GWAS metanalysis of atrial fibrillation reveals significant sex-related heterogeneity effects of the PITX2 and CFL2 loci

GWAS metanalysis of atrial fibrillation reveals significant sex-related heterogeneity effects of the PITX2 and CFL2 loci | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GWAS metanalysis of atrial fibrillation reveals significant sex-related heterogeneity effects of the PITX2 and CFL2 loci Jara Cárcel-Márquez, Paula Boldo, Laia Llucià-Carol, Elena Muiño, and 51 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7978651/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Atrial fibrillation (AF) exhibits notable sex differences in epidemiology and outcomes. This study investigates biological sex-specific differences in AF through sex-stratified genome-wide association studies (GWAS) and proteomic related analyses. Methods We performed a sex-stratified GWAS meta-analysis using data from the UK Biobank study: 4,375 male AF cases and 162,645 controls; 1,981 female AF cases and 192,193 controls. Significant loci and sex-specific associations were identified, and sex heterogeneity was assessed. Replication was done in an independent cohort of 12,614 individuals (1,207 AF cases, 55% female). Plasma proteomic analyses in 46,724 subjects assessed genotype–sex interactions stratifying by atrial fibrillation status. Heritability estimates and sex-specific polygenic risk scores (PRS) were also calculated. Results Two male-specific loci: CFL2 and ATXN1 were identified. The meta-analysis identified additional 22 known AF loci. Sex heterogeneity was found in 6 of the 24 loci, with TTN and SPATS2L showing stronger effects in females, and NTMT2 , PITX2 , GBF1 , and CFL2 stronger effects in males. Heritability estimation liability was higher in females (h²=0.19) than in males (h²=0.12). PRS performance was similar across sexes (AUC = 0.60–0.63). Replication confirmed heterogeneity for PITX2 and CFL2 , with CFL2 variant associated with AF only in males. Proteomics analyses suggested nominals association such as: myosin light chain 1/3 (MYL1) and biogenesis of lysosomal organelles complex 1 subunit 2 (BLOC1S2). Key associated pathways included SCF-KIT signaling, prolactin signaling, and the RAC1/PAK1/p38/MMP2. Conclusions Our findings indicate significant sex-based heterogeneity in the effects of well-known AF-associated loci. Proteomic-genetic integration suggested sex-specific differences and candidate pathways. Despite this heterogeneity, a sex-specific approach did not significantly enhance PRS prediction, underscoring the need for adequately powered sex-specific GWAS. Atrial Fibrillation Genomics Sex Differences Proteomics GWAS Figures Figure 1 Figure 2 Figure 3 Introduction Atrial fibrillation (AF), the most prevalent cardiac arrhythmia worldwide, affects millions of individuals and contributes significantly to the global burden of cardiovascular disease (CVD)[ 1 ]. While AF impacts both men and women, substantial sex-based disparities have been observed in prevalence, presentation, and outcomes with women having lower prevalence but poorer prognosis than men[ 2 , 3 ]. It has been suggested that body size such as measures of weight, and height play a key role in the sex impact of AF. The lower risk of AF observed among women who are generally smaller and shorter may have led to the common perception that being female provides a protective effect against AF. However, when models are adjusted for height and body size, women without baseline CVD show a 49% higher risk of AF compared with men[ 2 ], suggesting that differences in body size may account for much of the apparent protective effect in women. Studies to date have found 150 risk loci associated with AF[ 4 ]; showing the capability of genetics to identify key driver of the pathology. However, little is known about the sex-specific genetic architecture of AF, as so far there are no sex-stratified GWAS published. Such sex differences are paramount to understanding AF, not only to throw light on the disease, but also to advance cardiovascular research towards personalized and precision medicine. Research is especially relevant because the data used to develop drugs might lead to more effective treatments for each sex, which, in turn, highlights the need to account for sex differences in therapeutic strategies. Here, we study sex-related genetic heterogeneity in AF to better understand AF biology. We perform a sex-stratified genome-wide association study (GWAS), metanalyses, and downstream analyses to comprehensively analyze the genetic underpinnings of sex-specific variability in AF; and replicate our findings in an independent cohort. We further performed proteomic analyses aimed at shedding a light on the intricate molecular mechanisms underlying this variability, and studied male and female biology to determine the possible existence of distinct genetic AF risk factors that point to the presence of different biological mechanisms contributing to the disease. Results Study overview Analyses were conducted using genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) and an independent Spanish replication cohort. The UKBB dataset comprised 1,981 female and 4,375 male atrial fibrillation (AF) cases, together with 192,193 female and 162,645 male controls, identified using ICD-10 code I48. Participants were drawn from a population-based cohort of over 500,000 individuals aged 40–69 years, with extensive phenotypic and longitudinal health data. To validate findings, we examined an independent cohort of 12,614 participants recruited across Spain between 2003 and 2022, including 1,207 AF cases (669 females) and 11,407 controls (5,607 females). This cohort was specifically enriched for ischemic stroke and AF phenotypes and was characterized using harmonized clinical and genetic data. Figure 1 summarizes the study design and the cohorts included at each analytical stage. Sex-specific genetic architecture of Atrial Fibrillation Sex-stratified GWAS revealed 12 ( KCNN3 , NTMT2 , PPFIA4 , PITX2 , CAV1 , AOPEP , GBF1 , SH3PXD2A , TBX5 , ZFHX3, CFL2 and ATXN1 ) loci significantly associated with AF in males (p-value < 5e-8, Supplementary table XX) of which 2 loci ( CFL2 and ATXN1 , Fig. 2 ; Table 1 ) were identified in males-only. We further identified 5 loci were significantly associated with AF in females ( TTN , SPATS2L , PITX2 , CAV1 , and ZFHX3 ). Fixed-effects meta-analysis of sex-specific GWAS led to identification of twenty-two significant and independent loci associated with AF (Table 1 ), of which 14 were also identified in sex-stratified GWAS. All loci have been previously reported to be associated with AF in the GWAS Catalog results[ 5 ]. CFL2 and ATXN1 (Fig. 2 ; Table 1 ), identified as significantly associated in AF through male-only GWAS, did not reached genome-wide significance in the meta-analysis. While these two loci have been previously described to be associated with AF according to the GWAS Catalog[ 5 ], these were not associated (p > 0.05) in the female-only GWAS. Table 1 Sex-related heterogeneity in the independent and significant loci obtained in the UKBB sex-stratified data and GWAS metanalyses. For substantial heterogeneity, loci are identified as those with higher effects in males or females. Female-only GWAS Male-only GWAS Metanalysis # rs ID CHR BP Gene Gene OTG EA OA Z score p-value Z score p-value N eff Z score p-value I 2 Het P Heterogeneity status* 1 rs72694603 1 112458893 KCND3 KCND3 T C -2.97 2.94×10 − 03 -4.87 1.14×10 − 06 24885 -5.589 2.28×10 − 08 0 0.80 No heterogeneity 2 rs11264280 1 154862952 KCNN3 ADAM15 T C 4.37 1.26×10 − 05 8.02 1.03×10 − 15 24885 8.919 4.69×10 − 19 0 0.40 No heterogeneity 3 rs72700114 1 170193825 NTMT2 NTMT2 C G 3.16 1.60 ×10 − 03 8.88 6.55×10 − 19 24885 8.944 3.74×10 − 19 81.2 0.02 Higher effects in males 4 rs10753933 1 203026214 PPFIA4 PPFIA4 T G 3.06 2.21×10 − 03 5.71 1.10×10 − 08 24885 6.325 2.53×10 − 10 0 0.52 No heterogeneity 5 rs56181519 2 175555714 WIPF1 WIPF1 T C -5.05 4.36×10 − 07 -4.77 1.82×10 − 06 24885 -6.665 2.65×10 − 11 55.6 0.13 Moderate heterogeneity 6 rs3731747 2 179421186 TTN FKBP7 A C -5.90 3.59×10 − 09 -3.83 1.29×10 − 04 24885 -6.369 1.90×10 − 10 86.4 0.01 Higher effects in females 7 rs7605146 2 201183888 SPATS2L SPATS2L A G 4.93 8.13×10 − 07 3.43 5.94×10 − 04 24885 5.513 3.52×10 − 08 78.1 0.03 Higher effects in females 8 rs6599220 3 38637994 SCN5A SCN5A T C 3.11 1.85×10 − 03 4.87 1.13×10 − 06 24885 5.668 1.45×10 − 08 0 0.89 No heterogeneity 9 rs6843082 4 111718067 PITX2 PITX2 A G -10.73 7.61×10 − 27 -19.64 8.45×10 − 86 24885 -21.845 8.67×10 − 106 75.9 0.04 Higher effects in males 10 rs73299219 5 137285940 FAM13B FAM13B A G 4.88 1.08×10 − 06 4.74 2.11×10 − 06 24885 6.543 6.04×10 − 11 47 0.17 No heterogeneity 11 rs11773845 7 116191301 CAV1 CAV1 A C 6.15 7.99×10 − 10 7.42 1.15×10 − 13 24885 9.417 4.65×10 − 21 0 0.35 No heterogeneity 12 rs78332318 8 124546102 FBXO32 NTAQ1 T C 3.28 1.02×10 − 03 5.42 5.99×10 − 08 24885 6.21 5.31×10 − 10 0 0.77 No heterogeneity 13 rs10993463 9 97807233 AOPEP AOPEP T C 4.22 2.44×10 − 05 5.88 4.06×10 − 09 24885 7.102 1.23×10 − 12 0 0.83 No heterogeneity 14 rs2246775 10 104057251 GBF1 CUEDC2 C A 1.32 1.87×10 − 01 6.10 1.04×10 − 09 24885 5.676 1.38×10 − 08 80.7 0.02 Higher effects in males 15 rs35176054 10 105480387 SH3PXD2A SH3PXD2A A T 4.64 3.55×10 − 06 5.72 1.05×10 − 08 24885 7.204 5.87×10 − 13 0 0.52 No heterogeneity 16 rs78907918 11 128767957 KCNJ5 KCNJ5 T C -4.36 1.33×10 − 05 -4.33 1.47×10 − 05 24885 -5.923 3.17×10 − 09 27.3 0.24 No heterogeneity 17 rs2045172 12 32980161 PKP2 PKP2 T G 3.79 1.53×10 − 04 4.78 1.73×10 − 06 24885 5.972 2.35×10 − 09 0 0.64 No heterogeneity 18 rs883079 12 114793240 TBX5 TBX5 T C 5.31 1.13×10 − 07 7.70 1.37×10 − 14 24885 9.176 4.48×10 − 20 0 0.92 No heterogeneity 19 rs7140396 14 32983817 AKAP6 AKAP6 A G 2.39 1.67×10 − 02 5.29 1.21×10 − 07 24885 5.614 1.98×10 − 08 0 0.34 No heterogeneity 20 rs67329386 16 73048367 ZFHX3 HP T C 5.63 1.81×10 − 08 9.79 1.26×10 − 22 24885 11.048 2.24×10 − 28 0 0.43 No heterogeneity 21 rs10853573 18 46525480 SMAD7 SMAD7 A G 3.18 1.47×10 − 03 4.71 2.43×10 − 06 24885 5.58 2.40×10 − 08 0 1.00 No heterogeneity 22 rs6633992 X 137416958 ZIC3 - T C -3.89 1.01×10 − 04 -4.13 3.67×10 − 05 24885 -5.497 3.87×10 − 08 0 0.37 No heterogeneity 23 rs6571678 14 35185345 CFL2 CFL2 A G -0.79 4.30×10 − 01 -5.50 3.86×10 − 08 24885 -4.891 1.00×10 − 06 82.3 0.02 Higher effects in males 24 rs73724866 6 16415856 ATXN1 ATXN1 A G -1.51 1.30×10 − 01 -5.56 2.74×10 − 08 24885 -5.34 9.27×10 − 08 69.8 0.07 Moderate heterogeneity BP: base pair positions in Human Genome assembly version 19. CHR: chromosome. EA: effect allele. N eff: effective sample size. OA: other allele. OTG: Open Targets Genetics Variant-to-Gene prioritized gene. Sex heterogeneity of the effects Twenty-four loci were evaluated for heterogeneity, CFL2 and ATXN1 together with 22 from the GWAS metanalysis. Substantial heterogeneity (I 2 > 70%) was found in 6 loci: TTN and SPATS2L with a greater effect size in the female-only GWAS, and NTMT2 , PITX2, GBF1 , and CFL2 with a greater effect size in the male-only GWAS. Moderate heterogeneity (I 2 > 50%) was found in the WIPF1 and ATXN1 loci, with greater effect sizes in females and males, respectively (Table 1 ). These 6 candidate loci ( TTN , SPATS2L, NTMT2 , PITX2, GBF1 , and CFL2) were evaluated for replication in the independent cohort (AF cases and controls numbered 1,207 (669 females) and 11,407 (5,607 females)) (Fig. 3 , Table S5; see Figure S1 for Manhattan plots of the replication cohort). Loci PITX2 and CFL2 were considered replicated, given heterogeneity I 2 > 50%, consistent effect direction, and (at least) nominal association (p < 0.05) in the sex-stratified GWAS analyses. In the UKBB results PITX2 variant showed 2.04 higher effect sizes in males than in females, similarly in the replication we observed a difference of 2.55 higher effect sizes. The PITX2 locus was associated at genome-wide significance in the metanalysis and in the male-only GWAS. However, PITX2 locus variant rs6843082 was only nominally associated in the female-only GWAS (p = 6.8×10 − 3 ). Locus CFL2 variant rs6571678 was nominally associated in the male-only GWAS (p = 4.5×10 − 3 ); similarly, this locus was found associated only in males in the UKBB population (Table 1 ). Regarding the differences in the effect sizes, between men and women for CFL2 locus, it was more pronounced since the variant was not associated (p > 0.05) in the females results for the UKBB nor in the replication results. Risk factors for AF and sex-specific variants We sought to characterize potential factors that might account for the observed sex-heterogeneity at the 2 replicated loci, and therefore might be acting as confounding effect of the sex-difference. For locus PITX2 variant rs6843082, no confounding effects were observed body composition: standing height, weight, body mass index (BMI); nor for related risk factors: type 2 diabetes (DM2), LDL levels, hypertension nor cholesterol levels, with p > 0.05, but we did observe a modest association for smoking status (p = 0.02). This association was in the consistent for confounding effect, as smoking status is associated with the risk allele for AF (Table S9). To analyze sensitivity, the replication adjusted for smoking status was repeated, but no significant impact was observed on sex-related heterogeneity. Replication results for variant rs6843082-A were Z=-5.4, p = 6.65×10 − 08 , I 2 = 93.1, and p = 1.40×10 − 04 for heterogeneity. For locus CFL2 variant rs6571678, no confounding effects were observed, with p > 0.05 for smoking status, DM2, hypertension, BMI, and weight. For standing height, LDL, and cholesterol levels, although p < 0.05, the associations were in the opposite direction to that expected for the confounding effect (Table S9). Leveraging plasma proteomics to identify potential sex-differential mechanisms associated with the identified loci To elucidate the mechanisms potentially responsible for the sex-specific effects of the rs6843082 ( PITX2 ) and rs6571678 ( CFL2 ) variants, we conducted a systematic analysis of the differential associations between variant–sex interactions and circulating protein abundance, further evaluating the influence of AF status stratification. Several proteins were nominally associated with the interaction of the PITX2 locus and sex, for AF controls and AF cases, interestingly 171 proteins were found with significant heterogeneity for the AF status (Table S6), top modulated protein was MYL1 (Figure S2 ). Similarly, for the CFL2 locus, several proteins were nominally associated with the interaction with sex for AF controls and AF cases, 120 proteins showing significant heterogeneity for the AF status (Table S7), top modulated protein was BLOC1S2 (Figure S3). Enrichment pathway analyses revealed the involvement of SCF-KIT signaling by PITX2 locus and pathways prolactin signaling and RAC1/PAK1/p38/MMP2 by CFL2 locus. The three of them significant after multiple test comparison adjustments (Table S8). Sex stratified heritability estimation Given the substantial sex-heterogeneity observed at know AF-risk loci, we sought to characterize the estimated heritability in males and females. Observed heritability for AF was, for females, h 2 obs (SE) = 0.0173 (0.0028) and p = 6.47×10 − 10 , and for males, h 2 obs (SE) = 0.0244 (0.0047) and p = 2.09×10 − 07 . The AF liability scale for females was h 2 liab (SE) = 0.19 (0.03) and p = 7.80×10 − 10 and for males was h 2 liab (SE) = 0.12 (0.02) and p = 2.19×10 − 07 . When transformed to the liability scale, the estimated heritability was higher in females (h²=0.19, SE = 0.03) than in males (h²=0.12, SE = 0.02). Given the lower disease prevalence in females (359.9 vs. 569.5 per 100,000)[ 6 ], part of this difference may reflect the mathematical effect of the prevalence correction, which tends to inflate liability-scale estimates when prevalence is lower. Nevertheless, the magnitude of the observed difference suggests that this inflation alone is unlikely to fully account for the higher heritability observed in females. Evaluation of the performance of sex-specific PRS Considering the sex-specific patterns of AF we sought to evaluate the performance of sex-specific PRS vs a both sexes reference for PRS. The best PRS was obtained using LDpred-2, together with the metanalysis as the female reference and the sex-specific GWAS results as the male reference. Results for females and males with AF were AUC = 0.60 ((95% CI: 0.56–0.63) and p = 9.99×10 − 8 and AUC = 0.63 (95% CI: 0.59–0.67) and p = 1.03×10 − 9, respectively (Table S10). According to the DeLong test for correlated AUCs, there were no significant differences in the best PRS between the sex-specific GWAS and the metanalysis: males, AUC = 0.63 vs AUC = 0.61 (p = 0.13), and females, AUC = 0.58 vs AUC = 0.60 (p = 0.24) (Table S10). Discussion Through sex-stratified GWAS analyses, metanalysis, and downstream analyses, we analyzed in detail the genetic underpinnings of sex-related AF risk, finding and replicating 2 loci with strong sex-difference patterns in their genetic association with AF risk. As far as we are aware, ours is the first study suggesting that sex-related AF risk differences may be explained by distinct genetic risk factors. We identified 22 loci previously reported as associated with AF, thereby reinforcing the generalizability of the UKBB cohort. In addition, 2 loci were found to be only significant in the male-only GWAS for the UKBB cohort. For these 24 loci, we studied potential sex-related heterogeneity, finding 6 to show substantial heterogeneity, 2 with a greater effect size in females ( TTN and SPATS2L ) and 4 with a greater effect size in males ( NTMT2 , PITX2 , GBF1 , and CFL2 ). To confirm our findings, we performed replication in a cohort of 12,614 individuals, finding that we could replicate the sex-related heterogeneity of loci PITX2 and CFL2 . Direction remained consistent for both those loci, with both showing a greater standardized effect size in males than in females. PITX2 encodes a transcription factor involved in the regulation of multiple genes. To date, no sex-specific heterogeneity has been reported for this gene or its protein in humans. However, a previous study in mice found strong sex differences on evaluating the effects of deleting a 20-kb enhancer region of Pitx2 . The finding that male mice carrying this deletion exhibited higher rates of AF compared to females[ 7 ] reinforces the potential role of PITX2 as a sex-dependent mediator of AF risk. CFL2 encodes an intracellular protein that is involved in the regulation of actin-filament dynamics. To date, however, no significant sex differences related to cardiac function have been reported in humans. Interestingly, we observed, in 2 independent cohorts, that this locus was exclusively associated in males; i.e., there was not even nominal association in females. While a potential reason for this sex-related heterogeneity may be imbalanced sample sizes in the sex-stratified analyses, we corrected for this imbalance by performing a sample size-based metanalysis using the effective sample size of each analysis. In our replication, the more powered GWAS was the female analysis, in contrast with the UKBB, for which the male sample is more powered that the female sample. Therefore, sample size does not seem to have biased our sex-related heterogeneity results. Proteomic analyses provide additional biological context for the interaction between genotype, sex, and AF status. In our study evaluating the association between the two replicated variants and protein levels in plasma samples from the UKBB, we identified several nominal associations. Notably, the analysis of pathways associated with atrial fibrillation heterogeneity revealed significant pathways that may influence the interaction between AF status and variant-sex heterogeneity. Key associated pathways included SCF-KIT signaling by PITX2 locus, and pathways such as prolactin signaling, and the RAC1/PAK1/p38/MMP2 by CFL2 locus. The SCF-KIT pathway is suggested to be associated with the differential impact of the PITX2 genotype in men and women, this pathway it has been linked to mitochondrial dysfunction and impaired energy expenditure, as demonstrated in models of KIT loss-of-function.[ 8 ] Interestingly, estrogens play an important role in modulating mitochondrial biogenesis through the regulation of PGC-1 coactivators, exerting protective effects against oxidative stress and supporting mitochondrial efficiency.[ 9 ] This interaction suggests that the SCF-KIT pathway could underlie the differential response to atrial fibrillation observed between men and women, with estrogens providing a compensatory protective mechanism that may mitigate the impact of PITX2 variant in this pathway. The RAC1/PAK1/p38/MMP2 and prolactin signaling are suggested to be modulating the different impact of the CFL2 genotype in men and women, both pathways have been implicated in atrial fibrillation, through mechanisms involving oxidative stress, calcium handling, and extracellular matrix remodeling[ 10 – 13 ]. Importantly, both pathways are modulated by sex hormones and show sex-specific regulation: estradiol suppresses RAC1 expression and p38α activation in cardiomyocytes and modulates PAK1 activity in various cellular contexts[ 14 – 16 ], while prolactin signaling is also differentially regulated between sexes via estrogenic and pituitary mechanisms [ 17 ]. These sex-dependent effects may underlie differences in atrial fibrillation susceptibility and progression between men and women. The sex-related heterogeneity of PITX2 and CFL2 is possibly explained by the pleiotropy of these regions towards traits that are significantly more frequent in males than females; e.g., regarding a variant also associated with smoking, European men are more frequent smokers that European women. To account for this possible pleiotropy, we checked the association with potential traits, finding no potential confounding effects for either variant, except for a nominal association between smoking status and PITX2 . Our results after adjusting for smoking status revealed no changes confirming sex-related heterogeneity for PITX2. Our sex-stratified liability-scale estimates indicate that genetic factors may account for a larger proportion of variance in disease risk among women (h²=0.19, SE = 0.03) than among men (h²=0.12, SE = 0.02). Importantly, the prevalence of the disorder was lower in women (359.9 per 100,000) than in men (569.5 per 100,000), which sets a higher liability threshold for females. Because lower prevalence typically reduces liability-scale heritability, the finding of a higher estimate in women is more consistent with a genuine biological difference rather than a scaling artifact. This pattern suggests that genetic risk factors may exert relatively stronger effects in females, while environmental or non-additive influences could contribute more substantially to disease risk in males. Sex-specific PRS demonstrated moderate predictive ability. The best PRS yielded an AUC = 0.60 for females and a slightly higher AUC = 0.63 for males. The absence of significant differences between sex-specific PRS values and those derived from the metanalysis suggests that, while sex-specific loci add granularity, the overall genetic architecture captured by the metanalysis is broadly applicable to both sexes. However, the modest AUC values highlight the need for further refinement of PRS models to improve their clinical utility. Limitations of this study include the imbalanced sample sizes between males and females. While this imbalance could impact the statistical power of the analyses, especially in detecting sex-specific effects in smaller subgroups, efforts were made to correct this through metanalysis adjustments. In addition, our study only involves individuals of European ancestry, and replication in another subgroup of European ancestry, therefore further validation should be considered to evaluate the generalizability of the results in different ancestries. Additionally, the PRS models demonstrated only moderate predictive ability, suggesting the need for further refinement to enhance their clinical utility. While our study explored potential confounding factors such as smoking status, other unmeasured lifestyle and environmental factors might affect our findings. Finally, since the proteomic findings linking the PITX2 and CFL2 genotypes with different modulation of signaling by SCF-KIT, prolactin signaling and RAC1/PAK1/p38/MMP2 pathway which may not be generalizable across all populations, further functional studies are required to confirm the pathways linking genetic variants with AF risk. Our study underscores the importance of considering sex as a biological variable in genetic studies of AF. The identification of sex-specific loci, heterogeneity patterns, and differences in heritability estimates throws valuable light on the genetic underpinnings of AF and reinforce the need for sex-stratified analyses aimed at unraveling these complexities. Future research should focus on elucidating the biological mechanisms driving these sex differences, considering these sex differences as key aspect in drug development and on refining predictive models to enhance their clinical applicability. Abbreviations • AF Atrial Fibrillation • AUC Area Under the Curve • BMI Body Mass Index • CI Confidence Interval • CVD Cardiovascular Disease • DM2 Type 2 Diabetes • GIF Genomic Inflation Factor • GWAS Genome – Wide Association Studies • h² Heritability • I² Inconsistency Squared • LD Linkage Disequilibrium • LDL Low – Density Lipoprotein • MREC North West Multi – centre Research Ethics Committee • PRS Polygenic Risk Scores • RTB Research Tissue Bank • SE Standard Error • UKBB UK Biobank Online methods A table of resources with the versions for each software and the origin of files and phenotypes used can be found in Table S1. Study population The UK Biobank (UKBB) is a large, prospective population-based cohort that recruited over 500,000 participants aged 40–69 years across the United Kingdom between 2006 and 2010. Participants underwent baseline assessments including questionnaires on sociodemographic, lifestyle, and health-related factors, physical measurements, and biological sample collection. Follow-up data are available through linkage with electronic health records, hospital episode statistics, cancer registries, and death records, allowing long-term monitoring of health outcomes. ICD-10 code I48 was used to select GWAS AF and flutter phenotype results from the UKBB V2 analysis carried out by Neale et al[18]. Downloaded summary statistics for female- and male-only analyses comprised GWAS analyses of 1,981 female patients and 192,193 female controls, and 4,375 male patients and 162,645 male controls. We additionally used an independent replication cohort comprising a clinical and genetic cohort of 12,614 individuals aged ≥18 years recruited in Spain between 2003 and 2022; AF cases and controls numbered 1,207 (669 females) and 11,407 (5,607 females), respectively. This cohort, focused on ischemic stroke and AF cases and controls was defined based on clinical information and records on recruitment. Further details of the cohorts, inclusion and exclusion criteria, array information, and hospital contributions can be found in Tables S2-S4. Figure 1 depicts a flowchart of the project, highlighting the cohorts for which each step was performed. Quality control and GWAS analyses UKBB and its genotyping strategies and methods have been described elsewhere[19]. Quality control applied to the imputed variants were as follows: INFO score >0.8, minor allele frequency >0.001, and Hardy-Weinberg p>1×10 -10 . The GWAS analyses were performed using a liner regression model and selecting only European genetically defined samples, and the main covariates were age, age 2 , and the first 20 principal components[20]. Quality control and imputation of the replication cohort were performed according to a previous study by our group[21]. For the replication cohort, we performed the GWAS analysis for AF-only females and AF-only males, applying an additive genetic model using fastGWA from GCTA[22]. Age and the first 10 principal components were used as covariates. The genomic inflation factor (GIF) was estimated as lambda. Metanalysis To evaluate sex-related heterogeneity, we performed GWAS metanalyses based on sample sizes and p-values using METAL software[23]; Z -scores for each allele were combined across samples in a weighted sum, with weights proportional to the square root of the sample size for each study[24]. Correction was applied for the GIF. To account for sample size and unbalanced case/control ratios, we estimated the effective sample size using the following formula: For the individual GWAS analyses and metanalysis results, we considered genome-wide loci to be significant if p0.1 and a distance of 250kb between predefined LD blocks in Phase 3 of the European 1000 Genomes Project[25]. Locus names were determined according to the gene closest to the leading variant. Leading variants in the metanalysis and the independent GWAS analyses were tested for the sex-related heterogeneity of effects using Cochran’s Q-test. In line with previous studies[26], we considered variant heterogeneity between sexes to be substantial for I 2 >70%, moderate for I 2 >50%, and negligible for I 2 70%) were evaluated in the metanalysis data from the replication independent cohort. If the lead variant was not present, we checked genome-wide significantly associated variants from the same locus that presented r 2 >0.8 for LD with the lead variants. In this cohort, replication was considered to occur when I 2 >50%, the direction of effects was consistent, and the p-value of the association was at least nominally associated (p<0.05) in one of the sex-stratified GWAS analyses. Proteomic analysis Plasma proteomic was evaluated using Olink data in a subgroup of 46,724 individuals of the UKB cohort. Olink methodology in the UKB data has been previously described[27]. We assessed sex-specific effects of the index SNP on plasma protein levels by testing the interaction between SNP dosage and sex. For each protein, we fitted linear regression models with protein abundance as the outcome and included SNP dosage, sex, and their interaction term (SNP × sex) as predictors, adjusting for age. To evaluate whether the interaction effect differed between atrial fibrillation (AF) cases and non-cases, analyses were conducted separately within each stratum. Heterogeneity between strata was formally tested by comparing the estimated interaction coefficients (β) and their standard errors (SE) using a Z-test, calculated as Two-sided p-values were obtained from the standard normal distribution and corrected for multiple testing using the Benjamini–Hochberg false discovery rate (FDR). Gene lists ranked by the Z-score were used for gene-set enrichment analysis to identify overrepresented biological pathways. Enrichment was performed using String (https://string-db.org/), querying WikiPathways, Reactome, and KEGG . Enriched pathways with FDR 2 were considered significant. Confounding factors The replicated variants were analyzed for the potential impact of confounding factors on sex-related heterogeneity. As previously described and analyzed by Neale et al[18], the 2 replicated loci index variants were evaluated in the GWAS of phenotypes associated with AF risk in the UKBB cohort. Selected traits were based on CVD risk factors and body size measurements, namely, smoking status, type 2 diabetes (DM2), hypertension, body mass index (BMI), low-density lipoprotein (LDL) levels, total cholesterol levels, height, and weight. Traits with p<0.05 for the candidate variants were selected as covariates to evaluate the impact of adjustment in the replication step. Heritability estimation Observed heritability and heritability liability were estimated for the sex-stratified AF GWAS analyses using LD score regression[28]. In the case of heritability liability, the analysis accounted for age-adjusted estimates of AF prevalence rates of 569.5 and 359.9 per 100,000 population for men and for women, respectively[6]. Polygenic risk score To calculate the polygenic risk score (PRS), the replication cohort was divided randomly into validation and test samples in the proportion 60-40. As reference datasets we used summary statistics for the sex-stratified AF GWAS and for the AF GWAS metanalysis. To generate scores for the validation cohort and evaluate predictive value for the test cohort (60% and 40% of the replication cohort, respectively), we used 3 different strategies: (1) PRSice-2[29]. A clumping and thresholding strategy were used to evaluate the best threshold p-value to select predictive variants in the validation cohort, for a clumping window set to 250kb and r 2 >0.1. (2) LDpred-2 auto model[30]. This Bayesian method, which does not require the validation cohort to choose the best model, was used to infer heritability and polygenicity. (3) lassosum-2. With this software we applied penalized regression to generate PRS values. For LDpred-2 and lassosum-2, to ensure a dense panel that increased score generalizability, in line with the developer’s recommendations we selected variants that were present in the extended HapMap3 panel. Both those strategies were computed using the bigsnpR package v1.12.15 in R. Regarding the test set, for each model we estimated the area under the curve (AUC) and 95% confidence interval (CI), the pseudo r 2 , and the p-value of the association. Additionally, we used the best PRS strategy according to the AUC for females and males to compare difference with the others using the DeLong test for correlated AUCs. Declarations Ethics approval and consent to participate UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. The study protocol of the replication cohort was approved by an institutional review board and ethics committee at Hospital de la Santa Creu i Sant Pau on February 26 th 2024, approval identification code IIBSP-EGX-2023-152. Participants or their legal representatives provided prior written informed consent. Consent for publication Not applicable Availability of data and materials Summary statistics of GWAS and GWAS metanalysis will be available to the Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/). Additional files will be available from the corresponding authors upon reasonable requests. Competing interests The authors declare that they have no competing interests. Funding P. Villatoro-González is supported by a Joan Oró Contract from the AGAUR FI AJUTS (2023 FI-3 00065) predoctoral program. This study has been funded by ISCIII (grant numbers PI18/01338, PI20/00925), ERA-NET NEURON (AC19/00106), RICORS-ICTUS: Red de Investigación Cooperativa Orientada a Resultados en Salud – Enfermedades Vasculares Cerebrales (RD21/0006/0006; RD21/0006/0002; RD24/0009/0002; RD24/0009/0010), CIBER-Consorcio Centro de Investigación Biomédica en Red- Enfermedades Neurodegnerativas (CIBERNED) (CB22/05/00067), CERCA Programme/Generalitat de Catalunya, and Marató de 3cat (grant numbers 202306-30 and 202310-30). A subgroup of the controls samples were provided by the "Banco Nacional de ADN Carlos III (BNADN);www.bancoadn.org) and genotyping services were provided by the “Centro Nacional de Genotipado-Fundación Pública Galega de Medicina Xenómica". Authors’ contributions JCM and PB conceptualized, designed the study, performed the statistical data analyses. LLC performed the experiments and collected the data. EM, CGF, NC, ML, JMMC, PVG, LM, conducted data analyses and revision of the results. RIR, FC, AJM, MF, JFA, VO, JAS, CAM, MR, JJC, LMN, ELC, MM, RDN, STC, CVB, GSH, TS, LI, LH, PD, RD, JK, LPS, PCR, MG, GE, NB, LS, RdC, EC, GV, AFV, OB, JM, XU, MMCR, APM, JC, TSobrino, CC, JML, JMF and IFC collected the samples and data. JCM and IFC wrote the first draft. All authors contributed to manuscript revision and approved the final version. IFC, RD, JML, JM obtained the funding. IFC and JCM supervised the project. Acknowledgments Grateful thanks to the study participants and their families for their contributions. This work has been carried out within the framework of the Doctoral Program in Medicine of the Universitat Autònoma de Barcelona. Proteomic analyses were performed under UK biobank application number 44448. References Linz D, Gawalko M, Betz K, Hendriks JM, Lip GYH, Vinter N et al. Atrial fibrillation: epidemiology, screening and digital health. The Lancet Regional Health – Europe [Internet]. 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Nat Nat Publishing Group. 2023;622:329–38. https://doi.org/10.1038/s41586-023-06592-6 . Bulik-Sullivan BK, Loh P-R, Finucane H, Ripke S, Yang J et al. Consortium SWG of the PG,. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. Nature genetics. NIH Public Access; 2015;47:291. https://doi.org/10.1038/ng.3211 Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience. 2019;8:giz082. https://doi.org/10.1093/gigascience/giz082 . Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet. 2023;110:2042–55. https://doi.org/10.1016/j.ajhg.2023.10.010 . Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":117081,"visible":true,"origin":"","legend":"\u003cp\u003eProject flowchart highlighting the evaluated cohorts.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7978651/v1/07a046c35e1830990e8a5f3a.jpg"},{"id":96454050,"identity":"828e99c7-848d-49b8-a761-02bdd84239b6","added_by":"auto","created_at":"2025-11-21 10:02:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":284674,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot of atrial fibrillation GWAS analysis and metanalysis, highlighting the sex-related heterogeneity of the effects in the metanalysis. Panel A: GWAS metanalysis; Panel B: female-only GWAS; Panel C: male-only GWAS.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7978651/v1/e58c77fd42a0122d64bfc467.jpg"},{"id":96453645,"identity":"07c9877e-f120-4d24-97bd-3a3dc1235aa8","added_by":"auto","created_at":"2025-11-21 10:01:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91810,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the sex-heterogeneous variants in UKBB and replication cohort. For replication results of locus \u003cem\u003eTTN\u003c/em\u003e it is shown the results of variant rs35504893 used as proxy of rs3731747. Repl_f: Replication cohort results for AF in only females; Repl_m: Replication cohort results for AF in only males; UKBB_f: UKBB cohort results for AF in only females; UKBB_m: UKBB cohort results for AF in only males.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7978651/v1/eed0821675eff90e47bfff16.jpg"},{"id":96602786,"identity":"524ab904-f0f1-4d13-83c7-13a6abadbb50","added_by":"auto","created_at":"2025-11-24 09:01:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2235983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7978651/v1/52dcf75a-1799-4bfa-ad47-51a4f2247b3d.pdf"},{"id":96400627,"identity":"974ba5df-8c8e-48e6-8842-e1202f6c4472","added_by":"auto","created_at":"2025-11-20 16:06:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":624628,"visible":true,"origin":"","legend":"","description":"","filename":"edSupplementaryDataAuthorschecked.docx","url":"https://assets-eu.researchsquare.com/files/rs-7978651/v1/91cbf662b9444e89f723642a.docx"},{"id":96400638,"identity":"9f9238aa-54db-4ab5-a78f-76ffe2019401","added_by":"auto","created_at":"2025-11-20 16:06:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":903782,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesAuthorschecked.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7978651/v1/3d3053c2314b3cef65477858.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"GWAS metanalysis of atrial fibrillation reveals significant sex-related heterogeneity effects of the PITX2 and CFL2 loci","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF), the most prevalent cardiac arrhythmia worldwide, affects millions of individuals and contributes significantly to the global burden of cardiovascular disease (CVD)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While AF impacts both men and women, substantial sex-based disparities have been observed in prevalence, presentation, and outcomes with women having lower prevalence but poorer prognosis than men[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It has been suggested that body size such as measures of weight, and height play a key role in the sex impact of AF. The lower risk of AF observed among women who are generally smaller and shorter may have led to the common perception that being female provides a protective effect against AF. However, when models are adjusted for height and body size, women without baseline CVD show a 49% higher risk of AF compared with men[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], suggesting that differences in body size may account for much of the apparent protective effect in women.\u003c/p\u003e\u003cp\u003eStudies to date have found 150 risk loci associated with AF[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; showing the capability of genetics to identify key driver of the pathology. However, little is known about the sex-specific genetic architecture of AF, as so far there are no sex-stratified GWAS published. Such sex differences are paramount to understanding AF, not only to throw light on the disease, but also to advance cardiovascular research towards personalized and precision medicine. Research is especially relevant because the data used to develop drugs might lead to more effective treatments for each sex, which, in turn, highlights the need to account for sex differences in therapeutic strategies.\u003c/p\u003e\u003cp\u003eHere, we study sex-related genetic heterogeneity in AF to better understand AF biology. We perform a sex-stratified genome-wide association study (GWAS), metanalyses, and downstream analyses to comprehensively analyze the genetic underpinnings of sex-specific variability in AF; and replicate our findings in an independent cohort. We further performed proteomic analyses aimed at shedding a light on the intricate molecular mechanisms underlying this variability, and studied male and female biology to determine the possible existence of distinct genetic AF risk factors that point to the presence of different biological mechanisms contributing to the disease.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy overview\u003c/h2\u003e\u003cp\u003eAnalyses were conducted using genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) and an independent Spanish replication cohort. The UKBB dataset comprised 1,981 female and 4,375 male atrial fibrillation (AF) cases, together with 192,193 female and 162,645 male controls, identified using ICD-10 code I48. Participants were drawn from a population-based cohort of over 500,000 individuals aged 40\u0026ndash;69 years, with extensive phenotypic and longitudinal health data. To validate findings, we examined an independent cohort of 12,614 participants recruited across Spain between 2003 and 2022, including 1,207 AF cases (669 females) and 11,407 controls (5,607 females). This cohort was specifically enriched for ischemic stroke and AF phenotypes and was characterized using harmonized clinical and genetic data. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the study design and the cohorts included at each analytical stage.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSex-specific genetic architecture of Atrial Fibrillation\u003c/h3\u003e\n\u003cp\u003eSex-stratified GWAS revealed 12 (\u003cem\u003eKCNN3\u003c/em\u003e, \u003cem\u003eNTMT2\u003c/em\u003e, \u003cem\u003ePPFIA4\u003c/em\u003e, \u003cem\u003ePITX2\u003c/em\u003e, \u003cem\u003eCAV1\u003c/em\u003e, \u003cem\u003eAOPEP\u003c/em\u003e, \u003cem\u003eGBF1\u003c/em\u003e, \u003cem\u003eSH3PXD2A\u003c/em\u003e, \u003cem\u003eTBX5\u003c/em\u003e, \u003cem\u003eZFHX3, CFL2\u003c/em\u003e and \u003cem\u003eATXN1\u003c/em\u003e) loci significantly associated with AF in males (p-value\u0026thinsp;\u0026lt;\u0026thinsp;5e-8, Supplementary table XX) of which 2 loci (\u003cem\u003eCFL2\u003c/em\u003e and \u003cem\u003eATXN1\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were identified in males-only. We further identified 5 loci were significantly associated with AF in females (\u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eSPATS2L\u003c/em\u003e, \u003cem\u003ePITX2\u003c/em\u003e, \u003cem\u003eCAV1\u003c/em\u003e, and \u003cem\u003eZFHX3\u003c/em\u003e). Fixed-effects meta-analysis of sex-specific GWAS led to identification of twenty-two significant and independent loci associated with AF (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), of which 14 were also identified in sex-stratified GWAS. All loci have been previously reported to be associated with AF in the GWAS Catalog results[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u003cem\u003eCFL2\u003c/em\u003e and \u003cem\u003eATXN1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), identified as significantly associated in AF through male-only GWAS, did not reached genome-wide significance in the meta-analysis. While these two loci have been previously described to be associated with AF according to the GWAS Catalog[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], these were not associated (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the female-only GWAS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSex-related heterogeneity in the independent and significant loci obtained in the UKBB sex-stratified data and GWAS metanalyses. For substantial heterogeneity, loci are identified as those with higher effects in males or females.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"18\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eFemale-only GWAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eMale-only GWAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c18\" namest=\"c13\"\u003e\u003cp\u003eMetanalysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGene OTG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eZ score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eZ score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN eff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eZ score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003eHet P\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003eHeterogeneity status*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers72694603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e112458893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKCND3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKCND3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e2.94\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.14\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e-5.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e2.28\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers11264280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154862952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKCNN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eADAM15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.26\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.03\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e8.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e4.69\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ers72700114\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e170193825\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eNTMT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eNTMT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e3.16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1.60\u003c/b\u003e\u0026times;10\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;03\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e8.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e6.55\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;19\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e24885\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e8.944\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e3.74\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;19\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e81.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eHigher effects in males\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers10753933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e203026214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePPFIA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePPFIA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e2.21\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.10\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e6.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e2.53\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers56181519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e175555714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWIPF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWIPF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e4.36\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e-6.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e2.65\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eModerate heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ers3731747\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e179421186\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eTTN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eFKBP7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-5.90\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e3.59\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;09\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e-3.83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e1.29\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;04\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e24885\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e-6.369\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e1.90\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;10\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e86.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eHigher effects in females\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ers7605146\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e201183888\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSPATS2L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSPATS2L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e4.93\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e8.13\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;07\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e3.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e5.94\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;04\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e24885\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e5.513\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e3.52\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;08\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e78.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eHigher effects in females\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers6599220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38637994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSCN5A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSCN5A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.85\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.13\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e5.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e1.45\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ers6843082\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e111718067\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ePITX2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ePITX2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-10.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e7.61\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;27\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e-19.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e8.45\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;86\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e24885\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e-21.845\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e8.67\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;106\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e75.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eHigher effects in males\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers73299219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137285940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFAM13B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFAM13B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.08\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e2.11\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e6.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e6.04\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers11773845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116191301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCAV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCAV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e7.99\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.15\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e9.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e4.65\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;21\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers78332318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e124546102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFBXO32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNTAQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.02\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e5.99\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e5.31\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers10993463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97807233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAOPEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAOPEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e2.44\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e4.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e7.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e1.23\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ers2246775\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e104057251\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eGBF1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eCUEDC2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1.87\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;01\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e6.10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e1.04\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;09\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e24885\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e5.676\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e1.38\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;08\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e80.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eHigher effects in males\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers35176054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105480387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSH3PXD2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSH3PXD2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e3.55\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.05\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e7.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e5.87\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers78907918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e128767957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKCNJ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKCNJ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.33\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e-5.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e3.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers2045172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32980161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePKP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePKP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.53\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.73\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e5.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e2.35\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers883079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e114793240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTBX5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTBX5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.13\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.37\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e9.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e4.48\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers7140396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32983817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAKAP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAKAP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.67\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.21\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e5.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e1.98\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers67329386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73048367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZFHX3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e9.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e1.26\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;22\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e11.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e2.24\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;28\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers10853573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46525480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSMAD7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSMAD7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e2.43\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e2.40\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers6633992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137416958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZIC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.01\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e3.67\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e-5.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e3.87\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ers6571678\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e35185345\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCFL2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eCFL2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e4.30\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;01\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e-5.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e3.86\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;08\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e24885\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e-4.891\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e1.00\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;06\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e82.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eHigher effects in males\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers73724866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16415856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eATXN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eATXN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e\u003cp\u003e1.30\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;01\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-5.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c12\"\u003e\u003cp\u003e2.74\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e24885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e-5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c15\"\u003e\u003cp\u003e9.27\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e69.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eModerate heterogeneity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"18\"\u003eBP: base pair positions in Human Genome assembly version 19. CHR: chromosome. EA: effect allele. N eff: effective sample size. OA: other allele. OTG: Open Targets Genetics Variant-to-Gene prioritized gene.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eSex heterogeneity of the effects\u003c/h3\u003e\n\u003cp\u003eTwenty-four loci were evaluated for heterogeneity, \u003cem\u003eCFL2\u003c/em\u003e and \u003cem\u003eATXN1\u003c/em\u003e together with 22 from the GWAS metanalysis. Substantial heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;70%) was found in 6 loci: \u003cem\u003eTTN\u003c/em\u003e and \u003cem\u003eSPATS2L\u003c/em\u003e with a greater effect size in the female-only GWAS, and \u003cem\u003eNTMT2\u003c/em\u003e, \u003cem\u003ePITX2, GBF1\u003c/em\u003e, and \u003cem\u003eCFL2\u003c/em\u003e with a greater effect size in the male-only GWAS. Moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%) was found in the \u003cem\u003eWIPF1\u003c/em\u003e and \u003cem\u003eATXN1\u003c/em\u003e loci, with greater effect sizes in females and males, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese 6 candidate loci (\u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eSPATS2L, NTMT2\u003c/em\u003e, \u003cem\u003ePITX2, GBF1\u003c/em\u003e, and \u003cem\u003eCFL2)\u003c/em\u003e were evaluated for replication in the independent cohort (AF cases and controls numbered 1,207 (669 females) and 11,407 (5,607 females)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S5; see Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for Manhattan plots of the replication cohort). Loci \u003cem\u003ePITX2\u003c/em\u003e and \u003cem\u003eCFL2\u003c/em\u003e were considered replicated, given heterogeneity I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%, consistent effect direction, and (at least) nominal association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the sex-stratified GWAS analyses. In the UKBB results PITX2 variant showed 2.04 higher effect sizes in males than in females, similarly in the replication we observed a difference of 2.55 higher effect sizes. The \u003cem\u003ePITX2\u003c/em\u003e locus was associated at genome-wide significance in the metanalysis and in the male-only GWAS. However, \u003cem\u003ePITX2\u003c/em\u003e locus variant rs6843082 was only nominally associated in the female-only GWAS (p\u0026thinsp;=\u0026thinsp;6.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Locus \u003cem\u003eCFL2\u003c/em\u003e variant rs6571678 was nominally associated in the male-only GWAS (p\u0026thinsp;=\u0026thinsp;4.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); similarly, this locus was found associated only in males in the UKBB population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding the differences in the effect sizes, between men and women for \u003cem\u003eCFL2\u003c/em\u003e locus, it was more pronounced since the variant was not associated (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the females results for the UKBB nor in the replication results.\u003c/p\u003e\n\u003ch3\u003eRisk factors for AF and sex-specific variants\u003c/h3\u003e\n\u003cp\u003eWe sought to characterize potential factors that might account for the observed sex-heterogeneity at the 2 replicated loci, and therefore might be acting as confounding effect of the sex-difference. For locus \u003cem\u003ePITX2\u003c/em\u003e variant rs6843082, no confounding effects were observed body composition: standing height, weight, body mass index (BMI); nor for related risk factors: type 2 diabetes (DM2), LDL levels, hypertension nor cholesterol levels, with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, but we did observe a modest association for smoking status (p\u0026thinsp;=\u0026thinsp;0.02). This association was in the consistent for confounding effect, as smoking status is associated with the risk allele for AF (Table S9). To analyze sensitivity, the replication adjusted for smoking status was repeated, but no significant impact was observed on sex-related heterogeneity. Replication results for variant rs6843082-A were Z=-5.4, p\u0026thinsp;=\u0026thinsp;6.65\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;93.1, and p\u0026thinsp;=\u0026thinsp;1.40\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e for heterogeneity.\u003c/p\u003e\u003cp\u003eFor locus \u003cem\u003eCFL2\u003c/em\u003e variant rs6571678, no confounding effects were observed, with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for smoking status, DM2, hypertension, BMI, and weight. For standing height, LDL, and cholesterol levels, although p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the associations were in the opposite direction to that expected for the confounding effect (Table S9).\u003c/p\u003e\n\u003ch3\u003eLeveraging plasma proteomics to identify potential sex-differential mechanisms associated with the identified loci\u003c/h3\u003e\n\u003cp\u003eTo elucidate the mechanisms potentially responsible for the sex-specific effects of the rs6843082 (\u003cem\u003ePITX2\u003c/em\u003e) and rs6571678 (\u003cem\u003eCFL2\u003c/em\u003e) variants, we conducted a systematic analysis of the differential associations between variant\u0026ndash;sex interactions and circulating protein abundance, further evaluating the influence of AF status stratification. Several proteins were nominally associated with the interaction of the \u003cem\u003ePITX2\u003c/em\u003e locus and sex, for AF controls and AF cases, interestingly 171 proteins were found with significant heterogeneity for the AF status (Table S6), top modulated protein was MYL1 (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Similarly, for the \u003cem\u003eCFL2\u003c/em\u003e locus, several proteins were nominally associated with the interaction with sex for AF controls and AF cases, 120 proteins showing significant heterogeneity for the AF status (Table S7), top modulated protein was BLOC1S2 (Figure S3).\u003c/p\u003e\u003cp\u003eEnrichment pathway analyses revealed the involvement of SCF-KIT signaling by \u003cem\u003ePITX2\u003c/em\u003e locus and pathways prolactin signaling and RAC1/PAK1/p38/MMP2 by \u003cem\u003eCFL2\u003c/em\u003e locus. The three of them significant after multiple test comparison adjustments (Table S8).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSex stratified heritability estimation\u003c/h2\u003e\u003cp\u003eGiven the substantial sex-heterogeneity observed at know AF-risk loci, we sought to characterize the estimated heritability in males and females. Observed heritability for AF was, for females, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eobs\u003c/sub\u003e (SE)\u0026thinsp;=\u0026thinsp;0.0173 (0.0028) and p\u0026thinsp;=\u0026thinsp;6.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e, and for males, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eobs\u003c/sub\u003e (SE)\u0026thinsp;=\u0026thinsp;0.0244 (0.0047) and p\u0026thinsp;=\u0026thinsp;2.09\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e. The AF liability scale for females was h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eliab\u003c/sub\u003e (SE)\u0026thinsp;=\u0026thinsp;0.19 (0.03) and p\u0026thinsp;=\u0026thinsp;7.80\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e and for males was h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eliab\u003c/sub\u003e (SE)\u0026thinsp;=\u0026thinsp;0.12 (0.02) and p\u0026thinsp;=\u0026thinsp;2.19\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e. When transformed to the liability scale, the estimated heritability was higher in females (h\u0026sup2;=0.19, SE\u0026thinsp;=\u0026thinsp;0.03) than in males (h\u0026sup2;=0.12, SE\u0026thinsp;=\u0026thinsp;0.02). Given the lower disease prevalence in females (359.9 vs. 569.5 per 100,000)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], part of this difference may reflect the mathematical effect of the prevalence correction, which tends to inflate liability-scale estimates when prevalence is lower. Nevertheless, the magnitude of the observed difference suggests that this inflation alone is unlikely to fully account for the higher heritability observed in females.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation of the performance of sex-specific PRS\u003c/h3\u003e\n\u003cp\u003eConsidering the sex-specific patterns of AF we sought to evaluate the performance of sex-specific PRS vs a both sexes reference for PRS. The best PRS was obtained using LDpred-2, together with the metanalysis as the female reference and the sex-specific GWAS results as the male reference. Results for females and males with AF were AUC\u0026thinsp;=\u0026thinsp;0.60 ((95% CI: 0.56\u0026ndash;0.63) and p\u0026thinsp;=\u0026thinsp;9.99\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and AUC\u0026thinsp;=\u0026thinsp;0.63 (95% CI: 0.59\u0026ndash;0.67) and p\u0026thinsp;=\u0026thinsp;1.03\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9,\u003c/sup\u003e respectively (Table S10). According to the DeLong test for correlated AUCs, there were no significant differences in the best PRS between the sex-specific GWAS and the metanalysis: males, AUC\u0026thinsp;=\u0026thinsp;0.63 vs AUC\u0026thinsp;=\u0026thinsp;0.61 (p\u0026thinsp;=\u0026thinsp;0.13), and females, AUC\u0026thinsp;=\u0026thinsp;0.58 vs AUC\u0026thinsp;=\u0026thinsp;0.60 (p\u0026thinsp;=\u0026thinsp;0.24) (Table S10).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough sex-stratified GWAS analyses, metanalysis, and downstream analyses, we analyzed in detail the genetic underpinnings of sex-related AF risk, finding and replicating 2 loci with strong sex-difference patterns in their genetic association with AF risk. As far as we are aware, ours is the first study suggesting that sex-related AF risk differences may be explained by distinct genetic risk factors.\u003c/p\u003e\u003cp\u003eWe identified 22 loci previously reported as associated with AF, thereby reinforcing the generalizability of the UKBB cohort. In addition, 2 loci were found to be only significant in the male-only GWAS for the UKBB cohort. For these 24 loci, we studied potential sex-related heterogeneity, finding 6 to show substantial heterogeneity, 2 with a greater effect size in females (\u003cem\u003eTTN\u003c/em\u003e and \u003cem\u003eSPATS2L\u003c/em\u003e) and 4 with a greater effect size in males (\u003cem\u003eNTMT2\u003c/em\u003e, \u003cem\u003ePITX2\u003c/em\u003e, \u003cem\u003eGBF1\u003c/em\u003e, and \u003cem\u003eCFL2\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eTo confirm our findings, we performed replication in a cohort of 12,614 individuals, finding that we could replicate the sex-related heterogeneity of loci \u003cem\u003ePITX2\u003c/em\u003e and \u003cem\u003eCFL2\u003c/em\u003e. Direction remained consistent for both those loci, with both showing a greater standardized effect size in males than in females.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePITX2\u003c/em\u003e encodes a transcription factor involved in the regulation of multiple genes. To date, no sex-specific heterogeneity has been reported for this gene or its protein in humans. However, a previous study in mice found strong sex differences on evaluating the effects of deleting a 20-kb enhancer region of \u003cem\u003ePitx2\u003c/em\u003e. The finding that male mice carrying this deletion exhibited higher rates of AF compared to females[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] reinforces the potential role of \u003cem\u003ePITX2\u003c/em\u003e as a sex-dependent mediator of AF risk.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCFL2\u003c/em\u003e encodes an intracellular protein that is involved in the regulation of actin-filament dynamics. To date, however, no significant sex differences related to cardiac function have been reported in humans. Interestingly, we observed, in 2 independent cohorts, that this locus was exclusively associated in males; i.e., there was not even nominal association in females. While a potential reason for this sex-related heterogeneity may be imbalanced sample sizes in the sex-stratified analyses, we corrected for this imbalance by performing a sample size-based metanalysis using the effective sample size of each analysis. In our replication, the more powered GWAS was the female analysis, in contrast with the UKBB, for which the male sample is more powered that the female sample. Therefore, sample size does not seem to have biased our sex-related heterogeneity results.\u003c/p\u003e\u003cp\u003eProteomic analyses provide additional biological context for the interaction between genotype, sex, and AF status. In our study evaluating the association between the two replicated variants and protein levels in plasma samples from the UKBB, we identified several nominal associations. Notably, the analysis of pathways associated with atrial fibrillation heterogeneity revealed significant pathways that may influence the interaction between AF status and variant-sex heterogeneity. Key associated pathways included SCF-KIT signaling by \u003cem\u003ePITX2\u003c/em\u003e locus, and pathways such as prolactin signaling, and the RAC1/PAK1/p38/MMP2 by CFL2 locus.\u003c/p\u003e\u003cp\u003eThe SCF-KIT pathway is suggested to be associated with the differential impact of the \u003cem\u003ePITX2\u003c/em\u003e genotype in men and women, this pathway it has been linked to mitochondrial dysfunction and impaired energy expenditure, as demonstrated in models of KIT loss-of-function.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Interestingly, estrogens play an important role in modulating mitochondrial biogenesis through the regulation of PGC-1 coactivators, exerting protective effects against oxidative stress and supporting mitochondrial efficiency.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] This interaction suggests that the SCF-KIT pathway could underlie the differential response to atrial fibrillation observed between men and women, with estrogens providing a compensatory protective mechanism that may mitigate the impact of \u003cem\u003ePITX2\u003c/em\u003e variant in this pathway.\u003c/p\u003e\u003cp\u003eThe RAC1/PAK1/p38/MMP2 and prolactin signaling are suggested to be modulating the different impact of the \u003cem\u003eCFL2\u003c/em\u003e genotype in men and women, both pathways have been implicated in atrial fibrillation, through mechanisms involving oxidative stress, calcium handling, and extracellular matrix remodeling[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Importantly, both pathways are modulated by sex hormones and show sex-specific regulation: estradiol suppresses RAC1 expression and p38α activation in cardiomyocytes and modulates PAK1 activity in various cellular contexts[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while prolactin signaling is also differentially regulated between sexes via estrogenic and pituitary mechanisms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These sex-dependent effects may underlie differences in atrial fibrillation susceptibility and progression between men and women. The sex-related heterogeneity of \u003cem\u003ePITX2\u003c/em\u003e and \u003cem\u003eCFL2\u003c/em\u003e is possibly explained by the pleiotropy of these regions towards traits that are significantly more frequent in males than females; e.g., regarding a variant also associated with smoking, European men are more frequent smokers that European women. To account for this possible pleiotropy, we checked the association with potential traits, finding no potential confounding effects for either variant, except for a nominal association between smoking status and \u003cem\u003ePITX2\u003c/em\u003e. Our results after adjusting for smoking status revealed no changes confirming sex-related heterogeneity for \u003cem\u003ePITX2.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOur sex-stratified liability-scale estimates indicate that genetic factors may account for a larger proportion of variance in disease risk among women (h\u0026sup2;=0.19, SE\u0026thinsp;=\u0026thinsp;0.03) than among men (h\u0026sup2;=0.12, SE\u0026thinsp;=\u0026thinsp;0.02). Importantly, the prevalence of the disorder was lower in women (359.9 per 100,000) than in men (569.5 per 100,000), which sets a higher liability threshold for females. Because lower prevalence typically reduces liability-scale heritability, the finding of a higher estimate in women is more consistent with a genuine biological difference rather than a scaling artifact. This pattern suggests that genetic risk factors may exert relatively stronger effects in females, while environmental or non-additive influences could contribute more substantially to disease risk in males.\u003c/p\u003e\u003cp\u003eSex-specific PRS demonstrated moderate predictive ability. The best PRS yielded an AUC\u0026thinsp;=\u0026thinsp;0.60 for females and a slightly higher AUC\u0026thinsp;=\u0026thinsp;0.63 for males. The absence of significant differences between sex-specific PRS values and those derived from the metanalysis suggests that, while sex-specific loci add granularity, the overall genetic architecture captured by the metanalysis is broadly applicable to both sexes. However, the modest AUC values highlight the need for further refinement of PRS models to improve their clinical utility.\u003c/p\u003e\u003cp\u003eLimitations of this study include the imbalanced sample sizes between males and females. While this imbalance could impact the statistical power of the analyses, especially in detecting sex-specific effects in smaller subgroups, efforts were made to correct this through metanalysis adjustments. In addition, our study only involves individuals of European ancestry, and replication in another subgroup of European ancestry, therefore further validation should be considered to evaluate the generalizability of the results in different ancestries. Additionally, the PRS models demonstrated only moderate predictive ability, suggesting the need for further refinement to enhance their clinical utility. While our study explored potential confounding factors such as smoking status, other unmeasured lifestyle and environmental factors might affect our findings. Finally, since the proteomic findings linking the \u003cem\u003ePITX2\u003c/em\u003e and \u003cem\u003eCFL2\u003c/em\u003e genotypes with different modulation of signaling by SCF-KIT, prolactin signaling and RAC1/PAK1/p38/MMP2 pathway which may not be generalizable across all populations, further functional studies are required to confirm the pathways linking genetic variants with AF risk.\u003c/p\u003e\u003cp\u003eOur study underscores the importance of considering sex as a biological variable in genetic studies of AF. The identification of sex-specific loci, heterogeneity patterns, and differences in heritability estimates throws valuable light on the genetic underpinnings of AF and reinforce the need for sex-stratified analyses aimed at unraveling these complexities. Future research should focus on elucidating the biological mechanisms driving these sex differences, considering these sex differences as key aspect in drug development and on refining predictive models to enhance their clinical applicability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eAF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eAtrial Fibrillation\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eArea Under the Curve\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eBody Mass Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eConfidence Interval\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eCardiovascular Disease\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eDM2\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eType 2 Diabetes\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eGIF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eGenomic Inflation Factor\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eGWAS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eGenome\u003c/em\u003e\u0026ndash;\u003cem\u003eWide Association Studies\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eh\u0026sup2;\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eHeritability\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eI\u0026sup2;\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eInconsistency Squared\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eLD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eLinkage Disequilibrium\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u0026ndash;\u003cem\u003eDensity Lipoprotein\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eMREC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eNorth West Multi\u003c/em\u003e\u0026ndash;\u003cem\u003ecentre Research Ethics Committee\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003ePRS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ePolygenic Risk Scores\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eRTB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eResearch Tissue Bank\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eStandard Error\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eUKBB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eUK Biobank\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Online methods","content":"\u003cp\u003eA table of resources with the versions for each software and the origin of files and phenotypes used can be found in Table S1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank (UKBB) is a large, prospective population-based cohort that recruited over 500,000 participants aged 40\u0026ndash;69 years across the United Kingdom between 2006 and 2010. Participants underwent baseline assessments including questionnaires on sociodemographic, lifestyle, and health-related factors, physical measurements, and biological sample collection. Follow-up data are available through linkage with electronic health records, hospital episode statistics, cancer registries, and death records, allowing long-term monitoring of health outcomes. ICD-10 code I48 was used to select GWAS AF and flutter phenotype results from the UKBB V2 analysis carried out by Neale et al[18]. Downloaded summary statistics for female- and male-only analyses comprised GWAS analyses of 1,981 female patients and 192,193 female controls, and 4,375 male patients and 162,645 male controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe additionally used an independent replication cohort comprising a clinical and genetic cohort of 12,614 individuals aged \u0026ge;18 years recruited in Spain between 2003 and 2022;\u0026nbsp;AF cases and controls numbered 1,207 (669 females) and 11,407 (5,607 females), respectively. This cohort, focused on ischemic stroke and AF cases and controls was defined based on clinical information and records on recruitment. Further details of the cohorts, inclusion and exclusion criteria, array information, and hospital contributions can be found in Tables S2-S4.\u003c/p\u003e\n\u003cp\u003eFigure 1 depicts a flowchart of the project, highlighting the cohorts for which each step was performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality control and GWAS analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUKBB and its genotyping strategies and methods have been described elsewhere[19]. Quality control applied to the imputed variants were as follows: INFO score \u0026gt;0.8, minor allele frequency \u0026gt;0.001, and Hardy-Weinberg p\u0026gt;1\u0026times;10\u003csup\u003e-10\u003c/sup\u003e. The GWAS analyses were performed using a liner regression model and selecting only European genetically defined samples, and the main covariates were age, age\u003csup\u003e2\u003c/sup\u003e, and the first 20 principal components[20]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuality control and imputation of the replication cohort were performed according to a previous study by our group[21]. For the replication cohort, we performed the GWAS analysis for AF-only females and AF-only males, applying an additive genetic model using fastGWA from GCTA[22]. Age and the first 10 principal components were used as covariates. The genomic inflation factor (GIF) was estimated as lambda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMetanalysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate sex-related heterogeneity, we performed GWAS metanalyses based on sample sizes and p-values using METAL software[23]; \u003cem\u003eZ\u003c/em\u003e-scores for each allele were combined across samples in a weighted sum, with weights proportional to the square root of the sample size for each study[24]. Correction was applied for the GIF. To account for sample size and unbalanced case/control ratios, we estimated the effective sample size using the following formula:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1763653081.png\"\u003e\u003c/p\u003e\n\u003cp\u003eFor the individual GWAS analyses and metanalysis results, we considered genome-wide loci to be significant if p\u0026lt;5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e for leading variants. Of the significant variants, independent loci were described using a linkage disequilibrium (LD) threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.1 and a distance of 250kb between predefined LD blocks in Phase 3 of the European 1000 Genomes Project[25]. Locus names were determined according to the gene closest to the leading variant. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeading variants in the metanalysis and the independent GWAS analyses were tested for the sex-related heterogeneity of effects using Cochran\u0026rsquo;s Q-test. In line with previous studies[26], we considered variant heterogeneity between sexes to be substantial for I\u003csup\u003e2\u003c/sup\u003e\u0026gt;70%, moderate for I\u003csup\u003e2\u003c/sup\u003e\u0026gt;50%, and negligible for I\u003csup\u003e2\u003c/sup\u003e\u0026lt;50%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReplication\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLead variants with substantial heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026gt;70%) were evaluated in the metanalysis data from the replication independent cohort. If the lead variant was not present, we checked genome-wide significantly associated variants from the same locus that presented r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.8 for LD with the lead variants. In this cohort, replication was considered to occur when I\u003csup\u003e2\u003c/sup\u003e\u0026gt;50%, the direction of effects was consistent, and the p-value of the association was at least nominally associated (p\u0026lt;0.05) in one of the sex-stratified GWAS analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProteomic analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma proteomic was evaluated using Olink data in a subgroup of 46,724 individuals of the UKB cohort. Olink methodology in the UKB data has been previously described[27]. We assessed sex-specific effects of the index SNP on plasma protein levels by testing the interaction between SNP dosage and sex. For each protein, we fitted linear regression models with protein abundance as the outcome and included SNP dosage, sex, and their interaction term (SNP \u0026times; sex) as predictors, adjusting for age. To evaluate whether the interaction effect differed between atrial fibrillation (AF) cases and non-cases, analyses were conducted separately within each stratum. Heterogeneity between strata was formally tested by comparing the estimated interaction coefficients (\u0026beta;) and their standard errors (SE) using a Z-test, calculated as \u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1763653150.png\"\u003e\u0026nbsp;Two-sided p-values were obtained from the standard normal distribution and corrected for multiple testing using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR).\u003c/p\u003e\n\u003cp\u003eGene lists ranked by the Z-score were used for gene-set enrichment analysis to identify overrepresented biological pathways. Enrichment was performed using String (https://string-db.org/), querying \u003cstrong\u003eWikiPathways, Reactome, and KEGG\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eEnriched pathways with FDR \u0026lt; 0.05 and enrichment score \u0026gt; 2 were considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConfounding factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe replicated variants were analyzed for the potential impact of confounding factors on sex-related heterogeneity. \u0026nbsp;As previously described and analyzed by Neale et al[18], the 2 replicated loci index variants were evaluated in the GWAS of phenotypes associated with AF risk in the UKBB cohort. Selected traits were based on CVD risk factors and body size measurements, namely, smoking status, type 2 diabetes (DM2), hypertension, body mass index (BMI), low-density lipoprotein (LDL) levels, total cholesterol levels, height, and weight. Traits with p\u0026lt;0.05 for the candidate variants were selected as covariates to evaluate the impact of adjustment in the replication step.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHeritability estimation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObserved heritability and heritability liability were estimated for the sex-stratified AF GWAS analyses using LD score regression[28]. In the case of heritability liability, the analysis accounted for age-adjusted estimates of AF prevalence rates of 569.5 and 359.9 per 100,000 population for men and for women, respectively[6].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePolygenic risk score\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo calculate the polygenic risk score (PRS), the replication cohort was divided randomly into validation and test samples in the proportion 60-40. As reference datasets we used summary statistics for the sex-stratified AF GWAS and for the AF GWAS metanalysis. To generate scores for the validation cohort and evaluate predictive value for the test cohort (60% and 40% of the replication cohort, respectively), we used 3 different strategies:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) PRSice-2[29]. A clumping and thresholding strategy were used to evaluate the best threshold p-value to select predictive variants in the validation cohort, for a clumping window set to 250kb and r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) LDpred-2 auto model[30]. This Bayesian method, which does not require the validation cohort to choose the best model, was used to infer heritability and polygenicity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(3) lassosum-2. With this software we applied penalized regression to generate PRS values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor LDpred-2 and lassosum-2, to ensure a dense panel that increased score generalizability, in line with the developer\u0026rsquo;s recommendations we selected variants that were present in the extended HapMap3 panel. Both those strategies were computed using the bigsnpR package v1.12.15 in R.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the test set, for each model we estimated the area under the curve (AUC) and 95% confidence interval (CI), the pseudo r\u003csup\u003e2\u003c/sup\u003e, and the p-value of the association. Additionally, we used the best PRS strategy according to the AUC for females and males to compare difference with the others using the DeLong test for correlated AUCs.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. The study protocol of the replication cohort was approved by an institutional review board and ethics committee at Hospital de la Santa Creu i Sant Pau on February 26\u003csup\u003eth\u003c/sup\u003e 2024, approval identification code IIBSP-EGX-2023-152. Participants or their legal representatives provided prior written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary statistics of GWAS and GWAS metanalysis will be available to the Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/). Additional files will be available from the corresponding authors upon reasonable requests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP. Villatoro-González is supported by a Joan Oró Contract from the AGAUR FI AJUTS (2023 FI-3 00065) predoctoral program. This study has been funded by ISCIII (grant numbers PI18/01338, PI20/00925), ERA-NET NEURON (AC19/00106), RICORS-ICTUS: Red de Investigación Cooperativa Orientada a Resultados en Salud – Enfermedades Vasculares Cerebrales (RD21/0006/0006; RD21/0006/0002; RD24/0009/0002; RD24/0009/0010), CIBER-Consorcio Centro de Investigación Biomédica en Red- Enfermedades Neurodegnerativas (CIBERNED) (CB22/05/00067), CERCA Programme/Generalitat de Catalunya, and Marató de 3cat (grant numbers 202306-30 and 202310-30). A subgroup of the controls samples were provided by the \"Banco Nacional de ADN Carlos III (BNADN);www.bancoadn.org) and genotyping services were provided by the “Centro Nacional de Genotipado-Fundación Pública Galega de Medicina Xenómica\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJCM and PB conceptualized, designed the study, performed the statistical data analyses. LLC performed the experiments and collected the data. EM, CGF, NC, ML, JMMC, PVG, LM, conducted data analyses and revision of the results. RIR, FC, AJM, MF, JFA, VO, JAS, CAM, MR, JJC, LMN, ELC, MM, RDN, STC, CVB, GSH, TS, LI, LH, PD, RD, JK, LPS, PCR, MG, GE, NB, LS, RdC, EC, GV, AFV, OB, JM, XU, MMCR, APM, JC, TSobrino, CC, JML, JMF and IFC collected the samples and data. JCM and IFC wrote the first draft. All authors contributed to manuscript revision and approved the final version. IFC, RD, JML, JM obtained the funding. IFC and JCM supervised the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrateful thanks to the study participants and their families for their contributions. This work has been carried out within the framework of the Doctoral Program in Medicine of the Universitat Autònoma de Barcelona. Proteomic analyses were performed under UK biobank application number 44448.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLinz D, Gawalko M, Betz K, Hendriks JM, Lip GYH, Vinter N et al. Atrial fibrillation: epidemiology, screening and digital health. The Lancet Regional Health \u0026ndash; Europe [Internet]. 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Am J Hum Genet. 2023;110:2042\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2023.10.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2023.10.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atrial Fibrillation, Genomics, Sex Differences, Proteomics, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-7978651/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7978651/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAtrial fibrillation (AF) exhibits notable sex differences in epidemiology and outcomes. This study investigates biological sex-specific differences in AF through sex-stratified genome-wide association studies (GWAS) and proteomic related analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed a sex-stratified GWAS meta-analysis using data from the UK Biobank study: 4,375 male AF cases and 162,645 controls; 1,981 female AF cases and 192,193 controls. Significant loci and sex-specific associations were identified, and sex heterogeneity was assessed. Replication was done in an independent cohort of 12,614 individuals (1,207 AF cases, 55% female). Plasma proteomic analyses in 46,724 subjects assessed genotype\u0026ndash;sex interactions stratifying by atrial fibrillation status. Heritability estimates and sex-specific polygenic risk scores (PRS) were also calculated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo male-specific loci: \u003cem\u003eCFL2\u003c/em\u003e and \u003cem\u003eATXN1\u003c/em\u003e were identified. The meta-analysis identified additional 22 known AF loci. Sex heterogeneity was found in 6 of the 24 loci, with \u003cem\u003eTTN\u003c/em\u003e and \u003cem\u003eSPATS2L\u003c/em\u003e showing stronger effects in females, and \u003cem\u003eNTMT2\u003c/em\u003e, \u003cem\u003ePITX2\u003c/em\u003e, \u003cem\u003eGBF1\u003c/em\u003e, and \u003cem\u003eCFL2\u003c/em\u003e stronger effects in males. Heritability estimation liability was higher in females (h\u0026sup2;=0.19) than in males (h\u0026sup2;=0.12). PRS performance was similar across sexes (AUC\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.63). Replication confirmed heterogeneity for \u003cem\u003ePITX2\u003c/em\u003e and \u003cem\u003eCFL2\u003c/em\u003e, with \u003cem\u003eCFL2\u003c/em\u003e variant associated with AF only in males. Proteomics analyses suggested nominals association such as: myosin light chain 1/3 (MYL1) and biogenesis of lysosomal organelles complex 1 subunit 2 (BLOC1S2). Key associated pathways included SCF-KIT signaling, prolactin signaling, and the RAC1/PAK1/p38/MMP2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur findings indicate significant sex-based heterogeneity in the effects of well-known AF-associated loci. Proteomic-genetic integration suggested sex-specific differences and candidate pathways. Despite this heterogeneity, a sex-specific approach did not significantly enhance PRS prediction, underscoring the need for adequately powered sex-specific GWAS.\u003c/p\u003e","manuscriptTitle":"GWAS metanalysis of atrial fibrillation reveals significant sex-related heterogeneity effects of the PITX2 and CFL2 loci","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 16:06:30","doi":"10.21203/rs.3.rs-7978651/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f633eda8-b3cf-41cc-b33e-9b69b029b34b","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-20T16:06:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 16:06:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7978651","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7978651","identity":"rs-7978651","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}