Shared Genetic Loci Between Body Mass Index and Major Psychiatric Disorders: A Genome-wide Association Study | Bipolar and Related Disorders | JAMA Psychiatry | JAMA Network
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Figure 1.  Conditional Quantile-Quantile Plots
Conditional Quantile-Quantile Plots

A, C, and E, Conditional quantile-quantile plots of nominal vs empirical body mass index (BMI) −log10 P values (corrected for inflation) below the standard genome-wide association study threshold of P < 5 × 10−8 as a function of the significance of the association with schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) at the level of P ≤ .10, P ≤ .01, and P ≤ .001, respectively. B, D, and F, Conditional quantile-quantile plots of nominal vs empirical SCZ, BIP, and MD −log10 P values (corrected for inflation) below the standard genome-wide association study threshold of P < 5 × 10−8 as a function of the significance of the association with BMI, at the level of P ≤ .10, P ≤ .01, and P ≤ .001, respectively. The dashed line indicates the null hypothesis. SNP indicates single-nucleotide polymorphism.

Figure 2.  Common Genetic Variants Jointly Associated With Body Mass Index, Schizophrenia, Bipolar Disorder, and Major Depression at Conjunctional False Discovery Rate (conjFDR) Less Than 0.01
Common Genetic Variants Jointly Associated With Body Mass Index, Schizophrenia, Bipolar Disorder, and Major Depression at Conjunctional False Discovery Rate (conjFDR) Less Than 0.01

Manhattan plot showing the –log10 transformed conjFDR values for each single-nucleotide polymorphism on the y-axis and chromosomal positions along the x-axis. The dotted horizontal line represents the threshold chosen for reporting shared associations (conjFDR < 0.01). Independent lead single-nucleotide polymorphisms are encircled in outlined circle. The significant shared signal in the major histocompatibility complex region (chr6:25119106–33854733) is represented by 1 independent lead single-nucleotide polymorphism. Further details are provided in eTables 3, 12, and 19 in Supplement 1.

Figure 3.  Distribution of the Annotation for All SNPs Jointly Associated With Body Mass Index and Schizophrenia, Bipolar Disorder, and Major Depression at Conjunctional False Discovery Rate Less Than 0.10 Including Functional Consequences of SNPs
Distribution of the Annotation for All SNPs Jointly Associated With Body Mass Index and Schizophrenia, Bipolar Disorder, and Major Depression at Conjunctional False Discovery Rate Less Than 0.10 Including Functional Consequences of SNPs

A low RegulomeDB score indicates a higher likelihood of having a regulatory function. A lower minimum chromatin state across 127 tissue and cell types indicates higher accessibility, and states 1 to 7 refer to open chromatin states. NA indicates not applicable; ncRNA, noncoding RNA; SNP, single-nucleotide polymorphism; UTR, untranslated region.

Supplement 1.

eMethods

eResults.

eReferences.

eTable 1. Test for enrichment of strata in the QQ plots for BMI and SCZ

eTable 2. Distinct genomic loci associated with SCZ condFDR<0.01 given association with BMI

eTable 3. Distinct genomic loci associated with both BMI and SCZ at conjFDR<0.01

eTable 4. Distinct genomic loci associated with both BMI and SCZ at conjFDR<0.05

eTable 5. Significant eQTL functionality of SNPs in conj_BMI_SCZ in the GTEx database

eTable 6. cisEQTL- SNPs in conj_BMI_SCZ in the Braineac database

eTable 7. Gene Ontology gene-sets significantly associate with genes nearest loci in conj_BMI_SCZ at conjFDR<0.01

eTable 8. Pathway analysis for gene-set with concordant effect direction in conj_BMI vs MPDs at conjFDR<0.05

eTable 9. Pathway analysis for gene-set with opposite effect direction in conj_BMI vs MPDs at conjFDR<0.05

eTable 10. Test for enrichment of strata in the QQ plots for BMI and BIP

eTable 11. Distinct genomic loci associated with BIP condFDR<0.01 given association with BMI

eTable 12. Distinct genomic loci associated with both BMI and BIP at conjFDR<0.01

eTable 13. Distinct genomic loci associated with both BMI and BIP at conjFDR<0.05

eTable 14. Significant eQTL functionality of SNPs in conj_BMI_BIP in the GTEx database

eTable 15. cisEQTL- SNPs in conj_BMI_BIP in the Braineac database

eTable 16. Gene Ontology gene-sets significantly associated with genes nearest loci in conj_BMI_BIP at conjFDR<0.01

eTable 17. Test for enrichment of strata in the QQ plots for BMI and MD

eTable 18. Distinct genomic loci associated with major depression(MD) condFDR<0.01 given association with BMI

eTable 19. Distinct genomic loci associated with both BMI and major depression (MD) at conjFDR<0.01

eTable 20. Distinct genomic loci associated with both BMI and major depression (MD) at conjFDR<0.05

eTable 21. Significant eQTL functionality of SNPs in conj_BMI_major depression (MD) in the GTEx database

eTable 22. cisEQTL- SNPs in conj_BMI_major depression (MD) in the Braineac database

eTable 23. Gene Ontology gene-sets significantly associated with genes nearest loci in conj_BMI_major depression (MD) at conjFDR<0.01; pathway analysis for gene-sets significantly associated with genes nearest loci in conj_BMI_MDD at conjFDR<0.01

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Candilis  PJ, Geppert  CMA, Fletcher  KE, Lidz  CW, Appelbaum  PS.  Willingness of subjects with thought disorder to participate in research.   Schizophr Bull. 2006;32(1):159-165. doi:10.1093/schbul/sbj016PubMedGoogle ScholarCrossref
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    Original Investigation
    January 8, 2020

    Shared Genetic Loci Between Body Mass Index and Major Psychiatric Disorders: A Genome-wide Association Study

    Author Affiliations
    • 1NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
    • 2Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
    • 3Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
    • 4Department of Radiology, University of California, San Diego, La Jolla
    • 5Department of Cognitive Science, University of California, San Diego, La Jolla
    • 6Multimodal Imaging Laboratory, University of California, San Diego, La Jolla
    • 7Department of Psychiatry, University of California, San Diego, La Jolla
    • 8Department of Neurosciences, University of California, San Diego, La Jolla
    • 9NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
    JAMA Psychiatry. 2020;77(5):503-512. doi:10.1001/jamapsychiatry.2019.4188
    Key Points

    Question  Are there genome-wide genetic factors underlying both body mass index and major psychiatric disorders?

    Findings  In this study of combined genome-wide association data from 1 380 284 individuals, genetic overlap between body mass index and major psychiatric disorders (ie, schizophrenia, bipolar disorder, and major depression) was found.

    Meaning  These findings identify common genetic loci body mass index and major psychiatric disorders and indicate mixed association directions with mainly opposite associations in schizophrenia and body mass index.

    Abstract

    Importance  People with major psychiatric disorders (MPDs) have a 10- to 20-year shorter life span than the rest of the population, and this difference is mainly due to comorbid cardiovascular diseases. Genome-wide association studies have identified common variants involved in schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) and body mass index (BMI), a key cardiometabolic risk factor. However, genetic variants jointly influencing MPD and BMI remain largely unknown.

    Objective  To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them.

    Design, Setting, and Participants  Using a conditional false discovery rate statistical framework, independent genome-wide association study data on individuals with SCZ (n = 82 315), BIP (n = 51 710), MD (n = 480 359), and BMI (n = 795 640) were analyzed. The UK Biobank cohort (n = 29 740) was excluded from the MD data set to avoid sample overlap. Data were collected from August 2017 to May 2018, and analysis began July 2018.

    Main Outcomes and Measures  The primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways.

    Results  Genome-wide association study data from 1 380 284 participants were analyzed, and the genetic correlation between BMI and MPDs varied (SCZ: r for genetic = −0.11, P = 2.1 × 10−10; BIP: r for genetic = −0.06, P = .0103; MD: r for genetic = 0.12, P = 6.7 × 10−10). Overall, 63, 17, and 32 loci shared between BMI and SCZ, BIP, and MD, respectively, were analyzed at conjunctional false discovery rate less than 0.01. Of the shared loci, 34% (73 of 213) in SCZ, 52% (36 of 69) in BIP, and 57% (56 of 99) in MD had risk alleles associated with higher BMI (conjunctional false discovery rate <0.05), while the rest had opposite directions of associations. Functional analyses indicated that the overlapping loci are involved in several pathways including neurodevelopment, neurotransmitter signaling, and intracellular processes, and the loci with concordant and opposite association directions pointed mostly to different pathways.

    Conclusions and Relevance  In this genome-wide association study, extensive polygenic overlap between BMI and SCZ, BIP, and MD were found, and 111 shared genetic loci were identified, implicating novel functional mechanisms. There was mixture of association directions in SCZ and BMI, albeit with a preponderance of discordant ones.

    Introduction

    Cardiometabolic comorbidity in major psychiatric disorders (MPDs) is a major public health concern. The high rates of cardiometabolic risk factors, particularly obesity,1,2 contribute greatly to cardiovascular disease among individuals with MPDs, which is a main cause of the 10- to 20-year shorter life expectancy.3,4 Cardiometabolic comorbidity and the associated mortality have remained high during the last decades,5-7 illustrating that most patients with MPDs such as schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) have not benefited from the recent improvements in medicine. However, comorbidity may be a source of new pathophysiologic knowledge as indicated in recent genetic studies.8-10

    Cardiometabolic risk factors and overt cardiovascular disease in MPD are closely linked to lifestyle, including diet, physical activity, and smoking habits. Further, several psychopharmacologic agents, in particular antipsychotics, are obesogenic and have metabolic adverse events.2 At the same time, the polygenic nature of MPDs11,12 and cardiometabolic risk factors13-15 is becoming increasingly clear. Both body mass index (BMI) obesity and MPDs have substantial heritability, estimated to be 24% to 90% for BMI,16 40% to 70% for obesity,17 31% to 42% for MD,18 79% to 93% for BIP,19 and 79% for SCZ.20 Various genetic studies have established a relationship between BMI and MPD,21-24 and increased weight has been associated with treatment response across MPDs.25,26 Owing to the strong association between obesity and MPD, neurobiologic hypotheses associated with potential underlying mechanisms have been proposed.27 However, the relationship is complex, as both weight gain and loss are associated with depressive episodes, and there seems to be a link between low BMI and SCZ,28,29 recently supported by genetic studies.30,31 A bidirectional relationship is also suggested from studies of obesity and depression.32

    New powerful statistical methods specifically designed to analyze the polygenic architectures of complex traits have enabled improved gene discovery and replication rates.8,33,34 Specifically, we have shown how genetic pleiotropy enrichment increases the statistical power for identifying shared genetic variants.8,34 By assessing the contribution of all single-nucleotide polymorphisms (SNPs) from 2 independent genome-wide association studies (GWAS), we can determine their common associations, both in the presence and in the absence of overall genetic correlation.34-37

    In this study, we analyzed GWAS summary statistics of BMI, SCZ, BIP, and MD using the pleiotropy-based conditional and conjunctional false discovery rate (FDR) statistics8,34 to investigate the shared genetic architectures of BMI and SCZ, BIP, and MD. We hypothesized that the conditional and conjunctional FDR statistics would enhance the discovery of genetic loci for BMI and MPDs, thereby disclosing more of their shared polygenic architecture independent of their overall association directions.

    Methods
    Genome-wide Association Study Samples

    We used GWAS summary statistics results for BMI,38 SCZ,11 BIP,39 and MD.40 The BMI data were obtained from GIANT and UK BioBank (n = 795 640). The MD data were generated by meta-analyzing the Psychiatric Genomics Consortium GWAS and the 23andMe GWAS, which also included cases with self-reported depression. The meta-analysis was performed using the inverse-weighted fixed-effects model implemented in the software METAL (http://csg.sph.umich.edu//abecasis/Metal/).41 GWAS summary statistics for SCZ11 and BIP39 were provided by the Psychiatric Genomics Consortium. All GWASs performed and investigated in the present study were approved by the local ethics committees, and written informed consent was obtained from all participants. Additional details are provided in the original GWAS articles.11,38-40 Furthermore, the Norwegian Institutional Review Board for the southeast Norway region evaluated the current protocol and found that no additional institutional review board approval was needed because no individual data were used. Data were collected from August 2017 to May 2018.

    Statistical Analyses

    Analysis began July 2018. To visually assess the presence of enrichment, we generated conditional quantile-quantile (Q-Q) plots,8 conditioning BMI on SCZ, BIP, and MD and vice versa. We also estimated the genetic correlation between BMI and SCZ, BIP, and MD using linkage disequilibrium (LD) score regression.42,43 Details about these methods can be found in the eMethods in Supplement 1.

    To improve the discovery of genetic variants associated with BMI and SCZ, BIP, and MD, we computed conditional FDR statistics.8 The conditional FDR method builds on an empirical Bayesian statistical framework and uses GWAS summary statistics from a trait of interest (eg, BMI) together with those of a conditional trait (eg, MD) to estimate the posterior probability that a SNP has no association with the primary trait, given that the P values for that SNP in both the primary and conditional traits are as small as or smaller than the observed P value. Thus, the method improves the detection of genetic variants associated with the primary trait via reranking the test statistics of the primary phenotype based on the strength of the association with the secondary phenotype.

    To determine any loci likely to be shared by 2 phenotypes, we computed conjunctional FDR statistics.8,34,35 The conjunctional FDR is an extension of the conditional FDR and is defined as the maximum of the 2 conditional FDR statistics for a specific SNP and estimates the posterior probability that a SNP is null for either trait or both, given that the P values for both phenotypes are as small as or smaller than the P values for each trait individually. For more details, see the original8 and subsequent publications.35,44,45 In the present study, we used a conservative FDR level of 0.01 per pairwise comparison for conditional FDR and conjunctional FDR, but for subsequent analyses of association directions, we also included shared SNPs with conjunctional FDR less than 0.05. Manhattan plots were constructed based on the conjunctional FDR to show the genomic location of the shared genetic loci.8

    All analyses were performed after excluding SNPs in the extended major histocompatibility complex (hg19 location chr 6: 25119106-33854733) and the 8p23.1 (hg19 location chr 8: 7242715-12483982) regions to avoid potential biases owing to intricate LD patterns.

    Genomic Loci Definition and Functional Annotation

    To define the independent genomic loci, we used FUMA.46 Single-nucleotide polymorphisms with FDR less than 0.01 and at LD r2 < 0.6 with each other were considered as independent significant SNPs, and a fraction of the independent significant SNPs in approximate LD with each other at r2 < 0.1 were considered lead SNPs. We outlined the distinct genomic loci and their borders based on FUMA’s default parameters.46

    FUMA was also deployed to annotate the significantly associated SNPs with functional categories, Combined Annotation Dependent Depletion scores,47 RegulomeDB scores,48 and chromatin states.49,50 A Combined Annotation Dependent Depletion score above 12.37 shows an association of deleterious protein with outcomes.47 The RegulomeDB score indicates the regulatory functionality of SNPs based on expression quantitative trait loci and chromatin marks.48 The chromatin state indicates the accessibility of genomic regions using 15 categories, as predicted by ChromHMM based on 5 chromatin marks for 127 epigenomes.49,50 To place them in potential biologic context, we matched the candidate loci to the brain tissue expression quantitative trait loci (expression quantitative trait loci) databases from GTEx (http://gtexportal.org) and Braineac (http://www.braineac.org).51,52 Further details are provided in the eMethods in Supplement 1.

    To identify overrepresented pathways for the genes nearest the identified shared loci for each MPD, we used ConsensusPathDB.53 All analyses were corrected for multiple comparisons. Significance was set at a 2-sided P value less than .05.

    Results

    The MD sample originally consisted of 135 458 individuals with MD and 344 901 controls.40 To avoid sample overlaps, we excluded the UK Biobank cohort (n = 29 740). The SCZ sample included 34 241 individuals with SCZ and 45 604 controls.11 The BIP sample consisted of 20 352 individuals with BIP and 31 358 controls.39 A total of 1 380 284 individuals were included in this analysis.

    Genetic Overlap and Correlation Between BMI and MPDs

    The stratified Q-Q plots indicate successive increments of SNP enrichment for BMI conditioned on association P values for SCZ, BIP, and MD (Figure 1A, C, and E). These suggest polygenic overlap between the phenotypes. The reverse stratified Q-Q plots also display enrichment of associations for SCZ, BIP, and MD given BMI (Figure 1B, D, and F).

    Successive leftward shifts from the dashed line of no association for decreasing nominal BMI P values indicate that the proportion of non-null SNPs in BMI increase with higher levels of association with the MPDs (SCZ, BIP, or MD) (Figure 1).

    We used partitioned LD score regression43 to assess the statistical significance of the enrichment for the Q-Q plot strata and the analyses returned significant enrichment for BMI given SCZ, BIP, and MD as well as for SCZ, BIP, and MD given BMI for all strata (eTables 1, 10, and 17 in Supplement 1).

    Genome-wide LD score regression analyses showed significant negative genetic correlation between BMI and SCZ (r for genetic = −0.11; P = 2.4909 × 10−10), nonsignificant negative genetic correlation between BMI and BIP (r for genetic = −0.06; P = .0103), and significant positive genetic correlation between BMI and MD (r for genetic = 0.12; P = 6.7040 × 10−10).

    BMI-Associated Loci Identified With Conditional FDR

    To enhance the discovery of genetic variants associated with BMI and MPDs, we applied the conditional FDR statistical analysis on the association of BMI with MPDs, and vice versa. At conditional FDR less than 0.01, we identified 723, 679, and 710 loci associated with BMI conditionally on SCZ, BIP, and MD, respectively (Supplement 2, Supplement 4, and Supplement 6). The reversed conditional FDR analysis showed 170, 52, and 70 loci associated with SCZ, BIP, and MD, respectively, conditionally on BMI (eTables 2, 11, and 18 in Supplement 1).

    Loci Shared Between BMI and MPDs

    To identify loci shared between BMI and MPDs, we performed conjunctional FDR analyses. A total of 63 distinct loci were shared between BMI and SCZ at conjunctional FDR less than 0.01; of those, 12 were not identified in the original BMI GWAS,38 26 were not identified in the original SCZ GWAS,11 and 7 were novel for both phenotypes (Figure 2 and eTable 3 in Supplement 1). Seventeen loci emerged as shared between BMI and BIP at conjunctional FDR less than 0.01 (Figure 2 and eTable 12 in Supplement 1). One of these was not significantly associated in the original BMI GWAS, while 9 were not identified in the original BIP GWAS39 (eTable 12 in Supplement 1). Finally, 32 distinct genomic loci were associated with both BMI and MD at conjunctional FDR less than 0.01 (Figure 2 and eTable 19 in Supplement 1); 6 of these were not identified in the original BMI GWAS, 14 were novel for MD,40 and 4 were novel for both traits. All identified shared SNPs at conjunctional FDR less than 0.05 are reported in eTables 4, 13, and 20 in Supplement 1.

    In addition to those shared with BMI, the MPDs had also several genetic loci shared among them. Of the SNPs in the loci shared by SCZ and BMI, 2 were observed in the original BIP39 and 3 in the MD GWASs.40 Seventeen of the shared loci were below conjunctional FDR 0.05 for BMI and BIP (eTable 3 in Supplement 1). Further, 12 of the loci shared between BMI and SCZ at conjunctional FDR less than 0.01 were below conjunctional FDR 0.05 for BMI and MD, and 6 of these were below conjunctional FDR 0.05 for BMI and BIP (eTable 3 in Supplement 1). Of the 9 new loci BIP shared with BMI, 2 were also identified in the conjunctional FDR analyses for BMI and SCZ and also for BMI and MD (eTable 12 in Supplement 1). Of the loci shared by MD and BMI, 9 were identified in the conjunctional FDR analysis for BMI and SCZ and 2 of these were novel for MD (eTable 19 in Supplement 1). Of the 4 novel loci, 1 was also identified at conjunctional FDR 0.05 for BMI and SCZ. However, 3 and 1 of the 32 shared loci were identified in the original SCZ and BIP GWASs, respectively (eTable 19 in Supplement 1).

    By comparing the association directions of the top lead SNPs at conjunctional FDR less than 0.05, a clear pattern of mixed association directions appears, with SNPs with concordant association direction in 73 of 213 loci (34%) in BMI and SCZ, 56 of 99 loci (57%) in BMI and MD, and 36 of 69 loci (52%) in BMI and BIP (eTables 4, 13, and 20 in Supplement 1). This can explain the observed negative, negligible, and positive genetic correlations between BMI and SCZ, BMI and BIP, and BMI and MD, respectively.

    Annotation of Loci Shared Between BMI and the MPDs

    The functional annotation of all SNPs at conjunctional FDR less than 0.10 for BMI vs SCZ, BIP, and MD are shown in Figure 3, Supplement 3, Supplement 5, and Supplement 7. The shared loci revealed variants associated with brain tissue gene expression and several biologic and molecular processes including central nervous system development, hormone, GABAergic and glutamatergic signaling, and intracellular processes. Interestingly, the analyses of genes with concordant and opposite association directions for BMI and the MPDs indicated only minor overlap in the overrepresented pathways. More genetic analyses are provided in the eResults and eTables 5, 6, 8, 9, 14, 15, 21, and 22 in Supplement 1.

    Pathway Analysis of Loci Shared Between BMI and the MPDs

    To determine the overrepresented pathways among the genes nearest the identified loci shared between BMI and MPDs at conjunctional FDR less than 0.01, we carried out pathway overrepresentation analyses. There were 20 pathways significantly overrepresented among the genes nearest the identified loci shared between BMI and SCZ with the proton-pump inhibitor pathway and AKT phosphorylates targets in the nucleus pathway as the most significant (eTable 7 in Supplement 1). We found the neural cell adhesion molecule signaling for neurite outgrowth pathway and L1 cell adhesion molecule interactions pathway to be significantly overrepresented among the genes nearest the 17 loci shared between BMI and BIP (P = .001, Q = 0.007 and P = .003, Q = 0.011, respectively) (eTable 16 in Supplement 1). Finally, the RAB guanine nucleotide exchange factors exchange GTP for GDP on RABs pathway and RAB regulation of trafficking pathway were significantly overrepresented among the genes nearest the loci shared between BMI and MD (P = 9.29 × 10−5, Q = 0.000557 and P = 0.000244, Q = 0.000731, respectively) (eTable 23 in Supplement 1). For analysis of loci with concordant and opposite association directions respectively, see eResults in Supplement 1.

    Discussion

    In the current study, we demonstrated polygenic overlap between BMI and MPDs. We identified 63 genetic loci shared between BMI and SCZ, 17 loci shared between BMI and BIP, and 32 loci shared between BMI and MD (conjunctional FDR < 0.01). There was a striking pattern of bidirectional associations for the shared loci. In total, 57% of the shared MD SNPs had positive associations with BMI, against 52% of the shared BIP SNPs and as few as 34% of the shared SCZ SNPs (conjunctional FDR < 0.05), suggesting different genetic liability to weight gain across these disorders.

    The current findings of polygenic overlap between BMI and MPDs are in line with epidemiologic evidence of associations between BMI and psychotic and affective disorders,1,22,54 which has been suggested as an important contributor to increased cardiovascular morbidity and mortality in MPDs. However, the current findings of a mixture of association directions for the shared loci underscores the complexity of this genetic relationship and suggest that factors other than disease-specific genetics play a significant role in weight gain in MPDs, particularly in SCZ. Most (66%) of the SNPs overlapping between BMI and SCZ (conjunctional FDR < 0.05) have opposite association directions, in line with the estimated negative genetic correlation (r for genetic = −0.11). The finding has clinical implications suggesting that factors such as antipsychotic treatment, diet, or lifestyle may be the main drivers of weight gain in treated patient groups with long-term disease. Further, low BMI has been indicated as a risk factor for SCZ,55-57 and there seems to be an increased frequency of underweight in patients with SCZ.29,58 Body mass index forms the basis of nutritional status definitions, and genetic loci associated with variation in BMI could be associated with clinical subphenotypes ranging from underweight to obesity. Weight gain associated with negative symptoms in SCZ59 may constitute such a subgroup. The current findings could be of relevance for the variation in proneness to weight gain during antipsychotic medication, which seems to be partly genetically mediated.60

    Our findings of overlapping genetic architecture between BMI and MD and BIP are in line with clinical and epidemiologic evidence.1,22,54 Most BIP and MD loci shared with BMI (52% and 57%, respectively) have positive associations with BMI, while only MD showed significant positive genetic correlation with BMI (r for genetic = 0.12). The mixed association directions can explain the clinical features of both loss and gain of weight during a depressive episode.61 A recent mendelian randomization study indicated elevated BMI as a causal factor in the development of depression.62 Moreover, our results are supported by findings of preponderance of genetic risk variants for BMI in MD with the atypical features weight gain and/or increased appetite,63 suggesting genetically determined subphenotypes. It is also of interest that several drugs used in the treatment of affective disorders are prone to weight gain adverse events, including antidepressants and mood stabilizers.

    Our results go beyond standard genetic correlations as the conditional FDR tool can assess the direction of association of each overlapping genetic variant, independently of the overall genetic correlation. For example, despite the lack of genetic correlation in BIP and BMI (r for genetic = −0.06), we identified 17 loci shared by the 2. The discovery of shared loci may form the basis for developing risk prediction tools of comorbid obesity, enabling targeted interventions of importance for cardiometabolic health. Of interest, we observed that several of the discovered risk loci are shared across the MPDs, implying common mechanisms of comorbid obesity across several mental disorders.64 The notion of common genetic mechanisms across mental illness is supported by recent gene expression studies65 as well as the recent finding of overlapping SNPs from the large Psychiatric Genomics Consortium cross-disorder group66 showing moderate to high genetic correlations between BIP, MD, and SCZ. However, a major part of the genetic loci are still to be discovered.11 A large Swedish registry-based study recently confirmed the shared genetic liability between BIP and MD/SCZ, respectively.67

    The functional annotations of the shared loci revealed variants associated with brain tissue gene expression and several biologic and molecular processes including central nervous system development, hormone, GABAergic, and glutamatergic signaling, and intracellular processes. The identification of glucocorticoid receptor binding with variants conveying similar association directions in BIP and BMI may be of special clinical interest68,69 given the established role of the hypothalamic-pituitary-adrenal axis in MPD68,70,71 and the hypothalamic-pituitary-adrenal axis’s suggested contribution to obesity in MPD.27 Further, a large proportion of the shared genetic loci are brain-related, suggesting that BMI regulation is for a large part involving brain-related mechanisms.

    Here, we used summary statistics from GWAS of large cohorts, which we screened for overlapping samples. However, some cryptic overlap may persist across BMI and mental disorders samples owing to the comorbidity of these phenotypes. In fact, weight disturbances are often included in mental illness syndromes (eg, diagnostic criteria in MD). Owing to their more severely impaired functioning, individuals with SCZ are less likely to participate in scientific studies.72 Therefore, lower cryptic overlap is to be expected for SCZ than for MD, with BIP somewhere in between. As the level of genetic overlap will increase with increasing degree of sample overlap, the number of shared genetic loci may be overestimated, especially for MD whose samples include individuals with less severe conditions. Further, brain function determines behavior, which is a key factor in lifestyle choices such as diet and exercise, which in turn affect BMI. Thus, it is possible that similar brain mechanisms are involved in behavior related to mental illness and BMI. Although we were able to identify overlapping genetic loci and the direction of their association, the complexity of the mechanisms underlying the respective conditions and their comorbidity are apparent, as is the need for detailed characterized samples to provide additional insight. As the pathophysiologic mechanisms of the studied phenotypes are not fully known, we are cautious about claiming a specific type of pleiotropy73 based on findings of shared genetic loci.

    Limitations

    As with all GWAS findings, any SNP represents through LD a genomic region including potentially many causal SNPs. Hence, further studies are required to determine the true causal variants underlying the shared associations detected here and whether the same causal variants are involved in BMI, SCZ, BIP, and MD. Furthermore, it is challenging to evaluate small effect sizes and to speculate about molecular mechanisms behind the effective variants when examining such potentially overlapping phenotypes.

    Conclusions

    Here, we demonstrate extensive genetic overlap between BMI and psychotic and affective disorders with a striking pattern of bidirectional associations, suggesting a complex interplay of metabolism-related gene pathways in the pathophysiology of SCZ, BIP, and MD. While two-thirds of the genetic overlap had opposite associations directions in SCZ and BMI, suggesting environmental causes of observed weight gain rather than disease-specific genetics, most had concordant association directions in BMI and BIP and MD, pointing to genetic susceptibility as a potential cause of weight gain. The findings have implications for the discovery of drugs with fewer adverse events and potential future individualized treatment to reduce weight gain.

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    Article Information

    Corresponding Authors: Shahram Bahrami, PhD, University of Oslo, Universitetssykehus HF-Ullevål Avdeling for Psykoseforskning Bygg 49, 0424 Oslo, Norway (shahram.bahrami@medisin.uio.no); Ole A. Andreassen, MD, PhD, NORMENT, Psychiatry, Irkeveien 166, 0407 Oslo, Norway (o.a.andreassen@medisin.uio.no).

    Accepted for Publication: October 30, 2019.

    Published Online: January 8, 2020. doi:10.1001/jamapsychiatry.2019.4188

    Author Contributions: Dr Bahrami 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. Drs Bahrami and Steen are co–first authors.

    Concept and design: Bahrami, Fan, Dale, Andreassen.

    Acquisition, analysis, or interpretation of data: Bahrami, Steen, Shadrin, O’Connell, Frei, Bettella, Wirgenes, Krull, Dale, Smeland, Djurovic, Andreassen.

    Drafting of the manuscript: Bahrami, Steen, Smeland, Andreassen.

    Critical revision of the manuscript for important intellectual content: Bahrami, Steen, Shadrin, O’Connell, Frei, Bettella, Wirgenes, Krull, Fan, Dale, Djurovic, Andreassen.

    Statistical analysis: Bahrami, Shadrin, O’Connell, Frei, Bettella, Fan, Andreassen.

    Obtained funding: Dale, Andreassen.

    Administrative, technical, or material support: Bahrami, O’Connell, Wirgenes, Krull, Fan, Dale, Smeland, Djurovic, Andreassen.

    Supervision: Bahrami, Andreassen.

    Conflict of Interest Disclosures: Dr Steen reports grants from the National Institutes of Health, the Research Council of Norway, South-Eastern Norway Regional Health Authority, and the Kristian Gerhard Jebsen Foundation during the conduct of the study. Dr Fan reports personal fees from MultiModal Imaging Service, dba HealthLytix outside the submitted work. Dr Dale reports grants from the National Institutes of Health outside the submitted work; has US Provisional Patent Application Serial No. 61/751,420 pending about systems and methods for identifying polymorphisms; is founder of and holds equity in CorTechs Labs and serves on its scientific advisory board; is a member of the scientific advisory board of Human Longevity; and receives funding through research grants with GE Healthcare. Dr Andreassen reports grants from the Research Council of Norway, the Kristian Gerhard Jebsen Foundation, and South-Eastern Norway Regional Health Authority during the conduct of the study; personal fees from Lundbeck outside the submitted work; consultant fees from HealthLytix outside the submitted work; and has US Provisional Patent Application Serial No. 61/751,420 pending about systems and methods for identifying polymorphisms. No other disclosures were reported.

    Funding/Support: We gratefully acknowledge support from the National Institutes of Health (grants NS057198 and EB00790), the Research Council of Norway (grants 229129, 213837, and 223273), the South-Eastern Norway Regional Health Authority (grant 2017-112), and Kristian Gerhard Jebsen Foundation (grant SKGJ-MED-008).

    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 research participants and employees of the Body Mass Index Working Groups of GIANT and UK BioBank; the Schizophrenia, Bipolar Disorder and Major Depression Working Groups of the Psychiatric Genomics Consortium; 23andMe for granting access to their GWAS summary statistics, and the many people who provided DNA samples for their studies.

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