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat Genet 54, 548–559 (2022). https://doi.org/10.1038/s41588-022-01057-4

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