The genetics of fibromyalgia and its relationships to psychiatric and medical traits.

This research was not restricted or prohibited in the setting of any of the included researchers. All studies were approved by the relevant local institutional review boards and ethics review committees. AoU was approved by the AoU IRB, governed by the US National Institutes of Health (NIH). UKBB was approved by the North West Multi-center Research Ethics Committee as a research tissue bank. MVP was approved by the Veterans Affairs Central Institutional Review Board. We analyzed data from AoU, version 8 (whole-genome sequencing), MVP (imputation conducted using the 1000 Genomes project 138 as a reference panel), and UKBB (imputation conducted using the Haplotype Reference Consortium 139 , in combination with the merged UK10K and 1000 Genomes reference panels 140 ). For full description of genotyping and quality control procedures of AoU, MVP, and UKBB (EUR1, British EUR population; EUR2, non-British EUR population), see refs. 20 , 141 , 142 , 143 , respectively. In the meta-analysis, we also included summary statistics for fibromyalgia from Finngen subjects (release 12) 22 . To include only confirmed cases of a studied disorder, a phenotype is often defined by a stringent ICD-10 code-based phenotype definition (a): ≥ 2 outpatient and/or 1 inpatient visit/s. In this study, to create well-powered analyses, we aimed to assemble a larger sample size by considering inclusion of (b) fibromyalgia subjects meeting a less-stringent ICD-10 phenotype definition (1 outpatient visit), or (c) self-report (of having been diagnosed with fibromyalgia). To do this, we conducted separate GWAS of fibromyalgia, assigning subjects as cases according to the three different phenotype definitions: stringent ICD-10, less-stringent ICD-10 (with a single outpatient diagnosis considered sufficient to define affection), and self-report. For all the analyses that involved ICD-10 codes, we used ICD-10 code M79.7. Cases distribution and overlap between ICD-10 and self-report phenotype definitions in AoU and in UKBB are presented in Supplementary Fig.  1 . We then calculated the genetic correlations between GWAS based on these differing case definitions, to ascertain genetic similarity between these phenotypes. We conducted these preliminary phenotype definition tests in each of AoU, MVP and UKBB separately. In each of the cohorts, we defined fibromyalgia two or three ways, depending on data availability: a stringent ICD-10 code definition, less-stringent ICD-10 code definition, and self-report. Subjects that fit more than one of these phenotypes were excluded from this part of the analysis completely, i.e., they were not defined as cases nor controls (subjects that fit, for example, both strict ICD-10 code and self-report, were excluded; this exclusion applied only for these analyses conducted to evaluate the genetic relationship of subjects defined by these three phenotype methods). Only subjects that fit one category but not the others were included in the analysis as cases of the relevant category. Subjects that did not fit any category were classified as controls. Any subject classified as a case in one of these analyses, was excluded from the others (i.e., there was no situation where a subject was used as a case for one analysis and as a control subject in another). We conducted GWAS of these phenotypes separately, removing one individual from each pair of related subjects (kinship coefficient cutoff = 0.1), retaining as many cases as possible. GWAS were conducted with PLINK 2.0 using logistic regression, with sex, age, and the first ten genetic principal components (PCs) as covariates. In AoU, we extracted three different fibromyalgia phenotypes: stringent ICD-10 (n cases  = 2,932, n controls  = 212,390), less-stringent ICD-10 (n cases  = 1,951, n controls  = 210,571), and self-report (“Including yourself, who in your family has had fibromyalgia? Select all that apply. – Self”; n cases  = 5,339, n controls  = 208,813). In MVP, we extracted two different fibromyalgia phenotypes: stringent ICD-10 (n cases =24,041, n controls  = 384,196) and less-stringent ICD-10 (n cases =20,847, n controls  = 379,286). In UKBB, we extracted two different fibromyalgia phenotypes: stringent ICD-10 (EUR1: n cases  = 2,328, n controls  = 374,731; EUR2: n cases  = 329, n controls  = 52,072) and self-report (“Have you ever been told by a doctor that you have had any of the following conditions? -Fibromyalgia syndrome -Yes”; EUR1: n cases  = 1,666, n controls  = 374,840; EUR2: n cases  = 228, n controls  = 52,081; non-stringent data (defined elsewhere based on outpatient visits) were not available), and conducted an inverse variance weighing meta-analysis of EUR1 and EUR2 results of each phenotype (using METAL 144 ) before further analyses. For all the analyses, we used LDSC 145 to calculate the liability-scaled heritability estimate (h 2 ) and intercept (population prevalence was set at 2.7% 2 ). We also tested the genetic correlations for the fibromyalgia phenotypes between these fibromyalgia case definitions, defined by stringent ICD-10 code definition, less-stringent ICD-10 code definition, and self-report. A strong genetic correlation between these sub-cohorts would confirm that these different definitions of fibromyalgia correspond to genetically similar traits, enabling the valid inclusion of subjects of these different cohorts. We also included in our analysis EUR fibromyalgia summary statistics from Finngen, release 12 22 , defined by the stringent ICD-10 code (n cases = 3623, n controls  = 357,549). After we confirmed that the various fibromyalgia phenotypes (stringent ICD-10, less-stringent ICD-10 and self-report) are strongly genetically correlated (see previous section) – suggesting that they are likely representing a consistent trait – we defined a singular fibromyalgia phenotype in AoU, MVP and UKBB: subjects with stringent ICD-10 (M79.7), less-stringent ICD-10 and self-report definitions of fibromyalgia, including overlapping samples, were defined as cases. All other subjects were defined as controls. In AoU and MVP we conducted separate GWAS in subjects of European (EUR), African (AFR) and Latin American (AMR) genetically-defined ancestries. In UKBB we conducted the analysis in EUR. In each cohort, after removing one individual from each pair of related subjects (kinship coefficient cutoff = 0.1), retaining as many cases as possible, GWAS was conducted with PLINK 2.0 using logistic regression, with sex, age and the first ten genetic PCs as covariates. Variants with minor allele frequency (MAF) < 0.1%, Hardy-Weinberg equilibrium (HWE) p  < 1x10 −6 , or imputation quality < 0.6 were excluded. In UKBB, we meta-analyzed EUR1 and EUR2 results before further analyses. For each ancestry in each cohort we also conducted sex-stratified GWAS. EUR, AFR, AMR and cross-ancestry SE-based meta-analyses (for all subjects and sex-stratified) were conducted using METAL 144 , including heterogeneity analysis. The non-sex-stratified EUR meta-analysis included fibromyalgia summary statistics from the Finngen biobank 22 . In all the analyses we applied a standard genome-wide multiple testing correction ( p  < 5×10 −8 ). The results were visualized in a Manhattan plot using the R package qqman 146 . Regional Manhattan plots were created using FUMA 147 . Linkage disequilibrium (LD) was calculated between three variants on chromosome 3 (rs2681780, rs71080556, and rs763622663) that were identified as lead SNPs in three different GWAS. These variants are located within different genes yet in proximity to each other, and may represent the same GWS signal. This analysis was conducted using PLINK2 in EUR subjects in AoU. We used FUMA 147 to define genomic risk loci and map the lead variants’ exact location in the genome, pointing to a specific gene when applicable. We used MAGMA 148 , implemented in the FUMA platform, to conduct a gene-based analysis of the EUR, AFR, AMR and cross-ancestry meta-analyses. In the gene-based analyses, input SNPs were mapped to 19,069 protein-coding genes in EUR, 10,995 in AFR, 18,856 in AMR and 19,173 cross-ancestry, and corrected for false-discovery rate (FDR). We used LD score regression (LDSC) 145 based on the linkage disequilibrium reference from the 1000 Genomes data 138 for all EUR cohorts. We calculated the liability-scaled SNP-based heritability estimate (h 2 ) and intercept of fibromyalgia in every cohort separately (population prevalence was set on 2.7% 2 ), and the genetic correlations (r g ) between fibromyalgia phenotypes in the different cohorts. After meta-analysis, we calculated h 2 for the meta-analyzed fibromyalgia trait and its r g with a list of traits related to psychiatric disorders, autoimmune diseases, sleep, neurodegeneration, and general health 22 , 44 , 56 , 64 , 95 , 119 , 121 , 143 , 149 – 157 , phenotypically associated with fibromyalgia according to prior literature 1 , 2 , 112 , 113 . We also conducted similar analyses for the sex-stratified meta-analyses of fibromyalgia in EUR and for the EUR MTAG, using the same summary statistics for the comparative traits that we used for the main analysis (we did not use sex-specific summary statistics of other traits due to low availability of well-powered GWAS of most of the traits we analyzed, and because we aimed to maintain the analyses with as few differences as possible, for better comparison between the results). After Bonferroni correction for 108 tests (4 fibromyalgia phenotypes x 27 traits), the statistical significance threshold was set at p  = 0.0005. To enhance the statistical power of the EUR fibromyalgia GWAS, we used MTAG 158 , a method that enables joint analysis of traits that are genetically correlated with each other, and provides estimates of trait-specific effects. We used pain, a similar phenotype to the trait with the strongest genetic correlation with fibromyalgia we found using LDSC (chronic pain). A diagnosis of fibromyalgia may be categorized as pain, which raised the risk of using overlapping samples diagnosed under overlapping traits. Although MTAG is appropriate for overlapping samples, to minimize confounds we avoided using datasets with potentially high case overlap with fibromyalgia, and therefore we used summary statistics for pain among Finngen subjects, with 237,944 cases and 261,418 controls 22 . This pain phenotype was defined by 16 different diagnoses associated with limb, back, neck, head, and abdominal pain. There were only 817 fibromyalgia subjects in the same group, < 0.2% of the number of the pain patients, illustrating the low susceptibility for case overlap. The r g between these two traits was 0.72 ( ± 0.026). For an additional, more inclusive analysis, we aimed to capture the wider complexity of fibromyalgia as both a pain- and psychiatric-related trait. For this analysis we used depression for the MTAG analysis, because of the availability of a large-scaled GWAS of EUR major depressive disorder (MDD) that does not include any of the cohorts we used in our study. We used MDD summary statistics from a large meta-analysis 24 , taking a leave-one-out (LOO) approach, excluding subjects from MVP, UKBB, Finngen – and also 23andMe due to use restrictions (these four cohorts were part of the set of cohorts included in the aforementioned MDD study). After exclusion, the MDD dataset included 181,654 MDD cases and 862,012 controls. The r g between fibromyalgia and MDD was 0.63 ( ± 0.025), and between pain and MDD was 0.56 ( ± 0.021). In both sets of analysis, MTAG was conducted using fibromyalgia as the main trait, with variants restricted to only those common to the two GWAS, with MAF > 0.01. Because MDD had a relatively lower genetic correlation with fibromyalgia and with pain than the recommended threshold for MTAG (rg ≥ 0.7), we focused on the MTAG of fibromyalgia leveraged by pain (fibromyalgia MTAG leveraged by pain and MDD together is reported in the supplementary material). We conducted MR analyses to estimate the causality between fibromyalgia and 25 traits that had significant effects in the genetic correlations analysis (see previous section), assessing fibromyalgia both as exposure and outcome. All MR analyses were conducted using MRlap 159 , which is appropriate for MR analysis with potentially overlapping cohorts. We ran the inverse variance weighted (IVW) model, with two p-value thresholds of 1 × 10 −5 and 1 × 10 −8 to select genetic instruments. Due to a low number of variants left after pruning using the stricter threshold of 1 x 10 −8 , we report only the 1 × 10 −5 threshold analysis in our results (MR analysis using a p-value threshold of 1 x 10 −8 is reported in the supplementary material). Instrument pruning was conducted based on an LD threshold of 0.05. For every analysis, MRlap performs a correction for overlapping samples and other potential biases such as outliers and presents the statistical difference between the observed and corrected values. Where the difference between these values was significant ( p  < 0.05) we presented the corrected values in the results section; where the difference was not significant, we presented the observed values. To account for potential pleiotropy and for violations of instrumental variable assumptions, we applied complementary two-sample MR methods 25 , including MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode. PTSD could not be included in the two-sample MR analyses because the available summary statistics do not provide effect size and standard error 64 , which are essential for these analyses. After Bonferroni correction for 340 tests (MRlap: 25 traits as exposure and outcome, in two different p-value thresholds; two-sample MR: 24 traits as exposure and outcome, measured in five different tests), the p-value significance threshold set at 1.47 x 10 −4 . Pleiotropy was tested for all significant causal effects using the MR-Egger intercept test (p-value threshold after Bonferroni correction for 48 tests set at 0.001). We used LAVA 160 to calculate local genetic correlations between fibromyalgia and 27 traits of interest in EUR. The genome was divided into 2,495 genomic regions to provide minimal LD between the regions and maintain an approximately equal size of the regions of ~1 MB. Breakpoints between regions were computed according to the LD between neighboring SNPs as described previously 160 , maintaining regions as relatively independent. Univariate local correlations were calculated for each trait, and only regions that reached significance ( p  = 2 x 10 −5 after Bonferroni correction) were used to calculate genetic correlations among the cohorts (9295 regions in 27 pairs). Bonferroni correction for 9295 tests yielded a statistical significance threshold of p  = 5.38 x 10 −6 . We applied Popcorn 26 to calculate the cross-ancestry genetic correlations between fibromyalgia in AFR and AMR and fibromyalgia in EUR, as well as other traits in EUR. We conducted TWAS using GTEx_v8 161 , which provides expression data of 49 tissues in EUR samples. We used the 1000 Genomes dataset as LD reference. Using FUSION 162 , we identified associated genes, then processed the results to distinguish conditionally independent genes. In 49 tissues, there were a total of 300,187 genes measured (an average of 6126 ± 2787 per tissue). We therefore used a Bonferroni correction for 300,187 tests (0.05/300,187) to set a p -value threshold of 1.66 x 10 −7 . We conducted SMR to test whether genetic variants that regulate gene expression (eQTLs) influence fibromyalgia risk. We conducted this analysis across thousands of genes in 51 tissues (an average of 3,239 genes per tissue), using data from GTEx_v8 161 , implemented in the SMR portal 163 . GTEx_v8 data presents the influence of various SNPs on gene expression across different tissues. In this analysis, gene expression served as the exposure, and summary statistics of our fibromyalgia EUR meta-analysis as the outcome; for each tissue, we tested whether gene expression had an inferred causal effect on fibromyalgia (i.e., we tested whether variants that regulate gene expression affect fibromyalgia). Heterogeneity in dependent instruments (HEIDI) test was conducted to assess whether the association was consistent with a shared genetic signal rather than distinct variants in LD. Across all genes and tissues included in the analysis, we conducted 165,231 tests. After Bonferroni correction, p-value threshold was set 3.03 x 10 −7 . We conducted a gSEM analysis 164 on fibromyalgia and 22 traits that had significant genetic correlations with fibromyalgia. Three of these traits – academic degree, executive functioning and physical activity, which had a negative genetic correlation with fibromyalgia – were reverse-coded before the analysis. Exploratory factor analysis (EFA) was performed followed by confirmatory factor analysis (CFA) to identify and confirm the factor structure. EFA models containing 1–9 factors were evaluated based upon eigenvalues, sum of squared (SS) loadings, cumulative variance explained, and the distribution of variance explained across the respective factors. CFA models were assessed using traditional fit indices 164 . We used the drug.MATADOR database, which is implemented in ShinyGo 0.82 165 , to identify potential drug-target interactions, using 173 genes that had a significant effect in at least one of the methods we used in this study – GWAS, MAGMA, MTAG and TWAS – in any of the ancestries we studied. After we found a significant effect of ABO (a blood-type determining gene) in the MTAG, and based on known associations between blood type and pain perception 84 , 85 , we compared the ABO blood group blood type phenotype between fibromyalgia subjects and the general population, using data available in UKBB. This study used publicly available software, as follows: PLINK 2.0: plink-ng/2.0 at master · chrchang/plink-ng · GitHub LDSC 145 : https://github.com/bulik/ldsc . METAL 144 : https://github.com/statgen/METAL . MRlap 159 : https://github.com/n-mounier/MRlap . MAGMA 148 : https://cncr.nl/research/magma/ . LAVA 166 : https://github.com/josefin-werme/LAVA . MTAG 158 : https://github.com/JonJala/mtag . gSEM 164 : https://github.com/GenomicSEM/GenomicSEM . FUSION 162 : https://github.com/gusevlab/fusion_twas . MR-MEGA 167 : https://github.com/NIH-CARD/MA_MA_meta . SMR 163 : https://github.com/jianyanglab/SMR-Portal . Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

In each of the cohorts we analyzed in this study (AoU, MVP, UKBB), we conducted a separate GWAS for every phenotype definition of fibromyalgia (stringent ICD-10, less-stringent ICD-10, self-report), and then calculated the heritability and the genetic correlations between them (Suppl. Data  1 ). Within AoU and MVP, we found significant genetic correlations between all the different phenotypes, with r g  ~ =1.00. Within UKBB, the genetic correlation between the two defined phenotypes was not significant, likely due to relatively small sample sizes. We therefore tested the genetic correlation between the UKBB phenotypes and their closest equivalents in AoU (self-report in UKBB vs self-report in AoU, and stringent ICD-10 in UKBB vs stringent ICD-10 in AoU) and found a nominally significant effect of r g  = 0.77 between the self-report traits, and a significant effect for the stringent ICD-10 traits, with r g  = 1.07 (Suppl. Data  2 ). In view of these results, we combined all the phenotypic variations and assembled inclusive fibromyalgia phenotypes in all cohorts (Table  1 ). Table 1 Demographics Ancestry Sex* Cohort Measure Cases Controls Total Effective** % Cases EUR All Subjects AoU SR + ICD 11,878 208,531 220,409 44,952 5.39% MVP ICD 41,847 384,192 426,039 150,947 9.82% UKBB SR + ICD 5460 427,444 432,904 21,565 1.26% Finngen ICD-10 3623 357,549 361,172 14,347 1.00% Meta 62,808 1,377,716 1,440,524 240,278 4.36% Females AoU SR + ICD 10,775 120,767 131,542 39,570 8.19% MVP ICD 7970 22,751 30,721 23,609 25.94% UKBB SR + ICD 4464 234,875 239,339 17,523 1.87% Meta 23,209 378,393 401,602 87,471 5.78% Males AoU SR + ICD 1113 87,915 89,028 4396 1.25% MVP ICD 33,877 361,441 395,318 123,896 8.57% UKBB SR + ICD 782 204,850 205,632 3116 0.38% Meta 35,772 654,206 689,978 135,670 5.18% AFR All Subjects AoU SR + ICD 2295 70,616 72,911 8891 3.15% MVP ICD 13,468 94,580 108,048 47,157 12.46% Meta 15,763 165,196 180,959 57,560 8.71% Females AoU SR + ICD 2189 40,271 42,460 8305 5.16% MVP ICD 3465 11,153 14,618 10,575 23.70% Meta 5654 51,424 57,078 20,376 9.91% Males AoU SR + ICD 108 30,431 30,539 430 0.35% MVP ICD 10,003 83,427 93,430 35,728 10.71% Meta 10,111 113,858 123,969 37,145 8.16% AMR All Subjects AoU SR + ICD 2517 67,184 69,701 9704 3.61% MVP ICD 4051 32,337 36,388 14,400 11.13% Meta 6568 99,521 106,089 24,645 6.19% Females AoU SR + ICD 2356 43,104 45,460 8935 5.18% MVP ICD 823 2607 3430 2502 23.99% Meta 3179 45,711 48,890 11,889 6.50% Males AoU SR + ICD 163 24,149 24,312 648 0.67% MVP ICD 3228 29,730 32,958 11,647 9.79% Meta 3391 53,879 57,270 12,761 5.92% Females and males sample sizes do not necessarily sum up to be exactly as the All Subjects figures, due to kinship removal that was done separately for each analysis. **Effective sample size = 4/(1/nCases + 1/nControls) [ SR self-report; ICD ICD-10 code, AoU All of Us version 8, MVP Million Veterans Program, Meta meta-analysis, UKBB UK Biobank, EUR European, AFR African, AMR Latin American]. Demographics Females and males sample sizes do not necessarily sum up to be exactly as the All Subjects figures, due to kinship removal that was done separately for each analysis. **Effective sample size = 4/(1/nCases + 1/nControls) [ SR self-report; ICD ICD-10 code, AoU All of Us version 8, MVP Million Veterans Program, Meta meta-analysis, UKBB UK Biobank, EUR European, AFR African, AMR Latin American]. We conducted GWAS of fibromyalgia in three EUR cohorts. For this analysis we used an inclusive fibromyalgia phenotype (see previous section), defined by stringent ICD-10 code, less-stringent ICD-10 code and/or self-report in AoU, by stringent ICD-10 code and less-stringent ICD-10 code in MVP and by stringent ICD-10 code and self-report in UKBB. We found one lead variant in each, all different. In an EUR meta-analysis (n effective  = 240,278), which included fibromyalgia summary statistics of the aforementioned AoU, MVP and UKBB analyses, plus summary statistics of stringent ICD-10 definition of fibromyalgia from Finngen (which did not have any significant results on its own), we discovered ten independent lead SNPs: LOC105378797 *rs1993709, FBLN7 *rs72831629, TRAIP *rs71080556, PRR16 *rs56405820, BAG6 *rs2242656, TSBP1-AS1 *rs9279546, PBX3 *rs6478712, rs2587363, rs181388182 and rs11395028 (Table  2 , Fig.  1a ). For AFR and AMR ancestries, GWAS analyses of fibromyalgia – conducted using an inclusive fibromyalgia phenotype – were possible in two cohorts – AoU and MVP. In AFR, there were no significant regions in AoU and one in MVP - POLR1C *rs186798404 – which survived in the AFR meta-analysis (Suppl. Fig.  2 ). In AMR there were no significant effects in any of the analyses (Suppl. Fig.  3 ). A cross-ancestry meta-analysis, which included the three ancestral meta-analyses, revealed 12 significant lead variants: LOC105378797*rs10889947, FBLN7 *rs72831629, CAMKV *rs2681780, PRR16 *rs190161089, BAG6 *rs2242656, TSBP1-AS1 *rs9279546, UHRF1BP1 *rs16894959, POLR1C *rs186798404, rs12555516, rs10819064, CNNM2 *rs75970938 and rs9536401 (Table  2 , Fig.  1b ). In all the meta-analyses, heterogeneity was not detected for any of the significant SNPs (HetPVal > 0.05; see also Suppl. Fig.  10 ). Regional Manhattan plots of all our lead SNPs in the various meta-analyses are presented in Suppl. Figs.  11 – 33 . We also conducted sex-stratified analyses, with no significant results for males in any of the individual and meta-analyzed AFR and AMR cohorts. In EUR, in UKBB we found one GWS locus in SUCLG2-DT *rs10539712, which did not survive the meta-analysis. In females, in EUR we found one significant lead SNP in UKBB: CRYBG3 *rs188327717; one in AoU: rs1030125981; and none in MVP. In the EUR meta-analysis we found three lead SNPs: IP6K1 *rs763622663, PRR16 *rs62381083 and PCLO *rs73167394. We did not find any female-specific effects in AFR and AMR. (Table  2 , Suppl. Figs.  4 – 9 ). The LD between TRAIP *rs71080556, CAMKV *rs2681780, and IP6K1* rs763622663, which were GWS in three different analyses, was relatively high (r 2  > 0.6; rs71080556:rs2681780, r 2  = 0.62, d’ = 0.8; rs71080556:rs763622663, r 2  = 0.95, d’ = 0.98; rs763622663:rs2681780, r 2  = 0.64, d’ = 0.81). Fig. 1 Main analysis. GWAS meta-analysis of fibromyalgia in ( a ) EUR population (n total  = 1,440,524, n eff  = 240,278), and ( b ) cross-ancestry (EUR-AFR-AMR; n total  = 1,727,572, n eff  = 323,773). ( c ) MTAG of fibromyalgia leveraging pain data (n eff  = 240,278). Significant variants that are located within or near a gene are annotated according to the relevant gene; significant variants which are not located in the proximity of a gene are annotated with their rsID. Association tests were performed using a logistic regression model adjusted for age, sex, and the first ten genetic PCs as covariates. All tests were two-sided. Genome-wide significance was defined at p  < 5 × 10 − 8 . Table 2 Lead SNPs in GWAS meta-analyses of fibromyalgia Anc. Sex Cohort rsID Chr Pos (GRCh38) EA Beta SE P Gene EUR All AoU rs112902269 3 49,824,991 CA 0.078 0.014 1.21E-08 - MVP rs6478712 9 125,752,329 C 0.045 0.008 5.66E-09 PBX3 UKBB rs188327717 3 97,935,433 T 0.112 0.018 2.87E-10 CRYBG3 Meta rs1993709 1 72,372,846 A −0.086 0.014 3.24E-10 LOC105378797 rs72831629 2 112,175,548 T −0.068 0.012 6.45E-09 FBLN7 rs71080556 3 49,828,612 G −0.074 0.012 6.44E-10 TRAIP rs56405820 5 120,795,518 A 0.035 0.007 4.55E-08 PRR16 rs2242656 6 31,646,325 T 0.043 0.008 1.56E-08 BAG6 rs9279546 6 32,264,129 CT −0.043 0.007 6.95E-09 TSBP1-AS1 rs6478712 9 125,752,329 T −0.040 0.006 1.54E-10 PBX3 rs2587363 13 53,341,798 A −0.057 0.010 1.45E-08 - rs181388182 15 47,119,288 T 0.037 0.007 4.78E-08 - rs11395028 17 52,208,371 CT 0.066 0.012 2.18E-08 - F AoU rs1030125981 3 49,824,985 CA 0.081 0.014 1.33E-08 - UKBB rs188327717 3 97,935,433 T 0.124 0.019 3.48E-09 CRYBG3 Meta rs763622663 3 49,758,958 A −0.077 0.013 1.37E-09 IP6K1 rs62381083 5 120,794,269 T −0.062 0.011 1.22E-08 PRR16 rs73167394 7 82,797,010 A 0.060 0.011 2.66E-08 PCLO M UKBB rs10539712 3 67,842,251 CTT 0.538 0.096 2.33E-08 SUCLG2-DT AFR All MVP rs186798404 6 44,338,731 T 0.778 0.141 3.27E-08 POLR1C Meta rs186798404 6 44,338,731 T 0.778 0.141 3.27E-08 POLR1C Cross-Anc. All Meta rs10889947 1 72,362,538 T −0.065 0.010 5.90E-11 LOC105378797 rs72831629 2 112,175,548 T −0.062 0.011 3.43E-08 FBLN7 rs2681780 3 49,860,397 T 0.061 0.010 2.54E-10 CAMKV rs190161089 5 120,651,192 A −0.070 0.012 5.50E-09 PRR16 rs2242656 6 31,646,325 T 0.035 0.006 1.67E-08 BAG6 rs9279546 6 32,264,129 CT −0.041 0.007 3.54E-09 TSBP1-AS1 rs16894959 6 34,857,885 T −0.038 0.007 2.56E-08 UHRF1BP1 rs186798404 6 44,338,731 T 0.778 0.141 3.26E-08 POLR1C rs12555516 9 31,234,019 C 0.033 0.006 1.20E-08 - rs10819064 9 125,737,677 T 0.038 0.006 2.64E-10 - rs75970938 10 103,033,891 T 0.056 0.010 1.20E-08 CNNM2 rs9536401 13 53,343,730 T 0.054 0.010 1.66E-08 - Fibromyalgia was defined by stringent ICD-10 code, less-stringent ICD-10 code and/or self-report. SNPs were assigned by FUMA to the nearest gene based on physical distance ( ± 10 kb from gene boundaries). Chr chromosome, Pos GRCh38-based position, EA effect allele, AoU All of Us version 8, MVP Million Veterans Program, UKBB UK Biobank, Meta meta-analysis, EUR European, AFR African, AMR Latin American, M males, F females, Anc ancestry. Association tests were performed using a logistic regression model adjusted for age, sex, and the first ten genetic PCs as covariates. All tests were two-sided. Genome-wide significance was defined at p  < 5 × 10 −8 . GWAS meta-analysis of fibromyalgia in ( a ) EUR population (n total  = 1,440,524, n eff  = 240,278), and ( b ) cross-ancestry (EUR-AFR-AMR; n total  = 1,727,572, n eff  = 323,773). ( c ) MTAG of fibromyalgia leveraging pain data (n eff  = 240,278). Significant variants that are located within or near a gene are annotated according to the relevant gene; significant variants which are not located in the proximity of a gene are annotated with their rsID. Association tests were performed using a logistic regression model adjusted for age, sex, and the first ten genetic PCs as covariates. All tests were two-sided. Genome-wide significance was defined at p  < 5 × 10 − 8 . Lead SNPs in GWAS meta-analyses of fibromyalgia Fibromyalgia was defined by stringent ICD-10 code, less-stringent ICD-10 code and/or self-report. SNPs were assigned by FUMA to the nearest gene based on physical distance ( ± 10 kb from gene boundaries). Chr chromosome, Pos GRCh38-based position, EA effect allele, AoU All of Us version 8, MVP Million Veterans Program, UKBB UK Biobank, Meta meta-analysis, EUR European, AFR African, AMR Latin American, M males, F females, Anc ancestry. Association tests were performed using a logistic regression model adjusted for age, sex, and the first ten genetic PCs as covariates. All tests were two-sided. Genome-wide significance was defined at p  < 5 × 10 −8 . Using MAGMA gene-based analysis we found 148 genes significantly associated with fibromyalgia in EUR. The strongest effects were for PCLO , RNF123 and DCC (Suppl. Data  3 ). In AFR, we found one gene associated with fibromyalgia: TEX22 (Suppl. Data  4 ). In AMR, we found no significant genes associated with fibromyalgia (Suppl. Data  5 ). Cross-ancestry, we found 38 genes significantly associated with fibromyalgia (Suppl. Data  6 ). We used LD score regression (LDSC) to calculate the heritability estimates (h 2 ) of the individual and meta-analyzed EUR cohorts, and found that the SNP heritability of fibromyalgia in the EUR meta-analysis is 6.9% (h 2  = 0.069 ± 0.004) (Supplementary Data  7 ). We also calculated inter-cohort (i.e., AoU, MVP, UKBB, Finngen) genetic correlations, and found significant values for all the pairs tested, with r g values ranging between 0.72 ( ± 0.1) and 0.95 ( ± 0.1) (Supplementary Data  8 ). We repeated these tests for the sex-stratified analyses (Supplementary Data  7 , 8 ). The genetic correlation between fibromyalgia in males vs females was r g  = 0.73 ( ± 0.064; p  = 3.7 x 10 −30 ). We then calculated the genetic correlations between the EUR meta-analysis of fibromyalgia and 27 traits which are associated with cognitive function, physical activity, substance use, psychiatric disorders, autoimmune, cardiovascular, neurodegeneration, and general health measures. Except for the genetic correlations with Parkinson’s disease (PD) and Alzheimer’s disease (AD), all the r g values were statistically significant. The strongest negative genetic correlation was with physical activity (r g  = −0.5 ± 0.027). The strongest positive genetic correlations were with chronic pain (r g  = 0.8 ± 0.023), PTSD (r g  = 0.72 ± 0.026) and depression (r g  = 0.69 ± 0.021), while the next five were of the PTSD subphenotypes hyperarousal (r g  = 0.68 ± 0.032), avoidance (r g  = 0.63 ± 0.034) and re-experiencing (r g  = 0.62 ± 0.032), migraine (r g  = 0.58 ± 0.034), and ADHD (rg = 0.57 ± 0.029) (Supplementary Data  9 , Fig.  2 ). For male-only and female-only analyses, the genetic correlations with all 27 traits were in the same direction as in the main analysis, and all the values that were significant in the main analysis were significant also in the female-only analysis. All but six correlations in the male-only analysis were significant after Bonferroni correction (Supplementary Data  9 , Fig.  2 ). We also repeated the genetic correlation calculation for all 27 traits using the fibromyalgia MTAG, with almost identical and all-significant results (Supplementary Fig.  34 ). Fig. 2 Genetic Correlations. Genetic correlations between fibromyalgia and 27 traits in EUR (sample size for each trait is provided in Supplementary Data  9 ) in all subjects (purple), in females (orange), and in males (blue), tested using two-sided LD score regression ( p -value threshold for significance after Bonferroni correction: p  = 5×10 −4 ). The same sets of summary statistics were used for all the analyses (all except endometriosis included subjects of both sexes). Non-significant values are marked with empty circles. Error bars represent standard error. AcaDeg academic degree, ExecFunc executive functioning, PhysAct physical activity, ADHD attention-deficit/hyperactivity disorder, PTSD post-traumatic stress disorder, av avoidance, hyp hyperarousal, re-ex re-experiencing, GAD generalized anxiety disorder, PAU problematic alcohol use, CanUD cannabis use disorder, OUD opioid use disorder, BMI body mass index, BP blood pressure, T2D type 2 diabetes, AD Alzheimer’s disease PD Parkinson’s disease. Source data are provided as a Source Data file. Genetic correlations between fibromyalgia and 27 traits in EUR (sample size for each trait is provided in Supplementary Data  9 ) in all subjects (purple), in females (orange), and in males (blue), tested using two-sided LD score regression ( p -value threshold for significance after Bonferroni correction: p  = 5×10 −4 ). The same sets of summary statistics were used for all the analyses (all except endometriosis included subjects of both sexes). Non-significant values are marked with empty circles. Error bars represent standard error. AcaDeg academic degree, ExecFunc executive functioning, PhysAct physical activity, ADHD attention-deficit/hyperactivity disorder, PTSD post-traumatic stress disorder, av avoidance, hyp hyperarousal, re-ex re-experiencing, GAD generalized anxiety disorder, PAU problematic alcohol use, CanUD cannabis use disorder, OUD opioid use disorder, BMI body mass index, BP blood pressure, T2D type 2 diabetes, AD Alzheimer’s disease PD Parkinson’s disease. Source data are provided as a Source Data file. MTAG was conducted using pain summary statistics 22 to increase the available information for the fibromyalgia GWAS results. The pain phenotype was a generalized trait that included diagnoses of limb, back, neck, head and abdominal pain. The genetic correlation between fibromyalgia and pain was r g  = 0.72 ( ± 0.026; p  = 1.54 x 10 −169 ). SNP-based heritability of the MTAG was 8% (h 2  = 0.08 ± 0.003) (Suppl. Data  7 ), and the genetic correlation between the fibromyalgia GWAS and MTAG (based on it but including also fibromyalgia variance retrieved from the pain GWAS) was r g  = 0.93 ( ± 0.007) (Supplementary Data  8 ). The genetic correlations between the fibromyalgia MTAG and a variety of other traits were almost identical to the genetic correlations with the fibromyalgia GWAS (Supplementary Data  9 , Supplementary Fig.  34 ), indicating that the “enhanced” GWAS behaved similarly to the original GWAS in terms of its genetic relationships. The EUR fibromyalgia MTAG resulted in 45 independent significant lead SNPs, an increase of 35 compared to the EUR fibromyalgia GWAS. Of the ten GWS loci in the fibromyalgia GWAS, seven were retained in MTAG filtering (that is, only seven of the ten original independent SNPs could be studied in the MTAG context; two were filtered out because they did not exist in the pain summary statistics, and one because it was multiallelic). The effects of all 45 lead SNPs in the MTAG were in the same direction as in the EUR fibromyalgia main analysis. Effect size estimates were smaller for all but one SNP in the MTAG analysis compared to the GWAS, yet with smaller SE values and higher statistical significance (Fig.  3 , Supplementary Data  10 ). The strongest effects in the MTAG were for rs62098042* DCC ( p  = 1.95 x 10 −14 ) and rs3129905* TSBP1 ( p  = 2.22 x 10 −13 ) (Table  3 , Fig.  1c ). We also conducted MTAG using both pain and leave-one-out (LOO)-summary statistics of MDD together 24 , excluding for the MDD data cohorts that overlap with those we used for the EUR fibromyalgia GWAS. The genetic correlation between fibromyalgia and the LOO-MDD data was r g  = 0.63 ( ± 0.025; p  = 1.05 x 10 −141 ), and between pain and LOO-MDD was r g  = 0.56 ( ± 0.021; p  = 3.18 x 10 −150 ). MTAG resulted in 94 independent significant lead SNPs, 34 of which were significant in the pain-leveraged MTAG (Supplementary Data  11 ). Fig. 3 MTAG-GWAS comparison. Effect size of all the significant SNPs in the fibromyalgia MTAG analysis (n eff  = 1,016,498), and comparison with their effect size in the EUR fibromyalgia GWAS meta-analysis (n eff  = 240,278). Source data are provided as a Source Data file. Error bars represent standard error. Table 3 Lead SNPs in fibromyalgia MTAG in subjects of EUR genetic ancestry rsID Chr Pos (GRCh38) EA Beta SE P Gene rs1993709 1 72372846 A −0.019 0.003 3.26E-10 LOC105378797 rs6690570 1 174239038 T 0.013 0.002 5.54E-09 RABGAP1L rs10920885 1 191058818 T −0.017 0.003 2.72E-09 - rs12469476 2 22536001 A −0.013 0.002 5.77E-09 LINC01884 rs12617392 2 27113959 A −0.016 0.002 2.01E-12 CGREF1 rs2668732 2 60259880 A −0.015 0.002 3.72E-11 - rs17782474 2 99649474 A −0.021 0.004 1.09E-08 AFF3 rs73961845 2 143721414 A 0.013 0.002 1.86E-08 ARHGAP15 rs77585733 2 206212000 A 0.027 0.004 5.20E-11 CMKLR2 rs13063621 3 49784192 T −0.016 0.002 2.66E-12 IP6K1 rs34811474 4 25407216 A −0.015 0.003 3.79E-08 ANAPC4 rs12646543 4 140015960 T 0.015 0.002 1.15E-10 MAML3 rs62344664 4 150457762 A −0.014 0.003 4.58E-08 LRBA rs416223 5 104655775 A −0.013 0.002 2.26E-08 LOC105379109 rs77583774 5 124623861 A 0.023 0.004 4.80E-08 - rs9357048 6 27779263 A −0.026 0.004 7.97E-10 - rs3131854 6 29644108 T −0.016 0.003 9.64E-09 - rs9265878 6 31345256 A 0.017 0.003 1.70E-11 - rs3129905 6 32326786 T −0.018 0.002 2.22E-13 TSBP1 rs9469897 6 34823688 A 0.017 0.003 1.50E-08 UHRF1BP1 rs61417237 6 83614440 A 0.015 0.002 3.78E-10 SNAP91 rs62427982 6 107115962 T −0.015 0.002 1.40E-09 BEND3 rs73167394 7 82797010 A 0.015 0.002 3.06E-10 PCLO rs10262103 7 114451789 A 0.015 0.002 7.88E-12 FOXP2 rs75645829 9 31228381 A −0.017 0.002 1.14E-11 - rs10123941 9 117755884 T −0.014 0.003 2.10E-08 - rs7020938 9 119902930 A 0.015 0.002 1.96E-11 LOC107987122 rs6478712 9 125752329 T −0.015 0.002 2.54E-10 PBX3 rs7857390 9 133253159 A −0.013 0.002 4.93E-09 ABO rs28470994 9 137387536 T −0.022 0.004 1.17E-09 EXD3 rs2225962 10 19034984 A −0.018 0.003 1.84E-09 - rs10883806 10 102953319 T −0.024 0.004 1.90E-09 CNNM2 rs3907482 12 60160296 A −0.013 0.002 1.13E-08 - rs10785159 12 74946958 T −0.013 0.002 7.11E-09 - rs2587363 13 53341798 A −0.015 0.002 1.07E-10 - rs1538857 13 58901289 T −0.015 0.002 2.74E-11 - rs17057528 13 60026176 T −0.014 0.003 2.46E-08 DIAPH3 rs34883191 14 46792330 T −0.014 0.002 3.12E-10 - rs12323738 14 69033926 T 0.014 0.002 4.39E-09 - rs200493300 15 86382193 T 0.014 0.003 3.37E-08 AGBL1 rs62086903 17 68019890 T −0.018 0.003 1.77E-12 - rs2472610 18 23558901 T 0.013 0.002 1.28E-08 NPC1 rs62098042 18 53376190 A 0.017 0.002 1.95E-14 DCC rs71367544 18 79814374 T 0.016 0.003 5.57E-09 KCNG2 rs4811123 20 51098956 T 0.013 0.002 7.16E-09 - SNPs were assigned by FUMA to the nearest gene based on physical distance ( ± 10 kb from gene boundaries). Chr chromosome, Pos GRCh38-based position, EA effect allele. Association tests were performed using a logistic regression model adjusted for age, sex, and the first ten genetic PCs as covariates. All tests were two-sided. Genome-wide significance was defined at p  < 5×10 − 8 . Effect size of all the significant SNPs in the fibromyalgia MTAG analysis (n eff  = 1,016,498), and comparison with their effect size in the EUR fibromyalgia GWAS meta-analysis (n eff  = 240,278). Source data are provided as a Source Data file. Error bars represent standard error. Lead SNPs in fibromyalgia MTAG in subjects of EUR genetic ancestry SNPs were assigned by FUMA to the nearest gene based on physical distance ( ± 10 kb from gene boundaries). Chr chromosome, Pos GRCh38-based position, EA effect allele. Association tests were performed using a logistic regression model adjusted for age, sex, and the first ten genetic PCs as covariates. All tests were two-sided. Genome-wide significance was defined at p  < 5×10 − 8 . We used MRlap to assess the inferred causality between genetic liability to fibromyalgia and genetic liability to traits that had a significant effect in the LDSC analyses. We used a p -value threshold of 1 x 10 −5 to define genetic instruments. We found significant causal effects of fibromyalgia (as exposure) on 20 of the 25 traits we examined and a significant causal effect of 22 traits on fibromyalgia (as outcome). All the effects in this test were in the same directions as in the genetic correlation analyses. Problematic alcohol use (PAU) and generalized anxiety disorder (GAD) were the only traits which had no causal correlation with fibromyalgia in either direction (Fig.  4 , Supplementary Data  12 , 13 ) (an MR analysis with an instrument p -value threshold of 1 x 10 −8 was conducted as well; results are presented in Supplementary Data  14 , 15 ). While MRlap was used to account for sample overlap, additional methods were implemented to assess the robustness of the causal estimates under different assumptions, using two-sample MR 25 . All significant effects detected by MRlap were in the same direction when using different MR methods (Supplementary Data  16 ). We also tested horizontal pleiotropy between traits, and found potential horizontal pleiotropy, with low but statistically significant MR-Egger intercept values, between fibromyalgia and chronic pain, neuroticism, high blood pressure (BP), and migraine (Supplementary Data  17 ). Therefore, MR results regarding the causality between fibromyalgia and these traits should be taken with caution. Fig. 4 Causal Genetic Correlations. Causal genetic correlations between fibromyalgia (as exposure and as outcome) and 25 traits in EUR (sample size for each trait is provided in Supplementary Data  9 ), tested using Mendelian randomization with MRlap (inverse-variance MR, p -value threshold for significance after Bonferroni correction: p  = 1.47 × 10 −4 ). Non-significant values are marked with empty circles. Error bars represent standard error. AcaDeg academic degree, ExecFunc executive functioning, PhysAct physical activity, ADHD attention-deficit/hyperactivity disorder, PTSD post-traumatic stress disorder, av avoidance, hyp hyperarousal, re-ex re-experiencing, GAD generalized anxiety disorder, PAU problematic alcohol use, CanUD cannabis use disorder, OUD opioid use disorder, BMI body mass index, BP blood pressure, T2D type 2 diabetes. Source data are provided as a Source Data file. Causal genetic correlations between fibromyalgia (as exposure and as outcome) and 25 traits in EUR (sample size for each trait is provided in Supplementary Data  9 ), tested using Mendelian randomization with MRlap (inverse-variance MR, p -value threshold for significance after Bonferroni correction: p  = 1.47 × 10 −4 ). Non-significant values are marked with empty circles. Error bars represent standard error. AcaDeg academic degree, ExecFunc executive functioning, PhysAct physical activity, ADHD attention-deficit/hyperactivity disorder, PTSD post-traumatic stress disorder, av avoidance, hyp hyperarousal, re-ex re-experiencing, GAD generalized anxiety disorder, PAU problematic alcohol use, CanUD cannabis use disorder, OUD opioid use disorder, BMI body mass index, BP blood pressure, T2D type 2 diabetes. Source data are provided as a Source Data file. We used LAVA to calculate the local genetic correlations between fibromyalgia and the 27 traits that were used to calculate r g in EUR (see above). Seventeen traits had at least one local genetic correlation with fibromyalgia – including all three PTSD sub-phenotypes – most prominently PTSD (six loci) and chronic pain (5 loci). In total, there were 43 local genetic correlations considering all pairs, involving 31 different genetic loci. The region between chr2: 59,024,862:60,547,931 correlated five different traits – chronic pain, depression, PTSD and two of its subdimensions (hyperarousal, re-experiencing) – with fibromyalgia. The region between chr11:112,755,447:113,889,019 correlated four different traits – cannabis use disorder (CanUD), executive functioning, neuroticism and PTSD – with fibromyalgia (Suppl. Data  18 ). Using Popcorn 26 , we found that the heritability estimates of fibromyalgia in AFR and AMR subjects were not statistically significant ( p  = 0.18 and p  = 0.58, respectively). Therefore, genetic correlations between fibromyalgia within those populations and other traits in EUR could not be calculated. We used TWAS to evaluate predicted changes in differential gene expression in EUR. We identified four independent associated genes in four different tissues: DPYSL5 with a positive enrichment in the cerebellum, and DAG1 , GPX1 and PBX3 with negative enrichment in the tibial artery, skeletal muscles and pancreas, respectively (Supplementary Data  19 ). One variant, PBX3* rs6478712, remained significant after Bonferroni correction and after excluding genes that failed the HEIDI test (p_HEIDI < 0.05, indicating that the association was likely driven by two distinct variants in LD rather than a single shared causal variant). This variant had a significant negative effect in two tissues: non-sun-exposed skin ( β  = −0.157 ± 0.03) and tibial artery ( β  = −0.131 ± 0.025), suggesting that the effect of this variant on PBX3 expression in these tissues may be causal with respect to its effect on fibromyalgia (Supplementary Data  20 ). We performed gSEM to examine the overarching genetic relationships between fibromyalgia and 22 traits of interest that had demonstrated significant genetic correlations with fibromyalgia. EFA suggested a seven-factor model fit the data best, explaining 68.9% of cumulative variance. Factor 1 (SS loading: 3.09) explained 13.4% of the variance, factor 2 (SS loading: 3.03) explained 13.2%, factor 3 (SS loading: 2.75) explained 12%, factor 4 (SS loading: 2.32) explained 10%, factor 5 (SS loading: 1.81) explained 8%, factor 6 (SS loading: 1.77) explained 8%, and factor 7 (SS loading: 1.09) explained 5% of the overall variance. Traits with EFA loadings > 0.25 were evaluated on the respective factors in CFA. CFA suggested that the seven-factor model fit the data well via traditional fit indices including a comparative fit index of 0.89 and a standardized root mean square residual (SRMR) of 0.054 (Supplementary Data  21 ). Fibromyalgia co-loaded on factor 3 with insomnia, physical activity, and traits related to pain and autoimmune response. Depression and PTSD co-loaded on factor 1 with neuroticism and generalized anxiety disorder (GAD) and on factor 6 with ADHD and suicidality. Factor 6 had a strong genetic correlation (r g  = 0.75) with factor 3. Suicidality co-loaded on factor 2 with substance dependence traits (Fig.  5 ). Fig. 5 Genomic structural equation modeling (gSEM). The overarching genetic relationship between fibromyalgia and 22 traits of interest. Fibromyalgia loaded on factor 3 AcaDeg academic degree, ExecFunc executive functioning, PhysAct physical activity, ADHD attention-deficit/hyperactivity disorder, PTSD post-traumatic stress disorder, av avoidance, hyp hyperarousal, re-ex re-experiencing, GAD generalized anxiety disorder, PAU problematic alcohol use, CanUD cannabis use disorder, OUD opioid use disorder, BMI body mass index, BP blood pressure, T2D type 2 diabetes, SCZ schizophrenia]. Source data are provided as a Source Data file. The overarching genetic relationship between fibromyalgia and 22 traits of interest. Fibromyalgia loaded on factor 3 AcaDeg academic degree, ExecFunc executive functioning, PhysAct physical activity, ADHD attention-deficit/hyperactivity disorder, PTSD post-traumatic stress disorder, av avoidance, hyp hyperarousal, re-ex re-experiencing, GAD generalized anxiety disorder, PAU problematic alcohol use, CanUD cannabis use disorder, OUD opioid use disorder, BMI body mass index, BP blood pressure, T2D type 2 diabetes, SCZ schizophrenia]. Source data are provided as a Source Data file. After correction for FDR, we found no significantly enriched gene-drug pathways. We find no differences between blood type distribution in fibromyalgia subjects compared to the general population (Supplementary Data  22 ).

Fibromyalgia is a common syndrome that impairs the well-being of about 2.7% of the population (point prevalence). Existing knowledge regarding its etiology and potential treatments is sparse 2 , especially considering its population-level impact. In this study, we identified ten independent GWS risk loci associated with fibromyalgia in EUR, one in AFR, and twelve cross-ancestry. In an MTAG analysis that leveraged pain summary statistics, we found 45 independent loci associated with fibromyalgia. These findings, alongside genetic correlation, MR and gSEM analyses, provide a genotypic basis that supports the strong observed phenotypic associations between fibromyalgia and chronic pain, mental illnesses, autoimmune diseases, and unhealthy lifestyle as reflected by physical activity. Genetic correlation analyses revealed that although fibromyalgia is more prevalent in females (75% of the cases are females) 2 , its genetic architecture in both sexes is similar. Among the genetic variants that we identified, several map to genes that are associated with a variety of neuronal pathways, pathologies, protein expression, and mental states, that can help explain the complex genetics behind the complex syndrome that is fibromyalgia: FBLN7 (fibulin 7; GWS in the EUR and cross-ancestry meta-analyses) has a role in inflammation 27 and in anti-angiogenic activity 28 . It is also strongly associated with levels of MER proto-oncogene tyrosine kinase (MERTK) 29 , a regulator of immune response 30 – 32 that is involved in retinal diseases 33 , in myelin phagocytosis in multiple-sclerosis (MS) patients 34 , and in systemic lupus erythematosus (SLE) 35 . The former is an autoimmune disease that was previously genetically associated with fibromyalgia through a polygenic risk score (PRS)-based pheWAS 8 , in line with a moderate genetic correlation (r g =0.31) we found between fibromyalgia and autoimmune diseases. The neuromodulatory role of MERTK has also been implicated in neurodegenerative diseases such as PD and AD 36 . PBX3 , GWS in the EUR meta-analysis and in the MTAG, is involved in various processes, from embryonic neurodevelopment to adult locomotion and respiration 37 , 38 . It has a role in the etiology of several types of cancer 39 and it influences systolic BP 40 , 41 ; it affects the structure of the brain basal ganglia 42 , which play important roles in executive functioning and emotional regulation 43 . It is also associated with gastroesophageal reflux disease (GERD) 44 , which was genetically associated with fibromyalgia through PRS 8 . In the TWAS, PBX3 was found to be significantly enriched in the pancreas, which could indicate a potential role in the high rates of gastrointestinal disease and pancreatitis among fibromyalgia patients (4-10 times compared to the general population) 45 . In the SMR, lower enrichment of PBX3 in the tibial artery and in non-sun-exposed skin was found to be associated with fibromyalgia too. Considering that fibromyalgia is characterized by widespread pain 1 and that genome-wide analyses associate PBX3 with various types of pain 46 , 47 , it is likely that this gene, which had a significant effect in the GWAS, MTAG, and post-GWAS analyses, is a contributor to a variety of fibromyalgia symptoms. In three of the GWAS we conducted - the EUR, the female-specific EUR and the cross-ancestry meta-analyses, there were three different lead variants on chromosome 3, which map to three different genes but may represent the same GWS signal. This assumption is based on FUMA outputs, which pointed to a single genomic risk locus at this region in each of the analyses (despite that all three are within a ~ 100 kb region), and on LD of r 2  > 0.6 between all three SNPs, as calculated within EUR subjects in AoU. Regional Manhattan plots of this locus visually demonstrate the proximity of these genes to each other and the distribution of significant variants across all three of them (Supplementary Figs.  13 , 24 ). These genes are TRAIP , IP6K1 and CAMKV , and they all have significant associations in the EUR and cross-ancestry gene-based analyses (MAGMA) too. TRAIP encodes the TRAF-interacting protein and is important for DNA repair 48 . It is a strong determinant of the expression of TXNDC12, a protein involved in ferroptosis 49 , 50 , a cell death process triggered by lipid peroxidation, which may cause neuropathic pain 51 and increased fibromyalgia symptoms 52 . TRAIP is also genetically associated with ADHD 53 , cognitive functioning 54 , intelligence 55 , insomnia 56 and pain intensity 46 – all are traits that we found to be genetically correlated with fibromyalgia, directly (ADHD, insomnia) or indirectly (e.g., academic degree is a proxy to intelligence, chronic pain and migraine are associated with pain intensity). IP6K1 is associated with insomnia 56 , cognitive ability 57 , and with levels of MST1 29 , a protein that affects inflammation and autoimmune response 58 . MST1 , as well as MST1R (which encodes the MST1 receptor), are closely located to TRAIP and IP6K1 on chromosome 3, and both had a significant effect in the gene-based analysis. RNF123 , which resides only 83 bp from MST1 , adds another strong association with fibromyalgia in the EUR and cross-ancestral gene-based analyses; this gene is implicated in neuroimmune pathways 59 and was previously associated with chronic widespread musculoskeletal pain, a common symptom of fibromyalgia 10 . CAMKV , significant in our cross-ancestry meta-analysis, was previously shown to affect intelligence 60 , educational attainment 61 , 62 and the pleiotropy between body mass index (BMI) and osteoarthritis 63 . TRAIP, IP6K1 and CAMKV were all significant in the largest GWAS of PTSD published to date – the only mutual associations between that study and ours 64 . TSBP1-AS1 – GWS in the EUR and cross-ancestry analyses – has a strong effect on hypothyroidism and rheumatoid arthritis 44 , 65 , autoimmune disorders that share symptoms with fibromyalgia, like pain and muscle tenderness, in line with the genetic correlation between fibromyalgia and autoimmune diseases. TSBP1-AS1 also has a robust effect on the levels of the immunomodulatory C2, C4, and LILRB4 29 , 66 , 67 , the deficiency of which is associated with obesity and type 2 diabetes (T2D) 68 . Both of these traits were moderately genetically correlated with fibromyalgia and may lead to lupus-like effects 69 , further establishing the role of autoimmune response in fibromyalgia. In addition, obesity, BMI, and autoimmune response are locally genetically correlated with fibromyalgia in loci that do not overlap but are adjacent ( < 1.5 MB) to TSBP1-AS1 . Also within this region, we found BMI to be locally genetically correlated with fibromyalgia in a locus that includes SNRPC - significant in the EUR gene-based analysis, and previously associated with lupus erythematosus 70 and with smoking initiation 71 . BAG6 , closely located to TSBP1-AS1 on chromosome 6 and significant in our GWAS, is associated with the autoimmune condition psoriasis 72 , which may cause joint pain and arthritis. We conducted an MTAG analysis, leveraging pain summary statistics to create a better-powered genome-wide analysis of fibromyalgia. High r g between the GWAS and MTAG, and almost identical genetic correlations with other traits, suggest that the MTAG results indeed represent the genetics of fibromyalgia. The effects of all the lead SNPs in the MTAG were in the same direction as in the EUR GWAS; out of 28 MTAG GWS loci in gene-coding regions, 15 had a significant effect in at least one of the EUR or cross-ancestry GWAS or MAGMA analyses, further confirming the MTAG validity. PCLO , GWS in the MTAG and in the female EUR GWAS, had the strongest effect in the EUR gene-based analysis. It encodes a presynaptic cytoskeletal protein involved in neurotransmitter release 73 , and implicated in neuropsychiatric disorders such as MDD, bipolar disorder, and other brain-related phenotypes 56 , 74 , 75 , consistent with pathways influencing central pain processing and the affective components of chronic pain. It also affects bone mineral density (BMD) 76 , which is associated with fibromyalgia symptoms (i.e., BMD is lower in fibromyalgia subjects) 77 , as is DCC 76 , which had the strongest effect in the MTAG. DCC encodes a protein that has an important role in axon guidance in spinal neurons 78 , 79 , and it is associated with various types of pain 22 , 80 ; it is also associated with smoking initiation 71 , educational attainment 81 , and neuroticism 82 . MAML3 and FOXP2 were also previously associated with pain intensity 46 . Another MTAG-GWS gene of interest is ABO . A blood type (ABO blood group)-determining protein, ABO has a major impact on a variety of blood protein expression phenotypes 66 , 83 . Several studies point to an interaction between blood type and pain perception 84 , 85 . However, we did not find any phenotypic differences in blood type distribution among fibromyalgia patients compared to the general population. Several other genes that were GWS in our GWAS and/or MTAG will be discussed briefly: PRR16 and UHRF1BP1 were GWS in a prior multi-ancestral GWAS of chronic pain 46 . UHRF1BP1 and ARHGAP15 were associated with BMD 76 . ARHGAP15 was also found to be the strongest genetic factor contributing to diverticular disease 86 and was associated with educational attainment, as were KCNG2 , CNNM2 , EXD3 and AFF3 81 . AFF3 also affects rheumatoid arthritis 87 , and is part of a list of genes that affect substance use traits such as smoking initiation and alcohol consumption, along with SNAP91 , CNNM2 , NPC1 and RABGAP1L 71 . Though not mapping to a coding gene, rs1993709 was associated with BMI, obesity and weight 88 , 89 and also with neuroticism 90 . Data obtained from GTEx portal ( gtexportal.org ) 91 indicated that rs1993709 is a very strong negative eQTL of the pseudogene RPL31P12 transcription in the cerebellum (normalized effect size = −0.87; p  = 2.7 x 10 −16 ). Though its function is not clear, RPL31P12 had a significant effect in studies of BMI, weight and obesity 22 . The non-coding rs2587363 was previously associated with pain 9 . An intronic variant of POLR1C – a gene that is associated with Treacher Collins Syndrome, sensorineural hearing loss 22 and systolic BP 44 – POLR1C *rs186798404 was the only SNP that had a significant effect in the AFR meta-analysis, though it may be a false positive (there are no significant or close-to-significant high LD SNPs). The only significant effect in the AFR gene-based analysis was for TEX22 , a testis-expressed gene likely associated with male fertility 92 , but also with various blood counts, both in EUR and AFR populations 44 . Genetic correlation analysis revealed moderate-to-strong positive associations between fibromyalgia and several psychiatric traits, namely depression, suicidality, ADHD and PTSD. These correlations are in line with high comorbidity between fibromyalgia and these traits 11 – 13 . In people with both PTSD and fibromyalgia, the onset of fibromyalgia symptoms is usually later than the occurrence of the traumatic event 93 , a time course consistent with possible causality. While MR revealed bidirectional causality between these traits, the causal effect of PTSD (as exposure) on fibromyalgia (as outcome) was much stronger (although not for the sub-phenotypes of PTSD), suggesting that the genetic liability to PTSD may be more likely to cause fibromyalgia than vice versa. The same is true regarding depression and suicidality, but not ADHD: MR results show that genetic liability to fibromyalgia is more likely to cause ADHD; it is possible that what is reflected here are concentration and attention problems (including what is sometimes referred to as “brain fog”) – known to be common in fibromyalgia – that might be coded as ADHD. The moderate r g between fibromyalgia and neuroticism aligns with a known genetic correlation between neuroticism and various pain-related traits 94 – 96 , and with dominant neuroticism in fibromyalgia patients compared to other personality traits of the Big-Five model, as was found in a personality-trait questionnaire conducted among fibromyalgia patients 97 . The psychiatric traits described above are strongly associated with chronic pain 95 , 96 , one of the most dominant symptoms of fibromyalgia. Chronic pain was the trait most strongly genetically correlated with fibromyalgia in our study (r g  = 0.80 ± 0.02), alongside a relatively high correlation with migraine - a more specific pain-related trait. Migraine was also the trait most strongly affected by fibromyalgia in the MR analysis, though this effect is potentially influenced by horizontal pleiotropy. In a gSEM analysis, fibromyalgia co-loaded with chronic pain and migraine, as well as with endometriosis, autoimmune response, insomnia, and physical activity, but not with psychiatric traits. This suggests that fibromyalgia may be contextualized more as a pain and autoimmune trait than a psychiatric trait, even though the genetic correlations with psychiatric traits such as PTSD and depression are quite high. Genetic correlations between fibromyalgia and opioid use disorder (OUD; r g  = 0.41 ± 0.05) and CanUD (r g  = 0.33 ± 0.03) are of importance here, due to the use of opioids to alleviate pain, and the growing use of cannabis in recent years to treat symptoms of fibromyalgia 2 , 16 , 98 , 99 . We found associations between fibromyalgia and two genes previously associated with OUD 100 , representing a complex set of effects related to both OUD and fibromyalgia: FOXP2 , previously associated with pain 46 , but also with a range of psychiatric and neurological traits such as externalizing behavior 101 , ADHD 102 , and insomnia 56 , had a significant effect in our fibromyalgia MTAG; and NICN1 , associated with MST1 protein levels 29 , intelligence 57 , educational attainment 103 , and metabolic syndrome 104 , had an effect on fibromyalgia in the EUR gene-based analysis. We found no mutual GWS associations between fibromyalgia and CanUD. MR analysis revealed that fibromyalgia has a weak (yet statistically significant) causal effect on CanUD and OUD risk. The effect of OUD (as exposure) on fibromyalgia (as outcome) was stronger than any other effect in this MR analysis, indicating that the genetic variants associated with OUD risk may also be causal with respect to fibromyalgia. This is consistent with findings regarding increased pain sensitivity due to chronic opioid use 105 . The moderate genetic risk of developing dependence of opioids and cannabis among fibromyalgia patients should be taken into account when prescribing them as treatment. A moderate negative genetic correlation between fibromyalgia and physical activity (r g  = −0.5 ± 0.03) reinforces the importance of physical activity as a possible treatment for fibromyalgia symptoms 2 (alternatively, that fibromyalgia itself causes reduction in physical activity, as seen in the bidirectional results in the MR analysis). Along with a positive genetic correlation we found between fibromyalgia and BMI, obesity, high BP and T2D, our observations provide additional support for a biological connection between a healthy lifestyle and fibromyalgia. The moderate r g fibromyalgia had with endometriosis is also backed by the strong phenotypic association between these traits 106 . Negative genetic correlations between fibromyalgia and executive functioning and with academic degree indicate a genetic relationship underlying the phenotypic association between fibromyalgia and impaired cognitive performance 107 . The moderate genetic correlation we identified between fibromyalgia and autoimmune diseases (r g = 0.31 ± 0.03), along with the autoimmune roles of several GWS genes in our study, point to the possibility that fibromyalgia has autoimmune components. This notion was suggested previously 108 , with studies linking fibromyalgia and autoimmune diseases through genetic and inflammatory pathways 8 , 109 . Certain autoimmune diseases such as SLE are more prevalent in people of AFR ancestry 110 , 111 . While data regarding the prevalence of fibromyalgia in AFR is sparse, the prevalence of fibromyalgia in our AFR meta-analysis was twice as high as for EUR, suggesting that fibromyalgia may potentially be more prevalent in subjects of AFR ancestry. Conceivably this could relate to the effects of autoimmune disorders. Although there are known phenotypic associations between fibromyalgia and neurodegenerative diseases such as AD and PD 112 , 113 , genetic correlation analysis revealed no significant association between these traits. LAVA analysis revealed local genetic correlations between fibromyalgia and several traits in regions that map to genes of interest. Four traits – executive functioning (negative r g value), CanUD, neuroticism and PTSD (positive r g ) – were correlated with fibromyalgia in a region on chromosome 11 that maps to TTC12 , which had a significant effect in the EUR gene-based analysis. This gene affects neuroticism 114 , alcohol dependence 115 , and nicotine dependence traits 116 , 117 , and is an important factor in the expression of NCAM1, a cell adhesion molecule that is genetically associated with a variety of substance use traits 71 , 116 – 118 . It also is physically adjacent to DRD2 , which encodes the dopamine D2 receptor and is associated with psychiatric and behavioral traits such as MDD 119 , anxiety 120 neuroticism 82 , and a variety of substance use traits 71 , 121 , 122 . NRXN1 , which maps to a region that genetically correlates fibromyalgia with academic degree, was previously associated with educational attainment 81 . This gene, which had a significant effect in the EUR gene-based analysis, encodes a protein (neurexin-1) that participates in neurotransmission and is expressed in synaptic regions 37 . GRIA1 had a significant effect in the EUR gene-based analysis. It encodes the GluA1 subunit of the AMPA receptor, a major mediator of synaptic excitatory transmission; it is associated with insomnia 56 and personality disorders 22 , and it also maps to a region that genetically correlates fibromyalgia with PTSD. NRXN1 and GRIA1 are two of several genes that were identified in this study known to influence brain function and morphology; amongst the other such genes identified herein are PBX3 42 , PCLO 73 , MAPRE3 123 , 124 , and DPYSL5 123 , 124 . This is in line with the high enrichment of pain-related genes in the brain that was found in a recent trans-ancestral GWAS of pain intensity 46 . Specifically, GRIA1 -mediated alterations in AMPA receptors may influence the function of GABAergic interneurons 125 , 126 and alter pain sensation 127 , consistent with the high enrichment of pain-related genes in GABAergic neurons demonstrated in the same previous study 46 . Administration of the cannabis-derivatives Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) induced alterations in Gria1 expression in rodents 128 – 130 , consistent with growing evidence of cannabinoid-glutamatergic associations 131 , suggesting a potential pathway through which cannabis may ameliorate fibromyalgia symptoms 98 , 99 . Nine genes identified in the gene-based analysis map to a region on chromosome 2 that is locally positively genetically correlated with fibromyalgia and depression (there is a possible signal at the same region, which can be seen in Fig.  1a , though there are no GWS variants). Four of these genes ( DPYSL5 , MAPRE3 , AGBL5 , CGREF1 ) are associated with blood triglyceride levels 29 , 132 , 133 , two ( CENPA , SLC35F6 ) with BMD 76 , and two ( MAPRE3 , DPYSL5 ) with brain morphology 123 , 124 . Another gene, KHK , encodes an enzyme with a major role in fructose metabolism. This study included fibromyalgia data from four large biobanks. In AoU, UKBB and Finngen, heritability estimates were between 11–19%, and females were 82–92% of the total number of cases. In MVP, however, heritability was considerably lower (3%), and females accounted for only 19% of the fibromyalgia cases (compared to 7% of the sample). Nevertheless, the incidence of fibromyalgia in MVP was much higher, compared to the other cohorts we investigated (Table  1 ). While the different case distribution among sexes in MVP is attributable to the sex imbalance in the cohort, the low heritability is puzzling and may relate to the military nature of MVP. Indeed, soldiers tend to be more physically active in their daily routine compared to civilian population 134 . As MVP subjects are veterans, there may be higher likelihood that they were exposed to environmental factors that contributed to fibromyalgia symptoms during their service (e.g., injuries, traumatizing events). These two factors may explain the high prevalence but low genetic heritability of fibromyalgia among MVP participants. Nevertheless, genetic correlations between the EUR fibromyalgia cohorts were high (r g values ranged between 0.72 and 0.95; Supplementary Data  8 ), indicating that the fibromyalgia phenotype in all the cohorts we analyzed likely represents the same trait. A strong genetic correlation (r g =0.73 ± 0.06), between male and female EUR participants, as well as great similarities between the genetic correlations of males’ and females’ fibromyalgia datasets with other traits (Supplementary Data  9 ; Fig.  2 ), indicates that fibromyalgia likely represents the same trait in both sexes. Our GWAS analyses yielded a total of 15 genomic loci associated with fibromyalgia across different ancestries, ten of which map to a coding gene – and the MTAG resulted in 35 additional GWS loci, 22 of which map to a coding gene. Several of these genes are associated with traits that share with fibromyalgia symptomatic characteristics like pain, muscle tenderness, and insomnia; these may be important for the nociplastic pain experienced by fibromyalgia patients. This concept reflects central sensitization, a heightened central-nervous system response to sensory input, and is supported by genetic studies showing shared heritability across multiple pain traits, including this study. Other genes are associated with physical measures of general health like BP and BMI. Some of the genes we identified in this study are associated with neuronal pathways and with structural changes in brain regions that mediate emotional regulation and executive functioning, with mental and cognitive phenotypes such as ADHD, educational attainment and cognitive functioning, and with substance use traits. In addition, at least three of the genes we found are associated with autoimmune disorders. These findings are all in line with moderate genetic correlations observed between fibromyalgia and phenotypes of general health (physical activity, T2D, high BP, BMI, obesity) and of substance use (CanUD, PAU, OUD), and moderate-to-strong genetic correlations of fibromyalgia with chronic pain, with psychiatric traits (ADHD, depression, suicidality, PTSD), and with autoimmune traits. Our findings may indicate that fibromyalgia is a combination of several factors that create a complex trait, which can be categorized under several different factors that contribute to its phenotypic expression, with gSEM suggesting that it may be considered mainly as pain- and autoimmune-associated. This study has limitations. First, in two of the three cohorts in which we conducted a sex-stratified GWAS, the number of male participants with fibromyalgia (i.e., cases) was low and we did not detect GWS loci. Nevertheless, as discussed earlier, the genetic correlation between male and female fibromyalgia datasets was high, suggesting that the trait is similar or identical between the sexes. Second, only one GWS locus was detected in the AFR meta-analysis, and none in AMR, probably due to low sample sizes, but also possibly due to gaps in diagnosis and/or phenotype definition in these populations 135 , 136 ; non-significant heritability of fibromyalgia in those populations was also likely due to low sample size. Third, there were substantially more controls than cases in all cohorts (cases accounted for 1% to 12.5% of the various samples); such imbalances are typical in studies of this kind, but could potentially bias power and stability, particularly for low-frequency variants. In our study, however, all the lead SNPs had MAF > 7%. Fourth, fibromyalgia prevalence differs between geographic regions and populations (for example: 9.3% in Tunisia, 8.8% in Turkey and 0.4% in Greece, compared to the world prevalence of 2.7% 137 ) and also between different studies of the same region, suggesting that the true prevalence of this disorder in the population can be biased due to practices of diagnosis and environmental-cultural differences (in diet and exercise, as well as in symptom expression). Nevertheless, it may also represent actual genetic differences between populations that are underrepresented in large genetic biobanks, an issue that needs to be addressed in future studies, when more data regarding these populations are available. Fifth, the MTAG analysis was conducted using GWAS summary statistics of a broad pain phenotype defined by Finngen 22 and which includes multiple ICD codes of different pain measures. This increased the power of the analysis, but at the risk of false-positive results and loss of phenotype specificity. Despite the substantial genetic correlation between the two sets of summary statistics (fibromyalgia and pain; r g  = 0.72), the analysis may have introduced bias; therefore we did not treat this as our primary analysis. In total, our results provide novel insights into the genetic architecture of fibromyalgia, providing information about its genetic association with pain, autoimmune response, psychiatric traits, and lifestyle. These results allow for a deeper understanding of fibromyalgia and may provide tools for future identification of genetic risk factors that affect this complex disorder, which in turn may help with diagnosis. Genetic correlations between fibromyalgia and physical activity, as well as with traits that are highly associated with nutrition and cardiovascular function, support the importance of adopting a healthier lifestyle for the treatment and/or prevention of fibromyalgia. We studied multiple populations. While we identified the greatest number of findings in EUR, there was also independent GWS findings in AFR. All populations contributed to increasing findings in our trans-population meta-analysis. There is future potential for medication repurposing, personalization of treatments, and eventually, based on improved understanding of underlying biology, development of new treatments and even prevention strategies for fibromyalgia, but considering the lack of results for the former in the present analyses, this will require future studies with more subjects and better power.

Fibromyalgia is a chronic disorder characterized by nociplastic widespread pain, muscle stiffness, sleep disturbances, fatigue, depression, and anxiety. The syndromic pain is induced by altered nociception, without evident association to injuries, lesions or diseases, and is characterized by central sensitization - amplified pain response to noxious or non-noxious stimuli 1 , 2 . The point prevalence of fibromyalgia in the world population is 2.7%, with females making up 75% of cases 2 . There are no pathognomonic tests (e.g., blood-based or imaging biomarkers), and its diagnosis is thus based on identifying symptoms that are associated with the disease and exclusion of alternative explanations 1 . Fibromyalgia is influenced by genetic risk factors 3 : the risk of fibromyalgia among relatives of subjects who suffer from the disorder is eight times higher than in the general population 4 , and it may reach to 13-fold in siblings 5 . Genome-wide association studies (GWAS) have revealed dozens of genes associated with chronic pain and other pain-related disorders 6 . Two fibromyalgia-specific GWAS have been published to date; both used relatively small sample sizes and found no significant loci in European (EUR) or African (AFR) populations 7 , 8 . A study that examined the genetic basis of fibromyalgia as part of a long list of pain disorders also failed to find any significant single nucleotide polymorphism (SNP) specifically related to fibromyalgia 9 . A GWAS of chronic widespread musculoskeletal pain, a symptom of fibromyalgia, reported three genome-wide associations in RNF123 , ATP2C1 and COMT 10 . A phenome-wide association study (pheWAS) revealed significant associations between fibromyalgia and 304 phenotypes (out of 1740 phecodes tested); the strongest of these associations were with pain phenotypes (e.g., chronic pain, joint pain), traits directly related to pain (e.g., osteoarthrosis, spondylosis, migraine), autoimmune disease (erythematosus lupus) and psychiatric disorders (e.g., depression) 8 . Indeed, there is a strong association between fibromyalgia and psychiatric disorders: the lifetime prevalences of depression, anxiety, post-traumatic stress disorder (PTSD) and attention-deficit/hyperactivity disorder (ADHD) among fibromyalgia patients are reported to be 74%, 60%, 57% and 45%, respectively 11 – 13 . The therapeutic approach typically includes a combination of psychotherapy and pharmacotherapy (sometimes off-label) and lifestyle changes 2 . The former approaches may involve guided imagery, cognitive behavioral therapy, and group psychotherapy 14 , 15 , in addition to the introduction or increased frequency of aerobic physical exercise, a first-line treatment for the disorder. There are three FDA-approved medications: duloxetine and milnacipran – both of which are serotonin-norepinephrine reuptake inhibitors (SNRIs) – and pregabalin; the efficacy of opioids is quite limited 2 , 16 . Several small studies suggest that cannabis may be helpful to alleviate pain in fibromyalgia patients 17 . These treatments are variably effective; new therapeutic approaches are needed. Currently, it is unclear whether fibromyalgia can be prevented, and the available treatments are often unsatisfactory 18 . In this study, we aimed to discover genetic mechanisms that may identify novel treatment targets and open new paths for research regarding possible ways to alleviate symptoms of this disorder, that impairs the quality of life of so many people worldwide. Further, we hope to contribute to better prediction of who is at risk for the development of fibromyalgia which could lead to discoveries relevant to prevention. To these aims, we conducted a meta-analyzed GWAS in individuals of EUR, AFR, and Latin American (AMR) ancestries, as well as cross-ancestry and sex-stratified analyses, based on several different large samples including All of Us (AoU) 19 , the Million Veteran Program (MVP) 20 , the UK Biobank (UKBB) 21 , and Finngen 22 biobanks. We also conducted post-GWAS analyses to assess gene-based associations, transcriptome-wide associations, global and local genetic correlations, and genetic causality between fibromyalgia and a list of autoimmune diseases, psychiatric traits and general health phenotypes that are assumed to be associated with fibromyalgia 1 , 2 , 23 . Finally, we conducted genomic structural equation modeling (gSEM), to understand the genomic relationships between fibromyalgia and other psychiatric, autoimmune, general health and pain-related traits. In this work, we show that fibromyalgia is a complex genetic trait, affected by multiple genomic loci and is associated with a variety of pain-related traits, as well as with psychiatric phenotypes, autoimmune response, and general health. In this context, gSEM analysis places fibromyalgia mostly as a pain and autoimmune-related trait. Our results provide novel insights into the genetic architecture of fibromyalgia.

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