Genomic relationship between polycystic ovary syndrome and bipolar disorder

Genomic relationship between polycystic ovary syndrome and bipolar disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic relationship between polycystic ovary syndrome and bipolar disorder Piotr Jaholkowski, Markos Tesfaye, Vera Fominykh, Pravesh Parekh, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7629869/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Women with bipolar disorder (BIP) have a higher risk of developing polycystic ovary syndrome (PCOS). Shared genetic architecture may underlie this comorbidity. Valproate, a mood-stabilizer commonly used to treat BIP, increases the risk of PCOS. Still, the mechanism underlying PCOS in BIP remains unknown. Here, we aimed to identify genetic variants shared between BIP and PCOS, as well as their interaction with valproate. We used the results of large-scale genome-wide association studies of BIP (41,510 cases and 354,340 controls), and PCOS (3,609 cases and 229,788 controls). Using conditional false discovery rate, we discovered genetic variants jointly associated with BIP and PCOS. Gene mapping of identified variants was performed using the Open Targets platforms. We analyzed the tissue-specific expression, interaction with valproate, and involvement in biological pathways of the mapped genes. We identified two loci shared between BIP and PCOS. Among the 10 genes mapped to the locus on chromosome 8:11455262, GATA4 , NEIL2 , and FDFT1 showed expression profiles suggesting their role in the observed comorbidity. Mapped to the locus on chromosome 12:2499849, CACNA1C , FKBP4 , DCP1B , and ITFG2 are expressed in both the ovaries and the brain. CACNA1C expression is affected by valproate, and CACNA1C plays a role in biological pathways involving other valproate-affected genes. We identified shared genetic underpinnings of BIP and PCOS, and implicated genes which may explain the biological mechanisms of the comorbidity between these disorders and a potential mechanism for the role of valproate. bipolar disorder valproate polycystic ovary syndrome polycystic ovarian syndrome comorbidity genetic Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Bipolar disorder (BIP) is a complex mental disorder characterized by recurrent episodes of depression and mania or hypomania ( 1 ). A cornerstone in the treatment of BIP is mood-stabilizing drugs, and a commonly prescribed medication is the anti-epileptic drug valproate (VPA) ( 1 , 2 ). The choice of drug and the course of treatment in BIP are influenced by the fact that both BIP and VPA are independent risk factors for polycystic ovary syndrome (PCOS), characterized by hyperandrogenism, polycystic ovarian morphology, ovarian dysfunction, and subfertility ( 3 – 8 ). A systematic review and meta-analysis showed a 2-fold higher risk of BIP among women with PCOS ( 9 ). This relationship has also been shown in drug-naïve BIP patients, for whom the prevalence of PCOS was estimated at 30% (compared to approximately 10% in the healthy population) ( 3 ). Women with BIP, similarly to those diagnosed with PCOS, have higher serum testosterone and androstenedione levels and an increased risk of abnormal menstruation ( 3 , 7 , 8 , 10 , 11 ). Furthermore, both BIP and PCOS are associated with metabolic imbalances such as hyperinsulinemia, dyslipidemia, metabolic syndrome, and type 2 diabetes ( 7 , 8 , 12 – 14 ). Although research indicates dysfunction of the hypothalamic-pituitary-adrenal axis, chronic inflammation, altered adipokines, a disturbed gut microbiome, and disturbances in circadian rhythms as potential mechanisms linking BIP and PCOS, the basis of this comorbidity remains elusive ( 14 ). This lack of understanding of the mechanism has hindered the development of effective prevention and treatment, thereby increasing the burden of PCOS ( 7 , 8 ). Similarly, the mechanism by which VPA increases the risk of PCOS among female patients with BIP, raising its prevalence to over 50%, is unknown ( 3 ). Human and animal studies suggest several potential mechanisms: altered gonadotropin-releasing hormone secretion, attenuation of luteinizing hormone (LH) release from the pituitary gland due to enhanced GABAergic inhibitory neurotransmission, direct adenotoxic effects on ovarian tissue, increased ovarian androgen production by theca cells, and metabolic alterations associated with weight gain ( 3 , 15 ). Studies have shown that both PCOS and BIP are highly heritable, with heritability estimated at approximately 70% ( 7 , 16 – 18 ), and are under polygenic influences ( 19 , 20 ). Although genetic studies offer important insights into complex diseases and their comorbidity ( 21 – 23 ), our current comprehension of the shared genetic architecture between PCOS and BIP remains limited. Moreover, data-driven genotype phenotype mapping is a valuable resource for identifying drug safety issues, drug repurposing, and the discovery of new drugs ( 21 – 25 ). Therefore, elucidating the shared genetic architecture between BIP and PCOS may contribute to a better understanding of the mechanisms underlying their comorbidity, including those associated with VPA use. Combining signals from two genome-wide association studies (GWASs) can enhance the detection of shared genetic variants with a second trait for which the GWAS is underpowered ( 26 , 27 ). The conjunctional False Discovery Rate (conjFDR) approach can identify shared genetic loci between human traits and disorders irrespective of genetic correlation ( 26 ). To identify the genetic underpinnings shared between PCOS and BIP, and the potential mechanisms that might underlie PCOS as an adverse event of VPA treatment, we examined the shared genetic architecture using the conjFDR approach ( 26 , 28 ), followed by extensive functional analyses utilizing available repositories and tools ( 29 – 32 ). Materials and methods GWAS Samples The GWAS meta-analysis summary statistics for PCOS (3,609 cases and 229,788 controls) ( 33 ) were downloaded from the GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). The GWAS summary statistics for BIP (41,510 cases and 354,340 controls) were obtained from the BIP Working Group of the Psychiatric Genomics Consortium, after excluding samples from the Estonian Biobank to prevent sample overlap ( 19 ). All GWASs included in our analysis were approved by the relevant ethics committees, and informed consent was obtained from all participants. Conjunctional FDR To visualize cross-trait SNP enrichment, we constructed QQ plots that illustrate the distribution of p -values for the primary trait conditioned on significance levels in the secondary trait; that is, BIP conditioned on PCOS, and vice versa. To control for spurious enrichment, the QQ plots were generated after random pruning, averaging over 500 iterations. For each iteration of random pruning, all but one random SNP in each linkage disequilibrium (LD)-independent region (clump of SNPs in strong LD, r 2 > 0.1) were removed, and finally the results were averaged across all iterations. We excluded SNPs within three regions (major histocompatibility complex region chr6: 25119106–33854733; chr8:7200000–12500000; chr19:44909039–45912650) to prevent bias due to complex LD pattern within these regions. Successive leftward deflections on a QQ plot, associated with increasing levels of association with a secondary trait, indicate cross-trait enrichment of a primary trait ( 26 ). We applied the conjFDR approach to identify loci shared between PCOS and BIP ( 26 ). The conjFDR method involves conducting two conditional (condFDR) analyses—in our study, conditioning PCOS on BIP and vice versa—which re-rank test statistics and recalculate the associations between variants and a primary trait based on their associations with the secondary trait ( 26 ). Then, the conjFDR value for a given genetic variant is defined as the maximum of the two condFDR values, making it a conservative estimate of the association between variants and the traits of interest. In our study, we applied the FDR significance cutoffs at 0.05 for both condFDR and conjFDR. We performed the conjFDR analysis after removing three genomic regions with a complex LD pattern (chr6: 25119106–33854733; chr8:7200000–12500000; chr19:44909039–45912650). The FUMA protocol was applied to identify independent genomic loci ( 31 ). SNPs with conjFDR < 0.05 and an LD r² < 0.6 with each other were defined as independently significant. Lead SNPs were defined as independent significant SNPs with LD r² <0.1 with each other. We defined the boundaries of each genomic locus to include SNPs with an LD r² ≥ 0.6 with any of the independently significant SNPs within the locus. Loci separated by less than 250 kb were merged. A SNP within a given locus that had the lowest conjFDR value was designated as the lead SNP. We applied the 1000 Genomes Project European ancestry haplotype reference panel to compute all LD r 2 values ( 34 ). Functional annotation We applied Open Target Genetics ( https://genetics.opentargets.org/ ) to map genes for lead SNPs from the conjFDR analysis ( 29 ) using positional information and an overall score, which is an aggregated measure based on positional information, chromatin interactions, quantitative trait loci, and in silico functional prediction datasets. Adopting a broad inclusion strategy, for each lead SNP we selected the gene with the closest location, along with any genes that had an overall score equal to or higher than that of the closest gene. We used tissue expression data from the Adult Genotype Tissue Expression Project (GTEx; version 8) to assess the expression of mapped genes in various tissues. These include the brain (cortex, frontal cortex Brodmann area 24, anterior cingulate cortex Brodmann area 24, amygdala, substantia nigra, hippocampus, putamen basal ganglia, caudate basal ganglia, nucleus accumbens basal ganglia, cerebellar hemisphere, cerebellum, hypothalamus, spinal cord cervical C1), pituitary, ovary, subcutaneous adipose tissue, visceral omentum adipose tissue, adrenal gland, cultured fibroblast cells, liver, thyroid, and whole blood. According to the GTEx data presentation approach, isoforms were collapsed into a single gene, and no normalization or threshold procedures were applied. The data used for the analyses were obtained from the GTEx Portal on 26/06/24. To further explore potential interactions between the mapped genes in the three-dimensional space of the cell nucleus, which could suggest their co-expression ( 35 ), we examined published data on chromatin interactions in the brain and ovary ( 36 ). We utilized the results of the High-throughput Chromosome Conformation Capture (Hi-C) ( 37 ) method to reveal genome-wide 3D chromatin contacts, and visualized interactions using the 3D Genome Browser tool ( 38 ). These contacts are essential for understanding the spatial organization of the genome and its impact on gene regulation and cellular function ( 39 ). Regions of the genome characterized by a high frequency of interactions compared to other regions are defined as Topologically Associating Domains (TADs). Expression of genes located within a single TAD is more likely to be co-regulated than genes located in separate domains ( 40 ). We used data from the drug–gene interaction database (DGIdb, version 5) to illustrate the strength and type of drug interactions with mapped genes shared between PCOS and BIP ( 32 ). Next, we linked the drug-gene interaction data for VPA from DGIdb to data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) ( 30 ) to assess the number of pathways encompassing genes affected by VPA and mapped genes. To avoid the double inclusion of CACNA1C as both mapped and affected by the VPA gene, we excluded CACNA1C from the list of genes affected by VPA. We also assessed how many genes affected by VPA were included in pathways that contained the mapped genes. To evaluate whether the number of pathways and genes affected by VPA for each mapped gene exceeded that of a randomly selected protein-coding gene, we compared our estimates to those for 10,000 randomly selected protein-coding genes from a list provided by the GENCODE Project ( 41 ). We applied the Bonferroni correction, adjusted for the number of genes mapped to a given locus, to account for multiple testing. Results QQ plots of SNPs conditioned on association between BIP and PCOS The conditional QQ plot showed evidence of cross-trait enrichment between BIP and PCOS (Supplementary Fig. 1A). The earlier leftward deflection from the dashed line (no enrichment) indicated a greater proportion of true associations for a given nominal PCOS p -value. The more pronounced leftward shifts for decreasing nominal PCOS p -value thresholds demonstrated that the proportion of non-null effects in BIP increased as the strength of the association with PCOS increased. Similarly, the conditional QQ plot displayed polygenic enrichment for PCOS given the strength of association with BIP (Supplementary Fig. 1B). ConjFDR analysis Using condFDR (condFDR < 0.05) we identified 19 distinct genomic loci to be associated with PCOS after conditioning on association with BIP (Supplementary Fig. 2 and Supplementary Table 1). At condFDR < 0.05, we identified 175 loci associated with BIP conditional on PCOS (Supplementary Fig. 3, Supplementary Table 2). Applying the conjFDR approach (conjFDR < 0.05), we identified two independent loci jointly associated with PCOS and BIP (Fig. 1 C and Fig. 2 ). One of these loci, with the lead SNP rs34876360, was not identified in the original PCOS GWAS ( 33 ) nor in the BIP GWAS ( 19 ). The second locus, associated with the lead SNP rs740417, was not identified in the BIP GWAS utilized in our study, nor in the recently published largest BIP GWAS ( 19 , 42 ). Functional analysis Functional annotation analysis revealed that the lead SNP and 26 SNPs in LD with the lead SNP for the locus shared between PCOS and BIP on chromosome 8:11455262, displayed an intergenic character. The lead SNP and 10 SNPs in LD with the lead SNP for the locus identified on chromosome 12:2499849 were recognized as intronic of CACNA1C (Fig. 2 ). The overall Open Targets variant-to-gene analysis for the lead SNP of the locus shared between PCOS and BIP and located on chromosome 8:11455262 (rs34876360) revealed 9 genes ( BLK , SLC35G5 , XKR6 , DEFB134 , FDFT1 , DEFB136 , FAM167A , NEIL2 , CTSB ) in addition to the nearest GATA4 . Similar analysis for the lead SNP on chromosome 12:2499849 (rs740417) identified CACNA1C (an intronic variant), FKBP4 , ITFG2 , and DCP1B as the nearest gene. Tissue-specific gene expression (GTEx) analysis for genes mapped to the first locus showed that FDFT1 and NEIL2 were expressed at moderate level in both the brain and ovary (Fig. 3 ). Other genes ( BLK , CTSB, DEFB134, DEFB136 , FAM167A, SLC35G5, XKR6) were either not detected or expressed at low levels in both the brain and ovary, or in either organ individually (Supplementary Fig. 4). Interestingly, GATA4 was specifically and highly expressed in ovarian tissue but not expressed in the brain (Fig. 3 ). For the second locus, we observed expression of CACNA1C and FKBP4 within a similar range in both the ovary and most brain tissues, while DCP1B and ITFG2 tended to have higher expression levels in the ovary compared to the brain (Supplementary Fig. 5). Our analysis revealed that regions on chromosome 8:11455262 (Fig. 3 A) and chromosome 12:2499849 (Fig. 3 B), which encompass the identified genes of interest, are enriched for chromatin contacts in both the brain cortex (upper panel) and ovary (lower panel). The DGIdb analysis revealed that CACNA1C is one of the genes affected by VPA (Supplementary Fig. 4–6). Furthermore, it revealed that other therapeutic substances are known to block or modulate the expression of CACNA1C (Supplementary Fig. 4). In our analysis of KEGG-annotated pathways containing genes affected by VPA, as well as those mapped to loci jointly associated with PCOS and BIP, we identified 33 pathways associated with CANCA1C , five with CTSB , four with GATA4 , and one each involving NEIL2 and FKBP4 (Fig. 4 A). This analysis further revealed that CACNA1C shares biological pathways with 23 other genes, GATA4 with eight, CTSB with seven, and both NEIL2 and FKBP2 with one gene influenced by VPA (Fig. 4 B). We observed that for CACNA1C , both the number of biological pathways and the number of genes affected by VPA were statistically higher than those observed for randomly selected protein-coding genes ( p -values = 0.019 and 0.032, respectively). Discussion In our study, we applied the conjFDR framework to genome-wide data to identify the genetic underpinnings of BIP and PCOS comorbidity. We identified two independent loci jointly associated with BIP and PCOS and mapped several genes to them, such as GATA4 , NEIL2 , and CACNA1C , highlighting the potential role of these gene in disorder comorbidity. Furthermore, the results indicate that the mapped genes may be involved in the development of PCOS associated with VPA use. This provides new insight into the molecular pathways involved in PCOS and BIP comorbidity, as well as PCOS associated with VPA, which can form the basis of experimental work to determine the mechanisms and form the basis of future drug development to avoid these adverse events. We observed cross-trait polygenic enrichment between PCOS and BIP, supporting previous epidemiological observations of comorbidities ( 3 , 9 ). The two overlapping loci between PCOS and BIP exhibited both concordant and discordant directions, similar to those observed in many other pairs of human traits ( 43 – 45 ). The functional analysis included gene mapping of identified shared loci, and we utilized broad inclusion criteria based on OpenTargets to minimize the risk of omitting genes related to the traits of interest. For the loci on chromosome 8:11455262, apart from the nearest GATA4 , we identified nine genes ( BLK , SLC35G5 , XKR6 , DEFB136 , FDFT1 , DEFB134 , FAM167A , NEIL2 , CTSB ). Importantly, although GATA4 was not identified in the original GWAS for PCOS that we utilized for the conjFDR analysis, GATA4 / NEIL2 locus (rs804279; chr8:11623889) was identified in an independent GWAS for PCOS ( 46 ). This finding confirms the involvement of this genetic locus in the pathogenesis of PCOS and validates the conjFDR methodology used in our study. The lead SNP (rs740417) within the locus shared between PCOS and BIP on chromosome 12:2499849 was identified as an intronic variant of CACNA1C , and located in proximity to FKBP4 , ITFG2 , and DCP1B genes. The results of tissue-specific gene expression analysis for genes mapped to the locus on chromosome 8:11455262 showed that GATA4 is highly specific to the ovary, while other mapper genes (i.e., FDFT1 and NEIL2 = are expressed in both the brain and the ovary. These findings provide further evidence for their possible involvement in the comorbidity of PCOS and BIP. For the locus on chromosome 12, tissue expression analysis indicates that CACNA1C , FKBP4 , DCP1B , and ITFG2 are all expressed in both the ovary and the brain, with a noteworthy similarity in the expression patterns of DCP1B and ITFG2 . Moreover, the results of the Hi-C analysis demonstrated that, for both loci, the mapped genes are not only closely located in terms of genomic distance but also maintain physical proximity within nuclear space, suggesting that they may be co-expressed. This implies that genetic variants or modulators of the mapped genes such as VPA, could influence the transcription of multiple genes, with several genes potentially contributing to the comorbidity of PCOS and BIP. The DGIdb analysis revealed that VPA affects CACNA1C . Additionally, we observed that the genes FKBP4 , NEIL2 , GATA4 , CTSB , and CACNA1C participate in common biological pathways with those affected by VPA. This is particularly relevant for CACNA1C , which is involved in biological pathways such as MAPK signaling and calcium signaling that may explain the observed comorbidity ( 47 – 50 ), as well as underlie the metabolic disorders observed in both PCOS and BIP ( 51 , 52 ). GATA4 , the nearest gene to the lead SNP (rs34876360) in the locus shared between PCOS and BIP on chromosome 8:11455262, is a zinc-finger transcription factor. It is a transcriptional inducer of p15INK4B, a cyclin-dependent kinase inhibitor, leading to the reduction of cyclin D1 ( 53 ). GATA4 is critical in organogenesis, particularly in the development of the heart and gonads ( 54 , 55 ). Moreover, animal studies have shown that GATA4 plays an important role in the development of GnRH neurons and GnRH gene expression ( 56 , 57 ). Combined with the observed dysfunctions of the hypothalamic-pituitary-gonadal axis in PCOS and the crucial role of GnRH analogues in its treatment, these findings further suggest that GATA4 may play a role in the pathogenesis of PCOS ( 7 , 8 ). It is expressed in the developing and adult brain and negatively regulates the proliferation and growth of astrocytes ( 53 ). Interestingly, VPA exerts the opposite effect of stimulation of astrocyte proliferation in the developing brain ( 57 ). The co-occurrence of cardiovascular disease in BIP patients ( 58 , 59 ), and the association of PCOS with major adverse cardiovascular events from a young age, independent of body-mass index ( 60 , 61 ), further underscores the significance of GATA4 as a potential unifying factor. NEIL2 encodes a DNA glycosylase that initiates base excision DNA repair by cleaving oxidatively damaged bases, and it is crucial for long-term genomic maintenance ( 62 ). NEIL2 has been linked with PCOS by the previous GWAS ( 63 ). It has been shown that mice lacking Neil2 display hyperactivity and reduced anxiety, endophenotypes typical of animal models of BIP ( 64 , 65 ). Moreover, Neil2 knock-out mice were associated with reduced reactivity of NR2A subunits of NMDA receptors, which have been linked with the BIP pathogenesis ( 66 ). Noteworthy, altered Nr2a expression has been observed in VPA-exposed rats ( 67 ). CTSB encodes cathepsin B, a lysosomal protease and has been linked with pathogenesis of PCOS ( 68 ). Previous study based on microarray-based gene expression profiling indicated that Ctsb effects on depression-like behavior in mice ( 69 ). Moreover, it has been demonstrated in clinical settings that VPA increases cellular level of cathepsin B ( 70 ). FDFT1 encodes farnesyl-diphosphate farnesyltransferase 1, an evolutionarily conservative enzyme involved in the cholesterol biosynthesis ( 71 , 72 ). It has been demonstrated in an animal study that Fdft1 is involved in follicular development in ovaries ( 72 ). The recessive variants of FDFT1 have been linked with profound developmental delay, brain abnormality, irritability, and sleep disturbances ( 71 ). CACNA1C encodes calcium voltage-gated channel subunit alpha1 C. Multiple genetic studies, including GWAS, have linked CACNA1C with BIP ( 19 , 73 , 74 ). Increased serum levels of CACNA1C in BIP has been recently reported ( 75 ). CACNA1C , apart from its involvement in processes such as neurotransmitter release and synaptic plasticity ( 76 ), also affects the functioning of mitochondria and lysosomes ( 80 ), which are linked to the pathogenesis of PCOS ( 77 ). FKBP4 encodes FKBP prolyl isomerase 4 (FKBP4), which is involved in immunoregulation and cellular trafficking of steroid hormone receptors ( 78 ). Higher expression of Fkbp4 has been observed in a rat model of PCOS ( 78 ). Human chorionic gonadotropin stimulation upregulates expression of FKBP4 in human ovulatory follicles ( 79 ). Moreover, FKBP4 was shown to be involved in the intranuclear translocation of the glucocorticoid receptor (NR3C1) which was in turn linked to the pathogenesis of PCOS ( 79 – 82 ). Increased expression of Fkbp4 has been observed in the hypothalamus of a rat model of depression ( 83 ). Genetic association analysis showed a relationship between FKBP4 polymorphism and stressful life events in patients with BIP ( 83 ). Furthermore, decreased FKBP4 mRNA level has been observed in schizophrenia ( 84 ). The observations of the mapped genes may provide a basis for further targeted analyses in animal models ( 85 ) and clinical conditions. It is particularly important to evaluate the utility of identifying genetic variations within mapped genes, especially CACNA1C , to assess the risk of developing PCOS prior to initiating VPA treatment. However, it should be emphasized that the results indicating potential co-expression of several genes within the identified locus, along with biological pathways connecting CACNA1C to genes modified by VPA, highlight the complexity of the observed processes. This complexity advises caution against adopting overly simplified experimental assumptions. The main limitation of our study is that we used only summary statistics from GWASs of individuals with European ancestry, which limits the generalization of the results. Due to the absence of a large-scale female-specific GWAS for BIP, we conducted our analysis using data from both sexes. However, the genetic loci we identified show consistent effect direction and nominal significance in available female-specific GWAS. Further, our functional analysis is based on associations and should be verified by experimental studies. Conclusions Here, we identified genetic variants shared between BIP and PCOS, and functional analyses implicated genes representing molecular pathways underlying the comorbidity between PCOS and BIP. The identified pathways were also linked to the pharmacological mechanisms of VPA. These findings provide new understanding of the pathophysiology of PCOS associated with BIP and VPA use. Declarations Conflict of interest Dr. Andreassen has received speaker fees from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion and is a consultant to Cortechs.ai. and Precision Health. Dr. Anders M. Dale is Founding Director, holds equity in CorTechs Labs, Inc. (DBA Cortechs.ai), and serves on its Board of Directors and Scientific Advisory Board. Dr. Dale is the President of J. Craig Venter Institute (JCVI) and is a member of the Board of Trustees of JCVI. He is an unpaid consultant for Oslo University Hospital. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. Dr. Frei is a consultant to Precision Health. No other disclosures were reported. Acknowledgments This work was supported by the Research Council of Norway (grants: #324499, #324252, #326813, #334920, #296030, #344121), Nordforsk (grant #164218), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (grant #801133), the European Union’s Horizon 2020 research and innovation action programme (grants: #847776, #964874 and #847879 PRIME), and the European Commission Grant Committee (grant #964874). AEB was supported by the National Institute of Mental Health (grant # K01MH120352). PT was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases Intramural Research Program. This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH author were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. References Nierenberg AA, Agustini B, Köhler-Forsberg O, Cusin C, Katz D, Sylvia LG et al (2023) Diagnosis and Treatment of Bipolar Disorder: A Review. JAMA 330. https://doi.org/10.1001/jama.2023.18588 Crapanzano C, Casolaro I, Amendola C, Damiani S (2022) : Lithium and Valproate in Bipolar Disorder: From International Evidence-based Guidelines to Clinical Predictors. Clinical Psychopharmacology and Neuroscience , vol. 20. https://doi.org/10.9758/cpn.2022.20.3.403 Liu J, Teng Z, Xie H, Yuan H, Liu M, Chen J et al (2023) Prevalence and characteristics of polycystic ovarian syndrome in patients with bipolar disorder. J Affect Disord 340. https://doi.org/10.1016/j.jad.2023.08.007 O’Donovan C, Kusumakar V, Graves GR, Bird DC (2002) Menstrual abnormalities and polycystic ovary syndrome in women taking valproate for bipolar mood disorder. J Clin Psychiatry 63. https://doi.org/10.4088/JCP.v63n0409 Isojarvi J, Laatikainen TJ, Pakarinen AJ, Juntunen K, Myllyla VV (1993) Polycystic Ovaries and Hyperandrogenism in Women Taking Valproate for Epilepsy. N Engl J Med 329. https://doi.org/10.1056/nejm199311043291904 Joffe H, Cohen LS, Suppes T, McLaughlin WL, Lavori P, Adams JM et al (2006) Valproate Is Associated with New-Onset Oligoamenorrhea with Hyperandrogenism in Women with Bipolar Disorder. Biol Psychiatry 59. https://doi.org/10.1016/j.biopsych.2005.10.017 Azziz R, Carmina E, Chen Z, Dunaif A, Laven JSE, Legro RS et al (2016) Polycystic ovary syndrome. Nat Reviews Disease Primers 2. https://doi.org/10.1038/nrdp.2016.57 Walters KA, Gilchrist RB, Ledger WL, Teede HJ, Handelsman DJ, Campbell RE (2018) : New Perspectives on the Pathogenesis of PCOS: Neuroendocrine Origins. Trends in Endocrinology and Metabolism , vol. 29. https://doi.org/10.1016/j.tem.2018.08.005 Shahraki Z, Rastkar M, Ramezanpour M-R, Ghajarzadeh M (2024) The prevalence and odds of bipolar disorder in women with polycystic ovary syndrome (PCO) disease: a systematic review and meta-analysis. Arch Womens Ment Health. https://doi.org/10.1007/s00737-024-01420-w Matsunaga H, Sarai M (1993) Elevated Serum LH and Androgens in Affective Disorder Related to the Menstrual Cycle: With Reference to Polycystic Ovary Syndrome. Psychiatry Clin Neurosci 47. https://doi.org/10.1111/j.1440-1819.1993.tb01830.x Joffe H, Kim DR, Foris JM, Baldassano CF, Gyulai L, Hwang CH et al (2006) Menstrual dysfunction prior to onset of psychiatric illness is reported more commonly by women with bipolar disorder than by women with unipolar depression and healthy controls. J Clin Psychiatry 67. https://doi.org/10.4088/JCP.v67n0218 Godin O, Olié E, Fond G, Aouizerate B, Aubin V, Bellivier F et al (2024) Incidence and predictors of metabolic syndrome onset in individuals with bipolar disorders: A longitudinal study from the FACE-BD cohort. Acta Psychiatr Scand n/a. https://doi.org/https://doi.org/10.1111/acps.13655 Charles EF, Lambert CG, Kerner B (2016) Bipolar disorder and diabetes mellitus: evidence for disease-modifying effects and treatment implications. Int J Bipolar Disorders 4. https://doi.org/10.1186/s40345-016-0054-4 Dai W, Liu J, Qiu Y, Teng Z, Li S, Huang J et al (2022) : Shared postulations between bipolar disorder and polycystic ovary syndrome pathologies. Progress in Neuro-Psychopharmacology and Biological Psychiatry , vol. 115. https://doi.org/10.1016/j.pnpbp.2021.110498 Taubøll E, Isojärvi JIT, Harbo HF, Pakarinen AJ, Gjerstad L (1999) Long-term valproate treatment induces changes in ovarian morphology and serum sex steroid hormone levels in female Wistar rats. Seizure 8. https://doi.org/10.1053/seiz.1999.0342 Vink JM, Sadrzadeh S, Lambalk CB, Boomsma DI (2006) Heritability of Polycystic Ovary Syndrome in a Dutch Twin-Family Study. J Clin Endocrinol Metab 91:2100–2104 Kieseppä T, Partonen T, Haukka J, Kaprio J, Lönnqvist J (2004) High concordance of bipolar I disorder in a nationwide sample of twins. Am J Psychiatry 161. https://doi.org/10.1176/ajp.161.10.1814 Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM (2009) Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373:234–239 Mullins N, Forstner AJ, O’Connell KS, Coombes B, Coleman JRI, Qiao Z et al (2021) Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet 53. https://doi.org/10.1038/s41588-021-00857-4 Li X, Xiao H, Ma Y, Zhou Z, Chen D (2022) Identifying novel genetic loci associated with polycystic ovary syndrome based on its shared genetic architecture with type 2 diabetes. Front Genet 13. https://doi.org/10.3389/fgene.2022.905716 Krebs K, Milani L (2023) Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery. Annu Rev Pharmacol Toxicol 63. https://doi.org/10.1146/annurev-pharmtox-051421-111324 Reay WR, Cairns MJ (2021) Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 22. https://doi.org/10.1038/s41576-021-00387-z Malik MA, Faraone SV, Michoel T, Haavik J (2023) Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders. Pharmacol Ther 250. https://doi.org/10.1016/j.pharmthera.2023.108530 Bovijn J, Krebs K, Chen CY, Boxall R, Censin JC, Ferreira T et al (2020) Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics. Sci Transl Med 12. https://doi.org/10.1126/scitranslmed.aay6570 Parker N, Koch E, Shadrin AA, Fuhrer J, Hindley GFL, Stinson S et al (2024) : Leveraging the Genetics of Psychiatric Disorders to Prioritize Potential Drug Targets and Compounds. medRxiv 2024.09.24.24314069. Smeland OB, Frei O, Shadrin A, O’Connell K, Fan CC, Bahrami S et al (2020) Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet 139. https://doi.org/10.1007/s00439-019-02060-2 O’Connell KS, Koch E, Lenk HÇ, Akkouh IA, Hindley G, Jaholkowski P et al (2023) Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia. Psychiatry Res 325. https://doi.org/10.1016/j.psychres.2023.115217 Andreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR et al (2013) Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate. PLoS Genet 9. https://doi.org/10.1371/journal.pgen.1003455 Ochoa D, Hercules A, Carmona M, Suveges D, Baker J, Malangone C et al (2023) The next-generation Open Targets Platform: reimagined, redesigned, rebuilt. Nucleic Acids Res 51. https://doi.org/10.1093/nar/gkac1046 Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M (2025) KEGG: biological systems database as a model of the real world. Nucleic Acids Res 53:D672–D677 Watanabe K, Taskesen E, Van Bochoven A, Posthuma D (2017) Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8. https://doi.org/10.1038/s41467-017-01261-5 Cannon M, Stevenson J, Stahl K, Basu R, Coffman A, Kiwala S et al (2024) DGIdb 5.0: rebuilding the drug–gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Res 52:D1227–D1235 Tyrmi JS, Arffman RK, Pujol-Gualdo N, Kurra V, Morin-Papunen L, Sliz E et al (2022) Leveraging Northern European population history: novel low-frequency variants for polycystic ovary syndrome. Hum Reprod 37:352–365 Auton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR, Chakravarti A et al (2015) A global reference for human genetic variation. Nature 526. https://doi.org/10.1038/nature15393 Bohrer CH, Fursova NA, Larson DR (2024) Enhancers: A Focus on Synthetic Biology and Correlated Gene Expression. ACS Synth Biol 13:3093–3108 Schmitt AD, Hu M, Jung I, Xu Z, Qiu Y, Tan CL et al (2016) A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome. Cell Rep 17. https://doi.org/10.1016/j.celrep.2016.10.061 Lieberman-Aiden E, Van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A et al (2009) : Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science (80-) 326. https://doi.org/10.1126/science.1181369 Wang Y, Song F, Zhang B, Zhang L, Xu J, Kuang D et al (2018) The 3D Genome Browser: A web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol 19. https://doi.org/10.1186/s13059-018-1519-9 Finn EH, Misteli T (2019) Molecular basis and biological function of variability in spatial genome organization. Science 365. https://doi.org/10.1126/science.aaw9498 Rocha PP, Raviram R, Bonneau R, Skok JA (2015) Breaking TADs: Insights into hierarchical genome organization. Epigenomics 7. https://doi.org/10.2217/epi.15.25 Mudge JM, Carbonell-Sala S, Diekhans M, Martinez JG, Hunt T, Jungreis I et al (2025) GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Res 53:D966–D975 O’Connell KS, Koromina M, van der Veen T, Boltz T, David FS, Yang JMK et al (2025) Genomics yields biological and phenotypic insights into bipolar disorder. Nature. https://doi.org/10.1038/s41586-024-08468-9 Jaholkowski P, Hindley GFL, Shadrin AA, Tesfaye M, Bahrami S, Nerhus M et al (2023) Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci. Schizophr Bull. https://doi.org/10.1093/schbul/sbad063 Fominykh V, Shadrin AA, Jaholkowski PP, Bahrami S, Athanasiu L, Wightman DP et al (2023) Shared genetic loci between Alzheimer’s disease and multiple sclerosis: Crossroads between neurodegeneration and immune system. Neurobiol Dis 183. https://doi.org/10.1016/j.nbd.2023.106174 Tesfaye M, Jaholkowski P, Hindley GFL, Shadrin AA, Rahman Z, Bahrami S et al (2023) Shared genetic architecture between irritable bowel syndrome and psychiatric disorders reveals molecular pathways of the gut-brain axis. Genome Med 15. https://doi.org/10.1186/s13073-023-01212-4 Day F, Karaderi T, Jones MR, Meun C, He C, Drong A et al (2018) Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet 14. https://doi.org/10.1371/journal.pgen.1007813 Zhu X, Han Y, Feng Y, Shan Y, Liu C, Wang K et al (2025) Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis. Sci Rep 15:2970 Hu M, Zhang Y, Li X, Cui P, Li J, Brännström M et al (2020) Alterations of endometrial epithelial-mesenchymal transition and MAPK signalling components in women with PCOS are partially modulated by metformin in vitro. Mol Hum Reprod 26. https://doi.org/10.1093/molehr/gaaa023 Do Prado CH, Rizzo LB, Wieck A, Lopes RP, Teixeira AL, Grassi-Oliveira R, Bauer ME (2013) Reduced regulatory T cells are associated with higher levels of Th1/TH17 cytokines and activated MAPK in type 1 bipolar disorder. Psychoneuroendocrinology 38. https://doi.org/10.1016/j.psyneuen.2012.08.005 Berridge MJ (2014) Calcium signalling and psychiatric disease: Bipolar disorder and schizophrenia. Cell Tissue Res 357. https://doi.org/10.1007/s00441-014-1806-z Song Z, Wang Y, Zhang F, Yao F, Sun C (2019) Calcium signaling pathways: Key pathways in the regulation of obesity. Int J Mol Sci 20. https://doi.org/10.3390/ijms20112768 Schultze SM, Hemmings BA, Niessen M, Tschopp O (2012) PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis, 2012/01/19. Expert Rev Mol Med 14:e1 Agnihotri S, Wolf A, Picard D, Hawkins C, Guha A (2009) GATA4 is a regulator of astrocyte cell proliferation and apoptosis in the human and murine central nervous system. Oncogene 28. https://doi.org/10.1038/onc.2009.159 Afouda BA (2022) Towards Understanding the Gene-Specific Roles of GATA Factors in Heart Development: Does GATA4 Lead the Way? Int J Mol Sci 23. https://doi.org/10.3390/ijms23095255 Padua MB, Fox SC, Jiang T, Morse DA, Tevosian SG (2014) Simultaneous gene deletion of Gata4 and Gata6 leads to early disruption of follicular development and germ cell loss in the murine ovary. Biol Reprod 91. https://doi.org/10.1095/biolreprod.113.117002 Lawson MA, Whyte DB, Mellon PL (1996) GATA Factors Are Essential for Activity of the Neuron-Specific Enhancer of the Gonadotropin-Releasing Hormone Gene. Mol Cell Biol 16. https://doi.org/10.1128/mcb.16.7.3596 Lee HJ, Dreyfus C, DiCicco-Bloom E (2016) Valproic acid stimulates proliferation of glial precursors during cortical gliogenesis in developing rat. Dev Neurobiol 76. https://doi.org/10.1002/dneu.22359 Rødevand L, Bahrami S, Frei O, Chu Y, Shadrin A, O’Connell KS et al (2021) Extensive bidirectional genetic overlap between bipolar disorder and cardiovascular disease phenotypes. Transl Psychiatry 11. https://doi.org/10.1038/s41398-021-01527-z Hiller JK, Jangmo A, Tesli MS, Jaholkowski PP, Hoseth EZ, Steen NE, Haram M (2023) Lipid Biomarker Research in Bipolar Disorder: A Scoping Review of Trends, Challenges, and Future Directions. Biol Psychiatry Global Open Sci 3. https://doi.org/10.1016/j.bpsgos.2023.07.004 Glintborg D, Rubin KH, Nybo M, Abrahamsen B, Andersen M (2018) Cardiovascular disease in a nationwide population of Danish women with polycystic ovary syndrome. Cardiovasc Diabetol 17. https://doi.org/10.1186/s12933-018-0680-5 Ollila MM, Arffman RK, Korhonen E, Morin-Papunen L, Franks S, Junttila J, Piltonen TT (2023) Women with PCOS have an increased risk for cardiovascular disease regardless of diagnostic criteria—a prospective population-based cohort study. Eur J Endocrinol 189. https://doi.org/10.1093/ejendo/lvad077 Chakraborty A, Wakamiya M, Venkova-Canova T, Pandita RK, Aguilera-Aguirre L, Sarker AH et al (2015) Neil2-null mice accumulate oxidized DNA bases in the transcriptionally active sequences of the genome and are susceptible to innate inflammation. J Biol Chem 290. https://doi.org/10.1074/jbc.M115.658146 Hayes MG, Urbanek M, Ehrmann DA, Armstrong LL, Lee JY, Sisk R et al (2015) Genome-wide association of polycystic ovary syndrome implicates alterations in gonadotropin secretion in European ancestry populations. Nat Commun 6. https://doi.org/10.1038/ncomms8502 Logan RW, McClung CA (2016) Animal models of bipolar mania: The past, present and future. Neuroscience 321. https://doi.org/10.1016/j.neuroscience.2015.08.041 Hildrestrand GA, Rolseth V, Kunath N, Suganthan R, Jensen V, Bugaj AM et al (2021) NEIL1 and NEIL2 DNA glycosylases modulate anxiety and learning in a cooperative manner in mice. Commun Biol 4. https://doi.org/10.1038/s42003-021-02864-x Zandi PP, Jaffe AE, Goes FS, Burke EE, Collado-Torres L, Huuki-Myers L et al (2022) Amygdala and anterior cingulate transcriptomes from individuals with bipolar disorder reveal downregulated neuroimmune and synaptic pathways. Nat Neurosci 25. https://doi.org/10.1038/s41593-022-01024-6 Schiavi S, La Rosa P, Petrillo S, Carbone E, D’Amico J, Piemonte F, Trezza V (2022) N-Acetylcysteine Mitigates Social Dysfunction in a Rat Model of Autism Normalizing Glutathione Imbalance and the Altered Expression of Genes Related to Synaptic Function in Specific Brain Areas. Front Psychiatry 13. https://doi.org/10.3389/fpsyt.2022.851679 Kaur S, Archer KJ, Devi MG, Kriplani A, Strauss JF, Singh R (2012) Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis. J Clin Endocrinol Metab 97. https://doi.org/10.1210/jc.2011-3441 Czibere L, Baur LA, Wittmann A, Gemmeke K, Steiner A, Weber P et al (2011) Profiling trait anxiety: Transcriptome analysis reveals cathepsin B (Xtsb) as a novel candidate gene for emotionality in mice. PLoS ONE 6. https://doi.org/10.1371/journal.pone.0023604 Yoon JY, Szwajcer D, Ishdorj G, Benjaminson P, Xiao W, Kumar R et al (2013) Synergistic apoptotic response between valproic acid and fludarabine in chronic lymphocytic leukaemia (CLL) cells involves the lysosomal protease cathepsin B. Blood Cancer J 3. https://doi.org/10.1038/bcj.2013.50 Coman D, Vissers LELM, Riley LG, Kwint MP, Hauck R, Koster J et al (2018) Squalene Synthase Deficiency: Clinical, Biochemical, and Molecular Characterization of a Defect in Cholesterol Biosynthesis. Am J Hum Genet 103. https://doi.org/10.1016/j.ajhg.2018.05.004 Yu J, Xie X, Ma Y, Yang Y, Wang C, Xia G et al (2022) Effects and potential mechanism of Ca2+/calmodulin-dependent protein kinase II pathway inhibitor KN93 on the development of ovarian follicle. Int J Mol Med 50. https://doi.org/10.3892/ijmm.2022.5177 Ferreira MAR, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L et al (2008) Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 40. https://doi.org/10.1038/ng.209 Sklar P, Smoller JW, Fan J, Ferreira MAR, Perlis RH, Chambert K et al (2008) Whole-genome association study of bipolar disorder. Mol Psychiatry 13. https://doi.org/10.1038/sj.mp.4002151 Allen O, Coombes BJ, Pazdernik V, Gisabella B, Hartley J, Biernacka JM et al (2024) : Differential Serum Levels of CACNA1C, Circadian Rhythm and Stress Response Molecules in Subjects with Bipolar Disorder: Associations with Genetic and Clinical Factors. medRxiv 2024.04.11.24305678 Harrison PJ, Husain SM, Lee H, Los Angeles A, De, Colbourne L, Mould A et al (2022) CACNA1C (CaV1.2) and other L-type calcium channels in the pathophysiology and treatment of psychiatric disorders: Advances from functional genomics and pharmacoepidemiology. Neuropharmacology 220. https://doi.org/10.1016/j.neuropharm.2022.109262 Wu H-H, Zhu Q, Liang N, Xiang Y, Xu T-Y, Huang Z-C et al (2024) CISD2 regulates oxidative stress and mitophagy to maintain the balance of the follicular microenvironment in PCOS. Redox Rep 29:2377870 Song S, Tan Y (2019) Expression of FKBP52 in the ovaries of PCOS rats. Int J Mol Med 43. https://doi.org/10.3892/ijmm.2018.3998 Jeon H, Choi Y, Brännström M, Akin JW, Curry TE, Jo M (2023) Cortisol/glucocorticoid receptor: a critical mediator of the ovulatory process and luteinization in human periovulatory follicles. Hum Reprod 38. https://doi.org/10.1093/humrep/dead017 Syed S, Gragnoli C (2024) The glucocorticoid receptor gene (NR3C1) is linked to and associated with polycystic ovarian syndrome in Italian families. J Ovarian Res 17. https://doi.org/10.1186/s13048-023-01329-5 Davies TH, Ning YM, Sánchez ER (2002) A new first step in activation of steroid receptors. Hormone-induced switching of FKBP51 and FKBP52 immunophilins. J Biol Chem 277. https://doi.org/10.1074/jbc.C100531200 Wochnik GM, Rüegg J, Abel GA, Schmidt U, Holsboer F, Rein T (2005) FK506-binding proteins 51 and 52 differentially regulate dynein interaction and nuclear translocation of the glucocorticoid receptor in mammalian cells. J Biol Chem 280. https://doi.org/10.1074/jbc.M407498200 Szczepankiewicz D, Narożna B, Celichowski P, Sakrajda K, Kołodziejski P, Banach E et al (2021) Genes involved in glucocorticoid receptor signalling affect susceptibility to mood disorders. World J Biol Psychiatry 22. https://doi.org/10.1080/15622975.2020.1766109 Debs SR, Rothmond DA, Zhu Y, Weickert CS, Purves-Tyson TD (2024) Molecular evidence of altered stress responsivity related to neuroinflammation in the schizophrenia midbrain. J Psychiatr Res 177:118–128 Stener-Victorin E, Padmanabhan V, Walters KA, Campbell RE, Benrick A, Giacobini P et al (2020) Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome. Endocr Rev 41. https://doi.org/10.1210/endrev/bnaa010 Additional Declarations The authors declare potential competing interests as follows: Dr. Andreassen has received speaker fees from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion and is a consultant to Cortechs.ai. and Precision Health. Dr. Anders M. Dale is Founding Director, holds equity in CorTechs Labs, Inc. (DBA Cortechs.ai), and serves on its Board of Directors and Scientific Advisory Board. Dr. Dale is the President of J. Craig Venter Institute (JCVI) and is a member of the Board of Trustees of JCVI. He is an unpaid consultant for Oslo University Hospital. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. Dr. Frei is a consultant to Precision Health. No other disclosures were reported. 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Andreassen","email":"data:image/png;base64,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","orcid":"","institution":"Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway","correspondingAuthor":true,"prefix":"","firstName":"Ole","middleName":"A.","lastName":"Andreassen","suffix":""}],"badges":[],"createdAt":"2025-09-16 11:26:23","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7629869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7629869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91962671,"identity":"10c83157-182b-4fe1-8618-11bb8fd37e58","added_by":"auto","created_at":"2025-09-23 07:57:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175587,"visible":true,"origin":"","legend":"","description":"","filename":"Genomicrelationshipbetweenpolycysticovarysyndrome16092025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/c32416f2e02dfcf613fbb435.docx"},{"id":91962670,"identity":"e48a9419-5ce4-4e1f-b2f0-13b04d87d674","added_by":"auto","created_at":"2025-09-23 07:57:51","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7629869.json","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/9ecbc95abb14bea5350934e6.json"},{"id":91963416,"identity":"998f8fdc-5b8d-4d9a-9eb6-25cecdffc031","added_by":"auto","created_at":"2025-09-23 08:05:51","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187144,"visible":true,"origin":"","legend":"","description":"","filename":"rs76298690enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/a2d91dc1c96f261d96927f6e.xml"},{"id":91960969,"identity":"67d117c1-4d52-406d-8637-2a094e9f6a52","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185595,"visible":true,"origin":"","legend":"","description":"","filename":"rs76298690structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/3d170ee60a0f88ada6e94546.xml"},{"id":91960968,"identity":"6d8e3b21-5422-42fd-9750-64c939bb82dc","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203196,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/7d1e7f2e226237ae93973f0f.html"},{"id":91960967,"identity":"9ee8da78-5e25-4c66-9b1f-8512b0079a21","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1220896,"visible":true,"origin":"","legend":"\u003cp\u003eThe “ConjFDR Manhattan plots” illustrate SNP FDR values for the joint association between bipolar disorder and polycystic ovary syndrome. The y-axis shows the −log10 transformed conjFDR values. Chromosomal number is presented along the x-axis. The threshold for significant shared associations (conjFDR \u0026lt; 0.05) is represented by the horizontal dotted line. Independent significant SNPs are indicated by a black perimeter.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/10f19c56a9b3fb99a2b685e9.png"},{"id":91960964,"identity":"dfb9a3ec-e43c-4506-a0fa-3ba4f5302465","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1664650,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic context for the strongest associations identified in the conjunctional false discovery rate (conjFDR) analysis: (A) rs34876360, (B) rs740417. The y-axis depicts −log10 (conjFDR) values. The dashed horizontal line represents the statistically significant threshold of FDR = 0.05. A single nucleotide polymorphism (SNP) with the strongest association is shown in the large purple circle. The color of the remaining markers reflects the degree of linkage disequilibrium (LD) with the SNP most strongly associated, measured as the r\u003csup\u003e2\u003c/sup\u003e coefficient and generated from the 1000 Genomes reference data. Genes found within the region are annotated according to \u003cem\u003eEnsDb.Hsapiens.v75\u003c/em\u003e. The figure is generated with \u003cem\u003elocuszoomr\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/ad9323bb2a6c95e14ff2e3ad.png"},{"id":91960971,"identity":"0f3b9e96-c7a8-44ef-9448-3e1727ebbb21","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6946301,"visible":true,"origin":"","legend":"\u003cp\u003eChromatin interactions (Hi-C) for regions surrounding: (A) locus located on chromosome 8:11455262 (rs34876360), (B) locus on chromosome 12:2499849 (rs740414) shared between bipolar disorder and polycystic ovary syndrome. Top (purple) panels illustrate Hi-C in brain cortex. Bottom (green) panels illustrate Hi-C in ovary tissue. Between the Hi‑C panels, the locations of genes mapped to the identified loci are shown. For clarity, the locations of the remaining genes are omitted. The intensity of the colors illustrates the strength of interactions between pairs of loci and regulatory elements. Results are displayed with 25kb resolution in the hg38 genome reference assemble.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/d60cc92df9dcc009138b0a7c.png"},{"id":91960970,"identity":"6fe17526-5790-4625-82e2-5c702326fa27","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3138809,"visible":true,"origin":"","legend":"\u003cp\u003eThe biological pathways encompass both the gene(s) affected by valproic acid (VPA) and the genes mapped for loci shared between bipolar disorder (BIP) and polycystic ovary syndrome (PCOS). (A) The number of unique biological pathways encompassing gene(s) affected by VPA and individual genes is depicted. The density distribution of the number of unique pathways containing genes regulated by VPA, along with 10,000 individual genes randomly selected from protein-coding genes, is shown in beige. The red dashed line represents the nominal\u003cem\u003e p\u003c/em\u003e-value for the given density distribution. Black lines represent the number of unique genes encompassing VPA-regulated gene(s) and a given gene mapped to loci shared between BIP and PCOS. (B) The number of unique gene(s) affected by VPA that are included in biological pathways also encompassing the gene of interest. Details are analogous to those described for the plot in (A). (C) The network illustrating organismal systems pathways (blue) encompassing \u003cem\u003eCACNA1C\u003c/em\u003e and VPA-modified genes (orange). (D) The network depicts environmental information processing pathways encompassing \u003cem\u003eCACNA1C\u003c/em\u003eand genes modified by VPA. To enable the above analyses, we removed \u003cem\u003eCACNA1C\u003c/em\u003efrom the list of genes affected by VPA.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/d0f931f8660ad2c9478b4932.png"},{"id":91964931,"identity":"1da191d5-431b-4298-967e-194b8f4c79d0","added_by":"auto","created_at":"2025-09-23 08:14:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9772509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/a545f6fd-5f9c-472d-8547-2195f91b3634.pdf"},{"id":91960962,"identity":"c4161dca-4359-4a24-ad88-ec7e8267a84a","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1145143,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary text\u003c/p\u003e","description":"","filename":"Genomicrelationshipbetweenpolycysticsuppmat16092025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/1f57b46278c2b8fa8271f33a.docx"},{"id":91960960,"identity":"39569993-a3a1-4e3b-b54b-6eceb3d1d5e5","added_by":"auto","created_at":"2025-09-23 07:49:51","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28897,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary tables\u003c/p\u003e","description":"","filename":"Genomicrelationshipbetweenpolycysticsuppltabl16092025.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7629869/v1/ed5f30b7b885a64081ebe8a4.xlsx"}],"financialInterests":"The authors declare potential competing interests as follows: Dr. Andreassen has received speaker fees from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion and is a consultant to Cortechs.ai. and Precision Health. Dr. Anders M. Dale is Founding Director, holds equity in CorTechs Labs, Inc. (DBA Cortechs.ai), and serves on its Board of Directors and Scientific Advisory Board. Dr. Dale is the President of J. Craig Venter Institute (JCVI) and is a member of the Board of Trustees of JCVI. He is an unpaid consultant for Oslo University Hospital. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. Dr. Frei is a consultant to Precision Health. No other disclosures were reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenomic relationship between polycystic ovary syndrome and bipolar disorder\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBipolar disorder (BIP) is a complex mental disorder characterized by recurrent episodes of depression and mania or hypomania (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). A cornerstone in the treatment of BIP is mood-stabilizing drugs, and a commonly prescribed medication is the anti-epileptic drug valproate (VPA) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The choice of drug and the course of treatment in BIP are influenced by the fact that both BIP and VPA are independent risk factors for polycystic ovary syndrome (PCOS), characterized by hyperandrogenism, polycystic ovarian morphology, ovarian dysfunction, and subfertility (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA systematic review and meta-analysis showed a 2-fold higher risk of BIP among women with PCOS (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This relationship has also been shown in drug-na\u0026iuml;ve BIP patients, for whom the prevalence of PCOS was estimated at 30% (compared to approximately 10% in the healthy population) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Women with BIP, similarly to those diagnosed with PCOS, have higher serum testosterone and androstenedione levels and an increased risk of abnormal menstruation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, both BIP and PCOS are associated with metabolic imbalances such as hyperinsulinemia, dyslipidemia, metabolic syndrome, and type 2 diabetes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Although research indicates dysfunction of the hypothalamic-pituitary-adrenal axis, chronic inflammation, altered adipokines, a disturbed gut microbiome, and disturbances in circadian rhythms as potential mechanisms linking BIP and PCOS, the basis of this comorbidity remains elusive (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This lack of understanding of the mechanism has hindered the development of effective prevention and treatment, thereby increasing the burden of PCOS (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, the mechanism by which VPA increases the risk of PCOS among female patients with BIP, raising its prevalence to over 50%, is unknown (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Human and animal studies suggest several potential mechanisms: altered gonadotropin-releasing hormone secretion, attenuation of luteinizing hormone (LH) release from the pituitary gland due to enhanced GABAergic inhibitory neurotransmission, direct adenotoxic effects on ovarian tissue, increased ovarian androgen production by theca cells, and metabolic alterations associated with weight gain (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies have shown that both PCOS and BIP are highly heritable, with heritability estimated at approximately 70% (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and are under polygenic influences (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although genetic studies offer important insights into complex diseases and their comorbidity (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), our current comprehension of the shared genetic architecture between PCOS and BIP remains limited. Moreover, data-driven genotype phenotype mapping is a valuable resource for identifying drug safety issues, drug repurposing, and the discovery of new drugs (\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, elucidating the shared genetic architecture between BIP and PCOS may contribute to a better understanding of the mechanisms underlying their comorbidity, including those associated with VPA use.\u003c/p\u003e\u003cp\u003eCombining signals from two genome-wide association studies (GWASs) can enhance the detection of shared genetic variants with a second trait for which the GWAS is underpowered (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The conjunctional False Discovery Rate (conjFDR) approach can identify shared genetic loci between human traits and disorders irrespective of genetic correlation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). To identify the genetic underpinnings shared between PCOS and BIP, and the potential mechanisms that might underlie PCOS as an adverse event of VPA treatment, we examined the shared genetic architecture using the conjFDR approach (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), followed by extensive functional analyses utilizing available repositories and tools (\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGWAS Samples\u003c/h2\u003e\u003cp\u003eThe GWAS meta-analysis summary statistics for PCOS (3,609 cases and 229,788 controls) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) were downloaded from the GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GWAS summary statistics for BIP (41,510 cases and 354,340 controls) were obtained from the BIP Working Group of the Psychiatric Genomics Consortium, after excluding samples from the Estonian Biobank to prevent sample overlap (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). All GWASs included in our analysis were approved by the relevant ethics committees, and informed consent was obtained from all participants.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eConjunctional FDR\u003c/h3\u003e\n\u003cp\u003eTo visualize cross-trait SNP enrichment, we constructed QQ plots that illustrate the distribution of \u003cem\u003ep\u003c/em\u003e-values for the primary trait conditioned on significance levels in the secondary trait; that is, BIP conditioned on PCOS, and vice versa. To control for spurious enrichment, the QQ plots were generated after random pruning, averaging over 500 iterations. For each iteration of random pruning, all but one random SNP in each linkage disequilibrium (LD)-independent region (clump of SNPs in strong LD, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1) were removed, and finally the results were averaged across all iterations. We excluded SNPs within three regions (major histocompatibility complex region chr6: 25119106\u0026ndash;33854733; chr8:7200000\u0026ndash;12500000; chr19:44909039\u0026ndash;45912650) to prevent bias due to complex LD pattern within these regions. Successive leftward deflections on a QQ plot, associated with increasing levels of association with a secondary trait, indicate cross-trait enrichment of a primary trait (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe applied the conjFDR approach to identify loci shared between PCOS and BIP (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The conjFDR method involves conducting two conditional (condFDR) analyses\u0026mdash;in our study, conditioning PCOS on BIP and vice versa\u0026mdash;which re-rank test statistics and recalculate the associations between variants and a primary trait based on their associations with the secondary trait (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Then, the conjFDR value for a given genetic variant is defined as the maximum of the two condFDR values, making it a conservative estimate of the association between variants and the traits of interest. In our study, we applied the FDR significance cutoffs at 0.05 for both condFDR and conjFDR. We performed the conjFDR analysis after removing three genomic regions with a complex LD pattern (chr6: 25119106\u0026ndash;33854733; chr8:7200000\u0026ndash;12500000; chr19:44909039\u0026ndash;45912650).\u003c/p\u003e\u003cp\u003eThe FUMA protocol was applied to identify independent genomic loci (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). SNPs with conjFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an LD r\u0026sup2; \u0026lt; 0.6 with each other were defined as independently significant. Lead SNPs were defined as independent significant SNPs with LD r\u0026sup2; \u0026lt;0.1 with each other. We defined the boundaries of each genomic locus to include SNPs with an LD r\u0026sup2; \u0026ge; 0.6 with any of the independently significant SNPs within the locus. Loci separated by less than 250 kb were merged. A SNP within a given locus that had the lowest conjFDR value was designated as the lead SNP. We applied the 1000 Genomes Project European ancestry haplotype reference panel to compute all LD r\u003csup\u003e2\u003c/sup\u003e values (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFunctional annotation\u003c/h3\u003e\n\u003cp\u003eWe applied Open Target Genetics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genetics.opentargets.org/\u003c/span\u003e\u003cspan address=\"https://genetics.opentargets.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to map genes for lead SNPs from the conjFDR analysis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) using positional information and an overall score, which is an aggregated measure based on positional information, chromatin interactions, quantitative trait loci, and in silico functional prediction datasets. Adopting a broad inclusion strategy, for each lead SNP we selected the gene with the closest location, along with any genes that had an overall score equal to or higher than that of the closest gene.\u003c/p\u003e\u003cp\u003eWe used tissue expression data from the Adult Genotype Tissue Expression Project (GTEx; version 8) to assess the expression of mapped genes in various tissues. These include the brain (cortex, frontal cortex Brodmann area 24, anterior cingulate cortex Brodmann area 24, amygdala, substantia nigra, hippocampus, putamen basal ganglia, caudate basal ganglia, nucleus accumbens basal ganglia, cerebellar hemisphere, cerebellum, hypothalamus, spinal cord cervical C1), pituitary, ovary, subcutaneous adipose tissue, visceral omentum adipose tissue, adrenal gland, cultured fibroblast cells, liver, thyroid, and whole blood. According to the GTEx data presentation approach, isoforms were collapsed into a single gene, and no normalization or threshold procedures were applied. The data used for the analyses were obtained from the GTEx Portal on 26/06/24.\u003c/p\u003e\u003cp\u003eTo further explore potential interactions between the mapped genes in the three-dimensional space of the cell nucleus, which could suggest their co-expression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), we examined published data on chromatin interactions in the brain and ovary (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). We utilized the results of the High-throughput Chromosome Conformation Capture (Hi-C) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) method to reveal genome-wide 3D chromatin contacts, and visualized interactions using the 3D Genome Browser tool (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These contacts are essential for understanding the spatial organization of the genome and its impact on gene regulation and cellular function (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Regions of the genome characterized by a high frequency of interactions compared to other regions are defined as Topologically Associating Domains (TADs). Expression of genes located within a single TAD is more likely to be co-regulated than genes located in separate domains (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe used data from the drug\u0026ndash;gene interaction database (DGIdb, version 5) to illustrate the strength and type of drug interactions with mapped genes shared between PCOS and BIP (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Next, we linked the drug-gene interaction data for VPA from DGIdb to data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) to assess the number of pathways encompassing genes affected by VPA and mapped genes. To avoid the double inclusion of CACNA1C as both mapped and affected by the VPA gene, we excluded CACNA1C from the list of genes affected by VPA. We also assessed how many genes affected by VPA were included in pathways that contained the mapped genes. To evaluate whether the number of pathways and genes affected by VPA for each mapped gene exceeded that of a randomly selected protein-coding gene, we compared our estimates to those for 10,000 randomly selected protein-coding genes from a list provided by the GENCODE Project (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). We applied the Bonferroni correction, adjusted for the number of genes mapped to a given locus, to account for multiple testing.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eQQ plots of SNPs conditioned on association between BIP and PCOS\u003c/h2\u003e\u003cp\u003eThe conditional QQ plot showed evidence of cross-trait enrichment between BIP and PCOS (Supplementary Fig.\u0026nbsp;1A). The earlier leftward deflection from the dashed line (no enrichment) indicated a greater proportion of true associations for a given nominal PCOS \u003cem\u003ep\u003c/em\u003e-value. The more pronounced leftward shifts for decreasing nominal PCOS \u003cem\u003ep\u003c/em\u003e-value thresholds demonstrated that the proportion of non-null effects in BIP increased as the strength of the association with PCOS increased. Similarly, the conditional QQ plot displayed polygenic enrichment for PCOS given the strength of association with BIP (Supplementary Fig.\u0026nbsp;1B).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eConjFDR analysis\u003c/h2\u003e\u003cp\u003eUsing condFDR (condFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) we identified 19 distinct genomic loci to be associated with PCOS after conditioning on association with BIP (Supplementary Fig.\u0026nbsp;2 and Supplementary Table\u0026nbsp;1). At condFDR \u0026lt; 0.05, we identified 175 loci associated with BIP conditional on PCOS (Supplementary Fig.\u0026nbsp;3, Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eApplying the conjFDR approach (conjFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we identified two independent loci jointly associated with PCOS and BIP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). One of these loci, with the lead SNP rs34876360, was not identified in the original PCOS GWAS (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) nor in the BIP GWAS (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The second locus, associated with the lead SNP rs740417, was not identified in the BIP GWAS utilized in our study, nor in the recently published largest BIP GWAS (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFunctional analysis\u003c/h3\u003e\n\u003cp\u003eFunctional annotation analysis revealed that the lead SNP and 26 SNPs in LD with the lead SNP for the locus shared between PCOS and BIP on chromosome 8:11455262, displayed an intergenic character. The lead SNP and 10 SNPs in LD with the lead SNP for the locus identified on chromosome 12:2499849 were recognized as intronic of \u003cem\u003eCACNA1C\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe overall Open Targets variant-to-gene analysis for the lead SNP of the locus shared between PCOS and BIP and located on chromosome 8:11455262 (rs34876360) revealed 9 genes (\u003cem\u003eBLK\u003c/em\u003e, \u003cem\u003eSLC35G5\u003c/em\u003e, \u003cem\u003eXKR6\u003c/em\u003e, \u003cem\u003eDEFB134\u003c/em\u003e, \u003cem\u003eFDFT1\u003c/em\u003e, \u003cem\u003eDEFB136\u003c/em\u003e, \u003cem\u003eFAM167A\u003c/em\u003e, \u003cem\u003eNEIL2\u003c/em\u003e, \u003cem\u003eCTSB\u003c/em\u003e) in addition to the nearest \u003cem\u003eGATA4\u003c/em\u003e. Similar analysis for the lead SNP on chromosome 12:2499849 (rs740417) identified \u003cem\u003eCACNA1C\u003c/em\u003e (an intronic variant), \u003cem\u003eFKBP4\u003c/em\u003e, \u003cem\u003eITFG2\u003c/em\u003e, and \u003cem\u003eDCP1B\u003c/em\u003e as the nearest gene. Tissue-specific gene expression (GTEx) analysis for genes mapped to the first locus showed that \u003cem\u003eFDFT1\u003c/em\u003e and \u003cem\u003eNEIL2\u003c/em\u003e were expressed at moderate level in both the brain and ovary (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Other genes (\u003cem\u003eBLK\u003c/em\u003e, \u003cem\u003eCTSB, DEFB134, DEFB136\u003c/em\u003e, \u003cem\u003eFAM167A, SLC35G5, XKR6)\u003c/em\u003e were either not detected or expressed at low levels in both the brain and ovary, or in either organ individually (Supplementary Fig.\u0026nbsp;4). Interestingly, \u003cem\u003eGATA4\u003c/em\u003e was specifically and highly expressed in ovarian tissue but not expressed in the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the second locus, we observed expression of \u003cem\u003eCACNA1C\u003c/em\u003e and \u003cem\u003eFKBP4\u003c/em\u003e within a similar range in both the ovary and most brain tissues, while \u003cem\u003eDCP1B\u003c/em\u003e and \u003cem\u003eITFG2\u003c/em\u003e tended to have higher expression levels in the ovary compared to the brain (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur analysis revealed that regions on chromosome 8:11455262 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and chromosome 12:2499849 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), which encompass the identified genes of interest, are enriched for chromatin contacts in both the brain cortex (upper panel) and ovary (lower panel).\u003c/p\u003e\u003cp\u003eThe DGIdb analysis revealed that \u003cem\u003eCACNA1C\u003c/em\u003e is one of the genes affected by VPA (Supplementary Fig.\u0026nbsp;4\u0026ndash;6). Furthermore, it revealed that other therapeutic substances are known to block or modulate the expression of \u003cem\u003eCACNA1C\u003c/em\u003e (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e\u003cp\u003eIn our analysis of KEGG-annotated pathways containing genes affected by VPA, as well as those mapped to loci jointly associated with PCOS and BIP, we identified 33 pathways associated with \u003cem\u003eCANCA1C\u003c/em\u003e, five with \u003cem\u003eCTSB\u003c/em\u003e, four with \u003cem\u003eGATA4\u003c/em\u003e, and one each involving \u003cem\u003eNEIL2\u003c/em\u003e and \u003cem\u003eFKBP4\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This analysis further revealed that \u003cem\u003eCACNA1C\u003c/em\u003e shares biological pathways with 23 other genes, \u003cem\u003eGATA4\u003c/em\u003e with eight, \u003cem\u003eCTSB\u003c/em\u003e with seven, and both \u003cem\u003eNEIL2\u003c/em\u003e and \u003cem\u003eFKBP2\u003c/em\u003e with one gene influenced by VPA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). We observed that for \u003cem\u003eCACNA1C\u003c/em\u003e, both the number of biological pathways and the number of genes affected by VPA were statistically higher than those observed for randomly selected protein-coding genes (\u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;=\u0026thinsp;0.019 and 0.032, respectively).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we applied the conjFDR framework to genome-wide data to identify the genetic underpinnings of BIP and PCOS comorbidity. We identified two independent loci jointly associated with BIP and PCOS and mapped several genes to them, such as \u003cem\u003eGATA4\u003c/em\u003e, \u003cem\u003eNEIL2\u003c/em\u003e, and \u003cem\u003eCACNA1C\u003c/em\u003e, highlighting the potential role of these gene in disorder comorbidity. Furthermore, the results indicate that the mapped genes may be involved in the development of PCOS associated with VPA use. This provides new insight into the molecular pathways involved in PCOS and BIP comorbidity, as well as PCOS associated with VPA, which can form the basis of experimental work to determine the mechanisms and form the basis of future drug development to avoid these adverse events.\u003c/p\u003e\u003cp\u003eWe observed cross-trait polygenic enrichment between PCOS and BIP, supporting previous epidemiological observations of comorbidities (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The two overlapping loci between PCOS and BIP exhibited both concordant and discordant directions, similar to those observed in many other pairs of human traits (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe functional analysis included gene mapping of identified shared loci, and we utilized broad inclusion criteria based on OpenTargets to minimize the risk of omitting genes related to the traits of interest. For the loci on chromosome 8:11455262, apart from the nearest \u003cem\u003eGATA4\u003c/em\u003e, we identified nine genes (\u003cem\u003eBLK\u003c/em\u003e, \u003cem\u003eSLC35G5\u003c/em\u003e, \u003cem\u003eXKR6\u003c/em\u003e, \u003cem\u003eDEFB136\u003c/em\u003e, \u003cem\u003eFDFT1\u003c/em\u003e, \u003cem\u003eDEFB134\u003c/em\u003e, \u003cem\u003eFAM167A\u003c/em\u003e, \u003cem\u003eNEIL2\u003c/em\u003e, \u003cem\u003eCTSB\u003c/em\u003e). Importantly, although \u003cem\u003eGATA4\u003c/em\u003e was not identified in the original GWAS for PCOS that we utilized for the conjFDR analysis, \u003cem\u003eGATA4\u003c/em\u003e/\u003cem\u003eNEIL2\u003c/em\u003e locus (rs804279; chr8:11623889) was identified in an independent GWAS for PCOS (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). This finding confirms the involvement of this genetic locus in the pathogenesis of PCOS and validates the conjFDR methodology used in our study. The lead SNP (rs740417) within the locus shared between PCOS and BIP on chromosome 12:2499849 was identified as an intronic variant of \u003cem\u003eCACNA1C\u003c/em\u003e, and located in proximity to \u003cem\u003eFKBP4\u003c/em\u003e, \u003cem\u003eITFG2\u003c/em\u003e, and \u003cem\u003eDCP1B\u003c/em\u003e genes.\u003c/p\u003e\u003cp\u003eThe results of tissue-specific gene expression analysis for genes mapped to the locus on chromosome 8:11455262 showed that \u003cem\u003eGATA4\u003c/em\u003e is highly specific to the ovary, while other mapper genes (i.e., \u003cem\u003eFDFT1\u003c/em\u003e and \u003cem\u003eNEIL2\u0026thinsp;=\u003c/em\u003e\u0026thinsp;are expressed in both the brain and the ovary. These findings provide further evidence for their possible involvement in the comorbidity of PCOS and BIP. For the locus on chromosome 12, tissue expression analysis indicates that \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eFKBP4\u003c/em\u003e, \u003cem\u003eDCP1B\u003c/em\u003e, and \u003cem\u003eITFG2\u003c/em\u003e are all expressed in both the ovary and the brain, with a noteworthy similarity in the expression patterns of \u003cem\u003eDCP1B\u003c/em\u003e and \u003cem\u003eITFG2\u003c/em\u003e. Moreover, the results of the Hi-C analysis demonstrated that, for both loci, the mapped genes are not only closely located in terms of genomic distance but also maintain physical proximity within nuclear space, suggesting that they may be co-expressed. This implies that genetic variants or modulators of the mapped genes such as VPA, could influence the transcription of multiple genes, with several genes potentially contributing to the comorbidity of PCOS and BIP.\u003c/p\u003e\u003cp\u003eThe DGIdb analysis revealed that VPA affects \u003cem\u003eCACNA1C\u003c/em\u003e. Additionally, we observed that the genes \u003cem\u003eFKBP4\u003c/em\u003e, \u003cem\u003eNEIL2\u003c/em\u003e, \u003cem\u003eGATA4\u003c/em\u003e, \u003cem\u003eCTSB\u003c/em\u003e, and \u003cem\u003eCACNA1C\u003c/em\u003e participate in common biological pathways with those affected by VPA. This is particularly relevant for \u003cem\u003eCACNA1C\u003c/em\u003e, which is involved in biological pathways such as MAPK signaling and calcium signaling that may explain the observed comorbidity (\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), as well as underlie the metabolic disorders observed in both PCOS and BIP (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). \u003cem\u003eGATA4\u003c/em\u003e, the nearest gene to the lead SNP (rs34876360) in the locus shared between PCOS and BIP on chromosome 8:11455262, is a zinc-finger transcription factor. It is a transcriptional inducer of p15INK4B, a cyclin-dependent kinase inhibitor, leading to the reduction of cyclin D1 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). \u003cem\u003eGATA4\u003c/em\u003e is critical in organogenesis, particularly in the development of the heart and gonads (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Moreover, animal studies have shown that \u003cem\u003eGATA4\u003c/em\u003e plays an important role in the development of GnRH neurons and GnRH gene expression (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Combined with the observed dysfunctions of the hypothalamic-pituitary-gonadal axis in PCOS and the crucial role of GnRH analogues in its treatment, these findings further suggest that \u003cem\u003eGATA4\u003c/em\u003e may play a role in the pathogenesis of PCOS (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It is expressed in the developing and adult brain and negatively regulates the proliferation and growth of astrocytes (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Interestingly, VPA exerts the opposite effect of stimulation of astrocyte proliferation in the developing brain (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe co-occurrence of cardiovascular disease in BIP patients (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), and the association of PCOS with major adverse cardiovascular events from a young age, independent of body-mass index (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), further underscores the significance of \u003cem\u003eGATA4\u003c/em\u003e as a potential unifying factor. \u003cem\u003eNEIL2\u003c/em\u003e encodes a DNA glycosylase that initiates base excision DNA repair by cleaving oxidatively damaged bases, and it is crucial for long-term genomic maintenance (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). \u003cem\u003eNEIL2\u003c/em\u003e has been linked with PCOS by the previous GWAS (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). It has been shown that mice lacking \u003cem\u003eNeil2\u003c/em\u003e display hyperactivity and reduced anxiety, endophenotypes typical of animal models of BIP (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Moreover, \u003cem\u003eNeil2\u003c/em\u003e knock-out mice were associated with reduced reactivity of NR2A subunits of NMDA receptors, which have been linked with the BIP pathogenesis (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Noteworthy, altered \u003cem\u003eNr2a\u003c/em\u003e expression has been observed in VPA-exposed rats (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). \u003cem\u003eCTSB\u003c/em\u003e encodes cathepsin B, a lysosomal protease and has been linked with pathogenesis of PCOS (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Previous study based on microarray-based gene expression profiling indicated that \u003cem\u003eCtsb\u003c/em\u003e effects on depression-like behavior in mice (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Moreover, it has been demonstrated in clinical settings that VPA increases cellular level of cathepsin B (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). \u003cem\u003eFDFT1\u003c/em\u003e encodes farnesyl-diphosphate farnesyltransferase 1, an evolutionarily conservative enzyme involved in the cholesterol biosynthesis (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). It has been demonstrated in an animal study that \u003cem\u003eFdft1\u003c/em\u003e is involved in follicular development in ovaries (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). The recessive variants of \u003cem\u003eFDFT1\u003c/em\u003e have been linked with profound developmental delay, brain abnormality, irritability, and sleep disturbances (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eCACNA1C\u003c/em\u003e encodes calcium voltage-gated channel subunit alpha1 C. Multiple genetic studies, including GWAS, have linked \u003cem\u003eCACNA1C\u003c/em\u003e with BIP (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Increased serum levels of \u003cem\u003eCACNA1C\u003c/em\u003e in BIP has been recently reported (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). \u003cem\u003eCACNA1C\u003c/em\u003e, apart from its involvement in processes such as neurotransmitter release and synaptic plasticity (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), also affects the functioning of mitochondria and lysosomes (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), which are linked to the pathogenesis of PCOS (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). \u003cem\u003eFKBP4\u003c/em\u003e encodes FKBP prolyl isomerase 4 (FKBP4), which is involved in immunoregulation and cellular trafficking of steroid hormone receptors (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Higher expression of \u003cem\u003eFkbp4\u003c/em\u003e has been observed in a rat model of PCOS (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Human chorionic gonadotropin stimulation upregulates expression of \u003cem\u003eFKBP4\u003c/em\u003e in human ovulatory follicles (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Moreover, FKBP4 was shown to be involved in the intranuclear translocation of the glucocorticoid receptor (NR3C1) which was in turn linked to the pathogenesis of PCOS (\u003cspan additionalcitationids=\"CR80 CR81\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Increased expression of \u003cem\u003eFkbp4\u003c/em\u003e has been observed in the hypothalamus of a rat model of depression (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Genetic association analysis showed a relationship between \u003cem\u003eFKBP4\u003c/em\u003e polymorphism and stressful life events in patients with BIP (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Furthermore, decreased \u003cem\u003eFKBP4\u003c/em\u003e mRNA level has been observed in schizophrenia (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe observations of the mapped genes may provide a basis for further targeted analyses in animal models (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e) and clinical conditions. It is particularly important to evaluate the utility of identifying genetic variations within mapped genes, especially \u003cem\u003eCACNA1C\u003c/em\u003e, to assess the risk of developing PCOS prior to initiating VPA treatment. However, it should be emphasized that the results indicating potential co-expression of several genes within the identified locus, along with biological pathways connecting \u003cem\u003eCACNA1C\u003c/em\u003e to genes modified by VPA, highlight the complexity of the observed processes. This complexity advises caution against adopting overly simplified experimental assumptions.\u003c/p\u003e\u003cp\u003eThe main limitation of our study is that we used only summary statistics from GWASs of individuals with European ancestry, which limits the generalization of the results. Due to the absence of a large-scale female-specific GWAS for BIP, we conducted our analysis using data from both sexes. However, the genetic loci we identified show consistent effect direction and nominal significance in available female-specific GWAS. Further, our functional analysis is based on associations and should be verified by experimental studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHere, we identified genetic variants shared between BIP and PCOS, and functional analyses implicated genes representing molecular pathways underlying the comorbidity between PCOS and BIP. The identified pathways were also linked to the pharmacological mechanisms of VPA. These findings provide new understanding of the pathophysiology of PCOS associated with BIP and VPA use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eDr. Andreassen has received speaker fees from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion and is a consultant to Cortechs.ai. and Precision Health. Dr. Anders M. Dale is Founding Director, holds equity in CorTechs Labs, Inc. (DBA Cortechs.ai), and serves on its Board of Directors and Scientific Advisory Board. Dr. Dale is the President of J. Craig Venter Institute (JCVI) and is a member of the Board of Trustees of JCVI. He is an unpaid consultant for Oslo University Hospital. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. Dr. Frei is a consultant to Precision Health. No other disclosures were reported.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by the Research Council of Norway (grants: #324499, #324252, #326813, #334920, #296030, #344121), Nordforsk (grant #164218), the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (grant #801133), the European Union\u0026rsquo;s Horizon 2020 research and innovation action programme (grants: #847776, #964874 and #847879 PRIME), and the European Commission Grant Committee (grant #964874). AEB was supported by the National Institute of Mental Health (grant # K01MH120352). PT was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases Intramural Research Program.\u003c/p\u003e\u003cp\u003eThis research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH author were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNierenberg AA, Agustini B, K\u0026ouml;hler-Forsberg O, Cusin C, Katz D, Sylvia LG et al (2023) Diagnosis and Treatment of Bipolar Disorder: A Review. JAMA 330. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2023.18588\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.18588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrapanzano C, Casolaro I, Amendola C, Damiani S (2022) : Lithium and Valproate in Bipolar Disorder: From International Evidence-based Guidelines to Clinical Predictors. \u003cem\u003eClinical Psychopharmacology and Neuroscience\u003c/em\u003e, vol. 20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9758/cpn.2022.20.3.403\u003c/span\u003e\u003cspan address=\"10.9758/cpn.2022.20.3.403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Teng Z, Xie H, Yuan H, Liu M, Chen J et al (2023) Prevalence and characteristics of polycystic ovarian syndrome in patients with bipolar disorder. J Affect Disord 340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2023.08.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.08.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Donovan C, Kusumakar V, Graves GR, Bird DC (2002) Menstrual abnormalities and polycystic ovary syndrome in women taking valproate for bipolar mood disorder. J Clin Psychiatry 63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4088/JCP.v63n0409\u003c/span\u003e\u003cspan address=\"10.4088/JCP.v63n0409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsojarvi J, Laatikainen TJ, Pakarinen AJ, Juntunen K, Myllyla VV (1993) Polycystic Ovaries and Hyperandrogenism in Women Taking Valproate for Epilepsy. N Engl J Med 329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/nejm199311043291904\u003c/span\u003e\u003cspan address=\"10.1056/nejm199311043291904\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoffe H, Cohen LS, Suppes T, McLaughlin WL, Lavori P, Adams JM et al (2006) Valproate Is Associated with New-Onset Oligoamenorrhea with Hyperandrogenism in Women with Bipolar Disorder. Biol Psychiatry 59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biopsych.2005.10.017\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsych.2005.10.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzziz R, Carmina E, Chen Z, Dunaif A, Laven JSE, Legro RS et al (2016) Polycystic ovary syndrome. Nat Reviews Disease Primers 2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrdp.2016.57\u003c/span\u003e\u003cspan address=\"10.1038/nrdp.2016.57\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalters KA, Gilchrist RB, Ledger WL, Teede HJ, Handelsman DJ, Campbell RE (2018) : New Perspectives on the Pathogenesis of PCOS: Neuroendocrine Origins. \u003cem\u003eTrends in Endocrinology and Metabolism\u003c/em\u003e, vol. 29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tem.2018.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tem.2018.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShahraki Z, Rastkar M, Ramezanpour M-R, Ghajarzadeh M (2024) The prevalence and odds of bipolar disorder in women with polycystic ovary syndrome (PCO) disease: a systematic review and meta-analysis. Arch Womens Ment Health. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00737-024-01420-w\u003c/span\u003e\u003cspan address=\"10.1007/s00737-024-01420-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatsunaga H, Sarai M (1993) Elevated Serum LH and Androgens in Affective Disorder Related to the Menstrual Cycle: With Reference to Polycystic Ovary Syndrome. Psychiatry Clin Neurosci 47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1440-1819.1993.tb01830.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1440-1819.1993.tb01830.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoffe H, Kim DR, Foris JM, Baldassano CF, Gyulai L, Hwang CH et al (2006) Menstrual dysfunction prior to onset of psychiatric illness is reported more commonly by women with bipolar disorder than by women with unipolar depression and healthy controls. J Clin Psychiatry 67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4088/JCP.v67n0218\u003c/span\u003e\u003cspan address=\"10.4088/JCP.v67n0218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodin O, Oli\u0026eacute; E, Fond G, Aouizerate B, Aubin V, Bellivier F et al (2024) Incidence and predictors of metabolic syndrome onset in individuals with bipolar disorders: A longitudinal study from the FACE-BD cohort. Acta Psychiatr Scand n/a. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1111/acps.13655\u003c/span\u003e\u003cspan address=\"10.1111/acps.13655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharles EF, Lambert CG, Kerner B (2016) Bipolar disorder and diabetes mellitus: evidence for disease-modifying effects and treatment implications. Int J Bipolar Disorders 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40345-016-0054-4\u003c/span\u003e\u003cspan address=\"10.1186/s40345-016-0054-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai W, Liu J, Qiu Y, Teng Z, Li S, Huang J et al (2022) : Shared postulations between bipolar disorder and polycystic ovary syndrome pathologies. \u003cem\u003eProgress in Neuro-Psychopharmacology and Biological Psychiatry\u003c/em\u003e, vol. 115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pnpbp.2021.110498\u003c/span\u003e\u003cspan address=\"10.1016/j.pnpbp.2021.110498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaub\u0026oslash;ll E, Isoj\u0026auml;rvi JIT, Harbo HF, Pakarinen AJ, Gjerstad L (1999) Long-term valproate treatment induces changes in ovarian morphology and serum sex steroid hormone levels in female Wistar rats. Seizure 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/seiz.1999.0342\u003c/span\u003e\u003cspan address=\"10.1053/seiz.1999.0342\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVink JM, Sadrzadeh S, Lambalk CB, Boomsma DI (2006) Heritability of Polycystic Ovary Syndrome in a Dutch Twin-Family Study. J Clin Endocrinol Metab 91:2100\u0026ndash;2104\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKiesepp\u0026auml; T, Partonen T, Haukka J, Kaprio J, L\u0026ouml;nnqvist J (2004) High concordance of bipolar I disorder in a nationwide sample of twins. Am J Psychiatry 161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1176/ajp.161.10.1814\u003c/span\u003e\u003cspan address=\"10.1176/ajp.161.10.1814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLichtenstein P, Yip BH, Bj\u0026ouml;rk C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM (2009) Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373:234\u0026ndash;239\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMullins N, Forstner AJ, O\u0026rsquo;Connell KS, Coombes B, Coleman JRI, Qiao Z et al (2021) Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet 53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-021-00857-4\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00857-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X, Xiao H, Ma Y, Zhou Z, Chen D (2022) Identifying novel genetic loci associated with polycystic ovary syndrome based on its shared genetic architecture with type 2 diabetes. Front Genet 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2022.905716\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.905716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrebs K, Milani L (2023) Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery. Annu Rev Pharmacol Toxicol 63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-pharmtox-051421-111324\u003c/span\u003e\u003cspan address=\"10.1146/annurev-pharmtox-051421-111324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReay WR, Cairns MJ (2021) Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41576-021-00387-z\u003c/span\u003e\u003cspan address=\"10.1038/s41576-021-00387-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalik MA, Faraone SV, Michoel T, Haavik J (2023) Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders. Pharmacol Ther 250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pharmthera.2023.108530\u003c/span\u003e\u003cspan address=\"10.1016/j.pharmthera.2023.108530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBovijn J, Krebs K, Chen CY, Boxall R, Censin JC, Ferreira T et al (2020) Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics. Sci Transl Med 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/scitranslmed.aay6570\u003c/span\u003e\u003cspan address=\"10.1126/scitranslmed.aay6570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParker N, Koch E, Shadrin AA, Fuhrer J, Hindley GFL, Stinson S et al (2024) : Leveraging the Genetics of Psychiatric Disorders to Prioritize Potential Drug Targets and Compounds. \u003cem\u003emedRxiv\u003c/em\u003e 2024.09.24.24314069.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmeland OB, Frei O, Shadrin A, O\u0026rsquo;Connell K, Fan CC, Bahrami S et al (2020) Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet 139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00439-019-02060-2\u003c/span\u003e\u003cspan address=\"10.1007/s00439-019-02060-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connell KS, Koch E, Lenk H\u0026Ccedil;, Akkouh IA, Hindley G, Jaholkowski P et al (2023) Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia. Psychiatry Res 325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.psychres.2023.115217\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2023.115217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR et al (2013) Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate. PLoS Genet 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgen.1003455\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1003455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOchoa D, Hercules A, Carmona M, Suveges D, Baker J, Malangone C et al (2023) The next-generation Open Targets Platform: reimagined, redesigned, rebuilt. Nucleic Acids Res 51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkac1046\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac1046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M (2025) KEGG: biological systems database as a model of the real world. Nucleic Acids Res 53:D672\u0026ndash;D677\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatanabe K, Taskesen E, Van Bochoven A, Posthuma D (2017) Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-017-01261-5\u003c/span\u003e\u003cspan address=\"10.1038/s41467-017-01261-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCannon M, Stevenson J, Stahl K, Basu R, Coffman A, Kiwala S et al (2024) DGIdb 5.0: rebuilding the drug\u0026ndash;gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Res 52:D1227\u0026ndash;D1235\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTyrmi JS, Arffman RK, Pujol-Gualdo N, Kurra V, Morin-Papunen L, Sliz E et al (2022) Leveraging Northern European population history: novel low-frequency variants for polycystic ovary syndrome. Hum Reprod 37:352\u0026ndash;365\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAuton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR, Chakravarti A et al (2015) A global reference for human genetic variation. Nature 526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature15393\u003c/span\u003e\u003cspan address=\"10.1038/nature15393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBohrer CH, Fursova NA, Larson DR (2024) Enhancers: A Focus on Synthetic Biology and Correlated Gene Expression. ACS Synth Biol 13:3093\u0026ndash;3108\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmitt AD, Hu M, Jung I, Xu Z, Qiu Y, Tan CL et al (2016) A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome. Cell Rep 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.celrep.2016.10.061\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2016.10.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLieberman-Aiden E, Van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A et al (2009) : Comprehensive mapping of long-range interactions reveals folding principles of the human genome. \u003cem\u003eScience (80-)\u003c/em\u003e 326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1181369\u003c/span\u003e\u003cspan address=\"10.1126/science.1181369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Song F, Zhang B, Zhang L, Xu J, Kuang D et al (2018) The 3D Genome Browser: A web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol 19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-018-1519-9\u003c/span\u003e\u003cspan address=\"10.1186/s13059-018-1519-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinn EH, Misteli T (2019) Molecular basis and biological function of variability in spatial genome organization. Science 365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aaw9498\u003c/span\u003e\u003cspan address=\"10.1126/science.aaw9498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRocha PP, Raviram R, Bonneau R, Skok JA (2015) Breaking TADs: Insights into hierarchical genome organization. Epigenomics 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2217/epi.15.25\u003c/span\u003e\u003cspan address=\"10.2217/epi.15.25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMudge JM, Carbonell-Sala S, Diekhans M, Martinez JG, Hunt T, Jungreis I et al (2025) GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Res 53:D966\u0026ndash;D975\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connell KS, Koromina M, van der Veen T, Boltz T, David FS, Yang JMK et al (2025) Genomics yields biological and phenotypic insights into bipolar disorder. Nature. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-024-08468-9\u003c/span\u003e\u003cspan address=\"10.1038/s41586-024-08468-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaholkowski P, Hindley GFL, Shadrin AA, Tesfaye M, Bahrami S, Nerhus M et al (2023) Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci. Schizophr Bull. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/schbul/sbad063\u003c/span\u003e\u003cspan address=\"10.1093/schbul/sbad063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFominykh V, Shadrin AA, Jaholkowski PP, Bahrami S, Athanasiu L, Wightman DP et al (2023) Shared genetic loci between Alzheimer\u0026rsquo;s disease and multiple sclerosis: Crossroads between neurodegeneration and immune system. Neurobiol Dis 183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2023.106174\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2023.106174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTesfaye M, Jaholkowski P, Hindley GFL, Shadrin AA, Rahman Z, Bahrami S et al (2023) Shared genetic architecture between irritable bowel syndrome and psychiatric disorders reveals molecular pathways of the gut-brain axis. Genome Med 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13073-023-01212-4\u003c/span\u003e\u003cspan address=\"10.1186/s13073-023-01212-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDay F, Karaderi T, Jones MR, Meun C, He C, Drong A et al (2018) Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgen.1007813\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1007813\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu X, Han Y, Feng Y, Shan Y, Liu C, Wang K et al (2025) Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis. Sci Rep 15:2970\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu M, Zhang Y, Li X, Cui P, Li J, Br\u0026auml;nnstr\u0026ouml;m M et al (2020) Alterations of endometrial epithelial-mesenchymal transition and MAPK signalling components in women with PCOS are partially modulated by metformin in vitro. Mol Hum Reprod 26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/molehr/gaaa023\u003c/span\u003e\u003cspan address=\"10.1093/molehr/gaaa023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDo Prado CH, Rizzo LB, Wieck A, Lopes RP, Teixeira AL, Grassi-Oliveira R, Bauer ME (2013) Reduced regulatory T cells are associated with higher levels of Th1/TH17 cytokines and activated MAPK in type 1 bipolar disorder. Psychoneuroendocrinology 38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.psyneuen.2012.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2012.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerridge MJ (2014) Calcium signalling and psychiatric disease: Bipolar disorder and schizophrenia. Cell Tissue Res 357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00441-014-1806-z\u003c/span\u003e\u003cspan address=\"10.1007/s00441-014-1806-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong Z, Wang Y, Zhang F, Yao F, Sun C (2019) Calcium signaling pathways: Key pathways in the regulation of obesity. Int J Mol Sci 20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms20112768\u003c/span\u003e\u003cspan address=\"10.3390/ijms20112768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchultze SM, Hemmings BA, Niessen M, Tschopp O (2012) PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis, 2012/01/19. Expert Rev Mol Med 14:e1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgnihotri S, Wolf A, Picard D, Hawkins C, Guha A (2009) GATA4 is a regulator of astrocyte cell proliferation and apoptosis in the human and murine central nervous system. Oncogene 28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/onc.2009.159\u003c/span\u003e\u003cspan address=\"10.1038/onc.2009.159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAfouda BA (2022) Towards Understanding the Gene-Specific Roles of GATA Factors in Heart Development: Does GATA4 Lead the Way? Int J Mol Sci 23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23095255\u003c/span\u003e\u003cspan address=\"10.3390/ijms23095255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadua MB, Fox SC, Jiang T, Morse DA, Tevosian SG (2014) Simultaneous gene deletion of Gata4 and Gata6 leads to early disruption of follicular development and germ cell loss in the murine ovary. Biol Reprod 91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1095/biolreprod.113.117002\u003c/span\u003e\u003cspan address=\"10.1095/biolreprod.113.117002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawson MA, Whyte DB, Mellon PL (1996) GATA Factors Are Essential for Activity of the Neuron-Specific Enhancer of the Gonadotropin-Releasing Hormone Gene. Mol Cell Biol 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mcb.16.7.3596\u003c/span\u003e\u003cspan address=\"10.1128/mcb.16.7.3596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee HJ, Dreyfus C, DiCicco-Bloom E (2016) Valproic acid stimulates proliferation of glial precursors during cortical gliogenesis in developing rat. Dev Neurobiol 76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/dneu.22359\u003c/span\u003e\u003cspan address=\"10.1002/dneu.22359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR\u0026oslash;devand L, Bahrami S, Frei O, Chu Y, Shadrin A, O\u0026rsquo;Connell KS et al (2021) Extensive bidirectional genetic overlap between bipolar disorder and cardiovascular disease phenotypes. Transl Psychiatry 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-021-01527-z\u003c/span\u003e\u003cspan address=\"10.1038/s41398-021-01527-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiller JK, Jangmo A, Tesli MS, Jaholkowski PP, Hoseth EZ, Steen NE, Haram M (2023) Lipid Biomarker Research in Bipolar Disorder: A Scoping Review of Trends, Challenges, and Future Directions. Biol Psychiatry Global Open Sci 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bpsgos.2023.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.bpsgos.2023.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlintborg D, Rubin KH, Nybo M, Abrahamsen B, Andersen M (2018) Cardiovascular disease in a nationwide population of Danish women with polycystic ovary syndrome. Cardiovasc Diabetol 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12933-018-0680-5\u003c/span\u003e\u003cspan address=\"10.1186/s12933-018-0680-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOllila MM, Arffman RK, Korhonen E, Morin-Papunen L, Franks S, Junttila J, Piltonen TT (2023) Women with PCOS have an increased risk for cardiovascular disease regardless of diagnostic criteria\u0026mdash;a prospective population-based cohort study. Eur J Endocrinol 189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ejendo/lvad077\u003c/span\u003e\u003cspan address=\"10.1093/ejendo/lvad077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChakraborty A, Wakamiya M, Venkova-Canova T, Pandita RK, Aguilera-Aguirre L, Sarker AH et al (2015) Neil2-null mice accumulate oxidized DNA bases in the transcriptionally active sequences of the genome and are susceptible to innate inflammation. J Biol Chem 290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M115.658146\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M115.658146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayes MG, Urbanek M, Ehrmann DA, Armstrong LL, Lee JY, Sisk R et al (2015) Genome-wide association of polycystic ovary syndrome implicates alterations in gonadotropin secretion in European ancestry populations. Nat Commun 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms8502\u003c/span\u003e\u003cspan address=\"10.1038/ncomms8502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLogan RW, McClung CA (2016) Animal models of bipolar mania: The past, present and future. Neuroscience 321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroscience.2015.08.041\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroscience.2015.08.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHildrestrand GA, Rolseth V, Kunath N, Suganthan R, Jensen V, Bugaj AM et al (2021) NEIL1 and NEIL2 DNA glycosylases modulate anxiety and learning in a cooperative manner in mice. Commun Biol 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42003-021-02864-x\u003c/span\u003e\u003cspan address=\"10.1038/s42003-021-02864-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZandi PP, Jaffe AE, Goes FS, Burke EE, Collado-Torres L, Huuki-Myers L et al (2022) Amygdala and anterior cingulate transcriptomes from individuals with bipolar disorder reveal downregulated neuroimmune and synaptic pathways. Nat Neurosci 25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41593-022-01024-6\u003c/span\u003e\u003cspan address=\"10.1038/s41593-022-01024-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchiavi S, La Rosa P, Petrillo S, Carbone E, D\u0026rsquo;Amico J, Piemonte F, Trezza V (2022) N-Acetylcysteine Mitigates Social Dysfunction in a Rat Model of Autism Normalizing Glutathione Imbalance and the Altered Expression of Genes Related to Synaptic Function in Specific Brain Areas. Front Psychiatry 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyt.2022.851679\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2022.851679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaur S, Archer KJ, Devi MG, Kriplani A, Strauss JF, Singh R (2012) Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis. J Clin Endocrinol Metab 97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1210/jc.2011-3441\u003c/span\u003e\u003cspan address=\"10.1210/jc.2011-3441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCzibere L, Baur LA, Wittmann A, Gemmeke K, Steiner A, Weber P et al (2011) Profiling trait anxiety: Transcriptome analysis reveals cathepsin B (Xtsb) as a novel candidate gene for emotionality in mice. PLoS ONE 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0023604\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0023604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoon JY, Szwajcer D, Ishdorj G, Benjaminson P, Xiao W, Kumar R et al (2013) Synergistic apoptotic response between valproic acid and fludarabine in chronic lymphocytic leukaemia (CLL) cells involves the lysosomal protease cathepsin B. Blood Cancer J 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/bcj.2013.50\u003c/span\u003e\u003cspan address=\"10.1038/bcj.2013.50\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eComan D, Vissers LELM, Riley LG, Kwint MP, Hauck R, Koster J et al (2018) Squalene Synthase Deficiency: Clinical, Biochemical, and Molecular Characterization of a Defect in Cholesterol Biosynthesis. Am J Hum Genet 103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2018.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2018.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu J, Xie X, Ma Y, Yang Y, Wang C, Xia G et al (2022) Effects and potential mechanism of Ca2+/calmodulin-dependent protein kinase II pathway inhibitor KN93 on the development of ovarian follicle. Int J Mol Med 50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijmm.2022.5177\u003c/span\u003e\u003cspan address=\"10.3892/ijmm.2022.5177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira MAR, O\u0026rsquo;Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L et al (2008) Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.209\u003c/span\u003e\u003cspan address=\"10.1038/ng.209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSklar P, Smoller JW, Fan J, Ferreira MAR, Perlis RH, Chambert K et al (2008) Whole-genome association study of bipolar disorder. Mol Psychiatry 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sj.mp.4002151\u003c/span\u003e\u003cspan address=\"10.1038/sj.mp.4002151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen O, Coombes BJ, Pazdernik V, Gisabella B, Hartley J, Biernacka JM et al (2024) : Differential Serum Levels of CACNA1C, Circadian Rhythm and Stress Response Molecules in Subjects with Bipolar Disorder: Associations with Genetic and Clinical Factors. \u003cem\u003emedRxiv\u003c/em\u003e 2024.04.11.24305678\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison PJ, Husain SM, Lee H, Los Angeles A, De, Colbourne L, Mould A et al (2022) CACNA1C (CaV1.2) and other L-type calcium channels in the pathophysiology and treatment of psychiatric disorders: Advances from functional genomics and pharmacoepidemiology. Neuropharmacology 220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuropharm.2022.109262\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropharm.2022.109262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu H-H, Zhu Q, Liang N, Xiang Y, Xu T-Y, Huang Z-C et al (2024) CISD2 regulates oxidative stress and mitophagy to maintain the balance of the follicular microenvironment in PCOS. Redox Rep 29:2377870\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong S, Tan Y (2019) Expression of FKBP52 in the ovaries of PCOS rats. Int J Mol Med 43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijmm.2018.3998\u003c/span\u003e\u003cspan address=\"10.3892/ijmm.2018.3998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeon H, Choi Y, Br\u0026auml;nnstr\u0026ouml;m M, Akin JW, Curry TE, Jo M (2023) Cortisol/glucocorticoid receptor: a critical mediator of the ovulatory process and luteinization in human periovulatory follicles. Hum Reprod 38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/humrep/dead017\u003c/span\u003e\u003cspan address=\"10.1093/humrep/dead017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSyed S, Gragnoli C (2024) The glucocorticoid receptor gene (NR3C1) is linked to and associated with polycystic ovarian syndrome in Italian families. J Ovarian Res 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13048-023-01329-5\u003c/span\u003e\u003cspan address=\"10.1186/s13048-023-01329-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavies TH, Ning YM, S\u0026aacute;nchez ER (2002) A new first step in activation of steroid receptors. Hormone-induced switching of FKBP51 and FKBP52 immunophilins. J Biol Chem 277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.C100531200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.C100531200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWochnik GM, R\u0026uuml;egg J, Abel GA, Schmidt U, Holsboer F, Rein T (2005) FK506-binding proteins 51 and 52 differentially regulate dynein interaction and nuclear translocation of the glucocorticoid receptor in mammalian cells. J Biol Chem 280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M407498200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M407498200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzczepankiewicz D, Narożna B, Celichowski P, Sakrajda K, Kołodziejski P, Banach E et al (2021) Genes involved in glucocorticoid receptor signalling affect susceptibility to mood disorders. World J Biol Psychiatry 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15622975.2020.1766109\u003c/span\u003e\u003cspan address=\"10.1080/15622975.2020.1766109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDebs SR, Rothmond DA, Zhu Y, Weickert CS, Purves-Tyson TD (2024) Molecular evidence of altered stress responsivity related to neuroinflammation in the schizophrenia midbrain. J Psychiatr Res 177:118\u0026ndash;128\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStener-Victorin E, Padmanabhan V, Walters KA, Campbell RE, Benrick A, Giacobini P et al (2020) Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome. Endocr Rev 41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1210/endrev/bnaa010\u003c/span\u003e\u003cspan address=\"10.1210/endrev/bnaa010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bipolar disorder, valproate, polycystic ovary syndrome, polycystic ovarian syndrome, comorbidity, genetic","lastPublishedDoi":"10.21203/rs.3.rs-7629869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7629869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWomen with bipolar disorder (BIP) have a higher risk of developing polycystic ovary syndrome (PCOS). Shared genetic architecture may underlie this comorbidity. Valproate, a mood-stabilizer commonly used to treat BIP, increases the risk of PCOS. Still, the mechanism underlying PCOS in BIP remains unknown. Here, we aimed to identify genetic variants shared between BIP and PCOS, as well as their interaction with valproate. We used the results of large-scale genome-wide association studies of BIP (41,510 cases and 354,340 controls), and PCOS (3,609 cases and 229,788 controls). Using conditional false discovery rate, we discovered genetic variants jointly associated with BIP and PCOS. Gene mapping of identified variants was performed using the Open Targets platforms. We analyzed the tissue-specific expression, interaction with valproate, and involvement in biological pathways of the mapped genes. We identified two loci shared between BIP and PCOS. Among the 10 genes mapped to the locus on chromosome 8:11455262, \u003cem\u003eGATA4\u003c/em\u003e, \u003cem\u003eNEIL2\u003c/em\u003e, and \u003cem\u003eFDFT1\u003c/em\u003e showed expression profiles suggesting their role in the observed comorbidity. Mapped to the locus on chromosome 12:2499849, \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eFKBP4\u003c/em\u003e, \u003cem\u003eDCP1B\u003c/em\u003e, and \u003cem\u003eITFG2\u003c/em\u003e are expressed in both the ovaries and the brain. \u003cem\u003eCACNA1C\u003c/em\u003e expression is affected by valproate, and \u003cem\u003eCACNA1C\u003c/em\u003e plays a role in biological pathways involving other valproate-affected genes. We identified shared genetic underpinnings of BIP and PCOS, and implicated genes which may explain the biological mechanisms of the comorbidity between these disorders and a potential mechanism for the role of valproate.\u003c/p\u003e","manuscriptTitle":"Genomic relationship between polycystic ovary syndrome and bipolar disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:49:47","doi":"10.21203/rs.3.rs-7629869/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58cfd39d-06d4-4a46-b19b-7ce59f830e7d","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T07:49:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:49:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7629869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7629869","identity":"rs-7629869","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}