{"paper_id":"046af63c-e74f-4219-98d3-354dc08bd4e4","body_text":"1\nGenome-Wide Association Study of Distressing Premenstrual 1 \nSymptoms in Two Nordic Populations  2 \n 3 \nElgeta Hysaj*1, Piotr Jaholkowski*2, Alexey A. Shadrin*2 3, Jacob Bergstedt1, Yi Lu4, 4 \nElizabeth Bertone-Johnson 5 6, Cynthia M. Bulik4 7 8, Mikael Landén4 9, Sven Sandin4 5 \n10 11, Kaarina Kowalec4 12 13, Sara Hägg4, Arianna Di Florio7 14, David Goldman15, 6 \nPeter J. Schmidt16, Unnur A. Valdimarsdóttir1 17 18, Ole A. Andreassen#2 3, Donghao 7 \nLu#1 8 \n 9 \n* Equal contribution  10 \n# Equal contribution 11 \n1 Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, 12 \nSweden. 13 \n2 Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo 14 \nUniversity Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, 15 \nNorway. 16 \n3 KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, 17 \nNorway. 18 \n4 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 19 \nStockholm, Sweden. 20 \n5 Department of Biostatistics and Epidemiology, University of Massachusetts, 21 \nAmherst, MA, USA. 22 \n6 Department of Health Promotion and Policy, University of Massachusetts, Amherst, 23 \nMA, USA. 24 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n \n 2\n7 Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, 25 \nNC, USA. 26 \n8 Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27 \nUSA. 28 \n9 Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg 29 \nUniversity, Gothenburg, Sweden. 30 \n10 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, 31 \nNew York. 32 \n11 Seaver Autism Center for Research and Treatment at Mount Sinai, New York, 33 \nNew York. 34 \n12 College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, 35 \nCanada 36 \n13 Department of Biochemistry & Medical Genetics, Max Rady College of Medicine 37 \nRady Faculty of Health Sciences, University of Manitoba, Canada 38 \n14 Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological 39 \nMedicine and Clinical Neurosciences, Cardiff University, Cardiff, UK 40 \n15 Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism 41 \n(NIAAA), NIH, Rockville, MD 20855, USA. 42 \n16 Behavioral Endocrinology Branch, National Institute of Mental Health (NIMH), NIH, 43 \n10 Center Drive MSC 1277, Bethesda, MD 20892, USA. 44 \n17 Center of Public Health Sciences, Faculty of Medicine, University of Iceland, IS-45 \n101 Reykjavík, Iceland. 46 \n18 Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard 47 \nUniversity, Boston, MA USA. 48 \n 49 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 3\nCorrespondence: Elgeta Hysaj, elgeta.hysaj@ki.se; Donghao Lu, donghao.lu@ki.se 50 \n 51 \n 52 \n 53 \n 54 \n 55 \n 56 \n 57 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 4\nABSTRACT 58 \nBackground 59 \nPremenstrual disorders (PMDs) are characterized by affective and physical 60 \nsymptoms before menses, likely due to abnormal sensitivity to normal hormone 61 \nfluctuations. While sizable heritability has been indicated in twin studies, there are no 62 \ngenome-wide association studies (GWAS) to inform the genetic architecture of 63 \nPMDs. 64 \n 65 \nMethods  66 \nWe conducted a GWAS of 17,511 women with distressing premenstrual symptoms 67 \n(DPS) and 54,789 women controls of European ancestry from two Nordic population-68 \nbased cohorts. DPS were assessed using questionnaire or identified as a clinical 69 \ndiagnosis of PMDs in the nationwide healthcare registers. GWAS was performed in 70 \neach study before meta-analysis, analyses of single nucleotide polymorphism (SNP)-71 \nbased heritability (h2\nSNP) and genetic correlations to psychosocial and gynecological 72 \nphenotypes, as well as blood levels of gonadal steroids.  73 \n 74 \nResults 75 \nIn the meta-analysis, one locus at 12p13.3 (rs758170, CACNA1C, P=1.53x10-8, 76 \nOR=0.93, 95% CI 0.90-0.95) was associated with DPS. The SNP-based heritability 77 \nwas estimated 0.072 (SE=0.01, P=2.46 x10-12). Statistically significant genetic 78 \ncorrelations (rg) were found between DPS and all major psychiatric disorders, with 79 \nthe strongest correlation with major depression (rg=0.62, CI 0.49-0.74, P=3.04x10-80 \n22). Weaker correlations were noted to gynecological conditions such as 81 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 5\nendometriosis (rg=0.17, CI 0.01-0.32, P=0.029), while gonadal steroid hormone 82 \nlevels in blood were uncorrelated. 83 \n 84 \nConclusion 85 \nThis study provides the first direct insights into the genetic architecture of PMDs by 86 \nidentifying a SNP associated with DPS and genetic correlations to other conditions. If 87 \nconfirmed in larger independent populations, these findings may advance our 88 \nunderstanding of the underlying mechanisms of PMDs.  89 \n 90 \nKeywords: GWAS, premenstrual disorders, genetic correlation, heritability, women’s 91 \nhealth, MoBa, LifeGene 92 \n 93 \n 94 \n 95 \n 96 \n 97 \n 98 \n 99 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 6\nINTRODUCTION 100 \nPremenstrual disorders (PMDs), encompassing premenstrual syndrome (PMS) and 101 \nthe more severe premenstrual dysphoric disorder (PMDD), are characterized by 102 \nsubstantial mood, behavioral, and physical symptoms that occur in the luteal phase 103 \nof the menstrual cycle and resolve with the onset of menstruation1. PMDs affect 104 \nmillions of women of reproductive age worldwide, with an estimated prevalence of 105 \n20%-30% for PMS and 2%-6% for PMDD1. Both severe PMS and PMDD are 106 \naccompanied by significantly impaired social activities and relationships2–4. Although 107 \nthe symptoms are often limited to the days before menses, the chronic and cyclical 108 \nnature of PMDs can have a significant impact on a woman’s life5,  including 109 \nincreasing the risk of suicidal behavior6,7.   110 \n 111 \nWhile epidemiological studies have highlighted potential links with other hormone-112 \nrelated conditions (e.g., perinatal depression (PND)8, menopause timing and 113 \nmenopause symptoms9), an experimental study has revealed that PMDs are 114 \ntriggered by an abnormal, or heightened, response to normal hormone fluctuations10. 115 \nHowever, the ontogeny of the abnormal response remains unknown, impeding the 116 \ndevelopment of new treatments. Thus, uncovering the underlying causes of PMDs is 117 \nessential for more effective detection and intervention. 118 \n 119 \nTwin studies suggest a genetic component in PMDs, with an estimated heritability of 120 \n35%-57% for premenstrual symptoms11–15. Moreover, research into candidate genes 121 \nhave indicated variants in estrogen receptor genes16 serotonin receptor 1A17, 122 \ntranscription factor AP-2 beta18, and steroid-5-alpha-reductase,19 alpha polypeptide 123 \n119 in PMDD, although conflicting results also have been noted20. A genome-wide 124 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 7\napproach opens for a comprehensive, hypothesis-free characterization of genetic 125 \nfactors involved in PMDs, yet such an undertaking has not yet been performed for 126 \nPMDs. Here, we conducted a genome-wide association study (GWAS) of distressing 127 \npremenstrual symptoms (DPS) in two European-ancestry samples, aiming to identify 128 \nthe genetic architecture and relationship of DPS to other psychiatric conditions and 129 \nunderlying mechanisms of PMDs.  130 \n 131 \nMETHODS AND MATERIALS 132 \nStudy Population  133 \nWe conducted a GWAS of 72,297 European-ancestry women nested from the 134 \nLifeGene cohort in Sweden and Mother, Father and Child Cohort Study (MoBa) 135 \ncohort in Norway.  136 \n 137 \nLifeGene is a large-scale Swedish prospective cohort launched in 2009 with 138 \nlongitudinal follow-ups21. It enrolled 39,862 people (24,265 women) aged 18-50 139 \nyears who were randomly selected from the Swedish population and their household 140 \nmembers. A thorough web-based questionnaire collecting information on lifestyle, 141 \nphysical, mental, and social well-being was administered at baseline and in five 142 \nannual follow-up cycles. Blood samples were collected during the in-person testing 143 \nat baseline. Participants were linked to the national population and health registers 144 \nusing their unique Swedish personal identification number, a lifelong identifier 145 \nassigned at birth or upon immigration to Sweden. Informed consent was obtained 146 \neither electronically from all participants upon registration online or in writing at the 147 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 8\nin-person testing center. The current study was approved by the Swedish Ethical 148 \nReview Authority (2021-02775). 149 \n 150 \nMoBa is a population-based pregnancy cohort study conducted by the Norwegian 151 \nInstitute of Public Health22,23,24. Participants were recruited from across Norway from 152 \n1999 to 2008. Women consented to participation in 41% of pregnancies. The cohort 153 \nincludes approximately 114,500 children, 95,200 mothers and 75,200 fathers. The 154 \ncurrent study is based on version 12 of the quality-assured data files released for 155 \nresearch in January 2019. The establishment of MoBa and initial data collection 156 \nwere approved via a license from the Norwegian Data Protection Agency and after 157 \nreview of the Regional Committees for Medical and Health Research Ethics. The 158 \nMoBa cohort is currently regulated by the Norwegian Health Registry Act. The 159 \ncurrent study was approved by The Regional Committees for Medical and Health 160 \nResearch Ethics (2016/1226/REK).  161 \n 162 \nAssessment of premenstrual symptoms    163 \nBoth questionnaire assessments and nationwide healthcare registers were sourced 164 \nfor case assessment. Cases with DPS were defined as either having a clinical 165 \ndiagnosis of PMD recorded in the registers or having met the criteria for a probable 166 \nPMD based on self-report questionnaires (as described in detail below). The controls 167 \nwere women with no clinical diagnosis of PMDs in registers and not meeting the 168 \nPMD criteria in all available questionnaire cycles. 169 \n 170 \nLifeGene 171 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 9\nA modified version of the Premenstrual Symptom Screening Tool (PSST)25 was 172 \nemployed to assess premenstrual symptoms at baseline and annual follow-ups for 5 173 \nyears26. The original PSST has been validated with a sensitivity of 79%27. The PSST 174 \nwas modified to start with three screening questions: 1) “During most menstruation 175 \ncycles during the last year, have you experienced mood changes and/or physical 176 \nsymptoms during the week before menstruation?”; 2) “Have your premenstrual 177 \nsymptoms been so severe that they have affected your relationships with others or 178 \nyour ability to perform work or other activities?”;  and 3) “Are you absolutely certain 179 \nthat the symptoms are limited to the premenstrual period, meaning that you are 180 \nalways completely symptom-free approximately a week after menstruation begins?” 181 \nUpon confirmation of all screening questions, participants were prompted to rate the 182 \nseverity of 15 physical and affective symptoms from 1 (none), 2 (moderate), 3 183 \n(considerably severe) to 4 (severe). As described elsewhere26, participants were 184 \nclassified as cases if they met (1) ≥ 1 out of 4 affective symptoms rated as 185 \nconsiderably severe to severe; and (2) ≥ 4 other symptoms rated as moderate to 186 \nsevere.  187 \n 188 \nTo complement the questionnaire assessment in LifeGene, for the Swedish 189 \nparticipants, we further identified clinical diagnosis of PMDs, as described 190 \nelsewhere7. According to the Swedish guidelines, a clinical diagnosis of PMDs 191 \nshould be based on prospective daily symptom ratings for at least 2 consecutive 192 \nmenstrual cycles28. Briefly, we identified PMD diagnoses based on ICD codes 193 \n(Supplementary Table S1) from the National Patient Register (1987-2023) and the 194 \nStockholm Primary Care Register (2001-2021), as 82% of the participants lived in 195 \nStockholm County. Since primary care data was unavailable for residents in other 196 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 10\ncounties, we also obtained information on filled prescriptions of antidepressants 197 \n(ATC codes: N06AB, N06AX, N06AA) and hormonal contraceptives (G03A) with a 198 \nwritten indication for PMD treatment from the National Prescribed Drug Register 199 \n(2006-2023).  200 \n 201 \nMoBa  202 \nQuestionnaire assessment was based on the women’s responses to the following 203 \nquestions at 15 th week of gestation: “Are you usually depressed or irritable before 204 \nyour period?” and “If yes, does this feeling disappear after you get your period?”. As 205 \ndescribed elsewhere 29, the cases were defined based on the response “yes, 206 \nnoticeably” or “yes, very much” to the first question, and “yes” to the second 207 \nquestion. We excluded individuals whose symptoms of PMD did not resolve after the 208 \nonset of menses. 209 \n 210 \nClinical diagnoses of PMDs were derived from the Primary Care Registry of Norway 211 \n(KUHR; years 2006-2023), which contains diagnoses given at the primary care level. 212 \nWe identified cases based on the Premenstrual Tension Syndrome (X89) diagnosis 213 \naccording to the International Classification of Primary Care, Second Edition (ICPC-214 \n2)30. 215 \n 216 \nGenotyping, quality control, and imputation  217 \nLifeGene 218 \nIn LifeGene, DNA was extracted from blood samples collected at baseline. 219 \nParticipants from the LifeGene were genotyped by four sub-studies including the 220 \npresent study (Supplementary Table S2). Quality control (QC) was performed using 221 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 11\nthe Ricopili bioinformatics pipeline for each sub-study31. Briefly, these steps included 222 \na call rate threshold of ≥ 0.98 for cases and controls, heterozygosity (FHET) within 223 \n±0.20, and exclusion of sex mismatches. SNP QC required a call rate of ≥ 0.98, 224 \nmissingness difference ≤ 0.02 and minor allele frequency (MAF) ≥ 0.01. Ungenotyped 225 \nSNPs were then imputed based on the Haplotype Reference Consortium (HRC) 226 \nreference panel (r1.1)32 via the Sanger imputation server and pooled after removing 227 \nduplicated individuals. Information on a total of 7,135,674 single-nucleotide 228 \npolymorphisms (SNPs) was thereby available for analysis. Admixture analysis was 229 \nperformed using the ADMIXTURE software to estimate genetic ancestry proportions, 230 \nleveraging reference populations from the 1000 Genomes Project (phase 3 v5)33. 231 \nAnalysis was restricted to autosomal variants. Individuals estimated with less than 232 \n90% probability of European ancestry were excluded. Briefly, genotype data were 233 \navailable for 8,826 women (36%); after excluding 862 participants who were first-234 \ndegree relatives (having identity-by-descent sharing ≥ 0.2), had significant non-235 \nEuropean ancestry (n=896), or had no information on phenotype (n=1,839), 5,229 236 \nparticipants were included in this analysis. 237 \n 238 \nIn MoBa, venous blood was collected from women around the 15th week of 239 \ngestation and immediately after giving birth. Genomic DNA is extracted and stored at 240 \nthe Norwegian Institute of Public Health 34. The MoBa cohort genotyping was 241 \nconducted through multiple research projects over several years35. A novel family-242 \nbased pipeline (MoBaPsychGen genotype QC pipeline) was implemented to handle 243 \nthe relatedness structure of the MoBa dataset, while appropriately accounting for the 244 \ndifferences resulting from array and batch effects35. The pipeline36 includes pre-245 \nimputation QC, phasing, imputation, and post-imputation QC, and prioritizes 246 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 12\nretaining individuals over SNPs. After QC procedure 6,981,748 SNPs were available 247 \nfor further analysis. Our analysis was restricted to individuals of European ancestry, 248 \nselected based on visual comparison of the first seven genetic principal components 249 \n(PCs) with PCs from 1000 Genomes phase 1 unrelated samples. 250 \n 251 \n 252 \nStatistical analysis   253 \nGWAS  254 \nCharacteristics of individuals with and without DPS (age, educational level, civil 255 \nstatus, depression, anxiety disorder, age at menarche and parity) in LifeGene and 256 \nMoBa were summarized using descriptive statistics. P-values were calculated using 257 \nchi-square test.  258 \n 259 \nIn LifeGene, GWAS was performed using logistic regression using PLINK237. 260 \nAnalysis was restricted to 6,508,434 genetic variants with a minor allele frequency 261 \n(MAF) ≥  0.01 and imputation quality score (INFO) ≥  0.90. In MoBa, logistic 262 \nregression was employed for GWAS analysis using Regenie (version 3.2.5)38. We 263 \nexcluded SNPs with imputation info score (minINFO) < 0.80 and SNPs with minor 264 \nallele count (MAC) below 20. For both cohorts, the estimates were adjusted for sub-265 \nstudy membership and the top 10 PCs in Model 1.  To reduce the genetic influence 266 \nof other common psychiatric conditions, model 2 was additionally adjusted for clinical 267 \ndiagnosis of depression and anxiety disorder (ICD codes in Supplementary Table 268 \nS1). In an additional analysis, we restricted analysis to cases confirmed by both 269 \nquestionnaire assessment and clinical diagnosis.  270 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 13\nWe generated the Q-Q plot using qqman39 package in R version 4.2.2 (2022-10-31). 271 \nSummary statistics from both LifeGene and MoBa were then meta-analyzed using 272 \ninverse variance-weighted analysis as implemented in METAL (version 2020-05-273 \n05)40. SNPs with P-value < 5x10-8 were considered genome-wide significant and P-274 \nvalue < 1x10-6 for borderline significance. The odds ratio (OR), 95%CI, and P-value 275 \nwere reported for any independent SNPs (LD r2<0.6) above marginal significance 276 \n(i.e., lead SNPs). Functional annotation was conducted using FUMA41 integrating 277 \neQTL data from brain and blood tissues. 278 \n 279 \nSNP-based heritability and genetic correlation 280 \nBased on the summary statistics, we estimated the SNP-based heritability on the 281 \nobserved scale of DPS using the linkage disequilibrium score regression (LDSC) 282 \nsoftware package (version 2.0.1)42. Several studies have suggested that PMDs are 283 \nlinked to a broad range of other disorders and traits43–45. We used LDSC to 284 \nundertake genetic correlation analyses with major psychiatric traits (e.g., major 285 \ndepression), gynecologic conditions (e.g., endometriosis), sex hormones (e.g., 286 \nabsolute level of estradiol in the blood), and known risk factors (e.g., age at 287 \nmenarche) for PMDs as described in the Supplementary Table S3.  288 \n 289 \nRESULTS 290 \nA total of 72,297 (5,229 from LifeGene and 67,068 from MoBa) women were 291 \nincluded in the final GWAS meta-analysis. Among them, 17,511 (24.2%) met the 292 \ncriteria for DPS (1,962 (37.5%); cases were oversampled for genotyping, while the 293 \nprevalence in the whole cohort was 10.8%) from LifeGene and 15,549 (23.1%) from 294 \nMoBa. In LifeGene, compared to the controls, women with DPS were more likely to 295 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 14\nhave lower educational attainment, not to have a partner, to have prior history of 296 \ndepression or anxiety disorder (Table 1). Similar characteristics were observed 297 \namong MoBa participants.  298 \n 299 \nGWAS analysis  300 \nQuantile-quantile analysis of the GWAS meta-analysis indicated moderate inflation 301 \n(λ =1.075, Supplementary Tables Figure S1). One locus at 12p13.3 was genome-302 \nwide significant (rs758170, P=1.53x10-8, OR=0.93, 95% CI 0.90–0.95, risk allele C) 303 \n(Figure 2 and Table 2). The top SNP rs758170 (intron) was positionally mapped to 304 \nCACNA1C (Calcium Voltage-Gated Channel Subunit Alpha1 C) gene. FUMA, 305 \nindicates that the C allele associated with DPS risk leads to increased expression of 306 \nCACNA1C in cerebellum (P=2.53x10-6) (Figure 3). Comparable point estimates for 307 \nrs758170 were found between LifeGene (OR = 0.95) and MoBA (OR = 0.92, P-for-308 \nheterogeneity = 0.59). Moreover, six other loci showed a suggestive level of 309 \nsignificance (P<1.0x10-6, Table 2).  310 \n 311 \nNext, we conducted several sensitivity analyses using LifeGene samples to test the 312 \nrobustness of our findings. To reduce the influence of comorbid psychiatric disorders, 313 \nwe additionally adjusted for depression and anxiety, yielding comparable 314 \nassociations for top loci (Supplementary Table S5). To minimize the misclassification 315 \nof probable PMD cases, we restricted analysis to cases confirmed by both 316 \nquestionnaire assessment and clinical diagnosis (n= 762) and found largely 317 \ncomparable point estimates despite the attenuation of P values due to smaller 318 \nsample size (Supplementary Table S6). 319 \n 320 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 15\nSNP heritability and genetic correlation  321 \nThe SNP-based heritability was estimated as 0.072 (SE=0.01, P=2.46x10-12).  We 322 \nobserved significant positive genetic correlations between DPS and all considered 323 \npsychiatric disorders  (rg= 0.32 - 0.62), with the strongest correlation with major 324 \ndepression (rg= 0.62, 95%CI = (0.49, 0.74), empirical P= 3.04x10-22, Figure 2). There 325 \nwas a significant positive genetic correlation between PMDs and endometriosis (rg= 326 \n0.17, 95%CI = (0.01 - 0.32), empirical P= 0.029). In addition, a positive genetic 327 \ncorrelation was found for BMI (rg= 0.1, 95%CI = (0.03, 0.17), empirical P= 0.003), 328 \nwhile significant negative genetic correlations were observed with age at first 329 \nchildbirth (rg= -0.35, 95%CI = (-0.47, -0.22), empirical P= 7.01x10-8), subjective well-330 \nbeing (rg= -0.34, 95%CI = (-0.50, -0.19), empirical P= 1.82x10-5), and educational 331 \nattainment (rg= -0.15, 95%CI = (-0.22, -0.07), empirical P= 8.39x10-5) 332 \n(Supplementary Table S4). Finally, we did not observe any significant correlation with 333 \nblood steroid hormone levels. 334 \n 335 \nDISCUSSION 336 \nTo our knowledge, this is the first GWAS focused on DPS, including 17,511 337 \nindividuals with DPS and 54,789 control individuals of European genetic ancestry. 338 \nWe identified a genome-wide significant risk locus on 12p13.3, with comparable 339 \npoint estimates in LifeGene and MoBa. A moderate SNP-based heritability of 7.2% 340 \nwas observed. Genetic correlations were found between DPS and a range of 341 \npsychiatric disorders, with the strongest genetic overlap noted for major depression.  342 \n 343 \nThe CACNA1C gene implicated by linkage to psychiatric disorders, encodes the 344 \ncalcium voltage-gated channel subunit alpha1C, crucial for calcium channel 345 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 16\nfunctioning essential to neurodevelopment46,47. Previous research has found that the 346 \ngene NUCB1, encoding a calcium-binding protein, is directly involved in regulating 347 \nintracellular Ca2+ within the endoplasmic reticulum (ER)-Golgi compartment, 348 \ncontributing to the abnormal response to steroid hormones in patients with PMDD48. 349 \nMoreover, studies on calcium signalling in neural cells suggest potential interactions 350 \nbetween KCNMA1 (calcium-dependent gene) and NUCB1 and CACNA1C, as part of 351 \ncalcium regulation pathways49,50. Since Ca2+ activity in developing neural cells is 352 \nmodulated by various membrane receptors, including GABA, these findings highlight 353 \npromising links between calcium regulation and GABA receptor function, which has 354 \nbeen widely studied in relation to PMDs51,52. 355 \n 356 \nCACNA1C has been linked to a range of psychiatric disorders53,54. In a study 357 \ninvestigating the effects of CACNA1C haploinsufficiency on mouse behavior in tests 358 \nwith relevance to human mood disorders, researchers found that an intronic region 359 \nof CACNA1C was involved in mood disorder pathophysiology55. Moreover, several 360 \nassociation studies have linked polymorphisms in CACNA1C to bipolar disorder and 361 \nschizophrenia53,54, potentially through variations in mean gray matter volume and 362 \nmedio temporal emotional processing56,57. Indeed, rs758170 is highly correlated (LD 363 \nr2>0.8) with variants that have been associated with bipolar disorder58,59, 364 \nschizophrenia60, autism  and attention deficit hyperactivity disorder (ADHD)61, 365 \nsuggesting potentially shared disease mechanisms between PMDs and these 366 \npsychiatric disorders. However, the association of rs758170 remains similar after 367 \nadjustment for depression and anxiety, indicating that our finding cannot be 368 \ncompletely explained by psychiatric comorbidities. In addition, rs758170 has been 369 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 17\nlinked to lipid metabolism62, for which several epidemiological studies have illustrated 370 \na salient relationship between adiposity and PMDs63,64.   371 \n 372 \nPrevious twin research indicates a sizable genetic contribution to premenstrual 373 \nsymptoms15, our study represents the first attempt to estimate the genetic liability of 374 \nprobable PMDs. As reported in previous genetic studies of other complex 375 \ntraits65,66,67, the SNP-based heritability estimate in our study (h2\nSNP = 0.07) is lower 376 \nthan reported in twin studies. Future research should focus on larger GWAS as well 377 \nas rare alleles, other alleles not well captured by GWAS and gene-environment 378 \ninterplay to better capture the genetic liability of PMDs. 379 \n 380 \nExtensive clinical and questionnaire-based research consistently demonstrate a high 381 \nprevalence of psychiatric comorbidities, particularly depressive and anxiety 382 \ndisorders, among individuals diagnosed with premenstrual disorders1,68. Additionally, 383 \nother conditions such as bipolar disorder and ADHD have been found to co-occur 384 \nwith PMDs44,69. In the present analyses, we found the largest and most significant 385 \ngenetic correlation of PMDs with major depression, aligning with the confirmed 386 \nphenotypic correlation in literature70. In addition, we observed genetic correlations 387 \nwith ADHD and bipolar disorder (including bipolar I, bipolar II, and schizoaffective 388 \ntype), echoing the previous report on the polygenetic association with these 389 \ndisorders observed in MoBa29. Moreover, we report a significant genetic correlation 390 \nbetween endometriosis and DPS. This supports previous research conducted on 391 \nendometriosis and the phenotypic relationship with major depression and other 392 \npsychiatric disorders, potentially explained by shared dysregulated immunological 393 \nfunctions71. Finally, no genetic correlation was observed with steroid hormone levels, 394 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 18\nlending support to the notion that PMDs are not driven by hormone levels but 395 \nsensitivity to these fluctuations as shown in previous experimental studies72. 396 \n 397 \nThis study is strengthened by its large sample size, the inclusion of two Nordic 398 \ncohorts with banked bio samples, and the use of rich register and questionnaire data 399 \nfor case identification. This study also has some limitations. First, a portion of the 400 \nindividuals with DPS were identified through screening tools. However, prospective 401 \nsymptom charting, as required to establish the diagnosis,  is not feasible in cohort 402 \nstudies and may result in a high attrition rate particularly for those with severe 403 \nsymptoms73. Moreover, to complement on questionnaire assessment, we have used 404 \nclinical diagnoses recorded from national and regional healthcare registers. While we 405 \nlacked information on clinical diagnostic process, prospective symptom charting has 406 \nbeen outlined in clinical guidelines in Sweden28. With both questionnaire assessment 407 \nand register-recorded diagnoses, we may have captured moderate/severe PMS, 408 \nPMDD, and false positive. In a sensitivity analysis, we however restricted cases to 409 \nthose confirmed by both questionnaire assessment and clinical diagnosis, 410 \npresumably with a higher validity, and yielded comparable point estimates for top 411 \nSNPs. Secondly, due to small numbers, we removed the participants of non-412 \nEuropean ancestry. Future studies should include diverse ancestral backgrounds to 413 \naim for generalizable results across different groups, making the findings relevant 414 \nand potentially beneficial to a wider population, as the lack of replication in the 415 \ncurrent study is a limitation. 416 \n 417 \nIn summary, in this first genome-wide association meta-analysis of DPS, we report a 418 \nsignificant locus and genetic correlations with a range of psychosocial and 419 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 19\ngynecological phenotypes. The identified genetic markers may help advance our 420 \nunderstanding of the underlying mechanisms, which may further aid early detection 421 \nand clinical management of PMDs. Future studies with larger, more diverse samples, 422 \nand cases confirmed with prospective symptom charting are needed to further 423 \nunderstand the genetic influence on PMDs, and potentially the mechanisms of mood 424 \nregulating effects of sex hormones. 425 \n 426 \n 427 \nACKNOWLEDGEMENTS 428 \nThis work was funded by the Swedish Research Council (grant number 2020-01003 429 \nand 2024-02592 to DL), European Research Council (101165552 to DL), Swedish 430 \nResearch Council for Health, Working Life and Welfare (2023-00399 to DL), 431 \nKarolinska Institutet (to DL), and Karolinska Institutet - National Institute of Health 432 \nNeuroscience Doctoral Program (to EH, DL, and PJS). OAA received support from 433 \nthe Research Council of Norway (324252, 324499, 300309, 326813, 334920, 434 \n271555/F21), the South-East Norway Regional Health Authority (2023-031 and 435 \n2022-073), The University of Oslo, KG Jebsen Stiftelsen (SKGJ-MED-021), the 436 \nEuropean Union’s Horizon 2020 research and innovation program (847776, 964874), 437 \nNordForsk (164218) and the US National Institutes of Health (R01MH123724-01). 438 \nCMB was supported by Swedish Research Council (Vetenskapsrådet, award: 538-439 \n2013-8864). YL was supported by US NIMH R01 MH123724, the Swedish Research 440 \nCouncil (2021-02615), and the European Research Council (101042183). ADF was 441 \nsupported by European Research Council (947763). We extend our gratitude to the 442 \nLifeGene and MoBa participants for their time and commitment, which made this 443 \nresearch possible.  444 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 20\n 445 \nLifeGene was supported by grants from the Swedish Research Council, Karolinska 446 \nInstitutet, Karolinska Institutet/Stockholm County Council core facility funds, the 447 \nRagnar and Torsten Söderberg Foundation, and AFA Insurance.  Genotyping was 448 \nperformed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se). 449 \nThe facility is part of the National Genomics Infrastructure supported by the Swedish 450 \nResearch Council for Infrastructures and Science for Life Laboratory, Sweden. The 451 \ncomputations/data handling/[SIMILAR] were/was enabled by resources provided by 452 \nthe National Academic Infrastructure for Supercomputing in Sweden (NAISS), 453 \npartially funded by the Swedish Research Council through grant agreement no. 454 \n2022-06725. 455 \n 456 \nMoBa is supported by the Norwegian Ministry of Health and Care Services and the 457 \nMinistry of Education and Research. We are grateful to all the participating families 458 \nin Norway who take part in this on-going cohort study. We thank the Norwegian 459 \nInstitute of Public Health (NIPH) for generating high-quality genomic data. This 460 \nresearch is part of the HARVEST collaboration, supported by the Research Council 461 \nof Norway (grant 229624). The Norwegian Centre for Mental Disorders Research 462 \n(NORMENT) provided genotype data, funded by the Research Council of Norway 463 \n(grant 223273), South East Norway Health Authorities and Stiftelsen Kristian 464 \nGerhard Jebsen. We further thank the Center for Diabetes Research, the University 465 \nof Bergen for providing genotype data and performing quality control and imputation 466 \nof the data funded by the ERC AdG project SELECTionPREDISPOSED, Stiftelsen 467 \nKristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council of Norway, 468 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 21\nthe Novo Nordisk Foundation, the University of Bergen, and the Western Norway 469 \nHealth Authorities. 470 \n 471 \nWe would like to thank Robert Karlsson, PhD (https://orcid.org/0000-0002-8949-472 \n2587) for his invaluable assistance with Ricopili and data management, which were 473 \ncrucial to the success of this study.  474 \n 475 \n . 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Forrester-Knauss C, Zemp Stutz E, Weiss C, T schudin S. The interrelation 708 \nbetween premenstrual syndrome and major depression: Results from a 709 \npopulation-based sample. BMC Public Health. 2011;11(1):795. doi:10.1186/1471-710 \n2\n458-11-795 711 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 28\n71. Chen LC, Hsu JW, Huang KL, et al. Risk of developing major depression and 712 \nanxiety disorders among women with endometriosis: A longitudinal follow-up 713 \nstudy. J Affect Disord. 2016;190:282-285. doi:10.1016/j.jad.2015.10.030 714 \n72. Dubey N, Hoffman JF, Schuebel K, et al. The ESC/E(Z) complex, an effector of 715 \nresponse to ovarian steroids, manifests an intrinsic difference in cells from 716 \nwomen with premenstrual dysphoric disorder. Mol Psychiatry. 2017;22(8):1172-717 \n1184. doi:10.1038/mp.2016.229 718 \n73. Cohen LS, Soares CN, Otto MW, Sweeney BH, Liberman RF, Harlow BL. 719 \nPrevalence and predictors of premenstrual dysphoric disorder (PMDD) in older 720 \npremenopausal women: The Harvard Study of Moods and Cycles. Journal of 721 \nAffective Disorders. 2002;70(2):125-132. doi:10.1016/S0165-0327(01)00458-X 722 \n 723 \n 724 \n 725 \n 726 \n 727 \n 728 \n 729 \n 730 \n 731 \n 732 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 29\nFigure 1.  Manhattan plot from the meta-analyzed GWAS of DPS. This analysis included 17,511 cases and 50,633 \ncontrols. Dashed red line indicates genome-wide significant threshold P=5E-8. Pale dashed red line indicates suggestive \nsignificance threshold P=1.0x10-6. Green dots show SNPs in LD (r2>0.8) with the lead SNP of each genome-wide \nsignificant or suggestive locus. The point estimates of lead SNPs are provided in Table 2. \n \n \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \nFigure 2. Genetic correlations (rg) between DPS and psychosocial, gynecological \ntraits, and steroid hormones. Dots indicate the point estimate while caps indicate the \n95% confidence interval. Green dots denote empirical P value <0.05. The actual \nestimates are provided in Supplementary Table S4. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \nFigure 3. Regional plot for the lead SNP rs758170. SNPs which are not in LD of the \nlead SNP in the selected region are colored grey. Only SNPs which are in LD of the \nlead SNP are displayed in the plot. \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 32\nTable 1. Characteristics of women with and without DPS in the LifeGene and MoBa cohorts. \n \n \n \n  \nLifeGene    MoBa   \n  Without DPS With DPS P-value Without DPS With DPS P-value \nIndividuals, N 3267 1962  51,519 15,549  \nAge (years) 34 ±/i18.43 33 ± /i17.79  29.47 ± 4.56 30.1 ± 4.68  \nEducational Levela      0.060   <0.001 \n  Pre-secondary \neducation 112 (3.42) 48 (2.5)  1137 (2.2) 427 (2.7)  \n  Secondary education 777 (23.7) 513 (26.1)  15815 (30.7) 5393 (34.7)  \n  Post-secondary \neducation 2378 (72.4) 1401 (71.1)  31936 (62.0) 8850 (56.9)  \n Unknown 12 (0.4) 5 (0.3)  2631 (5.1) 879 (5.7)  \nCivil Statusa     0.024   0.003 \n \nSingle/separated/widower 1922 (58.8) 1228 (62.5)  915 (1.8) 301 (1.9)  \nMarried/cohabitant  1342 (41.0) 733 (37.3)   46648 (90.5) 13937 (89.6)  \nUnknown 3 (0.09) 1 (0.05)   3956 (7.7) 1311 (8.4)  \nDepressionb     <0.001   <0.001 \n No 2795 (85.6) 1390 (70.9)  47111 (91.4) 13208 (84.9)  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 33\n \n  \nLifeGene    MoBa   \n  Without DPS With DPS P-value Without DPS With DPS P-value \n Yes 472 (14.4) 572 (29.1)  4408 (8.6) 2341 (15.1)  \nAnxiety disorderb     <0.001   <0.001 \n No 2738 (83.8) 1316 (67.1)  47895 (93.0) 13882 (89.3)  \n Yes 529 (16.2) 646 (32.9)  3624 (7) 1667 (10.7)  \n Age at menarche \n<= 10 years  \n13.39 ± 3.64 \n \n82 (2.5) \n13.20 ± 3.50 \n \n81 (4.1) \n0.005 13.05 ± 1.37 \n \n970 (1.9) \n12.93 ± 1.39 \n \n387 (2.5) \n<0.001 \n 11 – 14 years  2577 (78.9) 1552 (79.1)  43036 (83.5) 13172 (84.7)  \n >= 15 years  419 (12.8) 232 (11.9)  6860 (13.3) 1807 (11.6)  \nUnknown 189 (5.8) 97 (4.9)  653 (1.3) 183 (1.2)  \nParitya   0.134   <0.001 \n \n 0 \n \n \n1941 (59.5) \n \n \n1196 (60.9) \n  \n \n29505 (57.3) \n \n \n7611 (48.9) \n \n 1 398 (12.1) 247 (12.6)  14740 (28.6) 4912 (31.6)  \n 2 697 (21.3) 367 (18.7) \n  \n6031 (11.7) \n \n \n2458 (15.8) \n \n >=3  231 (7.1) 152 (7.8)  1243 (2.4) 568 (3.7)  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 34\n \nsd standard deviation, PND perinatal depression \nmean/i1±/i1sd or N (%). \nMoBa Norwegian Mother, Father and Child Cohort Study. \nP-values were obtained from chi-squared test. \na At baseline. \nb Lifetime (in LifeGene). \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 35\nTable 2.  Lead SNPs associated with DPS: a meta-analysis of the LifeGene and the MoBa. Lead SNPs are independent, significant \nSNPs that have reached either GWAS significant P-value <5x10-8 or borderline significance P-value <5x10-6 in the genomic rick \nloci. The SNPs are presented in ascending order or the P-values.  \n \n               \nChr Position rsID Gene A1 A2 Sample OR (95% CI) a P-value P for \nheterogeneity \n12 2361460 rs758170 CACNA1C C T LifeGene 0.95 (0.86–\n1.04) \n0.242  \n           MoBa 0.92 (0.90–\n0.95) \n2.68 x 10-8  \n           Meta-analysis 0.93 (0.90–\n0.95) \n1.53 x 10-8 0.5949 \n5 144181270 rs76665457 CTB-85P21.1 G C LifeGene 0.80 (0.63–\n0.99) \n0.032  \n           MoBa 0.86 (0.81–\n0.91) \n5.30 x 10-7  \n           Meta-analysis 0.86 (0.80–\n0.86) \n5.96 x 10-8 0.5668 \n10 56883744 rs12770903 PCDH15 \n \nG T LifeGene 0.81 (0.62–\n1.02) \n0.066  \n           MoBa 0.85 (0.80–\n0.91) \n7.31 x 10-7  \n           Meta-analysis 0.84 (0.78–\n0.90) \n1.34 x 10-7 0.7665 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 36\n               \nChr Position rsID Gene A1 A2 Sample OR (95% CI) a P-value P for \nheterogeneity \n4 15764240 rs147346386 CD38 C T LifeGene 0.60 (0.39–\n0.85) \n0.001  \n            MoBa 0.84 (0.78–\n0.90) \n5.89 x 10-6  \n            Meta-analysis 0.82 (0.77–\n0.89) \n1.95 x 10-7 0.0813 \n13 90887997 rs4773561 KRT18P27 G A LifeGene 0.89 (0.80–\n0.98) \n0.013  \n      MoBa 0.93 (0.91–\n0.96) \n2.96 x 10-6  \n      Meta-analysis 0.93 (0.90–\n0.95) \n2.06 x 10-7 0.3235 \n1 151581008 rs77519409 RP11-404E16.1 G A LifeGene - -  \n      MoBa 1.17 (1.10–\n1.25) \n4.61 x 10-7  \n      Meta- analysis 1.17 (1.10–\n1.25) \n4.61 x 10-7 - \n18 77629986 rs112526506 KCNG2 G A LifeGene 1.08 (0.95–\n1.23) \n0.183  \n      MoBa 1.10 (1.06– 1.50 x 10-6  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint \n\n \n 37\n               \nChr Position rsID Gene A1 A2 Sample OR (95% CI) a P-value P for \nheterogeneity \n1.15) \n      Meta- analysis 1.10 (1.06–\n1.14) \n6.12 x 10-7 0.8539 \n \n \nChr chromosome, Position on the chromosome (GRCh37 genomic build), SNP single-nucleotide polymorphism, A1 Risk allele, \nA2 reference allele, OR odds ratio, CI confidence interval, P-value, MoBa Norwegian Mother, Father and Child Cohort Study. \naEstimates were adjusted for 10 principal components in LifeGene and MoBa; and were additionally adjusted for the sub-study \nmembership and year of birth, respectively.  \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted December 2, 2025. ; https://doi.org/10.64898/2025.12.01.25341208doi: medRxiv preprint","source_license":"CC0","license_restricted":false}