Genome-wide study of somatic symptom and related disorders identifies novel genomic loci and map genetic architecture

Genome-wide study of somatic symptom and related disorders identifies novel genomic loci and map genetic architecture | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genome-wide study of somatic symptom and related disorders identifies novel genomic loci and map genetic architecture View ORCID Profile Vera Fominykh , Piotr Jaholkowski , View ORCID Profile Alexey A. Shadrin , View ORCID Profile Elise Koch , View ORCID Profile Laura B. Luitva , View ORCID Profile Dorte H. Mikkelsen , View ORCID Profile Mischa Lundberg , View ORCID Profile Ole Birger Pedersen , View ORCID Profile Sisse Rye Ostrowski , View ORCID Profile Christian Erikstrup , View ORCID Profile Maria Didriksen , View ORCID Profile Christina Mikkelsen , Erik Sørensen , View ORCID Profile Henrik Ullum , View ORCID Profile Mie Topholm Bruun , View ORCID Profile Bitten Aagaard , View ORCID Profile Kaarina Kowalec , View ORCID Profile Robert Karlsson , Håkan Karlsson , View ORCID Profile Christina Dalman , View ORCID Profile Pravesh Parekh , View ORCID Profile Viktoria Birkenæs , View ORCID Profile Yury Seliverstov , View ORCID Profile Bernhard Landwehrmeyer , View ORCID Profile Ida E. Sønderby , DBDS genetic consortium , Estonian Biobank research team , View ORCID Profile Olav B. Smeland , View ORCID Profile Kevin S. O’Connell , View ORCID Profile Lu Yi , Patrick F. Sullivan , View ORCID Profile Thomas M. Werge , View ORCID Profile Lili Milani , View ORCID Profile Ole A. Andreassen doi: https://doi.org/10.1101/2025.07.16.25331639 Vera Fominykh 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vera Fominykh For correspondence: vera.fominykh{at}medisin.uio.no ole.andreassen{at}medisin.uio.no Piotr Jaholkowski 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexey A. Shadrin 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 2 Section for Precision Psychiatry, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexey A. Shadrin Elise Koch 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elise Koch Laura B. Luitva 3 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia 4 Institute of Mathematics and Statistics, University of Tartu , Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura B. Luitva Dorte H. Mikkelsen 5 Institute of Biological Psychiatry , Mental Health Services Copenhagen, Copenhagen University Hospital , Copenhagen, Denmark 6 iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dorte H. Mikkelsen Mischa Lundberg 5 Institute of Biological Psychiatry , Mental Health Services Copenhagen, Copenhagen University Hospital , Copenhagen, Denmark 6 iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mischa Lundberg Ole Birger Pedersen 7 Department of Clinical Immunology, Zealand University Hospital , Køge, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ole Birger Pedersen Sisse Rye Ostrowski 8 Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet , Copenhagen, Denmark 9 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sisse Rye Ostrowski Christian Erikstrup 10 Department of Clinical Immunology, Aarhus University Hospital , Aarhus, Denmark & Department of Clinical Medicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Erikstrup Maria Didriksen 8 Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Didriksen Christina Mikkelsen 8 Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christina Mikkelsen Erik Sørensen 8 Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henrik Ullum 11 Statens Serum Institute , Copenhagen, DK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henrik Ullum Mie Topholm Bruun 12 Department of Clinical Immunology, Odense University Hospital , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mie Topholm Bruun Bitten Aagaard 13 Department of Clinical Immunology, Aalborg University Hospital , Aalborg, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bitten Aagaard Kaarina Kowalec 14 College of Pharmacy and Department of Biochemistry & Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba , Canada 15 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kaarina Kowalec Robert Karlsson 15 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robert Karlsson Håkan Karlsson 16 Department of Neuroscience, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christina Dalman 17 Department of Public Health Sciences, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christina Dalman Pravesh Parekh 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 18 Center for Multimodal Imaging and Genetics, J. Craig Venter Institute , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pravesh Parekh Viktoria Birkenæs 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Viktoria Birkenæs Yury Seliverstov 19 Department of Neurology, Ulm University , Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yury Seliverstov Bernhard Landwehrmeyer 19 Department of Neurology, Ulm University , Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bernhard Landwehrmeyer Ida E. Sønderby 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 2 Section for Precision Psychiatry, Oslo University Hospital , Oslo, Norway 20 Department of Medical Genetics, Oslo University Hospital , Oslo, Norway 21 KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ida E. Sønderby 3 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Olav B. Smeland 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 2 Section for Precision Psychiatry, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Olav B. Smeland Kevin S. O’Connell 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kevin S. O’Connell Lu Yi 15 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lu Yi Patrick F. Sullivan 15 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden 22 Center for Psychiatric Genetics, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas M. Werge 5 Institute of Biological Psychiatry , Mental Health Services Copenhagen, Copenhagen University Hospital , Copenhagen, Denmark 6 iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research , Denmark 23 Department of Clinical Medicine, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas M. Werge Lili Milani 3 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lili Milani Ole A. Andreassen 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 2 Section for Precision Psychiatry, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ole A. Andreassen For correspondence: vera.fominykh{at}medisin.uio.no ole.andreassen{at}medisin.uio.no Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Somatic symptom and related disorders (SSRD) are characterized by a mixture of neurological and psychiatric features and include functional neurological (FND) and somatic symptom disorders (SomD). While these complex neuropsychiatric disorders show evidence of genetic susceptibility, there are no genome-wide association studies (GWAS) of SSRD, and the heritability is unknown. We did a GWAS of a total of 22,203 patients with SSRD, and 1,831,107 controls of European ancestry. We identified one genome-wide significant locus (chromosome 8:65565084) in SSRD, and one additional locus (chromosome 16:49074278) in the SomD subgroup (n cases = 18,536). The observed-scale SNP heritability was estimated to be 7.3 % for SSRD, 15.7 % for FND and 7.7 % for SomD. FND and SomD were strongly genetically correlated (rg=0.94, SE=0.11, p=3.9E-18). SSRD showed significant genetic correlation with psychiatric disorders (highest with anxiety, post-traumatic stress disorders, depression, rg=0.3- 0.8), neurological disorders (migraine, chronic pain, rg=0.4-0.6) and immune-related diseases (rg=0.2-0.3). Functional follow-up analysis of SSRD loci implicated the genes CYP7B1, BHLHE22, and CBLN1, which are involved in metabolic and brain-related processes, suggesting common underlying pathways. We identified genomic loci associations with SSRD and showed strong genetic correlation between FND and SomD and with neurological and psychiatric disorders, as well as immune-related diseases. The current findings highlight shared underlying pathophysiological processes between SSRD diagnostic categories. Introduction Somatic symptom and related disorders (SSRD), including functional neurological disorders (FND) and somatoform disorders (SomD), are highly prevalent [Finkelstein SA. et al., 2025] and characterised by somatic symptoms associated with significant distress or impairment. These disorders involve a complex interplay of psychological, social, and biological factors, and often co-occurring mental health conditions. Despite some recent advances in understanding their pathophysiology, the disease mechanisms remain unclear [Galli S. et al., 2020, Pellicciari R. et al., 2014; Spagnolo P. et al., 2020, 2023]. The SSRD encompass FND and SomD, which are sometimes placed between the neurological and psychiatric areas of expertise and have been previously defined as “non- organic pathology” [Stone J. et al, 2017; Bell W. et al., 2020]. The FND are characterized by a range of clinical symptoms that mimic neurological conditions such as tremor, dystonia, seizures, and cognitive dysfunction, which often present together in complex combinations. They develop due to dysfunction of certain brain networks in the absence of identifiable organic pathology [Hallett M. et al.; 2022, Stone J. et al, 2017; Edwards MJ. et al., 2023]. SomD or somatic symptom disorders (DSM-5) are characterized by physical symptoms that are distressing and affect daily life and cannot be explained by coexisting organic conditions. These symptoms can manifest in various ways, including pain, fatigue, and gastrointestinal problems, with an impact on the quality of life [Stone J. et al., 2020]. Although SSRDs are the second most common reason for outpatient neurology consultations [Carson A. et al., 2011], have a significant impact on healthcare and societal costs [Carson A. et al., 2016], and have been described as a “crisis for neurology” [Stone J. et al., 2010; Perjoc RS. et al., 2023], the pathobiological underpinnings of these disorders are still mainly unknown [Serranová T. et al., 2024]. Brain imaging and neurophysiology have provided insights into the mechanisms behind the symptoms [Stone J. et al., 2014; Levenson et al., 2024], highlighting dysfunction in multimodal neuronal integration of information, which partially overlaps with psychiatric disorders [Patron VG. et al., 2022; Burton C. et al., 2020]. The overlapping clinical characteristics of SomD and FND indicate shared pathophysiology, and they are hypothesized to be genetically related [Patron VG. et al, 2022; Helgeland H. et al., 2025 ; Burton C. et al., 2020; Kurki M. et al., 2023, https://risteys.finngen.fi/endpoints/ ]. This is in line with the overlapping genetic architecture of neurological and psychiatric disorders [Smeland OB. et al, 2025]. The role of genetics in SSRD remains unclear. Some studies suggest that a positive family history correlates with higher morbidity risk among relatives of those with FND [Stamelou M. et al., 2013; Ljungberg L., 1957 ]. The existing genetic studies of SSRD [Asadi- Pooya AA. et al, 2016] are based on small sample sizes (n<100) and candidate gene approaches [Spagnola P. et al., 2020, 2023; Weber S. et al., 2024; Leu C. et al., 2020; Apazoglou K. et al., 2018; Jungilligens J. et al., 2022]. In fact, the number of studies on SSRD genetics is realtively low compared to other disorders (see Supplementary (Suppl.) Fig. 1 ). No adequately powered genome-wide association studies (GWAS) have been conducted in SSRD or subtypes FND and SomD: the SNP heritability remains unknown, and the underlying molecular-genetic pathways have not been identified yet. Download figure Open in new tab Fig. 1. Manhattan plot for the genome-wide association analysis (GWAS) of the combined meta-nalysis (purple plot) on somatic-symptoms related disorders (SSRD, combined functional neurological disorders (FND) and somatoform disorders (SomD)), and the separate GWAS on SomD (green plot). −log10 p -values are plotted for all variants across chromosomes 1–22. The bold dots indicate loci with a lead variant genome-wide association study p -value of < 5×10 −8 . The grey line indicates the threshold for genome-wide significance ( p -value of 5×10 −8 ). In the current study, we performed a GWAS meta-analysis involving a total of 22,203 SSRD patients, along with 1,831,107 controls. We aimed to identify genomic loci associated with these phenotypes, explore the genetic correlation and heritability, investigate potential genetic links with psychiatric, neurological, and somatic conditions, and identify molecular genetic pathways with functional annotation methods. Materials and methods Cohorts The GWAS discovery meta-analysis comprised n=22,203 SSRD patients and 1,831,107 controls across seven cohorts. The cases included n=4,269 FND patients and n=18,536 SomD patients. All participants were of European ancestry ( Table 1 ). We meta-analysed GWAS data from five Nordic countries – Norway, Denmark, Estonia, Iceland, and Finland, along with the United Kingdom (UK), and the USA. The dataset included the Norwegian Mother, Father and Child Cohort Study (MoBa, Norway, Magnus P. et al., 2016 ), Copenhagen Hospital Biobank (CHB) and The Danish Blood Donor Study (DBDS) (Denmark, Erikstrup C. et al., 2023), Estonian Biobank (Estonia, Milani L. et al., 2024), deCODE (Iceland, Jónsson H. et al., 2017 ), FinnGen (Finland, Kurki M. et al., 2023), UK Biobank (UKB, United Kingdom, Sudlow C. et al., 2015; Bycroft C. et al, 2018 ), and All of Us (USA, Ramirez AH. et al., 2022; All of us, 2024). For the replication phase and validation of findings, we used summary statistics from an independent Swedish sample (Sweden Schizophrenia Study, Lichtenstein P. et al., 2006). View this table: View inline View popup Download powerpoint Table 1. Study summary for cases and controls. SSRD cases were defined as having a lifetime main diagnosis according to ICD-10 criteria of FND (F44) or SomD (F45) ( https://www.cdc.gov/nchs/icd/icd-10/ , MOBA, UKB, Danish sample). If it was not possible to specify whether the patient had a primary or secondary diagnosis, we used the criteria that the diagnosis should have been made at a specialized healthcare facility (Estonia) or used available data for diagnosis of interest elsewhere (FinnGene, deCODE, All of Us). Participants from the same cohorts who did not have a primary or secondary diagnosis of FND or SomD were designated as control subjects. Detailed descriptions of the samples are provided in Table 1 and the Suppl. material. We also included information about comorbidity in Suppl. Table 1. For the SSRD sample GWAS analysis, cases were defined as having an FND AND/OR SomD diagnosis, accounting for overlapping comorbidity (except FinnGen, where this information was not available). The regional ethics committee of Norway approved the current study, and local ethics committees approved each of the studies included in the meta-analysis. All participants provided written informed consent. Genotyping, GWAS and meta-analysis The genotyping, imputation and GWAS methodology details for each cohort are available in the Suppl. Inverse variance-based meta-analysis of the seven case-control GWAS cohorts was performed using the METAL software [Willer CJ. et al., 2010]. Genome-wide significance was set at a p -value threshold of 5×10 −8 . For the meta-analysis of SSRD phenotypes, we used the METAL tool with the overlap function “on” to reduce the effect of overlapping subjects [for details, see METAL Documentation - Genome Analysis Wiki ], as the combined FinnGen FND+SomD GWAS with overlap accounting was not available. Before commencing meta- analysis, all GWAS summary statistics underwent uniform quality control and were harmonized using the v1.6.0 cleansumstats pipeline GitHub_BioPsyk/cleansumstats [Gadin JR. et al., 2023], and precimed/python-convert was used for creating Manhattan plots and QQ plots. Markers that exist only in one cohort were excluded from the meta-analysis. We used R (version 4.2.3) and git/bash Linux for simple data processing during data preparation and analysis. All results were provided on the GRCh37 build. Genetic correlation and heritability: LD score regression and SumHer/LD adjusted kinships (LDAK) SNP-based heritability for SSRD, FND, and SomD was calculated using the LD Score regression method [LDSC, Bulik-Sullivan B. et al., 2015] on the observed scale. Liability scale heritability was not estimated, as the population prevalence for SSRD, FND, and SomD has an extensive range. LDSC genetic correlation analysis was performed to investigate a possible genetic overlap between FND and SomD, as well as the phenotypes of interest and other psychiatric, neurological, and immune-mediated disorders. GWAS summary statistics [Suppl. Table 2] were used to calculate genetic correlation between SSRD, FND, SomD, and major psychiatric disorders (bipolar disorder – BD [O’Connell KS. et al., 2025], major depressive disorder – MDD [Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium et al., 2025], schizophrenia – SCZ [Trubetskoy V. et al., 2022]), the most often comorbid psychiatric disorders (anxiety, Purves KL. et al., 2020, attention deficit hyperactivity disorder (ADHD, Demontis D. et al., 2023), post-traumatic stress-related disorder (PTSD, Nievergelt CM. et al., 2024) and traits (neuroticism, Nagel M. et al., 2018), neurological disease (epilepsy, focal and generalised, ILAE, 2023, migraine [Bjornsdottir G. et al., 2023], multiple sclerosis [ International Multiple Sclerosis Genetics Consortium, 2019 ]), chronic widespread pain [Zorina-Lichtenwalter K. et al., 2023], irritable bowel syndrome (IBS, Eijsbouts C. et al., 2021) and immune-related disorders (psoriasis, type 1 diabetes, systemic lupus erythematosus, systemic sclerosis, Sjogren’s syndrome, rheumatoid arthritis, autoimmune thyroid disorders, Crohn’s disease, and ulcerative colitis [for the source Suppl. Table 2, Fominykh V. et al., 2025]). The resulting p -values were Bonferroni corrected for multiple testing (alpha level 0.0011), considering the number of investigated phenotypes. To support the LDSC estimates of the SNP-based heritability for FND and SomD, as well as the correlation between them, additional analysis was performed using the SumHer tool, as implemented in LDAK v6.1 [Speed D. et al., 2019], which allows the user to specify distinct heritability models. We used the Human Default Model with a power parameter set to -0.25, in which the contribution of each SNP to heritability depends on its MAF [Speed D. et al., 2019]. Genotypes of 12,926,669 common variants from 5,000 randomly selected unrelated European individuals from the UK Biobank were used as a reference panel to calculate SNP taggings. Variants that individually explain more than 1% of phenotypic variance were excluded from the analysis (LDAK option: --cutoff 0.01). We also did not apply genomic control (LDAK option: --genomic-control NO) as recommended by the LDAK authors [Speed D et al., 2019]. Follow-up analysis (validation analysis) For validation analysis, we used the Sweden Schizophrenia Study (Suppl.), SSRD n cases = 342, n controls = 11,340, FND: n cases = 87; n controls = 2,239. SomD: n cases = 128; n controls = 6,608. We examined the significance of the identified lead variants and effect direction in this independent Swedish cohort. Post-GWAS functional analysis We applied FUMA v1.6.1 protocol for the identification of genomic loci and gene mapping strategies (positional mapping according to FUMA with 10 kb window size) and supported it by the Open Targets modelling for the annotation [ Ghoussaini M et al., 2021 , database version from October 2022, Watanabe 2017, de Leeuw CA et al., 2015 ]. We applied MAGMA v1.08 with default parameters as implemented in FUMA v1.6.1 [Watanabe 2017, de Leeuw CA et al., 2015 ] to perform gene-set analysis (17023 gene sets from MsigDB v2023.1Hs) and tissue specificity analysis (54 different tissue types from GTEx eQTL v8). Briefly, the meta-analysed summary statistics for SSRD, FND, and SomD GWAS data were used as input. The p -value threshold of 5×10 −8 was used to define lead SNPs. An r 2 of ≥0.6 and a genomic window of ± 250 kb were used to determine linkage disequilibrium (LD) with independent lead SNPs. P -values were corrected using the Bonferroni procedure following the standard protocol in FUMA. Results The present study describes the largest up-to-date GWAS meta-analyses of 22,203 SSRD individuals and 1,831,107 controls ( Table 1 , and Suppl. Table 1 and Suppl. description, QQ plot for all GWASes are available in Suppl. Fig.2 ). Download figure Open in new tab Fig. 2. Genetic correlations between SSRD and psychiatric, neurological and immune-related phenotypes. Genetic correlations of SSRD with 22 phenotypes are shown: major psychiatric disorders (bipolar disorder ― BD, major depressive disorder ― MDD, schizophrenia ― SCZ), most-comorbid psychiatric disorders (anxiety ― ANX, attention deficit hyperactivity disorder ― ADHD, post-traumatic stress disorder ― PTSD), neurological disease (epilepsy, focal ― FE and generalised ― GGE, migraine ― MIG, multiple sclerosis ― MS), neuroticism ― NEU, chronic widespread pain ― CP (as a proxy of fibromyalgia), irritable bowel syndrome ― IBS, and immune-related disorders (psoriasis ― PS, type 1 diabetes ― T1D, systemic lupus erythematosus ― SLE, systemic sclerosis ― SS, Sjögren’s syndrome ― SjS, rheumatoid arthritis ― RA, autoimmune thyroid disorders ― AITD, Crohn’s disease ― CD and ulcerative colitis ― UC). For each phenotype, the genetic correlation (in numbers) is shown. Genetic correlations significant after Bonferroni correction ( p- value <0.05/67) are marked with an asterisk. Detailed information is available in the Suppl. Table 3. We performed a meta-analysis of SSRD, combining FND and SomD. One significant SNP was identified on chromosome 8 (rs7004685, Chr8:65565084) and mapped to the cytochrome P450 family 7 subfamily B member 1 gene ( CYP7B1) and the basic helix-loop- helix family member e22 ( BHLHE22) gene using the FUMA and OpenTarget approaches. In the subgroup SomD meta-analysis (n cases = 18,536, n controls = 1,819,203), two significant SNPs were identified, one lead SNP rs7817744, Chr8:65594917 (with a high LD – D value: 0.9781, R² 0.8977 – with rs7004685 for combined phenotype, mapped to the same genes, CYP7B1 and BHLHE22) and second one rs2908882, Chr16:49074278 (see Table 2 ., Fig. 1 , and Suppl. Fig. 4. for locus zoom plot). The gene mapped by the FUMA and the OpenTarget for rs2908882 was the cerebellin 1 precursor gene ( CBLN1) . In the subgroup FND meta- analysis (n cases = 4,269, n controls = 1,852,274), no significant SNPs were identified (the details are described in the Suppl., Fig 3A and B.). View this table: View inline View popup Download powerpoint Table 2. Genomic loci associated with SSRD and SomD phenotypes ( p -value < 5×10 −8 ). Chr — chromosome number, BP — base pair position on build GRCh37 with start and stop end, lead SNP — lead single-nucleotide polymorphism, CADD score — Combined Annotation Dependent Depletion Score. *The SNPs on the CHR8 in the high LD between each other. LDSC analysis revealed a strong correlation between FND and SomD (rg=0.942, SE=0.11, p= 3.85×10 -18 ). The observed genetic correlation between FND and SomD did not differ significantly from 1 ( p =0.299), supporting the combined meta-analysis and indicating a similar genetic background for both disorders. The observed-scale SNP heritability based on LDSC results for SSRD was 7.3% (SE=0.63), FND was 15.7% (SE=1) and SomD was 7.7% (SE=1). The SumHer (LDAK) tool revealed similar heritability for FND (was 15 %; SE=0.02), SomD (10 %; SE=0.01) and genetic correlation (rg=0.97, SE=0.08, p=1.83E-30) as LDSC. We calculated the genetic correlation between SSRD, their subtypes, and psychiatric disorders and traits, neurological disorders, and immune-linked disorders (for the complete list of disorders see Fig. 2 , Suppl. Fig. 5, and Suppl. Table 2 and 3). After Bonferroni correction for multiple testing, we found significant genetic correlation between SSRD and major psychiatric disorders (MDD, BD, SCZ), as well as anxiety, ADHD, and PTSD. Also, a significant correlation with migraine, IBS, and chronic pain was found. In addition, SSRD had significant correlation with autoimmune thyroiditis, multiple sclerosis, systemic scleroderma, and Sjögren’s syndrome ( Fig. 2 ). MAGMA: gene-set and tissue expression enrichment analyses MAGMA gene-set analysis did not reveal significantly enriched gene-sets or pathways (Suppl. Fig. 6) within all GWAS for the studied phenotypes. Validation analysis In the validation cohort (Suppl. Table 4) two of the four genome-wide significant SNPs were in the same effect direction as the primary analyses. However, none of the identified loci were statistically significant, likely due to the small number of cases and controls in this cohort. Discussion We have conducted the first GWAS meta-analysis on SSRD, identified the first genome-wide significant loci (chromosome 8), and one additional locus on chromosome 16, associated with SomD. We estimated the SNP-based heritability was 7.3 % and showed extensive genetic correlation with psychiatric, neurological, and immune-related diseases, as well as between the subgroups FND and SomD. Together, this indicates that genetics plays important role in the SSRD development, and SSRD has a common genetic background with psychiatric and neurological disorders. For the SSRD meta-analysis, we found one significant SNP on chromosome 8 (rs7004685) that maps to the same genes ( CYP7B1 and BHLHE22 ) as for SomD analysis. The functional analysis revealed that CYP7B1 is involved in the metabolism of neurosteroids, which play a crucial role in modulating synaptic activity, neuroplasticity, and neuroprotection [Stiles AR. et al., 2009; Yantsevich AV. et al., 2014]. Biallelic loss-of-function variants in CYP7B1 were shown to cause hereditary spastic paraplegia type 5 [Prestsæter S. et al., 2020; Parodi L. et al., 2018; Schöls L. et al., 2017] and the SNP rs118111353 in CYP7B1 is thought to be associated with Parkinson disease [Petkova-Kirova P. et al, 2024]. CYP7B1 involvement in the metabolism of neurosteroids might be a possible link between the gene and functional symptoms as the influence of steroids in remodelling of the hippocampus and amygdala which regulate emotional reactivity and stress processing has been previously shown [Brown ES. et al., 1999; Fowler CH. et al., 2021]. The BHLHE22 gene product belongs to the family of transcription factors and is expressed exclusively in the central nervous system and retina [Ge Y. et al., 2023]. Monoallelic and biallelic variants in this gene cause a neurodevelopmental disorder with agenesis of the corpus callosum, intellectual disability, and tone and movement abnormalities [Le C. et al., 2024]. Thus, we speculate that certain genetic variants in BHLHE22 may affect development of cortical circuitry (especially those involving sensory, emotional, and motor processing and integration), predisposing individuals to functional disorders. Overall, our study provides new insights into potential genetic causes of functional disorders, with a possible biological connection to the current pathophysiological framework of these conditions. In the SomD meta-analysis, one additional locus was revealed on chromosome 16: lead SNP rs2908882, the mapped gene CBLN1 . The genetic variant rs2908882 has been identified in complex, non-specific psychiatric conditions [Kurki M. et al., 2023]. This locus was mapped to the CBLN1, which is critical for Purkinje cell synapse formation and maintenance. Additionally, CBLN1 protein product influences synapse formation in cortical and hippocampal cells [McCormick LE. et al., 2022]. Given the cerebellum is believed to contribute to cognitive and emotional processing, CBLN1 -dependent cerebellar dysfunction might hypothetically influence how the brain integrates sensory and emotional processing, leading to the development of functional symptoms. Moreover, according to the 2021 study by Gandhi PJ. et al., 2021, cerebellin 1 plays a crucial role in the mechanism of chronic pain. It is also strongly linked to the repetitive process underlying functional and somatic symptoms. We found little support for previous findings of prior small-scale candidate gene studies. The tryptophan hydroxylase 2 ( TPH2 ) gene [Spagnola P. et al., 2020] had a p -value of 0.18. The TPH1 gene, rs1800532, and the oxytocin receptor (OXTR) gene, rs2254298 and rs53576 [Weber S. et al.; 2024, Jungilligens J. et al., 2022] were all non-significant ( p -values 0.68, 0.53 and 0.32, respectively). The estimated observed-scale SNP heritability was 7.3% for SSRD, and 15.7% for FND and 7.7% for SomD. These values are similar to the heritability estimates for psychiatric disorders such as anxiety disorders, ADHD or MDD, and neurological disorders such as migraine [Smeland OB. et al., 2024]. The level of comorbidity for psychiatric disorders in our study [see Suppl. 1., table 1 ] was in line with previous studies: the most common comorbid psychiatric disorder was MDD, generalized anxiety disorder, and PTSD. Previously, it has been reported that 51% [Thomas M. et al. 2006] to 95% [Feinstein A. et al., 2001; Patron VG. et al, 2023, Carle-Toulemonde G. et al., 2023] of adult FND patients have comorbid psychiatric conditions. FND and SomD were highly genetically correlated (rg=0.94), suggesting a similar genetic susceptibility. Significant high genetic correlations were revealed for SSRD with psychiatric disorders (ADHD, anxiety disorders, BD, SCZ, MDD, PTSD) and neuroticism. Among psychiatric disorders, the correlation was particularly strong with the internalising disorders such as MDD, PTSD and anxiety. These results can support the existing theory of “shared polygenetic vulnerability” in brain-related disorders [ Hindley G. et al, 2022 ; Greenwood TA. et al., 2020; Peterson RE. et al. 2021] and could explain high levels of comorbidity. The high correlation with neuroticism is also quite interesting: many studies implicate that psychopathological traits are highly pleiotropic and indicative for their largely interconnected nature crossing the borders of diagnostic classification [Grotzinger, AD. et al., 2019; Selzam et al., 2018 ]. The psychological studies also showed some common traits with personality traits: there are correlations with autistic traits [Ekanayake V. et al; 2017, McWilliams A et al., 2019 ; González-Herrero B. et al., 2023 ], alexithymia [Demartini B. et al., 2014] and altered emotional processing in people with FND [Pick S. et al., 2019]. A plausible hypothesis behind this significant correlation might be that the individuals with high scores for neuroticism are more likely to experience feelings of anxiety and fear, which can lead to an increased vulnerability to develop functional symptoms [Patron VG. et al, 2023], and our findings can support this psychological concept from a genetic perspective. Taken together, the genetic correlations of FND and SomD show similarities with the genetic continuum of neuropsychiatric disorders. We demonstrated a significant genetic correlation between migraine and SSRD supporting previous findings [Bahrami S. et al., 2021; Smeland OB. et al, 2023; Brainstorm Consortium et al., 2018] about significant genetic correlation of migraine with different internalizing neuropsychiatric disorders (the correlation of migraine with depression was 0.32 which is in line with our data for SSRD) and high comorbidity level [Dresler T. et al., 2019]. This highlights the shared genetic architecture between migraine and SSRD, following their clinical overlap [McCracken HT. et al, 2024]. We also showed a significant correlation of SSRD with autoimmune disorders such as autoimmune thyroiditis, systemic scleroderma and primary Sjögren’s syndrome. This is in line with the recent paper of Joseph A. et al., 2024, which suggested a co-morbidity of FND with autoimmune disorders, and connective tissue disorders and endocrine disorders were more prevalent. These findings are in accordance with the previous studies which showed increased interleukin levels in SSRD patients [van der Feltz- Cornelis C. et al., 2023] as well in line with theory of the low-grade inflammation in functional somatic symptoms [McInnis PM. et al., 2020] which can activate stress system, fostering conditions that facilitate the onset of a functional disorder. This can also be supported by a highly significant genetic correlation between chronic pain and IBS, where the role of neuroinflammation in sensitization of chronic pain is well established [Ji RR. et al., 2018; Schumacher MA., 2024 ; Ishioh M. et al., 2024]. Our study has the strength of a large sample size, that enabled the discovery of the first GWAS significant loci for SSRD and the estimation of the heritability. We acknowledge that the diagnoses were collected from specialist health care diagnostics recorded in national health registries and electronic health records, which can be linked to a broader phenotype compared to prospective clinical assessments. However, for other brain-related diseases, the quality of data in these health registries is acceptable [Chen CY. et al., 2018; Smoller JV. et al., 2018]. Additionally, it was not possible to account for co-morbidities due to a small number of cases in each cohort. Therefore, we minimised this bias by using population controls in our GWAS design. However, we cannot rule out that SSRD comorbidities were excluded from all samples included in the large GWAS of neurological, psychiatric, and immune-related diseases, and thus affecting the genetic correlation results. We acknowledge that our focus was on individuals of European ancestry, mainly from Nordic populations, which may limit the generalizability of our findings. In conclusion, we demonstrated substantial SNP heritability and identified novel disease-related loci for SSRD. We showed significant genetic correlation between the FND and SomD, and between SSRD and psychiatric disorders, migraine and some immune-related diseases. Our findings provide insights into the causes and molecular mechanisms of SSRD, highlighting underlying pathophysiological processes in SSRD and the overlap between diagnostic categories. Together, our findings can form the basis for the development of new therapies and the identification of disease biomarkers. Declaration of Interests O.A.A. has received speaker fees from Lundbeck, Janssen, Otsuka, and Sunovion and is a consultant to Cortechs.ai. and Precision Health. The remaining authors have no conflicts of interest to declare. Data availability and Computational tools GWAS data for meta-analysis will be available upon request, and in the GWAS catalogue after the one-year embargo following this publication. Analyses were performed using existing tools available on: Cleansumstat GitHub_BioPsyk/cleansumstats FUMA https://fuma.ctglab.nl/ LDSC https://github.com/bulik/ldsc SumHer (LDAK) https://dougspeed.com/ MAGMA https://cncr.nl/research/magma/ METAL https://github.com/statgen/METAL Python Convert https://github.com/precimed/python_convert Abbreviation ADHD attention deficit hyperactivity disorder ADHFE1 alcohol dehydrogenase iron containing 1 gene AITD autoimmune thyroiditis ANX anxiety BD bipolar disease BHLHE22 basic helix-loop-helix family member e22 BP base pair position CADD score combined annotation dependent depletion score. CHR chromosome number. CNV copy number variation CHB Copenhagen Hospital Biobank FE epilepsy, focal FND functional neurological disorders FMD functional movement disorders DBDS The Danish Blood Donor Study CBLN1 cerebellin 1 Precursor gene CHR chromosome CD Crohn’s disease CP chronic widespread pain CYP7B1 Cytochrome P450 family 7 subfamily B member 1 gene IBS irritable bowel syndrome GGE epilepsy, generalised GWAS genome-wide association study LD linkage disequilibrium LDSC linkage disequilibrium score regression MDD major depressive disorder MHC major histocompatibility complex MIG migraine MOBA Norwegian Mother, Father and Child Cohort Study MRI magnetic resonance imaging MS multiple sclerosis NEU neuroticism OXTR oxytocin receptor gene PS psoriasis PTSD post-traumatic stress-related disorder RA rheumatoid arthritis SCZ schizophrenia SjS primary Sjögren’s syndrome SLE systemic lupus erythematosus SomD somatoform disorders SS systemic sclerosis SSRD somatic symptom related disorders Suppl. Supplementary T1D type 1 diabetes UC ulcerative colitis WES whole exome sequencing WGS whole genome sequencing Acknowledgements and Funding Many thanks to our patients, who continue to inspire us to learn more about neurology and psychiatry through their complex conditions. We thank the International Parkinson and Movement Disorder Society ( https://www.movementdisorders.org/ ) for their course on Neuropsychiatry in Movement Disorders, which motivated us to perform this analysis. We thank the participants and researchers contributing to the GWAS investigated in the present study. The authors thank the researchers of the International MS Genetics Consortium (IMSGC) https://imsgc.net/ and consortia for access to summary-level data, as well as all participants who provided DNA samples. We want to acknowledge the participants and investigators of the FinnGen study: https://www.finngen.fi/ [Kurki M. et al., 2023]. The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this ongoing cohort study. We thank the Norwegian Institute of Public Health (NIPH) for generating high-quality genomic data. This research is part of the HARVEST collaboration, supported by the Research Council of Norway (#229624). We also thank the NORMENT Centre for providing genotype data, funded by the Research Council of Norway (#223273), South-East Norway Version 7.0 3 Health Authorities, and Stiftelsen Kristian Gerhard Jebsen. We further thank the Center for Diabetes Research, the University of Bergen for providing genotype data and performing quality control and imputation of the data funded by the ERC AdG project SELECTionPREDISPOSED, Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western Norway Health Authorities. This research used data from the UK Biobank, a major biomedical database ( https://www.ukbiobank.ac.uk/ , accession number 27412). We gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health’s All of Us Research Program for making available the participant data examined in this study. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants. We gratefully acknowledge support from the Research Council of Norway (#223273, 296030, 324252, 326813, 334920, 344121), European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions Grant 801133 (Scientia fellowship); the South-Eastern Norway Regional Health Authority (#2020060, IES), KG Jebsen Stiftelsen (SKGJ-MED-021). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 847776 and 964874, EEA grants (EEA-RO-NO-2018-0535, EEA-RO-NO-2018-0573), National Institutes of Health grants U24DA041123 and U24DA055330, NIH 5R01MH124839-02 (PGC4), and the Estonian Research Council (PRG1197). The EstBB part of the research was conducted using the Estonian Center of Genomics/Roadmap II funded by the Estonian Research Council (project number TT17). We would like to thank all participants of the Estonian Biobank who have made this research possible, and the Estonian Biobank Research team – Andres Metspalu, Tõnu Esko, Reedik Mägi, Mait Metspalu, Mari Nelis and Georgi Hudjashov for data collection, genotyping, QC and imputation. Data analysis was carried out in part at the High-Performance Computing Center of the University of Tartu. Individual level data analysis in the Estonian Biobank was carried out under ethical approval 1.1-12/624 (24 March 2020) from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data according to release application 6-7/GI/8746 from the Estonian Biobank from the Estonian Biobank. In the Swedish Schizophrenia Study the computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. We acknowledge the deCODE genetics/Amgen Inc team, Reykjavik, Iceland and Faculty of Medicine, University of Iceland, Reykjavik, Iceland: Ástrós Th. Skúladóttir, G. Bragi Walters, Thorgeir E. Thorgeirsson, Gyda Bjornsdottir, Hreinn Steffanson and Kari Stefansson for thoughtful comments and providing the data. The acknowledgements for danish groups will be provide later. This work was partly performed on the TSD (Tjeneste for Sensitive Data, projects NS9666S, NS9703S) facilities, owned by the University of Oslo and operated and developed by the TSD service group at the University of Oslo, IT Department (USIT), with resources for computation provided by UNINETT Sigma2 — the National Infrastructure for High Performance Computing and Data Storage in Norway. Footnotes DBDS research consortium – detailed list of participants in the Supplementary information. Estonian Biobank research team: Andres Metspalu 3 , Lili Milani 3 , Tõnu Esko 3 , Reedik Mägi 3 , Mait Metspalu 3 , Mari Nelis 3 and Georgi Hudjashov 3 ↵ *** list of participants References 1. Finkelstein SA , Diamond C , Carson A , Stone J . Incidence and prevalence of functional neurological disorder: a systematic review . J Neurol Neurosurg Psychiatry . 2025 :jnnp- 2024-334767. doi: 10.1136/jnnp-2024-334767 . OpenUrl Abstract / FREE Full Text 2. Galli S , Béreau M , Magnin E , Moulin T , Aybek S. Functional movement disorders . Rev Neurol (Paris) . 2020 ;176(4):244-251. doi: 10.1016/j.neurol.2019.08.007 . OpenUrl CrossRef 3. Pellicciari R , Superbo M , Gigante AF , Livrea P , Defazio G . Disease modeling in functional movement disorders . Parkinsonism Relat Disord . 2014 ; 20 ( 11 ): 1287 – 9 . doi: 10.1016/j.parkreldis.2014.09.017 . OpenUrl CrossRef PubMed 4. Spagnolo PA , Norato G , Maurer CW , Goldman D , Hodgkinson C , Horovitz S , Hallett M . Effects of TPH2 gene variation and childhood trauma on the clinical and circuit- level phenotype of functional movement disorders . J Neurol Neurosurg Psychiatry . 2020 ; 91 ( 8 ): 814 – 821 . doi: 10.1136/jnnp-2019-322636 . OpenUrl Abstract / FREE Full Text 5. Spagnolo PA , Johnson K , Hodgkinson C , Goldman D , Hallett M . Methylome changes associated with functional movement/conversion disorder: Influence of biological sex and childhood abuse exposure . Prog Neuropsychopharmacol Biol Psychiatry . 2023 ; 125 : 110756 . doi: 10.1016/j.pnpbp.2023.110756 . OpenUrl CrossRef PubMed 6. Stone J , Carson A . ’Organic’ and ’non-organic’: a tale of two turnips . Pract Neurol . 2017 ; 17 ( 5 ): 417 – 418 . doi: 10.1136/practneurol-2017-001660 . OpenUrl FREE Full Text 7. Bell V , Wilkinson S , Greco M , Hendrie C , Mills B , Deeley Q . What is the functional/organic distinction actually doing in psychiatry and neurology? Wellcome Open Res . 2020 ; 5 : 138 . doi: 10.12688/wellcomeopenres.16022.1 . OpenUrl CrossRef 8. Hallett M , Aybek S , Dworetzky BA , McWhirter L , Staab JP , Stone J . Functional neurological disorder: new subtypes and shared mechanisms . Lancet Neurol . 2022 ; 21 ( 6 ): 537 – 550 . doi: 10.1016/S1474-4422(21)00422-1 . OpenUrl CrossRef PubMed 9. Edwards MJ , Yogarajah M , Stone J . Why functional neurological disorder is not feigning or malingering . Nat Rev Neurol . 2023 ; 19 ( 4 ): 246 – 256 . doi: 10.1038/s41582-022-00765-z . OpenUrl CrossRef PubMed 10. Stone J . Functional neurological disorder 2.0? Brain Commun . 2020 ; 2 ( 2 ):fcaa217. doi: 10.1093/braincomms/fcaa217 . OpenUrl CrossRef 11. Carson A , Stone J , Hibberd C , et al. Disability, distress and unemployment in neurology outpatients with symptoms “unexplained by organic disease.” J Neurol Neurosurg Psychiatry . 2011 Jul ; 82 ( 7 ): 810 – 3 . doi: 10.1136/jnnp.2010.220640 OpenUrl Abstract / FREE Full Text 12. Carson A , Lehn A . Epidemiology . Handbook of Clinical Neurology 2016 ; 139 : 47 – 60 . OpenUrl PubMed 13. Stone J , Carson A , Duncan R , Roberts R , Warlow C , Hibberd C , Coleman R , Cull R , Murray G , Pelosi A , Cavanagh J , Matthews K , Goldbeck R , Smyth R , Walker J , Sharpe M . Who is referred to neurology clinics?--the diagnoses made in 3781 new patients . Clin Neurol Neurosurg . 2010 ;112(9):747-51. doi: 10.1016/j.clineuro.2010.05.011 . OpenUrl CrossRef PubMed 14. Perjoc RS , Roza E , Vladacenco OA , Teleanu DM , Neacsu R , Teleanu RI . Functional Neurological Disorder-Old Problem New Perspective . Int J Environ Res Public Health . 2023 ; 20 ( 2 ): 1099 . doi: 10.3390/ijerph20021099 . OpenUrl CrossRef PubMed 15. Serranová T , Di Vico I , Tinazzi M , Aybek S , Bilic E , Binzer S , Bøen E , Bruggeman A , Bratanov C , Cabreira VRA , Golder D , Dunalska A , Falup-Pecurariu C , Garcin B , Gelauff J , Laffan A , Podnar S , Pareés I , Plender T , Popkirov S , Romanenko V , Schwingenschuh P , Seliverstov Y , Sjöström C , Škorvánek M , Stamelou M , Zailskas D , Edwards MJ , Stone J . Functional neurological disorder in Europe: regional differences in education and health policy . Eur J Neurol . 2024 ; 31 ( 10 ): e16350 . doi: 10.1111/ene.16350 . OpenUrl CrossRef PubMed 16. Stone J , Hallett M , Carson A , Bergen D , Shakir R . Functional disorders in the Neurology section of ICD-11: A landmark opportunity . Neurology . 2014 ; 83 ( 24 ): 2299 – 301 . doi: 10.1212/WNL.0000000000001063 . OpenUrl CrossRef PubMed 17. Levenson , J. L. ( 2024 ). Somatic symptom disorder: Epidemiology, clinical features, and course of illness . In J. Dimsdale. https://www.uptodate.com/contents/somatic-symptom-disorder-epidemiology-clinical-features-and-course-of-illness 18. Patron VG , Rustomji Y , Yip C , Jenkins LM . Psychiatric Comorbidities in Functional Neurologic Symptom Disorder . Pract Neurol (Fort Wash Pa ). 2022 ; 21 ( 3 ): 71 – 75 . OpenUrl PubMed 19. Burton C , Fink P , Henningsen P , Löwe B , Rief W; EURONET-SOMA Group. Functional somatic disorders: discussion paper for a new common classification for research and clinical use . BMC Med . 2020 ; 18 ( 1 ): 34 . doi: 10.1186/s12916-020-1505-4 . OpenUrl CrossRef PubMed 20. ↵ Helgeland H. et al. The term ’functional disorders’ should be revitalised. Tidsskriftet , V 145 , 2025 . doi: 10.4045/tidsskr.24.0573 OpenUrl CrossRef 21. Kurki MI , Karjalainen J , Palta P , et al. FinnGen provides genetic insights from a well- phenotyped isolated population . Nature . 2023 ; 613 (7944): 508 -518. doi: 10.1038/s41586-022-05473-8 . OpenUrl CrossRef PubMed 22. Smeland OB , Kutrolli G , Bahrami S , Fominykh V , Parker N , Hindley GFL , Rødevand L , Jaholkowski P , Tesfaye M , Parekh P , Elvsåshagen T , Grotzinger AD; International Multiple Sclerosis Genetics Consortium (IMSGC); International Headache Genetics Consortium (IHGC); Steen NE, van der Meer D, O’Connell KS, Djurovic S, Dale AM, Shadrin AA, Frei O , Andreassen OA. The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis. medRxiv . 2023 .07.21.23292993. doi: 10.1101/2023.07.21.23292993 . Article in press in Nature Neuroscience. OpenUrl Abstract / FREE Full Text 23. Stamelou M , Cossu G , Edwards MJ , Murgia D , Pareés I , Melis M , Bhatia KP . Familial psychogenic movement disorders . Mov Disord . 2013 ; 28 ( 9 ): 1295 – 8 . doi: 10.1002/mds.25463 . OpenUrl CrossRef PubMed 24. ↵ Ljungberg L . Hysteria; a clinical, prognostic and genetic study . Acta Psychiatr Neurol Scand Suppl . 1957 ; 112 : 1 – 162 . PMID: 13444049 . OpenUrl PubMed 25. Asadi-Pooya AA . Biological underpinnings of psychogenic nonepileptic seizures: directions for future research . Neurol Sci . 2016 ; 37 ( 7 ): 1033 – 8 . doi: 10.1007/s10072-016-2540-y . OpenUrl CrossRef PubMed 26. Weber S , Rey Álvarez LT , Ansede-Bermejo J , Cruz R , Del Real Á , Bühler J , Carracedo Á , Aybek S . The impact of genetic variations in the serotonergic system on symptom severity and clinical outcome in functional neurological disorders . J Psychosom Res . 2024 ; 186 : 111909 . doi: 10.1016/j.jpsychores.2024.111909 . OpenUrl CrossRef PubMed 27. Leu C , Bautista JF , Sudarsanam M , Niestroj LM , Stefanski A , Ferguson L , Daly MJ , Jehi L , Najm IM , Busch RM , Lal D . Neurological disorder-associated genetic variants in individuals with psychogenic nonepileptic seizures . Sci Rep . 2020 ; 10 ( 1 ): 15205 . doi: 10.1038/s41598-020-72101-8 . OpenUrl CrossRef PubMed 28. Apazoglou K , Adouan W , Aubry JM , Dayer A , Aybek S . Increased methylation of the oxytocin receptor gene in motor functional neurological disorder: a preliminary study . J Neurol Neurosurg Psychiatry . 2018 ; 89 ( 5 ): 552 – 554 . doi: 10.1136/jnnp-2017-316469 . OpenUrl FREE Full Text 29. Jungilligens J , Popkirov S , Perez DL , Diez I . Linking gene expression patterns and brain morphometry to trauma and symptom severity in patients with functional seizures . Psychiatry Res Neuroimaging . 2022 ; 326 : 111533 . doi: 10.1016/j.pscychresns.2022.111533 . OpenUrl CrossRef 30. ↵ Magnus P. et al. Cohort Profile Update: The Norwegian Mother and Child Cohort Study (MoBa) . Int J Epidemiol 2016 ; 45 : 382 – 8 . OpenUrl CrossRef PubMed 31. Christian Erikstrup , Erik Sørensen , Kaspar R Nielsen , Mie T Bruun , Mikkel S Petersen , Klaus Rostgaard , Lise W Thørner , Margit Larsen , Susan Mikkelsen , Khoa M Dinh , Michael Schwinn , Andreas S Rigas , Maria Didriksen , Joseph Dowsett , Jakob H von Stemann , Thorsten Brodersen , Isabella W Paulsen , Lotte Hindhede , Susanne G Sækmose , Kathrine A Kaspersen , Jens K Boldsen , Bertram Kjerulff , Thomas Werge , Søren Brunak , Karina Banasik , Thomas F Hansen , Henrik Ullum , Henrik Hjalgrim , Sisse R Ostrowski , Ole B Pedersen , Cohort Profile: The Danish Blood Donor Study, International Journal of Epidemiology , Volume 52 , Issue 3 , June 2023 , Pages e162– e171 , doi: 10.1093/ije/dyac194 OpenUrl CrossRef 32. Milani , Lili & Alver , Maris & Laur , Sven & Reisberg , Sulev & Haller , Toomas & Aasmets , Oliver & Abner , Erik & Alavere , Helene & Allik , Annely & Annilo , Tarmo & Fischer , Krista & Hudjashov , Georgi & Joeloo , Maarja & Kals , Mart & Karo-Astover , Liis & Kasela , Silva & Kolde , Anastassia & Krebs , Kristi & Krigul , Kertu . ( 2024 ). From Biobanking to Personalized Medicine: the journey of the Estonian Biobank . doi: 10.1101/2024.09.22.24313964 . OpenUrl Abstract / FREE Full Text 33. ↵ Jónsson , H. et al. Data Descriptor: Whole genome characterization of sequence diversity of 15,220 Icelanders . Sci. Data 4 , 1 – 9 ( 2017 ). OpenUrl 34. ↵ Bycroft , C. , Freeman , C. , Petkova , D. et al. The UK Biobank resource with deep phenotyping and genomic data . Nature 562 , 203 – 209 ( 2018 ). doi: 10.1038/s41586-018-0579-z OpenUrl CrossRef PubMed 35. Sudlow C , Gallacher J , Allen N , Beral V , Burton P , Danesh J , Downey P , Elliott P , Green J , Landray M , Liu B , Matthews P , Ong G , Pell J , Silman A , Young A , Sprosen T , Peakman T , Collins R . UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age . PLoS Med . 2015 ; 12 ( 3 ): e1001779 . doi: 10.1371/journal.pmed.1001779 . OpenUrl CrossRef PubMed 36. Lichtenstein P , Björk C , Hultman CM , Scolnick E , Sklar P , Sullivan PF . Recurrence risks for schizophrenia in a Swedish national cohort . Psychol Med . 2006 Oct ; 36 ( 10 ): 1417 – 25 . doi: 10.1017/S0033291706008385 . OpenUrl CrossRef PubMed Web of Science 37. Ramirez AH , Sulieman L , Schlueter DJ , Halvorson A , Qian J , Ratsimbazafy F , Loperena R , Mayo K , Basford M , Deflaux N , Muthuraman KN , Natarajan K , Kho A , Xu H , Wilkins C , Anton-Culver H , Boerwinkle E , Cicek M , Clark CR , Cohn E , Ohno- Machado L , Schully SD , Ahmedani BK , Argos M , Cronin RM , O’Donnell C , Fouad M , Goldstein DB , Greenland P , Hebbring SJ , Karlson EW , Khatri P , Korf B , Smoller JW , Sodeke S , Wilbanks J , Hentges J , Mockrin S , Lunt C , Devaney SA , Gebo K , Denny JC , Carroll RJ , Glazer D , Harris PA , Hripcsak G , Philippakis A , Roden DM; All of Us Research Program . The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y ). 2022 Aug 12; 3 ( 8 ): 100570 . doi: 10.1016/j.patter.2022.100570 . OpenUrl CrossRef PubMed 38. All of Us Research Program Genomics Investigators . Genomic data in the All of Us Research Program . Nature . 2024 ;627(8003):340-346. doi: 10.1038/s41586-023-06957-x . OpenUrl CrossRef PubMed 39. Willer CJ , Li Y , Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans . Bioinformatics . 2010 ; 26 ( 17 ): 2190 – 1 . doi: 10.1093/bioinformatics/btq340 . OpenUrl CrossRef PubMed Web of Science 40. Gadin J.R. , Zetterberg R. , Meijsen J. & Schork A.J . Cleansumstats: Converting GWAS sumstats to a common format to facilitate downstream applications . ( Zenodo , 2023 ) 41. Bulik-Sullivan BK , Loh PR , Finucane HK , Ripke S , Yang J; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Patterson N, Daly MJ, Price AL , Neale BM. LD Score regression distinguishes confounding from polygenicity in genome- wide association studies. Nat Genet . 2015 Mar ; 47 ( 3 ): 291 – 5 . doi: 10.1038/ng.3211 . OpenUrl CrossRef PubMed 42. O’Connell KS , Koromina M , van der Veen T , et al. Bipolar Disorder Working Group of the Psychiatric Genomics Consortium. Genomics yields biological and phenotypic insights into bipolar disorder . Nature . 2025 Mar ; 639 (8056): 968 -975. doi: 10.1038/s41586-024-08468-9 . OpenUrl CrossRef 43. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium . Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies . Cell . 2025 Feb 6;188(3):640-652.e9. doi: 10.1016/j.cell.2024.12.002 . OpenUrl CrossRef PubMed 44. Trubetskoy V , Pardiñas AF , Qi T et al. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Mapping genomic loci implicates genes and synaptic biology in schizophrenia . Nature . 2022 ; 604 (7906): 502 -508. doi: 10.1038/s41586-022-04434-5 . OpenUrl CrossRef PubMed 46. Purves KL , Coleman JRI , Meier SM , Rayner C , Davis KAS , Cheesman R , Bækvad- Hansen M , Børglum AD , Wan Cho S , Jürgen Deckert J , Gaspar HA , Bybjerg-Grauholm J , Hettema JM , Hotopf M , Hougaard D , Hübel C , Kan C , McIntosh AM , Mors O , Bo Mortensen P , Nordentoft M , Werge T , Nicodemus KK , Mattheisen M , Breen G , Eley TC . A major role for common genetic variation in anxiety disorders . Mol Psychiatry . 2020 ; 25 ( 12 ): 3292 – 3303 . doi: 10.1038/s41380-019-0559-1 . OpenUrl CrossRef PubMed 47. Demontis D , Walters GB , Athanasiadis G , et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains . Nat Genet . 2023 Feb ; 55 ( 2 ): 198 – 208 . doi: 10.1038/s41588-022-01285-8 . OpenUrl CrossRef 48. Nievergelt CM , Maihofer AX , Atkinson EG et al. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder . Nat Genet . 2024 ; 56 ( 5 ): 792 – 808 . doi: 10.1038/s41588-024-01707-9 . OpenUrl CrossRef 49. Nagel M , Jansen PR , Stringer S , Watanabe K , de Leeuw CA , Bryois J , Savage JE , Hammerschlag AR , Skene NG , Muñoz-Manchado AB; 23andMe Research Team; White T, Tiemeier H, Linnarsson S, Hjerling-Leffler J, Polderman TJC, Sullivan PF, van der Sluis S, Posthuma D. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways . Nat Genet . 2018 Jul;50(7):920-927. doi: 10.1038/s41588-018-0151-7 . OpenUrl CrossRef PubMed 50. International League Against Epilepsy Consortium on Complex Epilepsies . GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype- specific genetic architecture . Nat Genet. 2023 ;55(9):1471-1482. doi: 10.1038/s41588-023-01485-w . OpenUrl CrossRef PubMed 51. Bjornsdottir G , Chalmer MA , Stefansdottir L , et al. Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura . Nat Genet . 2023 ; 55 ( 11 ): 1843 – 1853 . doi: 10.1038/s41588-023-01538-0 . OpenUrl CrossRef PubMed 51. ↵ International Multiple Sclerosis Genetics Consortium . Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility . Science . 2019 ;365(6460):eaav7188. doi: 10.1126/science.aav7188 . OpenUrl Abstract / FREE Full Text 53. Zorina-Lichtenwalter K , Bango CI , Van Oudenhove L , Čeko M , Lindquist MA , Grotzinger AD , Keller MC , Friedman NP , Wager TD . Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling . Pain . 2023 ; 164 ( 10 ): 2239 – 2252 . doi: 10.1097/j.pain.0000000000002922 . OpenUrl CrossRef PubMed 54. Eijsbouts C , Zheng T , Kennedy NA , Bonfiglio F , Anderson CA , Moutsianas L , Holliday J , Shi J , Shringarpure S; 23andMe Research Team; Voda AI; Bellygenes Initiative; Farrugia G, Franke A, Hübenthal M, Abecasis G, Zawistowski M, Skogholt AH, Ness- Jensen E, Hveem K, Esko T, Teder-Laving M, Zhernakova A, Camilleri M, Boeckxstaens G, Whorwell PJ, Spiller R, McVean G, D’Amato M, Jostins L, Parkes M. Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders . Nat Genet. 2021 ;53(11):1543-1552. doi: 10.1038/s41588-021-00950-8 . OpenUrl CrossRef PubMed 55. Fominykh V , Shadrin AA , Jaholkowski P , Fuhrer J , Parker N , Wiström ED , Frei O , Smeland OB , Sanner H , Djurovic S , Andreassen OA . Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies . Front Immunol . 2025 ; 16 : 1543781 . doi: 10.3389/fimmu.2025.1543781 . OpenUrl CrossRef 56. Speed D , Balding DJ . SumHer better estimates the SNP heritability of complex traits from summary statistics . Nat Genet .; 51 ( 2 ): 277 – 284 . doi: 10.1038/s41588-018-0279-5 . OpenUrl CrossRef PubMed 57. ↵ Ghoussaini M , Mountjoy E , Carmona M , Peat G , Schmidt EM , Hercules A , Fumis L , Miranda A , Carvalho-Silva D , Buniello A , Burdett T , Hayhurst J , Baker J , Ferrer J , Gonzalez-Uriarte A , Jupp S , Karim MA , Koscielny G , Machlitt-Northen S , Malangone C , Pendlington ZM , Roncaglia P , Suveges D , Wright D , Vrousgou O , Papa E , Parkinson H , MacArthur JAL , Todd JA , Barrett JC , Schwartzentruber J , Hulcoop DG , Ochoa D , McDonagh EM , Dunham I , 2021 . Jan 8. Open targets genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics . Nucleic Acids Res . 49 ( D1 ), D1311 – D1320 . doi: 10.1093/nar/gkaa840 OpenUrl CrossRef 58. Watanabe K , Taskesen E , van Bochoven A , Posthuma D . Functional mapping and annotation of genetic associations with FUMA . Nat Commun . 2017 ; 8 ( 1 ): 1826 . doi: 10.1038/s41467-017-01261-5 . OpenUrl CrossRef PubMed 59. ↵ de Leeuw CA , Mooij JM , Heskes T , Posthuma D , 2015 . Apr 17 . MAGMA : generalized gene-set analysis of GWAS data . PLoS Comput Biol 11 (4), e1004219. doi: 10.1371/journal.pcbi.1004219 . OpenUrl CrossRef PubMed 60. Stiles AR , McDonald JG , Bauman DR , Russell DW . CYP7B1: one cytochrome P450, two human genetic diseases, and multiple physiological functions . J Biol Chem . 2009 Oct 16; 284 ( 42 ): 28485 – 9 . doi: 10.1074/jbc.R109.042168 . OpenUrl Abstract / FREE Full Text 61. Yantsevich AV , Dichenko YV , Mackenzie F , Mukha DV , Baranovsky AV , Gilep AA , Usanov SA , Strushkevich NV . Human steroid and oxysterol 7α-hydroxylase CYP7B1: substrate specificity, azole binding and misfolding of clinically relevant mutants . FEBS J . 2014 Mar ; 281 ( 6 ): 1700 – 13 . doi: 10.1111/febs.12733 . OpenUrl CrossRef PubMed 62. Prestsæter S , Koht J , Lamari F , Tallaksen CME , Hoven STJ , Vigeland MD , Selmer KK , Rydning SL . Elevated hydroxycholesterols in Norwegian patients with hereditary spastic paraplegia SPG5 . J Neurol Sci ; 419 : 117211 . doi: 10.1016/j.jns.2020.117211 . OpenUrl CrossRef 63. Parodi L , Coarelli G , Stevanin G , Brice A , Durr A . Hereditary ataxias and paraparesias: clinical and genetic update . Curr Opin Neurol . 2018 ; 31 ( 4 ): 462 – 471 . doi: 10.1097/WCO.0000000000000585 . OpenUrl CrossRef PubMed 64. Schöls L , Rattay TW , Martus P , Meisner C , Baets J , Fischer I , Jägle C , Fraidakis MJ , Martinuzzi A , Saute JA , Scarlato M , Antenora A , Stendel C , Höflinger P , Lourenco CM , Abreu L , Smets K , Paucar M , Deconinck T , Bis DM , Wiethoff S , Bauer P , Arnoldi A , Marques W , Jardim LB , Hauser S , Criscuolo C , Filla A , Züchner S , Bassi MT , Klopstock T , De Jonghe P , Björkhem I , Schüle R . Hereditary spastic paraplegia type 5: natural history, biomarkers and a randomized controlled trial . Brain . 2017 ; 140 ( 12 ): 3112 – 3127 . doi: 10.1093/brain/awx273 . OpenUrl CrossRef PubMed 65. Petkova-Kirova P , Kolchina A , Baas S , Wagenpfeil G , Unger MM , Schulze-Hentrich JM , Bernhardt R . SNPs in cytochromes P450 catalyzing cholesterol degradation in brain are associated with Parkinson’s disease . Front Pharmacol . 2024 ; 15 : 1477009 . doi: 10.3389/fphar.2024.1477009 . OpenUrl CrossRef 66. Brown ES , Rush AJ , McEwen BS . Hippocampal remodeling and damage by corticosteroids: implications for mood disorders . Neuropsychopharmacology . 1999 Oct ; 21 ( 4 ): 474 – 84 . doi: 10.1016/S0893-133X(99)00054-8 . OpenUrl CrossRef PubMed Web of Science 67. Fowler CH , Bogdan R , Gaffrey MS . Stress-induced cortisol response is associated with right amygdala volume in early childhood . Neurobiol Stress . 2021 ; 14 : 100329 . doi: 10.1016/j.ynstr.2021.100329 . OpenUrl CrossRef 68. Ge Y , Chen X , Nan N , Bard J , Wu F , Yergeau D , Liu T , Wang J , Mu X . Key transcription factors influence the epigenetic landscape to regulate retinal cell differentiation . Nucleic Acids Res . 2023 ; 51 ( 5 ): 2151 – 2176 . doi: 10.1093/nar/gkad026 . OpenUrl CrossRef PubMed 69. Le C , Argilli E , George E , Kalaycı T , Uyguner ZO , Karaman B , Demirören T , DiTroia S , Heron D , Sabatier I , Rodan LH , Girisha KM , Radhakrishnan P , Saunders C , Sullivan B , Fleming E , Alvi JR , Sultan T , Houlden H , Efthymiou S , Sacoto MJG , Goodman M , Pierron L , De Sainte-Agathe JM , Durr A , Sherr EH . Basic helix-loop-helix transcription factor BHLHE22 monoallelic and biallelic variants cause a neurodevelopmental disorder with agenesis of the corpus callosum, intellectual disability, tone and movement abnormalities . medRxiv . 2024 :2024.10.11.24312856. doi: 10.1101/2024.10.11.24312856 . OpenUrl Abstract / FREE Full Text 70. McCormick LE , Gupton SL . Cerebellin-1 leads the way . PLoS Biol . 2022 ; 20 ( 11 ): e3001880 . doi: 10.1371/journal.pbio.3001880 . OpenUrl CrossRef PubMed 71. Gandhi PJ , Gawande DY , Shelkar GP , Gakare SG , Kiritoshi T , Ji G , Misra B , Pavuluri R , Liu J , Neugebauer V , Dravid SM . Dysfunction of Glutamate Delta-1 Receptor- Cerebellin 1 Trans-Synaptic Signaling in the Central Amygdala in Chronic Pain . Cells . 2021 ; 10 ( 10 ): 2644 . doi: 10.3390/cells10102644 . OpenUrl CrossRef 72. Thomas M , Vuong KD , Jankovic J . Long-term prognosis of patients with psychogenic movement disorders . Parkinsonism Relat Disord . 2006 ; 12 ( 6 ): 382 – 7 . doi: 10.1016/j.parkreldis.2006.03.005 . OpenUrl CrossRef PubMed Web of Science 73. Feinstein A , Stergiopoulos V , Fine J , Lang AE . Psychiatric outcome in patients with a psychogenic movement disorder: a prospective study . Neuropsychiatry Neuropsychol Behav Neurol . 2001 Jul -Sep;14(3):169-76. PMID: 11513100 . OpenUrl PubMed Web of Science 74. Carle-Toulemonde G , Goutte J , Do-Quang-Cantagrel N , Mouchabac S , Joly C , Garcin B . Overall comorbidities in functional neurological disorder: A narrative review . Encephale . 2023 ; 49 ( 4S ): S24 – S32 . doi: 10.1016/j.encep.2023.06.004 . OpenUrl CrossRef PubMed 75. ↵ Hindley , G. , Frei , O. , Shadrin , A.A. , Cheng , W. , O’Connell , K.S. , Icick , R. , Parker , N. , Bahrami , S. , Karadag , N. , Roelfs , D. , Holen , B. , Lin , A. , Fan , C.C. , Djurovic , S. , Dale , A.M. , Smeland , O.B. , Andreassen , O.A ., 2022 . Charting the Landscape of Genetic Overlap Between Mental Disorders and Related Traits Beyond Genetic Correlation . Am. J. Psychiatry 179 ( 11 ), 833 – 843 . doi: 10.1176/appi.ajp.21101051 . OpenUrl CrossRef 76. Greenwood TA . Creativity and Bipolar Disorder: A Shared Genetic Vulnerability . Annu Rev Clin Psychol . 2020 May 7; 16 : 239 – 264 . doi: 10.1146/annurev-clinpsy-050718-095449 . OpenUrl CrossRef PubMed 77. Peterson RE , Bigdeli TB , Ripke S , Bacanu SA , Gejman PV , Levinson DF , Li QS , Rujescu D , Rietschel M , Weinberger DR , Straub RE , Walters JTR , Owen MJ , O’Donovan MC , Mowry BJ , Ophoff RA , Andreassen OA , Esko T , Petryshen TL , Kendler KS; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Fanous AH. Genome-wide analyses of smoking behaviors in schizophrenia: Findings from the Psychiatric Genomics Consortium . J Psychiatr Res . 2021 ; 137 : 215 – 224 . doi: 10.1016/j.jpsychires.2021.02.027 . OpenUrl CrossRef PubMed 78. Grotzinger AD , Rhemtulla M , de Vlaming R , Ritchie SJ , Mallard TT , Hill WD , Ip HF , Marioni RE , McIntosh AM , Deary IJ , Koellinger PD , Harden KP , Nivard MG , Tucker- Drob EM . Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits . Nat Hum Behav . 2019 ; 3 ( 5 ): 513 – 525 . doi: 10.1038/s41562-019-0566-x . OpenUrl CrossRef 79. ↵ Selzam S , Coleman JRI , Caspi A , Moffitt TE , Plomin R . A polygenic p factor for major psychiatric disorders . Transl Psychiatry . 2018 ; 8 ( 1 ): 205 . doi: 10.1038/s41398-018-0217-4 . OpenUrl CrossRef PubMed 80. Ekanayake V , Kranick S , LaFaver K , et al. Personality traits in psychogenic nonepileptic seizures (PNES) and psychogenic movement disorder (PMD): Neuroticism and perfectionism . J Psychosom Res 2017 ; 97 : 23 – 29 . doi: 10.1016/j.jpsychores.2017.03.018 OpenUrl CrossRef PubMed 81. ↵ McWilliams A , Reilly C , Gupta J , et al. Autism spectrum disorder in children and young people with non-epileptic seizures . Seizure 2019 ; 73 : 51 – 55 . doi: 10.1016/j.seizure.2019.10.022 OpenUrl CrossRef PubMed 82. ↵ González-Herrero , B. ; Morgante , F. ; Pagonabarraga , J. ; Stanton , B. ; Edwards , M.J . Autism Spectrum Disorder May Be Highly Prevalent in People with Functional Neurological Disorders . J. Clin. Med . 2023 , 12 , 299 . doi: 10.3390/jcm12010299 OpenUrl CrossRef 83. Demartini B , Petrochilos P , Ricciardi L , Price G , Edwards MJ , Joyce E . The role of alexithymia in the development of functional motor symptoms (conversion disorder) . J Neurol Neurosurg Psychiatry . 2014 ; 85 ( 10 ): 1132 – 7 . doi: 10.1136/jnnp-2013-307203 . OpenUrl Abstract / FREE Full Text 84. Pick S , Goldstein LH , Perez DL , Nicholson TR . Emotional processing in functional neurological disorder: a review, biopsychosocial model and research agenda . J Neurol Neurosurg Psychiatry . 2019 ; 90 ( 6 ): 704 – 711 . doi: 10.1136/jnnp-2018-319201 . OpenUrl Abstract / FREE Full Text 85. Bahrami S , Hindley G , Winsvold BS , O’Connell KS , Frei O , Shadrin A , Cheng W , Bettella F , Rødevand L , Odegaard KJ , Fan CC , Pirinen MJ , Hautakangas HM; HUNT All-In Headache; Dale AM, Djurovic S, Smeland OB , Andreassen OA. Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools. Brain . 2022 ; 145 ( 1 ): 142 – 153 . doi: 10.1093/brain/awab267 . OpenUrl CrossRef PubMed 85. Brainstorm Consortium ; Anttila V, Bulik-Sullivan B, Finucane HK, et al. Analysis of shared heritability in common disorders of the brain . Science . 2018 Jun 22;360(6395):eaap8757. doi: 10.1126/science.aap8757 . OpenUrl Abstract / FREE Full Text 87. Dresler T , Caratozzolo S , Guldolf K , Huhn JI , Loiacono C , Niiberg-Pikksööt T , Puma M , Sforza G , Tobia A , Ornello R , Serafini G; European Headache Federation School of Advanced Studies (EHF-SAS). Understanding the nature of psychiatric comorbidity in migraine: a systematic review focused on interactions and treatment implications . J Headache Pain . 2019 ; 20 ( 1 ): 51 . doi: 10.1186/s10194-019-0988-x OpenUrl CrossRef PubMed 88. McCracken HT , Thaxter LY , Smitherman TA . Psychiatric comorbidities of migraine . Handb Clin Neurol . 2024 ; 199 : 505 – 516 . doi: 10.1016/B978-0-12-823357-3.00013-6 . OpenUrl CrossRef PubMed 89. Joseph A , Baslet G , O’Neal MA , et al. Prevalence of autoimmune diseases in functional neurological disorder: influence of psychiatric comorbidities and biological sex Journal of Neurology , Neurosurgery & Psychiatry Published Online First : 21 March 2024. doi: 10.1136/jnnp-2023-332825 OpenUrl Abstract / FREE Full Text 90. van der Feltz-Cornelis C , Brabyn S , Ratcliff J , Varley D , Allgar V , Gilbody S , Clarke C , Lagos D. Assessment of cytokines, microRNA and patient related outcome measures in conversion disorder/functional neurological disorder (CD/FND): The CANDO clinical feasibility study . Brain Behav Immun Health . 2021 ; 13 : 100228 . doi: 10.1016/j.bbih.2021.100228 . OpenUrl CrossRef 91. McInnis PM , Braund TA , Chua ZK , Kozlowska K . Stress-system activation in children with chronic pain: A focus for clinical intervention . Clin Child Psychol Psychiatry . 2020 Jan ; 25 ( 1 ): 78 – 97 . doi: 10.1177/1359104519864994 . OpenUrl CrossRef PubMed 92. Ji RR , Nackley A , Huh Y , Terrando N , Maixner W . Neuroinflammation and Central Sensitization in Chronic and Widespread Pain . Anesthesiology . 2018 ; 129 ( 2 ): 343 – 366 . doi: 10.1097/ALN.0000000000002130 . OpenUrl CrossRef PubMed 93. ↵ Schumacher MA . Peripheral Neuroinflammation and Pain: How Acute Pain Becomes Chronic . Curr Neuropharmacol . 2024 ; 22 ( 1 ): 6 – 14 . doi: 10.2174/1570159X21666230808111908 . OpenUrl CrossRef PubMed 94. Ishioh M , Nozu T , Okumura T. Brain Neuropeptides, Neuroinflammation, and Irritable Bowel Syndrome . Digestion . 2024 ;105(1):34-39. doi: 10.1159/000533275 . Epub 2023 Sep 6. PMID: 37673052 . OpenUrl CrossRef PubMed 95. Chen CY , Lee PH , Castro VM , Minnier J , Charney AW , Stahl EA , Ruderfer DM , Murphy SN , Gainer V , Cai T , Jones I , Pato CN , Pato MT , Landén M , Sklar P , Perlis RH , Smoller JW . Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records . Transl Psychiatry . 2018 Apr 18; 8 ( 1 ): 86 . doi: 10.1038/s41398-018-0133-7 . OpenUrl CrossRef PubMed 96. Smoller JW . The use of electronic health records for psychiatric phenotyping and genomics . Am J Med Genet B Neuropsychiatr Genet . 2018 Oct ; 177 ( 7 ): 601 – 612 . doi: 10.1002/ajmg.b.32548 . Epub 2017 May 30. PMID: 28557243 ; PMCID: PMC6440216 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted July 17, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Genome-wide study of somatic symptom and related disorders identifies novel genomic loci and map genetic architecture Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genome-wide study of somatic symptom and related disorders identifies novel genomic loci and map genetic architecture Vera Fominykh , Piotr Jaholkowski , Alexey A. Shadrin , Elise Koch , Laura B. Luitva , Dorte H. Mikkelsen , Mischa Lundberg , Ole Birger Pedersen , Sisse Rye Ostrowski , Christian Erikstrup , Maria Didriksen , Christina Mikkelsen , Erik Sørensen , Henrik Ullum , Mie Topholm Bruun , Bitten Aagaard , Kaarina Kowalec , Robert Karlsson , Håkan Karlsson , Christina Dalman , Pravesh Parekh , Viktoria Birkenæs , Yury Seliverstov , Bernhard Landwehrmeyer , Ida E. Sønderby , DBDS genetic consortium , Estonian Biobank research team , Olav B. Smeland , Kevin S. O’Connell , Lu Yi , Patrick F. Sullivan , Thomas M. Werge , Lili Milani , Ole A. Andreassen medRxiv 2025.07.16.25331639; doi: https://doi.org/10.1101/2025.07.16.25331639 Share This Article: Copy Citation Tools Genome-wide study of somatic symptom and related disorders identifies novel genomic loci and map genetic architecture Vera Fominykh , Piotr Jaholkowski , Alexey A. Shadrin , Elise Koch , Laura B. Luitva , Dorte H. Mikkelsen , Mischa Lundberg , Ole Birger Pedersen , Sisse Rye Ostrowski , Christian Erikstrup , Maria Didriksen , Christina Mikkelsen , Erik Sørensen , Henrik Ullum , Mie Topholm Bruun , Bitten Aagaard , Kaarina Kowalec , Robert Karlsson , Håkan Karlsson , Christina Dalman , Pravesh Parekh , Viktoria Birkenæs , Yury Seliverstov , Bernhard Landwehrmeyer , Ida E. Sønderby , DBDS genetic consortium , Estonian Biobank research team , Olav B. Smeland , Kevin S. O’Connell , Lu Yi , Patrick F. Sullivan , Thomas M. Werge , Lili Milani , Ole A. Andreassen medRxiv 2025.07.16.25331639; doi: https://doi.org/10.1101/2025.07.16.25331639 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9219) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffb5cc4a92f0db4',t:'MTc3OTQ0ODEwMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();