Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness

Weiqiu Cheng; Oleksandr Frei; Dennis van der Meer; Yunpeng Wang; Kevin S O’Connell; Yunhan Chu; Shahram Bahrami; Alexey A Shadrin; Dag Alnæs; Guy F L Hindley; Aihua Lin; Naz Karadag; Chun-Chieh Fan; Lars T Westlye; Tobias Kaufmann; Espen Molden; Anders M Dale; Srdjan Djurovic; Olav B Smeland; Ole A Andreassen

doi:10.1001/jamapsychiatry.2021.1435

. 2021 Jun 23;78(9):1–11. doi: 10.1001/jamapsychiatry.2021.1435

Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness

Weiqiu Cheng ^1,^✉, Oleksandr Frei ¹, Dennis van der Meer ^1,², Yunpeng Wang ³, Kevin S O’Connell ¹, Yunhan Chu ¹, Shahram Bahrami ¹, Alexey A Shadrin ¹, Dag Alnæs ¹, Guy F L Hindley ^1,⁴, Aihua Lin ¹, Naz Karadag ¹, Chun-Chieh Fan ^5,⁶, Lars T Westlye ^1,⁷, Tobias Kaufmann ^1,⁸, Espen Molden ^9,¹⁰, Anders M Dale ^11,^12,¹³, Srdjan Djurovic ^14,¹⁵, Olav B Smeland ¹, Ole A Andreassen ^1,^✉

¹NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway

²School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands

³Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway

⁴Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, United Kingdom

⁵Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla

⁶Center for Human Development, University of California, San Diego, La Jolla

⁷Department of Psychology, University of Oslo, Oslo, Norway

⁸Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany

⁹Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway

¹⁰Department of Pharmacy, University of Oslo, Oslo, Norway

¹¹Department of Radiology, University of California, San Diego, La Jolla

¹²Department of Psychiatry, University of California, San Diego, La Jolla

¹³Department of Neurosciences, University of California San Diego, La Jolla

¹⁴Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

¹⁵NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway

^✉

Corresponding Author: Weiqiu Cheng, PhD (weiqiu.cheng@medisin.uio.no), and Ole A. Andreassen, MD, PhD (ole.andreassen@medisin.uio.no), Bldg 49, Norwegian Centre for Mental Disorders Research, Oslo University Hospital Ullevål, Kirkeveien 166, 0450 Oslo, Norway.

Accepted for Publication: April 28, 2021.

Published Online: June 23, 2021. doi:10.1001/jamapsychiatry.2021.1435

Author Contributions: Dr Cheng had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Cheng, O’Connell, Hindley, Karadag, Dale, Smeland, Andreassen.

Acquisition, analysis, or interpretation of data: Cheng, Frei, van der Meer, Wang, Chu, Bahrami, Shadrin, Alnaes, Lin, Karadag, Fan, Westlye, Kaufmann, Molden, Dale, Djurovic, Smeland, Andreassen.

Drafting of the manuscript: Cheng, Hindley, Dale, Andreassen.

Critical revision of the manuscript for important intellectual content: Frei, van der Meer, Wang, O'Connell, Chu, Bahrami, Shadrin, Alnaes, Hindley, Lin, Karadag, Fan, Westlye, Kaufmann, Molden, Djurovic, Smeland, Andreassen.

Statistical analysis: Cheng, Frei, Wang, Bahrami, Shadrin, Fan, Dale.

Obtained funding: Cheng, Westlye, Dale, Djurovic, Andreassen.

Administrative, technical, or material support: van der Meer, Chu, Bahrami, Hindley, Kaufmann, Molden, Dale, Djurovic, Smeland, Andreassen.

Supervision: Westlye, Dale, Andreassen.

Conflict of Interest Disclosures: Dr Andreassen reported grants from Stiftelsen Kristian Gerhard Jebsen, South-East Regional Health Authority, Research Council of Norway, and European Union’s Horizon 2020 during the conduct of the study; personal fees from HealthLytix (stock options), Lundbeck (speaker’s honorarium), and Sunovion (speaker’s honorarium) outside the submitted work; and had a pending patent for systems and methods for identifying polymorphisms. Dr Dale reported grants from the National Institutes of Health outside the submitted work; had a patent for US7324842 licensed to Siemens Healthineers; is a founder of and holds equity in Cortechs Labs and serves on its scientific advisory board; is member of the scientific advisory board of Human Longevity; a member of the scientific advisory board of Healthlytix; and receives funding through a research agreement with GE Healthcare. No other disclosures were reported.

Funding/Support: The authors were funded by the National Institutes of Health (grants NS057198 and EB00790), the Research Council of Norway (grants 229129, 276082, 213837, 223273, and 248980), the South-East Regional Health Authority (grant 2017-112), Stiftelsen Kristian Gerhard Jebsen (grants SKGJ-MED-008 and SKGJ-MED-021), the Psychiatric Genomics Consortium US Norway Collaboration (RCN 248980), part of convergence environment (MultiModal Mental Models [4MENT]) funded by University of Oslo Life Science and Scientia Fellows, European Union’s Horizon 2020 Research and Innovation Active Grant (847776 CoMorMent), and European Union’s Horizon 2020 research and innovation programme (801133 Marie Skłodowska-Curie grant agreement).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank the Psychiatric Genetics Consortium (https://www.med.unc.edu/pgc/), the UK Biobank (https://www.ukbiobank.ac.uk/), Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) (http://enigma.ini.usc.edu/), and the Norwegian Thematically Organized Psychosis (TOP) project for access to genome-wide association study data. We thank all participants and employees of Psychiatric Genomics Consortium, UK Biobank, ENIGMA, and TOP for their contribution to this study.

^✉

Corresponding author.

PMCID: PMC8223140 PMID: 34160554

This genetic association study investigates the extent of shared genetic architecture between schizophrenia and brain cortical surface area and thickness.

Key Points

Question

To what extent is the genetic architecture of schizophrenia shared with cortical brain surface area and thickness?

Findings

In this genetic association study, data sets revealed that 94% of the genetic variants associated with total cortical surface area and all variants associated with average cortical thickness were also associated with the genetic risk of schizophrenia, despite nonsignificant genetic correlations.

Meaning

The amount of shared genetic variants between schizophrenia and cortical brain structure suggests overlapping molecular genetic mechanisms between cortical development and schizophrenia.

Abstract

Importance

Schizophrenia is a complex heritable disorder associated with many genetic variants, each with a small effect. While cortical differences between patients with schizophrenia and healthy controls are consistently reported, the underlying molecular mechanisms remain elusive.

Objective

To investigate the extent of shared genetic architecture between schizophrenia and brain cortical surface area (SA) and thickness (TH) and to identify shared genomic loci.

Design, Setting, and Participants

Independent genome-wide association study data on schizophrenia (Psychiatric Genomics Consortium and CLOZUK: n = 105 318) and SA and TH (UK Biobank: n = 33 735) were obtained. The extent of polygenic overlap was investigated using MiXeR. The specific shared genomic loci were identified by conditional/conjunctional false discovery rate analysis and were further examined in 3 independent cohorts. Data were collected from December 2019 to February 2021, and data analysis was performed from May 2020 to February 2021.

Main Outcomes and Measures

The primary outcomes were estimated fractions of polygenic overlap between schizophrenia, total SA, and average TH and a list of functionally characterized shared genomic loci.

Results

Based on genome-wide association study data from 139 053 participants, MiXeR estimated schizophrenia to be more polygenic (9703 single-nucleotide variants [SNVs]) than total SA (2101 SNVs) and average TH (1363 SNVs). Most SNVs associated with total SA (1966 of 2101 [93.6%]) and average TH (1322 of 1363 [97.0%]) may be associated with the development of schizophrenia. Subsequent conjunctional false discovery rate analysis identified 44 and 23 schizophrenia risk loci shared with total SA and average TH, respectively. The SNV associations of shared loci between schizophrenia and total SA revealed en masse concordant association between the discovery and independent cohorts. After removing high linkage disequilibrium regions, such as the major histocompatibility complex region, the shared loci were enriched in immunologic signature gene sets. Polygenic overlap and shared loci between schizophrenia and schizophrenia-associated regions of interest for SA (superior frontal and middle temporal gyri) and for TH (superior temporal, inferior temporal, and superior frontal gyri) were also identified.

Conclusions and Relevance

This study demonstrated shared genetic loci between cortical morphometry and schizophrenia, among which a subset are associated with immunity. These findings provide an insight into the complex genetic architecture and associated with schizophrenia.

Introduction

Schizophrenia is a severe brain disorder with an estimated heritability up to 80%¹ in twin studies, indicating a prominent genetic component in its cause. Genetic risk of schizophrenia has been suggested to influence early brain development,² and patients with schizophrenia exhibit many brain structural abnormalities.^3,4 Recent structural magnetic resonance imaging studies have reported a smaller total cortical surface area (SA) and thinner average cortical thickness (TH) in individuals with schizophrenia, with the largest effect sizes found in the frontal and temporal lobes.^3,5 Similar frontotemporal patterns in SA and TH were also reported in nonaffected relatives of patients with schizophrenia⁶ and in comparison with polygenic risk for schizophrenia in healthy population.⁷ Because both SA and TH are highly heritable with common single-nucleotide variants (SNVs) explaining 26% to 34% of the phenotypic variation,⁸ the above findings proposed that the genetic mechanisms contributing to the risk of schizophrenia may also influence brain anatomy. This hypothesis is further supported by evidence that polygenic risk scores of schizophrenia is associated with global TH in healthy individuals^9,10 and that genetic variance of schizophrenia is shared with SA (0.5%) and TH (6.3%) in twins.¹¹

While previous studies have indicated an overlapping genetic basis between schizophrenia and brain anatomy,^12,13 the polygenic overlap of schizophrenia and cortical brain structure remains elusive. A recent genetic-wide association study (GWAS) of SA and TH found no significant genetic correlation with schizophrenia using linkage disequilibrium (LD) score regression.⁸ However, LD score regression¹⁴ is not able to capture genetic overlap with mixed-effect directions, which is often the case between complex phenotypes.^15,16 To address that, Frei et al¹⁶ developed MiXeR, which evaluates genetic overlap regardless of effect directions and estimates the overall shared polygenic architecture. Moreover, identifying specific shared genetic loci offers biological insights into the polygenic overlap between 2 traits. Because both schizophrenia and brain anatomy are associated with many small-effect genetic variants, applying statistical tools, such as conditional/conjunctional false discovery rate (cFDR),^15,17,18 to improve the yield of existing GWAS provides a cost-efficient approach to boost power for discovery. The cFDR was built on an empirical bayesian statistical framework, and it improves power of SNV detection by leveraging the combined power of 2 GWASs.^{19,20,21,22,23,24,25} For example, while conventional statistical tools reported no evidence of genetic overlap between schizophrenia and subcortical volumes,²⁶ cFDR identified shared genomic loci between schizophrenia and subcortical volumes using cross-trait SNV enrichment.²⁷

Here we applied MiXeR¹⁶ to investigate whether schizophrenia shares a genetic basis with cortical SA and TH and the cFDR approach^15,17,18 to identify specific shared loci using GWAS summary statistics. By exploring the shared genetic architecture of schizophrenia and brain structure, we ultimately aim to provide key insights into the pathophysiology underlying schizophrenia.

Methods

GWAS Data Sets

Details of data sets and methods are provided in the eMethods in Supplement 1 and corresponding publications.^{8,16,17,18,28,29} The GWAS data sets were collected from December 2019 to February 2021, and data analysis was performed from May 2020 to February 2021.

All GWAS data sets included in this study were approved by the relevant ethics committees, and informed consent was obtained from all participants. The Regional Committee for Medical and Health Research Ethics of South-East Norway approved the inclusion of data from UK Biobank.

For the discovery analyses, the GWAS summary statistics for schizophrenia (40 675 cases and 64 643 controls) were generated by meta-analyzing the schizophrenia cases from UK (CLOZUK) and an independent Psychiatric Genomics Consortium data sets.^28,29 The summary statistics of total SA and average TH were generated from UK Biobank (n = 33 735).³⁰ For validation of findings, we included summary statistics of independent cohorts, including schizophrenia from a Norwegian cohort (961 cases and 5000 controls) and total SA and average TH from the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) cohort of European ancestry (n = 23 909).⁷ We also included summary statistics for schizophrenia in East Asian cohorts (22 778 cases and 35 362 healthy controls).³¹

Summary statistics for regional SA and TH were acquired from the UK Biobank with 33 735 volunteers.³⁰ We selected regions that have been reported as significantly different with the largest effect sizes between schizophrenia cases and healthy controls in a previous large-scale study,³ including fusiform, inferior temporal gyrus, middle temporal gyrus, precentral, and superior frontal gyrus for SA and fusiform, inferior temporal gyrus, middle temporal gyrus, superior frontal gyrus, and superior temporal gyrus, insula and pars opercularis for TH.

As height showed phenotypic association,³² limited shared genomic regions,³³ and nonsignificant genetic correlation³⁴ with schizophrenia, we included a height GWAS data set as a heritable, polygenic, non–brain-related comparator.³⁵

Statistical Analysis

We constructed conditional quantile-quantile (QQ) plots to visualize polygenic enrichment, after excluding SNVs within 4 regions with complex LD pattern (major histocompatibility complex [MHC] region: chr6:25119106–33854733; 8p23.1: chr8:7200000–12500000; MAPT region: chr17:40000000–47000000; apolipoprotein E region: chr19:44909039–45912650).³⁶ Each QQ plot reveals the distribution of P values for the primary phenotype conditioning on the significance of association with the secondary trait at the level of P < .10, P < .010, and P < .001.

Polygenic overlap irrespective of genetic correlation between selected phenotypes was evaluated by MiXeR.^16,37 Univariate analysis estimated polygenicity (estimated number of variants) and discoverability (the average magnitude of additive genetic associations across variants) of each phenotype. Bivariate analysis modeled additive genetic associations with 2 traits as a mixture of 4 bivariate Gaussian components, representing variants influencing only 1 trait, variants affecting both traits, and variants have no association with either trait. The number of variants associated with the unique polygenic components and of the shared polygenic component were estimated. MiXeR calculated a Dice coefficient, a ratio of shared variants to the total number of variants, to evaluate the polygenic overlap. Based on Akaike information criterion, MiXeR evaluated model fitting based on current power of input summary statistics. A comparison between MiXeR and LD score regression is provided in the eMethods in Supplement 1.

The cFDR method,³⁸ including conditional FDR (condFDR) and conjunctional FDR (conjFDR) analysis, was applied to identify specific shared loci.^15,17,18 The condFDR analysis, as an extension of standard FDR, uses the associations between variants and the secondary phenotype to rerank test statistics and to recalculate the associations between these variants and the primary phenotype. Thus, we applied condFDR to boost the power of SNV discovery for each individual trait. The conjFDR analysis is a conservative estimate of the posterior probability that an SNV has no association with either trait, given that the P values for that SNV in both the primary and secondary traits are lower than the observed P values. After repeating condFDR for both traits, we applied conjFDR analysis to identify shared genetic loci. The FDR significance cutoffs were 0.05 for conjFDR and 0.01 for condFDR in line with prior studies.^15,17 We also included shared loci with conjFDR less than 0.01 in the eResults in Supplement 1.

For identified loci, we examined significance and directionality of allelic association in independent cohorts using lead SNVs. For genomic loci with a primary lead SNV missing in an independent data set with European ancestry, we assigned a secondary lead SNV if available. SNV sign test^26,29 verified directionality of allelic association between the discovery and independent cohorts with the null hypothesis of randomly oriented associations. Sign concordancy for shared lead SNVs was defined only when SNV associations of both phenotypes showed congruent associations in independent cohorts.

Identify Genomic Loci, Functional Annotation, and Novelty

Independent genomic loci were defined using FUMA protocol.³⁹ A locus that was not physically overlapping with findings from the original GWASs, National Human Genome Research Institute–European Bioinformatics Institute GWAS Catalog,⁴⁰ or previous cFDR studies^16,17,18 was considered as novel. The z scores from original GWASs reflected the directionality of allelic association. FUMA³⁹ annotated Combined Annotation Dependent Depletion,⁴¹ RegulomeDB scores,⁴² and chromatin states^43,44 for candidate SNVs with FDR of 0.1 or less. We performed 3 gene mapping strategies to candidate SNVs, including (1) positional mapping by physical position using a 10-kb window; (2) expression quantitative trait locus [eQTL] association; and (3) chromatin interaction mapping.³⁹ Genes mapped by all 3 mapping strategies were considered as credible genes. After excluding mapped genes in the regions with complex LD pattern (MHC, 8p23.1, MAPT, apolipoprotein E regions), we applied the rest in gene set analysis.³⁹ The Molecular Signatures Database evaluated enrichment in immunological signature gene sets.⁴⁵ GTEx portal (version 8) obtained eQTL associations in brain tissues for lead SNVs and expression trajectories for genes.⁴⁶

Results

Genetic Overlap Between Schizophrenia and Global Cortical Structural Measures

The stratified conditional QQ plots showed SNV enrichment for schizophrenia as a function of their associations with total SA and average TH and vice versa (eFigure 1 in Supplement 1), suggesting the existence of polygenic overlap. While the corresponding genetic correlations were not significant (schizophrenia and total SA: genetic r = −0.01; schizophrenia and average TH: genetic r = 0.01), 94% of variants associated with total SA (1966 of 2101; Dice coefficient = 0.33) and 97% of variants associated with average TH (1322 of 1363; Dice coefficient = 0.24) may contribute to the risk of schizophrenia (Figure 1; eTables 1 and 2 and eFigure 2 in Supplement 1). Specifically, 51% of shared variants between schizophrenia and total SA and 50% shared variants between schizophrenia and average TH showed concordant associations with both traits. MiXeR also revealed a higher polygenicity in schizophrenia than in total SA and average TH with 7737 and 8380 variants associated with schizophrenia but not associated with SA and TH, respectively. As a somatic control, height shared a smaller proportion of variants (1049 of 4026 [26%]; Dice coefficient = 0.15; eTables 1 and 2 and eFigure 2 in Supplement 1) with schizophrenia, with a nonsignificant genetic correlation.

Figure 1. — Bar diagram visualizing the estimated unique and shared variants, shown in thousands. Error bars indicate standard errors. SA indicates surface area; TH, thickness.

Shared and Novel Loci Between Schizophrenia and Global Cortical Structural Measures

The conjFDR analysis identified 44 genomic loci shared between schizophrenia and total SA (Figure 2), with 19 and 37 novel loci for schizophrenia and total SA, respectively. Schizophrenia and average TH shared 23 genomic loci (Figure 2), with 8 and 19 loci novel for schizophrenia and average TH, respectively. Four loci were jointly associated with schizophrenia, total SA, and average TH. The shared loci showed mixed directions of allelic associations, with 17 of 44 loci (39%) showing concordant associations with schizophrenia and total SA, and 8 of 23 loci (35%) had concordant associations with schizophrenia and average TH. The shared genomic loci, candidate SNVs, allelic associations, and novelty for each trait are summarized in Supplement 2, eTable 3 in Supplement 1, Supplement 3, and Supplement 4.

Among those shared loci, Combined Annotation Dependent Depletion scores indicated 3 lead SNVs (schizophrenia and SA: rs7146019; schizophrenia and TH: rs13107325 and rs11975) as pathogenic (Combined Annotation Dependent Depletion score >12.37; Supplement 2). A subset of lead SNVs had a minimum chromatin state of less than 8, indicating a location within regulatory regions (Supplement 2). We found 92 significant eQTLs for lead SNVs shared between schizophrenia and total SA and 61 significant eQTLs for lead SNVs shared between schizophrenia and average TH, implying shared loci alter gene expressions in brain tissues (eTable 4 in Supplement 1). After excluding high-LD regions, mapped genes for schizophrenia and total SA were enriched in 26 GO terms, including “GO_COMMITMENT_OF_NEURONAL_CELL_TO_SPECIFIC_NEURON_TYPE_IN_FOREBRAIN” and “GO_POSITIVE_REGULATION_OF_TYPE_I_INTERFERON_PRODUCTION” and 27 immunologic signature gene sets. Mapped genes for schizophrenia and average TH showed significant enrichments in 2 GO terms and 10 immunologic signature gene sets. Mapped genes and gene sets are listed in eTables 5 and 6 in Supplement 1, Supplement 5, and Supplement 6.

In addition, condFDR analysis identified 214 schizophrenia-associated genomic loci, in which 33 were novel for schizophrenia, conditional on their associations with SA (Supplement 7). Conditioning on SNV associations with TH, we found 201 genomic loci associated with schizophrenia, including 28 novel loci. Conditioning on SNV associations with schizophrenia, condFDR analysis reported 76 genomic loci for total SA and 57 loci for average TH, with 52 and 35 novel for total SA and average TH, respectively (Supplement 8).

Concordancy and Significance of Identified Genomic Loci in Independent Samples

Of 43 lead SNVs that were shared between schizophrenia and total SA and available in the independent Norwegian and ENIGMA data sets, 67% (29 of 43) showed a consistent direction of association between the discovery and independent cohorts (binomial P = .02), suggesting corresponding SNV associations replicated en masse in independent cohorts. As for schizophrenia and average TH, 13 of 23 shared lead SNVs showed the same direction of association (binomial P = .34) in independent cohorts. Examining the association of those 23 loci on each phenotype separately showed significant sign concordancy in average TH (concordant sign: 19 of 23 lead SNVs [83%]; binomial P = .001) but not in schizophrenia (concordant sign: 15 of 23 lead SNVs [65%]; binomial P = .11). Further, the genetic association of shared lead SNVs with schizophrenia replicated en masse in East Asian samples³¹ (schizophrenia and total SA: binomial P = 1.56 × 10⁻⁴; schizophrenia and average TH: binomial P = 3.64 × 10⁻⁴). Lead SNVs identified in condFDR analysis revealed en masse concordant association in independent cohorts as well as in East Asian samples (eResults in Supplement 1).

A group of shared loci (schizophrenia and total SA: 23 of 43 [54%]; schizophrenia and average TH: 12 of 23 [52%]) showed nominal significance between lead SNVs with at least 1 phenotype (P < .05; Table 1 and Table 2).^47,48 For those shared loci, 5 credible mapped genes, including INA, ATP5G1, SREBF2, MAPT, and DNPH1, showed a high expression in the brain cortex (transcripts per million >40⁴⁹; Figure 3). More details are included in the eResults in Supplement 1, Supplement 9, Supplement 10, Supplement 11, and Supplement 12.

Table 1. Twenty-Three Shared Genomic Loci Between Schizophrenia and Total Surface Area That Showed conjFDR Less Than 0.05 and Significance in Independent Cohorts^a.

Genomic loci				Discovery study						Independent GWAS data sets^c				Novelty^e
No.	CHR	Minimum BP	Maximum BP	Primary lead SNV	conjFDR	A1	A2	Credible mapped gene	Cor_Eff^b	Lead SNV	P value		Rep_Dir^d
No.	CHR	Minimum BP	Maximum BP	Primary lead SNV	conjFDR	A1	A2	Credible mapped gene	Cor_Eff^b	Lead SNV	SCZ GWAS	Total SA GWAS	Rep_Dir^d
3	1	243639859	244014490	rs1058304	4.74 × 10⁻⁴	T	C	NA	−	rs1058304	.675	2.35 × 10⁻⁴	0	Total SA
4	2	48177487	48707841	rs72872724	7.34 × 10⁻⁴	G	A	FOXN2	+	rs72872724	.972	1.17 × 10⁻⁴	+	SCZ, total SA
8	3	71481192	71611630	rs7610856	0.010	A	C	NA	−	rs7610856	.058	.005	+	Total SA
12	4	103112470	103387161	rs34316881	0.001	T	C	NA	+	rs34316881	.407	.001	+	Total SA
13	4	139927828	140130524	rs28565133	0.021	T	C	NA	−	rs28565133	.069	5.01 × 10⁻⁵	+	SCZ, total SA
14	5	5426431	5533414	rs3111175	0.030	G	T	NA	−	rs2578558	.532	.030	+	SCZ, total SA
15	5	38922826	39094257	rs12233987	0.038	C	T	NA	−	rs12233987	.220	.002	+	SCZ, total SA
19	5	170776747	170848124	rs7715167	0.047	T	C	NA	−	rs7715167	.031	1.37 × 10⁻⁵	+	SCZ, total SA
21	6	108868422	109035704	rs9398172	0.002	G	A	FOXO3	−	rs9398172	.272	4.69 × 10⁻⁴	+
23	7	1860728	2167939	rs11764960	0.035	A	G	NA	−	rs11764960	.472	.013	+	Total SA
24	7	28454580	28482115	rs2391665	0.029	G	A	NA	−	rs2391665	.383	.008	0	SCZ, total SA
25	7	104587323	105063372	rs3779210	0.017	T	C	NA	+	rs3779210	.101	8.44 × 10⁻⁶	+	Total SA
26	8	40712159	40771375	rs1004412	0.017	T	C	NA	−	rs1004412	.036	.664	0	SCZ, total SA
28	10	104965551	105176914	rs7082288	9.11 × 10⁻⁴	T	C	INA; PDCD11	−	rs7082288	.013	3.35 × 10⁻⁸	+
29	11	46257757	47242761	rs6485671	7.75 × 10⁻⁵	A	G	NA	+	rs6485671	.106	.030	+	Total SA
31	12	66277698	66384362	rs61921611	0.001	C	T	NA	−	rs61921611	.632	6.94 × 10⁻⁸	+	SCZ
32	12	79760388	79891269	rs2895728	0.041	C	T	NA	−	rs2895728	.327	4.01 × 10⁻⁴	0	SCZ, total SA
35	12	123447928	123891209	rs61955214	0.021	T	C	OGFOD2; PITPNM2; MPHOSPH9; RP11-282O18.3; hsa-mir-8072; SETD8	+	rs61955214	.665	.018	0	Total SA
37	14	99733954	99751588	rs7146019	6.16 × 10⁻⁴	A	G	NA	+	rs7146019	.700	.004	0	Total SA
39	17	43463493	44865603	rs2532392	0.008	A	G	MAPT-AS1; MAPT; STH; KANSL1-AS1; RP11-259G18.1; WNT3	+	rs199504	.301	1.99 × 10⁻²²	+	SCZ
40	17	46835629	47047868	rs35929648	0.007	A	G	TTLL6; SUMO2P17; ATP5G1; UBE2Z	−	rs35929648	.774	.001	0	Total SA
43	22	29360297	29399244	rs2213645	0.046	A	G	NA	−	rs2213645	.976	5.50 × 10⁻⁵	+	SCZ, total SA
44	22	41437526	42308739	rs12170228	0.004	T	C	POLR3H; CSDC2; DESI1; MEI1; SREBF2	−	rs12170228	.023	.899	+	Total SA

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Abbreviations: A1, allele 1 (effect allele); A2, allele 2 (noneffect allele); BP, genomic position (hg19); conjFDR, conjunctional false discovery rate; CHR, chromosome; Cor_Eff, concordancy of association directions between 2 traits; GWAS, genome-wide association study; NA, not applicable; Rep_Dir, concordancy of association directions between the discovery and independent cohorts; SA, surface area; SCZ, schizophrenia; SNV, single-nucleotide variant.

^{^a}

This table provides 23 genomic loci that identified as shared between SCZ and total SA in a discovery study and showed a significant association with at least 1 phenotype in the independent Norwegian and Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) cohorts.

^{^b}

The directionality of allelic associations with both phenotypes in discovery data sets using z scores is shown here. + Indicates the same direction and −, the opposite direction.

^{^c}

For genomic loci of which the primary lead SNV is not available in independent data sets, we assigned a secondary lead SNV.

^{^d}

The “Rep_Dir” shows the concordancy of association directions of lead SNVs between the discovery and independent samples using z scores. + For both phenotypes show the same direction between the discovery and independent cohorts and 0, for at least 1 phenotype shows opposite direction.

^{^e}

Whether a locus was novel for 2 traits, according to the original GWAS, National Human Genome Research Institute–European Bioinformatics Institute GWAS Catalog, and previous conditional FDR/conjFDR publications is indicated. More information about all identified genomic loci are listed in Supplement 2 and Supplement 9, with details of defining genomic loci, lead SNV, credible mapped genes, and novelty in the eMethods in Supplement 1.

Table 2. Twelve Shared Genomic Loci Between Schizophrenia and Average Thickness That Showed conjFDR Less Than 0.05 and Significance in Independent Cohorts^a.

Genomic loci				Discovery study						Independent GWAS data sets^c				Novelty^e
No.	CHR	Minimum BP	Maximum BP	Primary lead SNV	conjFDR	A1	A2	Credible mapped gene	Cor_Eff^b	LEAD_SNV	P value		Rep_Dir^d
No.	CHR	Minimum BP	Maximum BP	Primary lead SNV	conjFDR	A1	A2	Credible mapped gene	Cor_Eff^b	LEAD_SNV	SCZ GWAS	Average TH GWAS	Rep_Dir^d
1	1	98336823	98664991	rs10875133	0.012	A	G	NA	+	rs10875133	.632	.002	+	Average TH
2	2	26961166	27351279	rs34472003	0.007	C	T	SLC35F6; DPYSL5; KHK	−	rs7602823	.430	6.90 × 10⁻⁴	0	Average TH
4	2	97349315	98599499	rs78381888	.002	G	A	NA	+	rs78381888	.636	.025	+	Average TH
7	4	102702364	103387161	rs13107325	1.97 × 10⁻⁷	T	C	NA	−	rs13107325	.209	9.97 × 10⁻⁵	+	Average TH
9	6	26410800	33548090	rs2523548	0.008	A	G	NA	−	rs3130100	.225	.006	+	Average TH
10	6	43160375	43212493	rs9462876	0.024	G	A	CUL9; DNPH1	+	rs9462876	.026	.848	0	Average TH
11	8	26119170	26279173	rs147506820	7.01 × 10⁻⁵	A	G	SDAD1P1	+	rs147506820	.725	.012	0	Average TH
15	10	123902771	123912298	rs7902292	0.036	T	C	NA	−	rs7902292	.681	.023	0	SCZ, average TH
17	15	58873555	59115995	rs4774310	0.010	G	T	NA	+	rs4774310	.354	.003	+	Average TH
19	17	2532052	2586229	rs6502460	0.002	G	T	NA	+	rs6502460	.775	.022	+	SCZ, average TH
21	17	19897445	20384834	rs7209734	0.011	C	A	NA	−	rs7209734	.544	.004	+	Average TH
22	17	43463493	44865603	rs2532392	0.013	A	G	MAPT-AS1; MAPT; STH; KANSL1-AS1; RP11-259G18.1; WNT3	−	rs199504	.301	1.47 × 10⁻⁴	+	SCZ, average TH

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Abbreviations: A1, allele 1 (effect allele); A2, allele 2 (noneffect allele); BP, genomic position (hg19); conjFDR, conjunctional false discovery rate; CHR, chromosome; Cor_Eff, concordancy of association directions between 2 traits; GWAS, genome-wide association study; NA, not applicable; Rep_Dir, concordancy of association directions between the discovery and independent cohorts; SCZ, schizophrenia; SNV, single-nucleotide variant; TH, thickness.

^{^a}

This table provides 12 genomic loci that identified as shared between SCZ and average TH in a discovery study and showed significant associations with at least 1 phenotype in the independent Norwegian and Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) cohorts.

^{^b}

The directionality of allelic associations with both phenotypes in discovery data sets using z scores is shown here. + Indicates the same direction and −, the opposite direction.

^{^c}

For genomic loci of which the primary lead SNV is not available in independent data sets, we assigned a secondary lead SNV.

^{^d}

^{^e}

Figure 3. — Among the shared loci that were significantly associated with at least 1 phenotype in the independent cohorts, 31 credible genes were mapped by all 3 strategies: positional mapping, expression quantitative trait locus mapping, and chromatin interaction mapping. Corresponding expression patterns in brain tissue were extracted from the Genotype-Tissue Expression Portal. The expression pattern for *has-mir-8072* was not found. The *KMT5A* is also named as *SETD8*. Transcripts per million (TPM) is used to measure gene expression levels. BA indicates Brodmann area.

Shared Genetic Mechanisms Between Schizophrenia and Schizophrenia-Associated Regional Cortical Measurements

We observed SNV enrichment in QQ plots for 2 regions of interest for SA (superior frontal gyrus and middle temporal gyrus) and 3 for TH (superior temporal gyrus, inferior temporal gyrus, and superior frontal gyrus) with schizophrenia and vice versa (eFigures 3-6 in Supplement 1). The largest overlap as measured with Dice coefficient was observed in middle temporal gyrus among SA and schizophrenia (Dice coefficient = 0.32) and in superior temporal gyrus among TH and schizophrenia (Dice coefficient = 0.40; eTable 1 in Supplement 1). For the conjFDR analysis, schizophrenia shared 9, 16, 9, 11, and 10 genomic loci with regional SA (superior frontal gyrus and middle temporal gyrus) and regional TH (superior temporal gyrus, inferior temporal gyrus, and superior frontal gyrus), respectively (Supplement 13).

Discussion

In the present study, we identified a shared genetic basis between schizophrenia and SA and TH of cerebral cortex using MiXeR and cFDR. Specifically, we (1) showed that a substantial proportion of SNVs associated with total SA and average TH were associated with the risk of schizophrenia; (2) identified shared genomic loci between schizophrenia and total SA and average TH; (3) revealed polygenic overlap between schizophrenia and relevant regions of interest for SA (superior frontal gyrus and middle temporal gyrus) and for TH (superior temporal gyrus, inferior temporal gyrus, and superior frontal gyrus); and (4) suggested immune functions may be associated with cortical development and the pathology of schizophrenia.

Individual differences in cortical brain structure have been linked to cognition, mood, and behavior.^50,51 Previous studies have suggested cortical brain structure as an endophenotype of schizophrenia,⁵² mainly supported by phenotypic associations.^3,4 Here we identified genetic overlap between schizophrenia and cortical structure, suggesting that previously reported group differences in these brain magnetic resonance imaging features between patients with schizophrenia and healthy controls are partly genetically determined, in line with the reported association between polygenic risk score of schizophrenia and cortical TH.^7,9,10 Specifically, MiXeR revealed many variants shared between schizophrenia and cortical morphology with a distinct pattern of association distributions that approximately 50% of shared variants demonstrated concordant associations with schizophrenia and cortical structure. The mixed directions or associations across the shared genetic variants^15,16 help explain the polygenic overlap despite nonsignificant genetic correlation.⁸ Meanwhile, MiXeR reported a higher polygenicity for schizophrenia compared with cortical SA and TH, which is likely associated with the biological complexity and clinical heterogeneity of schizophrenia.^16,53,54 These findings indicate a polygenic pleiotropy¹⁵ model for the brain in which polygenic phenotypes related to brain may have overlapping genetic determinants along with comparatively distinct genetic characteristics, including different polygenicities and phenotype-specific association distributions. Regional TH measures showed a bigger genetic overlap with schizophrenia than global measures, while SA did not show this pattern. While the brain magnetic resonance imaging associations with schizophrenia show substantial between-individual heterogeneity,^7,10,55 this pattern generally complies with the anatomical distribution of associations observed for TH in schizophrenia while the association with SA appears to be global.³

Notably, after excluding the regions with complex LD patterns, genes implicated by the shared loci were significantly enriched in biological processes related to inflammation. Previous studies have suggested that the activity of neuroinflammatory receptors affects cortical development,⁵⁶ along with significant associations between inflammation makers with cortical SA⁵⁷ and TH.⁵⁸ Immune dysfunction has been proposed as an important factor in the pathogenesis of schizophrenia⁵⁹ as evidenced by the involvement of immune-related genes²⁹ and altered various inflammatory makers in schizophrenia.⁶⁰ Immune-related synapse pruning, which decreases cortical TH,⁶¹ has been reported in schizophrenia.⁶² Our results suggest immune processes may be involved in the shared genetic component between schizophrenia and cortical structure, providing an insight into the above observations. Although most prior reports highlighted the MHC region, the enrichment in inflammation-related gene sets without MHC implies that genetic factors linking to the immune system to cerebral cortex and schizophrenia are not only driven by the strong and complex association at the MHC region. Further experimental studies are needed to understand the roles of immunity in cortical structure and in schizophrenia.

Our study found 5 credible mapped genes, including INA, ATP5G1, SREBF2, MAPT, and DNPH1, have a high expression in the brain cortex. INA encodes neurofilaments, which are involved in the morphogenesis of neurons and work as biomarkers in neurological disorders.⁶³ Previous studies have suggested ATP5G1, encoding a subunit of mitochondrial ATP synthase, is associated with major depression disorder and Alzheimer disease.^64,65 SREBP2 has been indicated as a genetic susceptibility factor in schizophrenia and an antipsychotic-activated transcription factor. MAPT encodes microtubule-associated protein tau, which participates in axonal transport and neurite outgrowth.⁶⁶ Variants within MAPT are associated with neurodegenerative disorders,²³ learning disability,⁶⁷ and alterations in cortical TH, gray matter volume, and white matter integrity.^68,69 DNPH1 encodes a hydrolase, and a GWAS has reported DNPH1 is associated with risk-taking behavior.⁷⁰

Limitations

However, some limitations should be noted. First, the sample size of the independent Norwegian GWAS data set for schizophrenia was small with a limited power. Given the shared loci between schizophrenia and average TH failed to show concordancy in the Norwegian cohort, a future examination with a large cohort is necessary. Second, although gene set analysis provided potential insights, whether the overlapping genetic components reflect shared biological pathways or more unspecific associations of brain-expressed genes warrants further research.

Conclusions

In summary, our study uncovers shared polygenicity between schizophrenia and cortical brain structure. Mapped genes of shared loci along with previous reports suggest potential connections of immunity to the development of cortical brain structure and to the pathology of schizophrenia. These findings provide molecular genetic insights into the phenotypic correlations between schizophrenia and brain structure.

Supplement 1.

eMethods.

eResults.

eReferences.

eFigure 1. Conditional QQ plots for schizophrenia conditioned on global cortical measures and vice versa in discovery study

eFigure 2. Shared polygenicity underlying schizophrenia and global cortical measures in discovery study

eFigure 3. Conditional QQ plots for schizophrenia conditioned on regions of interest for surface area and vice versa

eFigure 4. Shared polygenicity underlying schizophrenia and regional surface area

eFigure 5. Conditional QQ plots for schizophrenia conditioned on regions of interest for thickness and vice versa

eFigure 6. Shared polygenicity underlying schizophrenia and regional thickness

eTable 1. Univariate analysis using MiXeR

eTable 2. Bivariate analysis using MiXeR

eTable 3. Previously reported significant associations of shared genomic loci based on the NHGRI-EBI GWAS Catalog using candidate SNPs as index

eTable 4. Significant eQTLs of lead SNPs of shared genomic loci in discovery study in the GTEx database (v8)

eTable 5. Gene-sets significantly enriched by mapped genes of shared loci between schizophrenia (SCZ) and total surface area (SA)

eTable 6. Gene-sets significantly enriched by mapped genes of shared loci between schizophrenia (SCZ) and average thickness (TH)

Click here for additional data file.^{(2MB, pdf)}

Supplement 2.

eTable. Distinct genomic loci shared between schizophrenia (SCZ) and brain cortical structural measures (total surface area, SA; average thickness, TH) at conjFDR<0.05 in discovery study

Click here for additional data file.^{(28.2KB, xlsx)}

Supplement 3.

eTable. Candidate SNPs shared between schizophrenia (SCZ) and total surface area (SA) in discovery study

Click here for additional data file.^{(354.3KB, xlsx)}

Supplement 4.

eTable. Candidate SNPs shared between schizophrenia (SCZ) and average thickness (TH) in discovery study

Click here for additional data file.^{(189KB, xlsx)}

Supplement 5.

eTable. Genes mapped to candidate SNPs shared between schizophrenia (SCZ) and total surface area (SA) in discovery study

Click here for additional data file.^{(139.4KB, xlsx)}

Supplement 6.

eTable. Genes mapped to candidate SNPs shared between schizophrenia (SCZ) and average thickness (TH) in discovery study

Click here for additional data file.^{(90KB, xlsx)}

Supplement 7.

eTable. Distinct genomic loci associated with schizophrenia (SCZ) at condFDR<0.01 given association with total surface area(SA) or average thickness (TH) in discovery study

Click here for additional data file.^{(91.3KB, xlsx)}

Supplement 8.

eTable. Distinct genomic loci associated with total surface area (SA) or average thickness (TH) at condFDR<0.01 given association with schizophrenia (SCZ) in discovery study

Click here for additional data file.^{(37.7KB, xlsx)}

Supplement 9.

eTable. Lead SNPs for conjFDR genomic loci in independent datasets

Click here for additional data file.^{(25.4KB, xlsx)}

Supplement 10.

eTable. Lead SNPs for conjFDR genomic loci in an East Asian GWAS dataset

Click here for additional data file.^{(23.1KB, xlsx)}

Supplement 11.

eTable. Lead SNPs for condFDR genomic loci in independent datasets

Click here for additional data file.^{(90.8KB, xlsx)}

Supplement 12.

eTable. Lead SNP for condFDR genomic loci in an East Asian GWAS dataset

Click here for additional data file.^{(69.3KB, xlsx)}

Supplement 13.

eTable. Distinct genomic loci shared between schizophrenia (SCZ) and regional cortical structural measures(regional surface area, SA; regional thickness, TH) at conjFDR<0.05

Click here for additional data file.^{(22.2KB, xlsx)}

Supplement 14.

eTable. Distinct genomic loci shared between schizophrenia (SCZ) and brain cortical structural measures (total surface area, SA; average thickness, TH) at conjFDR<0.01 in discovery study

Click here for additional data file.^{(16.7KB, xlsx)}

Supplement 15.

eTable. Lead SNPs for conjFDR genomic loci at conjFDR<0.01 in independent datasets

Click here for additional data file.^{(17.1KB, xlsx)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods.

eResults.

eReferences.

eFigure 1. Conditional QQ plots for schizophrenia conditioned on global cortical measures and vice versa in discovery study

eFigure 2. Shared polygenicity underlying schizophrenia and global cortical measures in discovery study

eFigure 3. Conditional QQ plots for schizophrenia conditioned on regions of interest for surface area and vice versa

eFigure 4. Shared polygenicity underlying schizophrenia and regional surface area

eFigure 5. Conditional QQ plots for schizophrenia conditioned on regions of interest for thickness and vice versa

eFigure 6. Shared polygenicity underlying schizophrenia and regional thickness

eTable 1. Univariate analysis using MiXeR

eTable 2. Bivariate analysis using MiXeR

eTable 3. Previously reported significant associations of shared genomic loci based on the NHGRI-EBI GWAS Catalog using candidate SNPs as index

eTable 4. Significant eQTLs of lead SNPs of shared genomic loci in discovery study in the GTEx database (v8)

eTable 5. Gene-sets significantly enriched by mapped genes of shared loci between schizophrenia (SCZ) and total surface area (SA)

eTable 6. Gene-sets significantly enriched by mapped genes of shared loci between schizophrenia (SCZ) and average thickness (TH)

Click here for additional data file.^{(2MB, pdf)}

Supplement 2.

eTable. Distinct genomic loci shared between schizophrenia (SCZ) and brain cortical structural measures (total surface area, SA; average thickness, TH) at conjFDR<0.05 in discovery study

Click here for additional data file.^{(28.2KB, xlsx)}

Supplement 3.

eTable. Candidate SNPs shared between schizophrenia (SCZ) and total surface area (SA) in discovery study

Click here for additional data file.^{(354.3KB, xlsx)}

Supplement 4.

eTable. Candidate SNPs shared between schizophrenia (SCZ) and average thickness (TH) in discovery study

Click here for additional data file.^{(189KB, xlsx)}

Supplement 5.

eTable. Genes mapped to candidate SNPs shared between schizophrenia (SCZ) and total surface area (SA) in discovery study

Click here for additional data file.^{(139.4KB, xlsx)}

Supplement 6.

eTable. Genes mapped to candidate SNPs shared between schizophrenia (SCZ) and average thickness (TH) in discovery study

Click here for additional data file.^{(90KB, xlsx)}

Supplement 7.

eTable. Distinct genomic loci associated with schizophrenia (SCZ) at condFDR<0.01 given association with total surface area(SA) or average thickness (TH) in discovery study

Click here for additional data file.^{(91.3KB, xlsx)}

Supplement 8.

eTable. Distinct genomic loci associated with total surface area (SA) or average thickness (TH) at condFDR<0.01 given association with schizophrenia (SCZ) in discovery study

Click here for additional data file.^{(37.7KB, xlsx)}

Supplement 9.

eTable. Lead SNPs for conjFDR genomic loci in independent datasets

Click here for additional data file.^{(25.4KB, xlsx)}

Supplement 10.

eTable. Lead SNPs for conjFDR genomic loci in an East Asian GWAS dataset

Click here for additional data file.^{(23.1KB, xlsx)}

Supplement 11.

eTable. Lead SNPs for condFDR genomic loci in independent datasets

Click here for additional data file.^{(90.8KB, xlsx)}

Supplement 12.

eTable. Lead SNP for condFDR genomic loci in an East Asian GWAS dataset

Click here for additional data file.^{(69.3KB, xlsx)}

Supplement 13.

eTable. Distinct genomic loci shared between schizophrenia (SCZ) and regional cortical structural measures(regional surface area, SA; regional thickness, TH) at conjFDR<0.05

Click here for additional data file.^{(22.2KB, xlsx)}

Supplement 14.

eTable. Distinct genomic loci shared between schizophrenia (SCZ) and brain cortical structural measures (total surface area, SA; average thickness, TH) at conjFDR<0.01 in discovery study

Click here for additional data file.^{(16.7KB, xlsx)}

Supplement 15.

eTable. Lead SNPs for conjFDR genomic loci at conjFDR<0.01 in independent datasets

Click here for additional data file.^{(17.1KB, xlsx)}

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PERMALINK

Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness

Weiqiu Cheng, PhD

Oleksandr Frei, PhD

Dennis van der Meer, PhD

Yunpeng Wang, PhD

Kevin S O’Connell, PhD

Yunhan Chu, PhD

Shahram Bahrami, PhD

Alexey A Shadrin, PhD

Dag Alnæs, PhD

Guy F L Hindley, MBBS, MRes

Aihua Lin, PhD

Naz Karadag, MSc

Chun-Chieh Fan, PhD

Lars T Westlye, PhD

Tobias Kaufmann, PhD

Espen Molden, PhD

Anders M Dale, PhD

Srdjan Djurovic, PhD

Olav B Smeland, MD, PhD

Ole A Andreassen, MD, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

GWAS Data Sets

Statistical Analysis

Identify Genomic Loci, Functional Annotation, and Novelty

Results

Genetic Overlap Between Schizophrenia and Global Cortical Structural Measures

Figure 1. Polygenic Comparison of Schizophrenia and Global Cortical Measures in Discovery Study.

Shared and Novel Loci Between Schizophrenia and Global Cortical Structural Measures

Figure 2. Shared Genetic Loci Between Schizophrenia and Global Cortical Measures in Discovery Study.

Concordancy and Significance of Identified Genomic Loci in Independent Samples

Table 1. Twenty-Three Shared Genomic Loci Between Schizophrenia and Total Surface Area That Showed conjFDR Less Than 0.05 and Significance in Independent Cohortsa.

Table 2. Twelve Shared Genomic Loci Between Schizophrenia and Average Thickness That Showed conjFDR Less Than 0.05 and Significance in Independent Cohortsa.

Figure 3. Expression of Credible Genes of Shared Loci in Brain Tissue.

Shared Genetic Mechanisms Between Schizophrenia and Schizophrenia-Associated Regional Cortical Measurements

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Twenty-Three Shared Genomic Loci Between Schizophrenia and Total Surface Area That Showed conjFDR Less Than 0.05 and Significance in Independent Cohorts^a.

Table 2. Twelve Shared Genomic Loci Between Schizophrenia and Average Thickness That Showed conjFDR Less Than 0.05 and Significance in Independent Cohorts^a.