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Associated genetics and connectomic circuitry in schizophrenia and bipolar disorder

  • Yongbin Wei
    Correspondence
    Corresponding Author: Yongbin Wei, PhD, Xi-Tu-Cheng Rd. 10, Haidian, Beijing, 100876, China. , Telephone: +86 156 523 15657; Fax: +86 010 203 66595
    Affiliations
    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, the Netherlands
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  • Siemon C. de Lange
    Affiliations
    Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, the Netherlands

    Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, 1105 BA, The Netherlands
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  • Jeanne E. Savage
    Affiliations
    Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, the Netherlands
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  • Elleke Tissink
    Affiliations
    Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, the Netherlands
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  • Ting Qi
    Affiliations
    Department of Neurology, School of Medicine, University of California San Francisco, San Francisco, CA 94143, the USA
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  • Jonathan Repple
    Affiliations
    Institute for Translational Psychiatry, University of Muenster, Muenster, 48149, Germany
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  • Marius Gruber
    Affiliations
    Institute for Translational Psychiatry, University of Muenster, Muenster, 48149, Germany
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  • Tilo Kircher
    Affiliations
    Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, 35037, Germany
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  • Udo Dannlowski
    Affiliations
    Institute for Translational Psychiatry, University of Muenster, Muenster, 48149, Germany
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  • Danielle Posthuma
    Affiliations
    Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, the Netherlands

    Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam 1081HV, the Netherlands
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  • Martijn P. van den Heuvel
    Correspondence
    Corresponding Author: Martijn P. van den Heuvel, PhD, De Boelelaan 1085, W&N B-651, Amsterdam, 1081 HV, the Netherlands. , Telephone: +31 20 59 83343, Fax: +31 20 59 83343
    Affiliations
    Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, the Netherlands

    Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam 1081HV, the Netherlands
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Open AccessPublished:November 09, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.11.006

      Abstract

      BACKGROUND

      Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions that can involve symptoms of psychosis and cognitive dysfunction. The two conditions share symptomatology and genetic etiology and are regularly hypothesized to share underlying neuropathology. Here we examined how genetic liability to SCZ and BD shapes normative variations in brain connectivity.

      METHODS

      We examined the effect of the combined genetic liability for SCZ and BD on brain connectivity from two perspectives. First, we examined the association between polygenic scores (PGS) for SCZ and BD for 19,778 healthy subjects from the UK Biobank and individual variation in brain structural connectivity reconstructed by means of diffusion weighted imaging data. Second, we conducted a genome-wide association study (GWAS) using genotypic and imaging data from the UK Biobank, taking SCZ-/BD-involved brain circuits as a phenotype of interest.

      RESULTS

      Our findings show brain circuits of superior parietal and posterior cingulate regions to be associated with polygenic liability for SCZ and BD, circuitry that overlaps with brain networks involved in disease conditions (r = 0.239, p < 0.001). GWAS analysis shows 9 significant genomic loci associated with SCZ-involved circuits and 14 loci associated with BD-involved circuits. Genes related to SCZ/BD-involved circuits are significantly enriched in gene sets previously reported in GWAS for SCZ and BD.

      CONCLUSIONS

      Our findings suggest that polygenic liability of SCZ and BD is associated with normative individual variation in brain circuitry.

      Keywords

      Introduction

      Schizophrenia (SCZ) and bipolar disorder (BD) are psychiatric disorders affecting an estimated 3% of the population worldwide (
      • Merikangas K.R.
      • Jin R.
      • He J.-P.
      • Kessler R.C.
      • Lee S.
      • Sampson N.A.
      • et al.
      Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative.
      ,
      • McGrath J.
      • Saha S.
      • Chant D.
      • Welham J.
      Schizophrenia: a concise overview of incidence, prevalence, and mortality.
      ). SCZ is characterized by symptoms of delusions, hallucinations, affective flattening, and cognitive dysfunction, and BD is characterized by periodic symptoms of mania and depression (
      • Cosgrove V.E.
      • Suppes T.
      Informing DSM-5: biological boundaries between bipolar I disorder, schizoaffective disorder, and schizophrenia.
      ). The two disorders share mood and psychotic symptoms (
      • Pearlson G.D.
      Etiologic, phenomenologic, and endophenotypic overlap of schizophrenia and bipolar disorder.
      ), show high comorbidity (
      • Laursen T.M.
      • Agerbo E.
      • Pedersen C.B.
      Bipolar disorder, schizoaffective disorder, and schizophrenia overlap: a new comorbidity index.
      ), are both highly heritable (
      • Nöthen M.M.
      • Nieratschker V.
      • Cichon S.
      • Rietschel M.
      New findings in the genetics of major psychoses.
      ), and are believed to overlap in their genetic background (
      Schizophrenia Working Group of the Psychiatric Genomics, Consortium
      Biological insights from 108 schizophrenia-associated genetic loci.
      ,
      • Stahl E.A.
      • Breen G.
      • Forstner A.J.
      • McQuillin A.
      • Ripke S.
      • Trubetskoy V.
      • et al.
      Genome-wide association study identifies 30 loci associated with bipolar disorder.
      ) with a high genetic correlation (∼0.7) (
      • Lee S.H.
      • Ripke S.
      • Neale B.M.
      • Faraone S.V.
      • Purcell S.M.
      • et al.
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.
      ) and several overlapping sets of involved risk genes and pathways (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ,

      Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. (2014): Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry 19: 1017–1024.

      ).
      Their shared genetic background suggests the involvement of common biological mechanisms. Neuropathological and neuroimaging studies have pointed out that SCZ and BD share major pathophysiological changes, such as a loss in dendritic spines of pyramidal neurons in the prefrontal cortex (
      • Konopaske G.T.
      • Lange N.
      • Coyle J.T.
      • Benes F.M.
      Prefrontal cortical dendritic spine pathology in schizophrenia and bipolar disorder.
      ), decreased density of interneurons in the parahippocampal cortex (
      • Wang A.Y.
      • Lohmann K.M.
      • Yang C.K.
      • Zimmerman E.I.
      • Pantazopoulos H.
      • Herring N.
      • et al.
      Bipolar disorder type 1 and schizophrenia are accompanied by decreased density of parvalbumin- and somatostatin-positive interneurons in the parahippocampal region.
      ), and gray matter volume disruptions (
      • Chang M.
      • Womer F.Y.
      • Edmiston E.K.
      • Bai C.
      • Zhou Q.
      • Jiang X.
      • et al.
      Neurobiological Commonalities and Distinctions Among Three Major Psychiatric Diagnostic Categories: A Structural MRI Study.
      ). The two conditions further show overlapping pathology in terms of abnormalities in white-matter tracts such as the uncinate fasciculus (
      • Sussmann J.E.
      • Lymer G.K.S.
      • McKirdy J.
      • TWJ Moorhead
      • Muñoz Maniega S.
      • Job D.
      • et al.
      White matter abnormalities in bipolar disorder and schizophrenia detected using diffusion tensor magnetic resonance imaging.
      ) and short-range connections among brain regions relevant to language processing, mood regulation, and working memory (
      • Ji E.
      • Guevara P.
      • Guevara M.
      • Grigis A.
      • Labra N.
      • Sarrazin S.
      • et al.
      Increased and Decreased Superficial White Matter Structural Connectivity in Schizophrenia and Bipolar Disorder.
      ). Specifying to what extent individual variation in brain connectivity is directly associated with underlying unique and shared polygenic liability can improve our knowledge of the combined genetic and connectomic etiology of psychiatric conditions (
      • van den Heuvel M.P.
      • Sporns O.
      A cross-disorder connectome landscape of brain dysconnectivity.
      ,
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders.
      ).
      We first examined polygenic scores (PGS) that quantitatively estimate an individual’s genetic predisposition for SCZ and BD based on genomic variants (
      • Choi S.W.
      • Mak T.S.-H.
      • O’Reilly P.F.
      Tutorial: a guide to performing polygenic risk score analyses.
      ). Combining genetic and neuroimaging data of the UK Biobank (
      • Sudlow C.
      • Gallacher J.
      • Allen N.
      • Beral V.
      • Burton P.
      • Danesh J.
      • et al.
      UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.
      ) with neuroimaging data from disease cohorts (Part I) (
      • Çetin M.S.
      • Christensen F.
      • Abbott C.C.
      • Stephen J.M.
      • Mayer A.R.
      • Cañive J.M.
      • et al.
      Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia.
      ,
      • Wang L.
      • Alpert K.I.
      • Calhoun V.D.
      • Cobia D.J.
      • Keator D.B.
      • King M.D.
      • et al.
      SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration.
      ), we examined structural brain circuits related to combined polygenic effects for SCZ and BD in the healthy brain and demonstrated their relationship to connectivity-based pathology in disease conditions. Second, we examined the genetic-connectomic association by means of conducting GWAS on SCZ- and BD-involved brain circuits (Part II), stressing genomic variants associated with brain circuits to play a role in psychiatric conditions.

      Methods and Materials

      Part I. Examining associations between polygenic scores and brain connectivity

      Participants from the UK Biobank. Neuroimaging and genotypic data of 38,436 subjects from the UK Biobank (UKB; access number 16406; February 2020) (
      • Sudlow C.
      • Gallacher J.
      • Allen N.
      • Beral V.
      • Burton P.
      • Danesh J.
      • et al.
      UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.
      ,
      • Alfaro-Almagro F.
      • McCarthy P.
      • Afyouni S.
      • Andersson J.L.R.
      • Bastiani M.
      • Miller K.L.
      • et al.
      Confound modelling in UK Biobank brain imaging.
      ) were used. A hold-out sample of 5,000 subjects was selected for validation purposes, making a discovery set of 33,436 participants and a replication set of 5,000 participants. Strict quality control (QC) was conducted for both neuroimaging and genotypic data to exclude unreliable data samples (Supplemental Methods). Remaining data samples (N = 26,703 out of 38,436) were divided into a healthy sample (N = 19,778; discovery/hold-out: 17,189/2,589) and a disease sample (N = 6,815; including 26 SCZ and 124 BD) (Supplemental Methods and Table S1).
      Participants from disease cohorts. Neuroimaging data from two independent cohorts of SCZ and BD were used to assess disease-associated brain circuits. The SCZ dataset included 58 SCZ and 77 age-, sex-matched healthy controls (HC), obtained from the open SchizoConnect COBRE dataset (
      • Çetin M.S.
      • Christensen F.
      • Abbott C.C.
      • Stephen J.M.
      • Mayer A.R.
      • Cañive J.M.
      • et al.
      Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia.
      ,
      • Wang L.
      • Alpert K.I.
      • Calhoun V.D.
      • Cobia D.J.
      • Keator D.B.
      • King M.D.
      • et al.
      SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration.
      ). The BD dataset included 84 BD and 326 age-, sex-matched HC, as part of the Marburg-Münster Affective Disorders Cohort Study (MACS) collected at two sites (University of Marburg and University of Münster) (

      Vogelbacher C, Möbius TWD, Sommer J, Schuster V, Dannlowski U, Kircher T, et al. (2018): The Marburg-Münster Affective Disorders Cohort Study (MACS): A quality assurance protocol for MR neuroimaging data. Neuroimage 172: 450–460.

      ,
      • Kircher T.
      • Wöhr M.
      • Nenadic I.
      • Schwarting R.
      • Schratt G.
      • Alferink J.
      • et al.
      Neurobiology of the major psychoses: a translational perspective on brain structure and function-the FOR2107 consortium.
      ) (Supplemental Methods and Table S1).
      Genotype data. Imputed genotype data of 9,203,453 genetic variants from the UK Biobank were studied (
      • Bycroft C.
      • Freeman C.
      • Petkova D.
      • Band G.
      • Elliott L.T.
      • Sharp K.
      • et al.
      The UK Biobank resource with deep phenotyping and genomic data.
      ) [details described in (
      • Bycroft C.
      • Freeman C.
      • Petkova D.
      • Band G.
      • Elliott L.T.
      • Sharp K.
      • et al.
      The UK Biobank resource with deep phenotyping and genomic data.
      ,
      • Savage J.E.
      • Jansen P.R.
      • Stringer S.
      • Watanabe K.
      • Bryois J.
      • de Leeuw C.A.
      • et al.
      Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence.
      )]. Subjects of European ancestry were included in the current study (
      • Savage J.E.
      • Jansen P.R.
      • Stringer S.
      • Watanabe K.
      • Bryois J.
      • de Leeuw C.A.
      • et al.
      Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence.
      ) (Supplemental Methods). Genetic principal components (PCs) were computed within the full UKB sample, based on a set of 145,432 independent (r2 < 0.1) autosomal SNPs using FlashPCA (
      • Abraham G.
      • Qiu Y.
      • Inouye M.
      FlashPCA2: principal component analysis of Biobank-scale genotype datasets.
      ). The first 20 PCs were used as covariates to correct for population stratification (
      • Jansen P.R.
      • Muetzel R.L.
      • Polderman T.J.C.
      • Jaddoe V.W.
      • Verhulst F.C.
      • van der Lugt A.
      • et al.
      Polygenic Scores for Neuropsychiatric Traits and White Matter Microstructure in the Pediatric Population.
      ).
      Polygenic score calculation. Polygenic scores (PGSs) were calculated on imputed genotype data for each individual from the UK Biobank, based on summary statistics from GWASs on SCZ and BD (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ) (Supplemental Table S2). PGS regarding combined polygenic effects for SCZ and BD (referred to as SCZ+BD) was computed using the GWAS comparing combined SCZ and BD patients to HCs (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ). PGS for the differentiated polygenic effects between SCZ and BD was also calculated (referred to as SCZ-BD) (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ). Summary statistics from the two most recent GWASs for SCZ (
      • Trubetskoy V.
      • Pardiñas A.F.
      • Qi T.
      • Panagiotaropoulou G.
      • Awasthi S.
      • Bigdeli T.B.
      • et al.
      Mapping genomic loci implicates genes and synaptic biology in schizophrenia.
      ) and BD (
      • Mullins N.
      • Forstner A.J.
      • O’Connell K.S.
      • Coombes B.
      • Coleman J.R.I.
      • Qiao Z.
      • et al.
      Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.
      ) were included for validation purposes. Eleven GWASs on other psychiatric and neurological conditions were used to examine to what extent results relevant to SCZ and BD are specific and/or are generalizable to other conditions (Supplemental Table S2). PGS calculation was performed using PRSice-2 (
      • Choi S.W.
      • Mak T.S.-H.
      • O’Reilly P.F.
      Tutorial: a guide to performing polygenic risk score analyses.
      ,
      • Choi S.W.
      • O’Reilly P.F.
      PRSice-2: Polygenic Risk Score software for biobank-scale data.
      ) for a range of p-value thresholds for inclusions of SNPs (p < 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, and 0.5). Results derived from p < .01 are reported in the main results, results on alternative thresholds are reported in the Supplemental Figures S2∼6. PGSs were also computed based on an optimal threshold [by PRSice-2 (
      • Choi S.W.
      • O’Reilly P.F.
      PRSice-2: Polygenic Risk Score software for biobank-scale data.
      )] identified according to case prediction within the entire UKB sample (Supplemental Results).
      MRI data. T1-weighted MRI and diffusion weighted imaging (DWI) data were used for reconstruction of brain connectivity circuits. Scanning parameters and data processing are summarized in (
      • Alfaro-Almagro F.
      • Jenkinson M.
      • Bangerter N.K.
      • Andersson J.L.R.
      • Griffanti L.
      • Douaud G.
      • et al.
      Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.
      ,
      • Miller K.L.
      • Alfaro-Almagro F.
      • Bangerter N.K.
      • Thomas D.L.
      • Yacoub E.
      • Xu J.
      • et al.
      Multimodal population brain imaging in the UK Biobank prospective epidemiological study.
      ) and Supplemental Methods. A 114×114 connectivity matrix describing all reconstructed region-to-region connections was formed for each subject using FreeSurfer (v6.0) and CATO (v3.1.2) (

      de Lange SC, van den Heuvel MP (2021, May 31): Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox. BioRxiv. p 2021.05.31.446012.

      ,
      • Wei Y.
      • Collin G.
      • Mandl R.C.W.
      • Cahn W.
      • Keunen K.
      • Schmidt R.
      • et al.
      Cortical magnetization transfer abnormalities and connectome dysconnectivity in schizophrenia.
      ,
      • Fischl B.
      ) (details in the Supplemental methods). Considering a high heritability as evidenced by both twin MRI studies (
      • Arnatkeviciute A.
      • Fulcher B.D.
      • Oldham S.
      • Tiego J.
      • Paquola C.
      • Gerring Z.
      • et al.
      Genetic influences on hub connectivity of the human connectome.
      ) and GWAS (

      Zhao B, Zhang J, Ibrahim JG, Luo T, Santelli RC (2021): Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n= 17,706). Molecular. Retrieved from https://idp.nature.com/authorize/casa?redirect_uri=https://www.nature.com/articles/s41380-019-0569-z&casa_token=1HHPV8ZGWrUAAAAA:2qwMIHCMZWaI_fxAc-BdLx8dJ0zvOktV2FkIwVp26DItE-YKEd_7ojFyh0eX4q-tVXJkLoU53BIYPmnW

      ), mean fractional anisotropy (FA) of reconstructed tractography streamlines was taken as a metric of the strength of connections. Results of streamline density weighted connectivity are summarized in Supplemental Results.
      Linear regression analysis. Linear regression analysis was used on the discovery dataset to identify relationships between polygenic effects for the different contrasts (SCZ, BD, SCZ+BD, and SCZ-BD) and brain connectivity (global-, regional-, and connection-wise). Global connectivity strength (mean strength across connections), regional connectivity strength (mean strength of connections of a region), and strength of single connections were used respectively for global-, regional-, and connection-wise analysis. For connection-wise analysis, group-thresholding was applied by selecting consistent connections (N = 1,311) that were mapped in >60% of the subjects (
      • de Reus M.A.
      • van den Heuvel M.P.
      Estimating false positives and negatives in brain networks.
      ). PGS-connectivity association was examined using:
      yi=β0+β1xi+β2ci1++βp+1cip+εi
      where xi indicates the standardized PGS of subject i, yi the standardized connectivity strength, cip the standardized pth covariate, and εi the residual. Age, sex, genotyping array, assessment center, and 20 ancestry PCs were included as the p covariates in the model. The standardized regression coefficient β indicates the effect size, with t-tests performed to express the corresponding p-value. Network-based statistic (NBS) analysis (
      • Zalesky A.
      • Fornito A.
      • Bullmore E.T.
      Network-based statistic: identifying differences in brain networks.
      ) was used to control family-wise error rate and identify subnetworks showing significant PGS-connectome associations (Supplemental Methods).
      Cross-reference to disease conditions. Connectivity maps were similarly formed for the 26 SCZ and 124 BD cases in the UKB dataset. Two-tailed two-sample t-tests were performed on the 1,311 connections in the connectivity matrix to assess connection-wise differences for SCZ+BD compared to matched healthy controls (150 randomly selected HC, matched for age, sex on respectively the SCZ and BD patient group). Resulting t-scores were correlated to the standardized regression coefficient β obtained in the PGS-connectivity association analysis across all 1,311 connections, with permutation testing performed to rule out the spatial auto-correlation effects (Supplemental Methods). Similar analyses were conducted for the COBRE SCZ dataset and the MACS BD dataset for validation.

      Part II. GWAS analysis on SCZ-/BD-involved brain circuits

      Above analyses focused on identifying brain connectivity in relationship to PGS for SCZ and BD in healthy subjects. We further examined the genetic-connectomic association using GWAS analysis on a phenotype capturing subnetworks of connections related to SCZ and BD. SCZ-involved connections and BD-involved connections were selected using the external COBRE SCZ dataset and the MACS BD dataset, computed by means of two sample t-tests on all connections between the patients and HC in these datasets. Disease-involved connections were selected if two-sided p < 0.05 and t-score < 0 (i.e. connectivity strength reduced in patients; resulting in 46 SCZ-involved connections and 100 BD-involved connections). Next, in the UKB sample (N = 22,799, including both healthy and disease samples; results on healthy samples only were shown in the Supplemental Results), the mean strength of the selected SCZ- and BD-involved connections was computed and taken as the phenotype of interest in a following GWAS analysis. GWAS was conducted in PLINK v2.00 (
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.R.
      • Bender D.
      • et al.
      PLINK: a tool set for whole-genome association and population-based linkage analyses.
      ), using an additive linear regression model controlling for covariates of age, sex, twenty European-based ancestry PCs, genotyping array, assessment center, and two QC metrics of DWI data (Supplemental Methods). Total brain volume was additionally taken as a covariate to rule out genetic effects that are generally related to the brain. Genetic correlation analysis was conducted between the resulting GWAS summary statistics and the summary statistics for SCZ+BD, SCZ, BD, and SCZ-BD, using linkage disequilibrium score regression (LDSC) (

      Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. (2015): LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47: 291–295.

      ,
      • Bulik-Sullivan B.
      • Finucane H.K.
      • Anttila V.
      • Gusev A.
      • Day F.R.
      • Loh P.-R.
      • et al.
      An atlas of genetic correlations across human diseases and traits.
      ).

      Results

      Part I. Connectome-wide associations of the combined polygenic effects for SCZ and BD

      Sample characteristics. Polygenic scores for SCZ, BD and SCZ+BD were computed for 17,189 UKB healthy subjects. PGS for SCZ+BD was positively associated with PGS for SCZ (r = 0.705, p < .001), PGS for BD (r = 0.465, p < .001), and PGS for SCZ-BD (r = 0.101, p < .001). UKB SCZ and BD samples showed significantly higher PGS compared to UKB healthy samples (SCZ+BD PGS: t = 5.564, p < .001; SCZ PGS: t = 2.412, p = .016; BD PGS: t = 5.011, p < .001; Supplemental Table S3). Similar results were observed in the entire UKB sample of 641 SCZ and 1,455 BD cases (Supplemental Results).
      PGS-connectomic association in the healthy population. PGS for SCZ+BD was negatively associated with global connectivity strength across healthy subjects (β = -0.021, pfdr = .012, FDR corrected across four tests), indicative of healthy individuals with a higher SCZ+BD PGS to show overall lower levels of connectivity strength in their brain network. Follow-up analysis showed significant associations when examining only SCZ PGS (β = -0.023, pfdr = .011), but not for PGS for BD (β = -0.003, pfdr = .661) and SCZ-BD (β = -0.008, pfdr = .274). Using PGS calculated on the more recent SCZ GWAS (
      • Trubetskoy V.
      • Pardiñas A.F.
      • Qi T.
      • Panagiotaropoulou G.
      • Awasthi S.
      • Bigdeli T.B.
      • et al.
      Mapping genomic loci implicates genes and synaptic biology in schizophrenia.
      ) revealed similar results (β = -0.021, pfdr = .012; Supplemental Results). Re-performing this analysis with a more recent BD GWAS describing a larger sample (
      • Mullins N.
      • Forstner A.J.
      • O’Connell K.S.
      • Coombes B.
      • Coleman J.R.I.
      • Qiao Z.
      • et al.
      Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.
      ) did indicate a potential significant effect for BD PGS (β = -0.018, p = .019). Results on PGS for BD in the following sections are thus based on this recent BD GWAS (
      • Mullins N.
      • Forstner A.J.
      • O’Connell K.S.
      • Coombes B.
      • Coleman J.R.I.
      • Qiao Z.
      • et al.
      Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.
      ).
      Significant associations between SCZ+BD PGS and regional connectivity strength were observed for the left posterior cingulate and superior parietal regions, and the right lateral occipital and anterior cingulate regions (q < .05, FDR corrected across 114 brain regions; Figure 2a; Supplemental Table S4). Similar results were observed for PGS for separately SCZ and BD (Supplemental Figure S7). Connection-wise regression analyses revealed two subnetworks of totally 72 connections that showed significant associations with SCZ+BD PGS (β = -0.020∼-0.039, pNBS = .001 and .006; Figure 2b). These connections linked intra-hemisphere cortical regions including inferior parietal cortex and insula/superior temporal cortex, supramarginal and lateral orbitofrontal cortex, etcetera. Post-hoc examinations revealed longer connections to show stronger PGS-connectivity associations (r = -0.240, p < .001; Supplemental Results). A stronger PGS-connectivity association was also observed for connections spanning between type2 Economo cortical areas (i.e. homotypic frontal cortex; t = -3.555, pfdr = .002) and connections spanning between regions involved in cognitive domains of “Cognition” and “Manipulation” (t = -3.312, pfdr = .006 and t = -2.723, pfdr = .020, respectively; Supplemental Results). Correlating SCZ PGS to connectivity strength showed significant associations in three subnetworks of totally 47 connections (pNBS < .05; 18 connections nested within the above SCZ+BD PGS identified network). PGS for BD was associated with a subnetwork of 8 connections (pNBS = .020; 6 connections nested within the SCZ+BD PGS identified network). No specific effects observed for PGS for SCZ-BD (pNBS = .478).
      Figure thumbnail gr2
      Figure 2Connectome-wide associations for PGS for SCZ+BD. (A) Brain regions with the mean connectivity strength associated with PGS for SCZ+BD. Yellow dot indicates FDR corrected p < .05. (B) Connections significantly associated with PGS for SCZ+BD. NBS p < 0.01. (C) Yeo-7 network division. VN: visual network; SMN: somatomotor network; DAN: dorsal attention network; VAN: ventral attention network; LN: limbic network; FPN: frontoparietal network; DMN: default-mode network. (D) Associations between within-network connectivity strength and PGS for SCZ+BD. Dark Red: FDR corrected p < .05. (E) Associations between between-network connectivity strength and PGS for SCZ+BD. Only significant between-network pairs are displayed (FDR corrected p < .05).
      Figure thumbnail gr1
      Figure 1Methods overview. (A) Data samples from the UK Biobank were divided into the healthy sample and the disease sample. (B) Connectome-wide association analysis was performed within the healthy sample to examine associations between structural connectivity and polygenic scores (PGS) for SCZ and BD. PGS was computed using genotype data from the UK Biobank and GWAS summary statistics from a previous GWAS study on SCZ and BD from the Psychiatric Genomics Consortium (PGC). Between-group connectivity differences were also examined between healthy individuals and individuals with SCZ and/or BD. (C) Spatial correlation analysis was performed between the spatial pattern of PGS-connectivity associations and the pattern of case-control connectivity differences. (D) Two independent datasets, namely the COBRE SCZ dataset and the MACS BD dataset, were used to study connectivity differences between healthy controls and patients with SCZ or BD. Spatial correlations between the spatial pattern of PGS-connectivity associations and the pattern of case-control connectivity differences were then validated. Connections showing deficits in disease conditions were identified and were taken as the phenotypes of interest in GWAS analysis, which was conducted using genotype data from the UK Biobank.
      Post-hoc analysis further revealed significant associations between SCZ+BD PGS and within-network connectivity strength of the default-mode network (DMN, See Supplemental Methods (
      • Yeo B.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      )) (β = -0.020, pfdr = .032; Figure 2d). PGS for SCZ+BD was associated with between-network mean connectivity strength among examined networks (Figure 2e), with the highest effect observed for connections spanning between the ventral attention network (VAN) and dorsal attention network (DAN) (β = -0.038, pfdr < .001). Results of SCZ PGS are shown in Supplementary Figure S8. No significant correlations were observed for PGS for BD and SCZ-BD. Additionally examining associations between SCZ+BD PGS and network topological properties showed trend-level, non-significant associations for the characteristic path length (β = 0.017, nominal p = .016) and the mean clustering coefficient (β = -0.016, nominal p = .042) (Supplemental Results).
      The specificity of the association between SCZ+BD PGS and brain circuits was tested by examining PGS for other mental conditions. The SCZ+BD PGS subnetwork significantly correlated to PGS for ADHD (
      • Demontis D.
      • Walters R.K.
      • Martin J.
      • Mattheisen M.
      • Als T.D.
      • Agerbo E.
      • et al.
      Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.
      ) (β = -0.026, pfdr = .009) and MDD (
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ) (β = -0.021, pfdr = .036), but not for other examined mental disorders (Supplemental Results).
      PGS-connectomic associations overlapped with disconnectivity of psychiatric disorders. The spatial pattern of correlations between SCZ+BD PGS and connectivity strength significantly correlated to the pattern of connectivity differences between the group of SCZ and BD patients and controls from the UK Biobank [r(1,309) = 0.239, ppermut < .001; Figure 3]. Similar spatial correlations were observed for the patterns of connectivity differences between SCZ and controls [r(1,309) = 0.214, ppermut < .001] and between BD and controls [r(1,309) = 0.216, ppermut = .002]. These findings suggest that connections associated with a higher SCZ+BD PGS in the healthy population display larger changes in brain circuitry in clinical groups (SCZ and BD). The association for SCZ remained significant when controlling for polygenic effects of BD [r(1,309) = 0.186, ppermut < .001], and vise versa (see Supplemental Results). Out-group analyses correlating the spatial pattern of PGS-connectivity associations to the pattern of connectivity differences between controls and non-SCZ and non-BD subjects (N = 6,665) with other mental conditions showed non-significant effect [r(1,309) = 0.028, p = .658]. Analyses separately on four distinct mental disorders, including depression (N = 4,731), anxiety (N = 34), autism (N = 31), and obsessive-compulsive disorder (N = 112) similarly resulted in non-significant effects (all p > 0.3), suggesting the observed spatial correlation between SCZ and BD PGS and brain patterns to be relative specific to SCZ and BD conditions.
      Figure thumbnail gr3
      Figure 3Clinical reference of SCZ+BD PGS-related connections. (A) Connectivity differences between a combined group of BD and SCZ and HC. Dark blue indicates lower connectivity in cases compared to HC. Connections with nominal p < 0.1 are shown. (B) The spatial profile of between-group connectivity difference is correlated to the spatial profile of SCZ+BD PGS-connectivity correlations across healthy subjects (r = 0.239). Permutation testing using randomly permuted group assignment confirms the observed association to be significant (two-sided p < 0.001, 1,000 permutations).
      PGS, connectivity and cognition. SCZ and BD share a genetic background with genetics of intelligence and cognition (
      • Smeland O.B.
      • Bahrami S.
      • Frei O.
      • Shadrin A.
      • O’Connell K.
      • Savage J.
      • et al.
      Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence.
      ). We thus further investigated potential interactions across PGS, brain connectivity, and cognitive functions [assessed by means of ‘fluid intelligence’ (UKB field: 20016), ‘reaction time’ (UKB field: 20023), ‘pairs matching’ (UKB field: 399), ‘numeric memory’ (UKB field: 4282), and ‘prospective memory’ (UKB field: 20018); see Supplemental Methods]. The SCZ+BD PGS subnetwork (72 connections) positively associated with higher scores on fluid intelligence test (β = 0.065, p < .001, Figure 4), suggesting that higher connectivity strength covaried with lower polygenic risk for SCZ/BP and overall higher scores on fluid intelligence tests. Post-hoc analysis using permutation testing showed that this observed effect was relatively specific to the SCZ+BD PGS network with effects significantly exceeding the null distribution of effect sizes obtained when we compute this correlation with randomly selected connections across the brain (ppermut = .025, 1,000 permutations). Mediation analysis showed connectivity strength of the SCZ+BD PGS derived subnetworks to significantly mediate the relationship between SCZ+BD PGS and cognitive function (β = -0.002, p < .001, effect accounting for 2.5% of the total effect size for the PGS-intelligence association, Figure 4). The SCZ+BD PGS subnetwork was also significantly associated with cognitive performance in reaction time (β = -0.062, p < .001), pairs matching (β = -0.033, p < .001), and numeric memory (β = 0.044, p < .001), with no effect observed for prospective memory (β = 0.004, p = .655).
      Figure thumbnail gr4
      Figure 4SCZ+BD PGS-associated connectivity and cognition. (A) The mean strength of SCZ+BD PGS-associated connections is significantly correlated with fluid intelligence (β = 0.065, p < .001). count: the number of subjects (B) The mean connectivity strength of SCZ+BD PGS-associated connections significantly mediates the relationship between SCZ+BD PGS and intelligence (direct effect: β = -0.077, p < .001; mediation effect: β = -0.002, p < .001; total effect: β = -0.079, p < .001).
      Validation. The robustness of the observed connectome-wide association with SCZ+BD PGS was tested using the hold-out dataset (N = 2,589, see Methods). First, the spatial pattern of PGS-connectivity association reported using the discovery sample again significantly correlated to the pattern of PGS-connectivity association observed in the hold-out sample [r(1,309) = 0.150, p < .001]. Second, the pattern of connectome-wide correlations between PGS for SCZ+BD and connectivity strength similarly showed a positive correlation to the pattern of disconnectivity in the combined group of SCZ and BD patients [r(1,309) = 0.160, p < .001], as well as to the pattern of disconnectivity in SCZ [r(1,309) = 0.167, p < .001] and BD [r(1,309) = 0.135, p < .001].
      Using the independent COBRE SCZ dataset (
      • Çetin M.S.
      • Christensen F.
      • Abbott C.C.
      • Stephen J.M.
      • Mayer A.R.
      • Cañive J.M.
      • et al.
      Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia.
      ,
      • Wang L.
      • Alpert K.I.
      • Calhoun V.D.
      • Cobia D.J.
      • Keator D.B.
      • King M.D.
      • et al.
      SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration.
      ) we further validated the overlapping patterns of SCZ+BD PGS-connectivity correlations (as observed in the UKB healthy sample) and the connectivity differences between SCZ and HC [r(1,309) = 0.18, ppermut = .006; Supplemental Figure S9]. Analyzing the independent MACS BD dataset (

      Vogelbacher C, Möbius TWD, Sommer J, Schuster V, Dannlowski U, Kircher T, et al. (2018): The Marburg-Münster Affective Disorders Cohort Study (MACS): A quality assurance protocol for MR neuroimaging data. Neuroimage 172: 450–460.

      ,
      • Kircher T.
      • Wöhr M.
      • Nenadic I.
      • Schwarting R.
      • Schratt G.
      • Alferink J.
      • et al.
      Neurobiology of the major psychoses: a translational perspective on brain structure and function-the FOR2107 consortium.
      ) (N = 84 patients, 346 HCs) validated the observation of the overlapping pattern between the PGS-connectivity correlations and connectivity difference between HC and BD [r(1,309) = 0.129, ppermut = .014; Supplemental Figure S9].

      Part II. GWASs on SCZ-/BD-involved brain circuits reveal genetic overlaps with the disorders

      GWAS on SCZ circuitry. GWAS results with minor allele frequency (MAF) > 0.0001 are shown in Figure 5a. GWAS on SCZ-involved connections revealed 32 independent, significant variants (p < 5 × 10−8), tagging 9 independent genomic loci (Supplemental Table S5). The SNP-based heritability (h2SNP) estimated by LDSC was 24.0% (SE = 2.9%). The LDSC intercept of 1.008 was close to 1 and the observed inflation level (λGC) was 1.099, suggesting that the inflation of genetic signals is mostly due to polygenicity rather than population stratification (
      • Yang J.
      • Weedon M.N.
      • Purcell S.
      • Lettre G.
      • Estrada K.
      • Willer C.J.
      • et al.
      Genomic inflation factors under polygenic inheritance.
      ). One of the observed genomic loci (rs3129171; chromosome 6; position 29155749; Figure 5b) was within the significant loci reported in a recent schizophrenia GWAS study (
      • Pardinas A.F.
      • Holmans P.
      • Pocklington A.J.
      • Escott-Price V.
      • Ripke S.
      • Carrera N.
      • et al.
      Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.
      ). Five out of the nine observed loci overlapped with the loci reported in a recent GWAS on brain-wide FA (

      Zhao B, Zhang J, Ibrahim JG, Luo T, Santelli RC (2021): Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n= 17,706). Molecular. Retrieved from https://idp.nature.com/authorize/casa?redirect_uri=https://www.nature.com/articles/s41380-019-0569-z&casa_token=1HHPV8ZGWrUAAAAA:2qwMIHCMZWaI_fxAc-BdLx8dJ0zvOktV2FkIwVp26DItE-YKEd_7ojFyh0eX4q-tVXJkLoU53BIYPmnW

      ).
      Figure thumbnail gr5
      Figure 5GWAS on SCZ-involved connections. (A) GWAS results on the mean strength of SCZ-involved connections (top) and of BD-involved connections (bottom). The Miami plot shows -log10-transformed two-tailed p-value for all SNP (y-axis) and base pair positions along the chromosomes (x-axis). Red line indicates Bonferroni-corrected genome-wide significance (p < 5 × 10−8). (B) Regional plot of the lead SNP rs3129171 on chromosome 6 in the GWAS on SCZ-involved connections. (C) LDSC genetic correlation between SCZ-involved connections and SCZ+BD, SCZ, BD, and SCZ-BD. Error bar indicates standard error. Orange indicates rg with p < .05 and ppermut < .05 in permutation testing simultaneously. (D) Enrichment of genes associated with SCZ-involved connections and BD-involved connections in GWAS Catalog terms. Significant terms are displayed (FDR corrected p < .05).
      The identified SNPs were mapped to 261 genes using positional mapping, eQTL mapping, and chromatin interaction mapping implemented in FUMA (
      • Watanabe K.
      • Taskesen E.
      • van Bochoven A.
      • Posthuma D.
      Functional mapping and annotation of genetic associations with FUMA.
      ). Gene-enrichment analysis based on previously curated gene sets showed significant enrichment of the identified genes in the GWAS catalog reported gene-sets “Autism spectrum disorder or schizophrenia” (pfdr = 1.70 × 10–52), “Schizophrenia” (pfdr = 4.05 × 10–11), “Bipolar I disorder (pfdr = 1.31 × 10–3), as well as other 12 traits related to sleep, lung cancer, social communication, and blood protein levels (Figure 5d).
      LDSC genetic correlation analysis showed a trend-level correlation between the here performed GWAS on SCZ-involved connectivity and previous GWAS on SCZ (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ) (rg = -0.111, nominal p = .015; pfdr = .058, corrected across four tests). Controlling for the effect of global FA (which showed phenotypic correlation with SCZ-involved circuits: r = 0.730), permutation testing (see Supplemental Methods) showed rg for SCZ and SCZ+BD (rg = -0.078, pfdr = .104) to significantly exceed the null distribution of rg yielded by GWAS analysis on same sized random connections (ppermut = .030 and .045 for SCZ and SCZ+BD, respectively; 200 permutations) (Figure 5c). Correlating to GWAS results of BD (rg = -0.052, pfdr = .208) and SCZ-BD (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ) (rg = -0.109, pfdr = .104) revealed no additional significant effects.
      GWAS on BD circuitry. GWAS on the subnetwork of BD-involved connections showed 45 genome-wide independent significant variants (p < 5 × 10−8) in 14 genomic loci (Supplemental Table S6). LDSC SNP-based heritability (h2SNP) was 28.6% (SE = 3.1%, LDSC intercept = 0.998 close to 1, λGC = 1.099). The 14 genomic loci did however not overlap with reported significant genomic loci in the recent BD GWAS (
      • Mullins N.
      • Forstner A.J.
      • O’Connell K.S.
      • Coombes B.
      • Coleman J.R.I.
      • Qiao Z.
      • et al.
      Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.
      ), but five out of the 14 observed loci overlapped with loci reported in GWAS on brain-wide FA (

      Zhao B, Zhang J, Ibrahim JG, Luo T, Santelli RC (2021): Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n= 17,706). Molecular. Retrieved from https://idp.nature.com/authorize/casa?redirect_uri=https://www.nature.com/articles/s41380-019-0569-z&casa_token=1HHPV8ZGWrUAAAAA:2qwMIHCMZWaI_fxAc-BdLx8dJ0zvOktV2FkIwVp26DItE-YKEd_7ojFyh0eX4q-tVXJkLoU53BIYPmnW

      ) and six loci overlapped with loci related to SCZ-involved circuits reported above. The identified set of significant SNPs mapped to 245 genes (
      • Watanabe K.
      • Taskesen E.
      • van Bochoven A.
      • Posthuma D.
      Functional mapping and annotation of genetic associations with FUMA.
      ), and showed significant enrichment in gene-sets related to “Bipolar I disorder (pfdr = 3.29 × 10–3)”, “Sleep duration” (pfdr = 5.82 × 10–5), and four traits related to neutrophil (Figure 5d). LDSC genetic correlation analysis revealed non-significant correlations between the GWAS on BD-involved connections and GWAS on SCZ+BD, SCZ, BD, SCZ-BD (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ) (pfdr > .116).

      Discussion

      Our study provides biological insights into associated brain connectivity and polygenic liability for schizophrenia and bipolar disorder. Combining PGS and neuroimaging shows healthy individuals with a higher polygenic score for SCZ and BP to display relatively lower levels of connectivity strength in brain circuits matching those involved in disease samples.
      Our findings suggest that normative variations of macroscale brain circuitry are associated with combined polygenic effects of SCZ and BD, findings that are in support of a general relationship between polygenic liability and structural (
      • Stauffer E.-M.
      • Bethlehem R.A.I.
      • Warrier V.
      • Murray G.K.
      • Romero-Garcia R.
      • Seidlitz J.
      • Bullmore E.T.
      Grey and white matter microstructure is associated with polygenic risk for schizophrenia.
      ,

      Kirschner M, Paquola C, Khundrakpam BS, Vainik U, Bhutani N, Benazir-Hodzic-Santor, et al. (2021, June 13): Schizophrenia polygenic risk during typical development reflects multiscale cortical organization. BioRxiv. p 2021.06.13.448243.

      ) and functional brain organization (
      • Meyers J.L.
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      ,
      • Cao H.
      • Zhou H.
      • Cannon T.D.
      Functional connectome-wide associations of schizophrenia polygenic risk.
      ). SCZ and BD are known to share a common genetic background and several molecular pathways (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ,

      Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. (2014): Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry 19: 1017–1024.

      ,
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.
      ). The observed association between polygenic liability and brain connectivity might be attributable to the role of disorder risk genes in white matter organization (
      • Stauffer E.-M.
      • Bethlehem R.A.I.
      • Warrier V.
      • Murray G.K.
      • Romero-Garcia R.
      • Seidlitz J.
      • Bullmore E.T.
      Grey and white matter microstructure is associated with polygenic risk for schizophrenia.
      ). Functional studies on disorder risk genes also pinpoint the role of genes in biological processes related to synaptic and oligodendrocytes (
      • Disorder Bipolar
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
      ,
      • Trubetskoy V.
      • Pardiñas A.F.
      • Qi T.
      • Panagiotaropoulou G.
      • Awasthi S.
      • Bigdeli T.B.
      • et al.
      Mapping genomic loci implicates genes and synaptic biology in schizophrenia.
      ), processes that are known to shape the cellular organization (
      • Whitaker K.J.
      • Vértes P.E.
      • Romero-Garcia R.
      • Váša F.
      • Moutoussis M.
      • Prabhu G.
      • et al.
      Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome.
      ) and dynamics of brain connectivity (
      • van den Heuvel M.P.
      • Scholtens L.H.
      • Kahn R.S.
      Multiscale Neuroscience of Psychiatric Disorders.
      ,
      • Scholtens L.H.
      • van den Heuvel M.P.
      Multimodal Connectomics in Psychiatry: Bridging Scales From Micro to Macro.
      ). Our findings converge on cross-scale interactions among genetic, cellular, and macroscale brain organization in the context of shared polygenic effects for schizophrenia and bipolar disorder (
      • van den Heuvel M.P.
      • Scholtens L.H.
      • Kahn R.S.
      Multiscale Neuroscience of Psychiatric Disorders.
      ,
      • Guan F.
      • Ni T.
      • Zhu W.
      • Williams L.K.
      • Cui L.-B.
      • Li M.
      • et al.
      Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction.
      ).
      Association analysis on PGS in a healthy population may identify new brain circuits that are potentially related to disease processes. This corroborates previous observations in functional connectivity, which have indicated that several functional networks are related to SCZ’s polygenic scores (
      • Cao H.
      • Zhou H.
      • Cannon T.D.
      Functional connectome-wide associations of schizophrenia polygenic risk.
      ). Structural circuits in the current study involve the superior and inferior parietal cortex and the posterior cingulate regions of the default-mode network, systems that have been broadly reported to be associated with both schizophrenia (
      • Ji E.
      • Guevara P.
      • Guevara M.
      • Grigis A.
      • Labra N.
      • Sarrazin S.
      • et al.
      Increased and Decreased Superficial White Matter Structural Connectivity in Schizophrenia and Bipolar Disorder.
      ,
      • Kelly S.
      • Jahanshad N.
      • Zalesky A.
      • Kochunov P.
      • Agartz I.
      • Alloza C.
      • et al.
      Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group.
      ,
      • Wheeler A.L.
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      ,
      • Ongur D.
      • Lundy M.
      • Greenhouse I.
      • Shinn A.K.
      • Menon V.
      • Cohen B.M.
      • Renshaw P.F.
      Default mode network abnormalities in bipolar disorder and schizophrenia.
      ) and bipolar disorder (
      • Ji E.
      • Guevara P.
      • Guevara M.
      • Grigis A.
      • Labra N.
      • Sarrazin S.
      • et al.
      Increased and Decreased Superficial White Matter Structural Connectivity in Schizophrenia and Bipolar Disorder.
      ,

      Favre P, Pauling M, Stout J, Hozer F, Sarrazin S, Abé C, et al. (2019): Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals. Neuropsychopharmacology 44: 2285–2293.

      ,
      • Perry A.
      • Roberts G.
      • Mitchell P.B.
      • Breakspear M.
      Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks.
      ). Combined, these and previous results suggest that accumulating liability for psychiatric disorders in the healthy population can target particular brain substrates that are vulnerable to disease processes.
      Neuroimaging and neurocircuitry analysis may provide valuable new endophenotypes to connect genetics and disease conditions (
      • van der Meer D.
      • Shadrin A.A.
      • O’Connell K.
      • Bettella F.
      • Djurovic S.
      • Wolfers T.
      • et al.
      Boosting Schizophrenia Genetics by Utilizing Genetic Overlap With Brain Morphology.
      ). GWAS analyses on SCZ- and BD-involved circuits point to a common genetic architecture of brain white matter integrity and mental health traits (

      Zhao B, Zhang J, Ibrahim JG, Luo T, Santelli RC (2021): Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n= 17,706). Molecular. Retrieved from https://idp.nature.com/authorize/casa?redirect_uri=https://www.nature.com/articles/s41380-019-0569-z&casa_token=1HHPV8ZGWrUAAAAA:2qwMIHCMZWaI_fxAc-BdLx8dJ0zvOktV2FkIwVp26DItE-YKEd_7ojFyh0eX4q-tVXJkLoU53BIYPmnW

      ). Overlapping genes of SCZ-involved circuits and SCZ traits include for example ZSCAN31, a gene that regulates pivotal schizophrenia risk genes such as VIPR2 and NPY, as well as the PI3K-AKT and the NOTCH signaling pathways in SCZ (
      • Li S.
      • Li X.
      • Liu J.
      • Huo Y.
      • Li L.
      • Wang J.
      • Luo X.-J.
      Functional variants fine-mapping and gene function characterization provide insights into the role of ZNF323 in schizophrenia pathogenesis.
      ); XPNPEP3, PCDHA7, and PCDHA8, genes reported to show altered expressions in SCZ and BD (
      • Hall L.S.
      • Pain O.
      • O’Brien H.E.
      • Anney R.
      • Walters J.T.R.
      • Owen M.J.
      • et al.
      Cis-effects on gene expression in the human prenatal brain associated with genetic risk for neuropsychiatric disorders.
      ).
      Several remarks have to be considered when interpreting our results. PGS explains only a small proportion of variance of case-control differences in schizophrenia (here, 3.2%∼11.5%) and bipolar disorder (2.3%∼9.2%) (
      • Plomin R.
      • von Stumm S.
      Polygenic scores: prediction versus explanation.
      ), but it should be noted that PGS explains an even smaller proportion of variance in brain connectivity (∼1%), which is similar to previous literature examining other neuroimaging traits (
      • Neilson E.
      • Shen X.
      • Cox S.R.
      • Clarke T.-K.
      • Wigmore E.M.
      • Gibson J.
      • et al.
      Impact of Polygenic Risk for Schizophrenia on Cortical Structure in UK Biobank.
      ,
      • Stauffer E.-M.
      • Bethlehem R.A.I.
      • Warrier V.
      • Murray G.K.
      • Romero-Garcia R.
      • Seidlitz J.
      • Bullmore E.T.
      February 8): Cortical and subcortical grey matter micro-structure is associated with polygenic risk for schizophrenia.
      ). Second, our samples are all from European ancestry, which limits the generalizability of the results to populations of different ancestry (
      • Choi S.W.
      • O’Reilly P.F.
      PRSice-2: Polygenic Risk Score software for biobank-scale data.
      ). Third, brain connectivity was reconstructed using tractography which is known to have several limitations regarding the reconstruction of complex oriented white matter fibers, for example fibers through the corpus callosum (
      • Jbabdi S.
      • Johansen-Berg H.
      Tractography: where do we go from here?.
      ,
      • Jones D.K.
      Studying connections in the living human brain with diffusion MRI.
      ). This might explain why no interhemispheric connection was found to correlate to PGS for SCZ+BD (12 out of the 142 interhemispheric connections showed nominal p < .05, β = -0.017∼-0.025; pfdr > .05). Fourth, information on antipsychotic treatment and disease duration was not available in the studied data cohorts. Future study on the impact of antipsychotic medication dosage on the reported genetic-connectomic associations is warranted (
      • Kraguljac N.V.
      • McDonald W.M.
      • Widge A.S.
      • Rodriguez C.I.
      • Tohen M.
      • Nemeroff C.B.
      Neuroimaging Biomarkers in Schizophrenia.
      ).

      Conclusion

      Our study shows a common genetic background for brain structural connectivity and schizophrenia and bipolar disorder, with a combined polygenic liability for the two disorders playing a central role in key macroscale brain circuits. The integration of genetics and connectomics may pave an avenue for the transition of the diagnostic practice of psychiatric disorders from a traditional descriptive manner to diagnosis built upon the underlying biological systems of the brain.
      Code availability
      Codes are available from the corresponding author on reasonable request. Data visualization uses the Gramm toolbox (
      • Morel P.
      Gramm: grammar of graphics plotting in Matlab.
      ) and the Simple Brain Plot (

      Scholtens LH, de Lange SC, van den Heuvel MP (2021): Simple Brain Plot. https://doi.org/10.5281/zenodo.5346593

      ) implemented in MATLAB.

      Data availability

      The UK Biobank genotype data and MRI data that support the findings of this study are available in the UK Biobank (accessed under application 16406; https://www.ukbiobank.ac.uk). The COBRE dataset that supports the findings of this study is available at http://schizconnect.org. The MACS BD data that supports the findings of this study is available from the corresponding author on reasonable request.

      Compliance with ethical standards

      The UK Biobank study protocol was approved by the National Research Ethics Service Committee North West Haydock (reference 11/NW/0382) and all procedures were conducted following the ethical principles for medical research declared in the World Medical Association Declaration of Helsinki. The COBRE SCZ study protocol was approved by the Institutional Review Board of the University of New Mexico. The MACS BD study was approved by the ethics committees of the medical faculties of the University of Marburg and the University of Münster.

      Acknowledgments

      Y.W. is supported by the National Natural Science Foundation of China (Grant No. 82202264). M.P.v.d.H. is supported by an ALW open (ALWOP.179), a VIDI (452-16-015) grant from the Netherlands Organization for Scientific Research (NWO), and an ERC Consolidator grant (ID 101001062) of the European Research Council. M.P.v.d.H. and D.P. is supported by an NWO Gravitation project BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology (024.004.012) and a European Research Council advanced grant ERC-2018-ADG 834057. S.C.d.L is supported by the ZonMw Open Competition, project REMOVE 09120011910032. The genetic analyses were carried out on the Genetic Cluster Computer, which is financed by the Netherlands Scientific Organization (NWO: 480-05-003), Vrije Universiteit, Amsterdam, The Netherlands, and the Dutch Brain Foundation, and is hosted by the Dutch National Computing and Networking Services SurfSARA.

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