Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases

Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases Gui-Juan Feng, Qian Xu, Jing-Jing Ni, Shan-Shan Yang, Bai-Xue Han, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-955340/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 Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 participants from 3 genome-wide association studies of European and East Asian ancestries. Twenty-one novel genomic loci were identified at the genome-wide significance level. Besides, we observed significant genetic correlations between AAM and 67 complex traits, and the highest genetic correlation was observed between AAM and body mass index ( r g =-0.19, P =6.11×10 −31 ). Latent causal variable analyses demonstrate that there is a genetically causal effect of AAM on high blood pressure (GCP=0.47, P =0.02), forced vital capacity (GCP=0.63, P =0.02), age at first live birth (GCP=0.51, P =0.03), impedance of right arm (GCP=0.41, P <1×10 -7 ) and right leg fat percentage (GCP=-0.10, P =0.02), etc. Enrichment analysis identified 5 enriched tissues and 51 enriched gene sets. Four of the five enriched tissues were related to the nervous system, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems and hypothalamus. The fifth tissue was the retina in the sensory organ. The most significant gene set was the ‘decreased circulating luteinizing hormone level’ ( P =2.45×10 -6 ). Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women. Internal Medicine Molecular Biology Joint Genome-wide association studies Age at menarche Functional analysis Figures Figure 1 Figure 2 Figure 3 Introduction Age at menarche (AAM), defined as the age of first menstrual bleeding, is a commonly reported marker of pubertal timing in females ( 1 ). AAM is determined by the overall duration of endocrine-tissue sex hormone exposure level ( 2 ), and has a series of clinical outcomes through the women's life. On one hand, earlier AAM is considered to be a risk factor for certain diseases, including obesity ( 3 ), type 2 diabetes ( 4 ), cardiovascular diseases ( 5 ), breast cancer ( 6 ) and ovarian cancer ( 7 ). On the other hand, later AAM may be associated with an increased risk of Alzheimer's disease ( 8 ) and stroke ( 9 ), as well as lower fertility ( 10 , 11 ). AAM is a highly heritable trait, with estimated heritability up to 50% in previous twin and family studies ( 12 , 13 ). To date, hundreds of variants associated with AAM have been identified by a number of genome-wide association studies (GWASs) and their meta-analyses ( 14 , 15 ). Nonetheless, variants identified by the largest GWAS meta-analyses to date only explain 7.4% of the total phenotypic variation, far less than the total 50% of heritability ( 14 ). Therefore, the majority of the genetic components underlying AAM remains to be discovered. The unidentified 'missing' heritability is probably carried by genetic variants with minor phenotypic effects that escaped detection by studies with insufficient sample size and statistical power ( 16 , 17 ), and enlarged sample is warranted to uncover them. To maximize power, it is critical to make use of meta-analyses of multiple independent GWASs whose increasing sample size boosts the statistical power for detecting modest associations. In the present study, aiming to identify more association signals that contribute to AAM, we conduct a joint GWAS meta-analysis of AAM in 438,089 participants by integrating 3 GWASs, namely the Reproductive Genetics (ReproGen) Consortium (N=182,416), the UK Biobank (UKB, N=188,644) and the Biobank Japan (BBJ, N=67,029). A series of bioinformatical analyses are then followed to explore in-depth annotations at the associated loci. Materials And Methods Study design Summary statistics of 3 large GWASs were included in this study, 2 of which are of European population and the third one is of East Asian population. The study design is shown in Figure 1 . In brief, we conducted two joint analyses. First, the European ancestry-specific meta-analysis was performed to combine results from the ReproGen and the UKB samples. Second, the BBJ was included for the trans-ancestry meta-analysis. The purpose of the second analysis was to identify more loci across populations by a maximal sample size. With European-specific meta-analysis results, we performed a series of follow-up analyses that are sensitive to linkage disequilibrium (LD) pattern, including LD score regression (LDSC) analysis, genetic correlation analysis, Generalized Summary data-based Mendelian Randomization (GSMR) analysis and latent causal variable (LCV) analysis. Functional enrichment and candidate gene prioritization were conducted with trans-ancestry meta-analysis results. Study populations The first study is the ReproGen study, which is a GWAS meta-analysis of 182,416 women of European descent from 58 samples ( 18 ). In brief, genome-wide SNPs were genotyped by genotyping arrays, and were imputed into the HapMap Phase II CEU build 35 or 36 reference panel. The second study is the UKB sample, which included 188,644 women of European descent ( 19 ). All participants were genotyped by the UK BiLEVE Axiom array or UKB Axiom array, and were imputed into UK10K haplotype, 1000 Genomes project phase 3 and Haplotype Reference Consortium (HRC) reference panels ( 20 ). Subjects who had a self-reported gender inconsistent with the genetic gender, who were genotyped but not imputed or who withdraw their consents were removed. The last sample is the BBJ study, which is a single GWAS of 67,029 women of East Asian descent ( 21 ). BBJ participants had DNA genotyped on more than 950,000 variants using either (a) a combination of Illumina Human OmniExpress BeadChip and Infinium HumanExome BeadChip or (b) Infinium OmniExpressExome BeadChip alone. Variants overlapping across these two sets of genotyping arrays were extracted. After quality control (QC), genome-wide genotypes were imputed into the 1000 Genomes Project Phase 3 reference panel ( 21 ). All participants had provided written informed consent and each study had its research protocol approved by the corresponding local ethics committee or Institutional Review Boards (IRB). No new IRB approval was required. Summary statistics for the 3 studies were downloaded from their respective websites. Data quality control We excluded the variants which were duplicated, poorly imputed, or without allele frequency information, only common or less common (minor allele frequency, MAF>1%) SNPs from each individual study were included into analyses ( 22 ). There were 2,441,815 genetic variants in the Reprogen released summary data. After QC, there were 2,400,657 variants remained. There were 13,788,288 genetic variants in the UKB released summary data. After removing X-chromosome variants, monomorphic site and duplicated variants (i.e., multiple variants correspond to one identifier), 13,752,112 variants are left. These 13,752,112 variants were matched with 2,400,657 variants in the Reprogen summary data. After removing variants that had intermediate locus incompatibility (i.e., A/G and A/T polymorphisms) in both studies, a total of 2,378,813 variants were eligible for European ancestry-specific meta-analysis. The BBJ study included 9,296,729 genetic variants, of which 15,851 duplicates were removed. The remaining 9,280,878 variables were matched with the 2,378,813 variants in the European ancestry-specific analysis, and then variants with incompatible alleles were excluded. Finally, 2,090,939 genetic variants were present in all three studies, which were used for trans-ancestry meta-analysis. Meta-analysis Summary statistics from each GWAS sample were combined by an inverse-variance weighted fixed-effects model implemented in METAL ( 23 ). In the European ancestry-specific meta-analysis, the ReproGen and the UKBB studies were meta-analyzed. In the trans-ancestry meta-analysis, all 3 GWASs were meta-analyzed. The potential heterogeneity effects of each variant were assessed using Q statistics and I 2 index in European populations and in all-ancestry populations. Identification of novel loci Genome-wide significance (GWS) level was set at 5.0×10 −8 . An independent locus was defined as a genomic region of 500kb on either side of the variant showing the strongest association signal. We declared an association locus to be novel if it contained GWS SNPs that were at least 500kb outside from any reported loci and the novel lead SNP was not in linkage disequilibrium (LD, r 2 <0.1) with previously reported signals. We further compared the results of the European ancestry-specific analysis and the trans-ancestry meta-analysis to investigate the ancestry specificity of the identified loci. A European-ancestry specific locus was defined if this locus was: (a) identified only in European ancestry-specific meta-analysis; or (b) identified in both meta-analyses, but was significantly heterogeneous ( I 2 ≥50%) in trans-ancestry meta-analysis and was not heterogeneous in European ancestry-specific analysis. For the overlapped loci in two analyses (a same locus with different lead SNP), we picked the most significant SNP in the two analyses as the lead SNP at this locus. Genetic architecture The LDSC method was applied to the European ancestry specific meta-analysis results to estimate the amount of genomic inflation due to confounding factors such as population stratification and cryptic relatedness ( 24 ). LDSC takes GWAS summary statistics as input and partitions overall inflated association statistic into one part attributable to polygenic architecture and the other part due to population stratification and cryptic relatedness. The relative contribution of confounding factors was measured by attenuation ratio (AR), which is defined as (intercept-1)/(mean χ 2 -1), where intercept and mean χ 2 are estimates of confounding and the overall association inflation, respectively ( 24 ). Genetic correlations and causal inference To evaluate genetic interplay between AAM and other complex traits/diseases, genetic correlation analyses were performed with a LDSC based web tool Complex-Traits Genetics Virtual Lab (CTG-VL) ( 25 ). A total of 1,387 traits were available at the CTG-VL database. Genetic correlation between AAM and each of these 1,387 traits was estimated. Significance threshold was set at a Bonferroni corrected significance level 3.60ⅹ10 −5 , e.g., 0.05/1387. To further investigate the causal relationship for significant genetic correlations, we performed GSMR to evaluate the causal associations between AAM and correlated traits. GSMR can account for LD between the variants, and is more powerful than existing summary data-based MR methods ( 26 ) ( 27 ) ( 28 ). GWS SNPs show no evidence of horizontal pleiotropy (HEIDI-outlier test P HEIDI <0.01) and independent of each other (LD r 2 <0.05 and window size=1 Mb) were selected as instrumental variables (IVs). We switched the exposure and outcome to explore the reverse causation in GSMR analysis. GSMR may suffer from horizontal pleiotropy even after correction. To resolve this problem, a recently developed LCV model was used to validate the significant results of GSMR analysis ( 29 ). The LCV method estimates a genetic causality proportion (GCP) for each pair of traits being evaluated, which represents the proportion of genetic components of one trait that influence the other trait. A positive GCP value indicates that a proportion of the genetic component of AAM affects the other trait, while a negative GCP value means that the proportion of genetic component of the other trait affects AAM. Candidate gene prioritization We incorporated the following five sources of information to prioritize candidate genes at the identified loci: (i) being the gene nearest the novel lead SNP; (ii) being the target gene for any local expression quantitative trait loci (cis-eQTL); (iii) containing a novel GWS missense SNP; (iv) being prioritized by summary data based mendelian randomization (SMR) analysis and (v) being prioritized by Summary-MultiXcan (S-MultiXcan) analysis. HaploReg was used to identify the nearest gene of the lead SNP ( 30 ). The Ensembl Variant Effect Predictor (VEP) was used to look over whether there is a missense mutation at the location of the GWS SNPs ( 31 ). Gene expression information from seven tissues (brain hypothalamus, breast mammary tissue, ovary, pituitary, uterus, vagina and whole blood) linked to AAM were included for cis-eQTL, SMR and S-MultiXcan analyses. We analyzed all novel lead SNPs for their cis-eQTL activity. Briefly, the cis-eQTL was defined as an association signal from SNPs located within 100 kb upstream and downstream of the target gene ( 32 ). Using a Bonferroni correction, we set the significance threshold to be 0.05/N tissue /N SNPs . Cis-eQTLs are accessed from the web portal ( http://genehopper.de/qtlizer ). SMR is another prioritization method developed by Zhu and his colleagues ( 33 ). SMR integrates GWAS summary results with data from eQTL studies to identify genes whose expression levels are associated with trait because of causal or pleiotropy effects ( 33 ). Using SMR analysis, we examined for pleiotropy between GWAS signal and cis-eQTL for genes within 1 megabase (Mb) of the sentinel SNP to identify a possible causal relationship between gene expression and AAM. Only probes with eQTL P< 5.0×10 −8 were included in the SMR analysis. HEIDI test P -values<0.05 were taken to indicate significant heterogeneity. S-MultiXcan is a summary result-based extension that leverages the substantial sharing of eQTLs across tissues and contexts to improve the ability to identify potential target genes ( 34 ). We applied MultiXcan to 7 tissues from the CTG-VL. The total number of gene-tissue pairs were used when determining the Bonferroni corrected significance threshold. Enrichment analysis Tissue/cell type enrichment analysis and gene set enrichment analysis were performed using DEPICT( 35 ). DEPICT is an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes and tissues/cell types where genes from associated loci are highly expressed. Results with FDR<0.05 was considered significant enrichment. We used all suggestive significant SNPs ( P <1×10 −5 ) from trans- ancestry meta-analysis as input. Results European ancestry-specific meta-analysis A total of 371,060 participants and 2,378,813 SNPs passed the QC in the European ancestry-specific GWAS meta-analysis. The LDSC estimates genome-wide heritability to be 10.54% for AAM. The distribution of genome-wide test statistics demonstrated significant inflation (genomic control inflation factor λ GC = 1.53). Results of LDSC analysis showed that 94.0% of the inflation in the mean χ 2 statistic is from polygenic architecture rather than from population stratification (LD score intercept=1.05±0.01 SE, mean χ 2 = 1.83). Manhattan plot of the GWAS meta-analyses results is displayed in Fig. 2 . 6,342 SNPs are significant at the GWS level, encompassing into 229 distinct loci. Of these, 14 loci contained 86 GWS variants that were at least 500kb outside from any reported loci. Lead SNPs in 2 of the 14 loci are in LD with previously reported signals (rs13094336 r 2 =0.53 with rs6445624, rs2764272 r 2 =0.17 with rs6931884) ( 36 ). We excluded 31 GWS variants located within these 2 loci and declared the remaining 12 loci contained 55 GWS variants to be novel ( Supplementary Table 1 ). These 12 novel loci are independent of each other ( r 2 50%), including rs13126656, rs1549864, rs11773714 and rs12220144 (Table 1 ). Table 1 Novel loci identified in European ancestry specific analysis Reprogen UKB Meta Marker CHR POS Locus EA OA EAF Beta SE P 1 Beta SE P 2 Beta SE P Het I 2 rs2002446 2 116473865 2q14.1 A C 0.11 0.03 0.009 3.3×10 −3 0.02 0.004 2.64×10 −6 0.02 0.003 4.86×10 −8 0 rs17400325 2 178565913 2q31.2 T C 0.96 0.04 0.014 6.5×10 −3 0.03 0.003 1.62×10 −6 0.03 0.006 4.35×10 −8 0 rs11248108 4 2481490 4p16.3 A G 0.15 -0.02 0.008 4.70×10 −3 -0.02 0.003 8.15×10 −7 -0.02 0.003 1.74×10 −8 0 rs1027013 4 102904490 4q24 A G 0.57 -0.02 0.006 5.80×10 −3 -0.01 0.002 2.93×10 −7 -0.01 0.002 6.86×10 −9 0 rs13126656 4 161955155 4q32.2 A T 0.22 -0.03 0.007 2.40×10 −4 -0.01 0.003 5.59×10 −6 -0.01 0.003 1.98×10 −8 61.5 rs1265093 6 31107187 6p21.33 A G 0.29 0.02 0.007 6.60×10 −3 0.01 0.003 1.70×10 −7 0.01 0.002 4.58×10 −9 0 rs2326574 6 5041470 6p25.1 T G 0.88 0.03 0.008 1.9×10 −3 0.02 0.004 1.13×10 −6 0.02 0.003 1.24×10 −8 0 rs17442489 7 10383553 7p21.3 T C 0.63 -0.03 0.006 1.10×10 −6 -0.01 0.002 4.77×10 −5 -0.01 0.002 1.80×10 −8 88.4 rs11773714 7 142523468 7q34 A C 0.26 0.22 0.03 7.60×10 −5 0.01 0.003 8.16×10 −6 0.01 0.003 1.84×10 −8 74.3 rs12220144 10 70364884 10q21.3 T G 0.17 0.03 0.008 1.6×10 −4 0.01 0.003 1.31×10 −5 0.02 0.003 4.77×10 −8 70.9 rs11186505 10 92982908 10q23.32 A G 0.30 0.01 0.007 0.26 0.01 0.003 3.87×10 −8 0.01 0.003 3.41×10 −8 2.5 rs6060752 20 34597962 20q11.23 A G 0.05 0.04 0.014 0.01 0.03 0.006 3.36×10 −7 0.03 0.005 1.27×10 −8 0 Note: CHR, chromosome; POS, genomic position based on GRCH37 genome assembly; EA, effect allele; OA, other allele; EAF, effect allele frequency; Beta, regression coefficient; SE, standard error of Beta; Het I 2 , heterogeneity test I square; P 1, the P value of ReproGen Consortium; P 2, the P value of UKB sample; P , the P value calculated in meta-analyses. The European-ancestry specific loci are shown with bold font. Trans-ancestry meta-analysis After systematic QC, association statistics for 2,100,684 autosomal SNPs were combined across all three studies. Manhattan plot of the GWAS meta-analyses are displayed in Fig. 3 . The results contain 6,370 SNPs significant at the GWS level, encompassing 239 distinct loci. By removing reported loci, we identified 93 novel SNPs mapping to 17 independent loci (Table 2 ). These 17 novel loci are independent of each other ( r 2 <0.01). 16 of 17 lead SNPs have consistent association direction in all three samples, except for rs11773714 at 7q34 (beta ReproGen =0.027, beta UKB =0.013 and beta BBJ= -0.007). 4 of the 17 novel lead SNPs show significant heterogeneity ( I 2 >50%), included rs7429712, rs1549864, rs11773714, and rs7159376 ( Supplementary Table 2 ). Table 2 Trans-ancestry GWAS meta-analysis identified 17 novel loci associated with AAM Reprogen UKB BBJ Meta Marker CHR POS Locus EA OA EAF Beta SE P 1 Beta SE P 2 Beta SE P 3 Beta SE P Het I 2 rs1881395 2 27838549 2p23.3 A G 0.22 -0.02 0.007 2.30×10 −3 -0.01 0.003 6.80×10 −6 -0.01 0.010 0.16 -0.01 0.003 3.51×10 −8 0 rs6730140 2 148460036 2q22.3 T C 0.14 -0.03 0.010 1.70×10 −3 -0.02 0.004 1.81×10 −5 -0.03 0.011 0.01 -0.02 0.003 1.45×10 −8 6.5 rs7429712 3 75265159 3p12.3 C G 0.60 -0.02 0.006 5.40×10 −4 -0.01 0.003 5.22×10 −5 -0.03 0.009 3.43×10 −3 -0.01 0.002 1.68×10 −8 63.7 rs1203863 4 2502637 4p16.3 T C 0.78 0.02 0.008 3.70×10 −3 0.01 0.003 4.15×10 −6 0.03 0.009 7.69×10 −3 0.02 0.003 2.62×10 −9 0 rs1027013 4 102904490 4q24 A G 0.68 -0.02 0.006 5.80×10 −3 -0.01 0.002 2.93×10 −7 -0.03 0.012 0.04 -0.01 0.002 1.23×10 −9 0 rs13126656 4 161955155 4q32.2 A T 0.17 -0.03 0.007 2.40×10 −4 -0.01 0.003 5.59×10 −6 -0.02 0.018 0.27 -0.02 0.003 1.09×10 −8 25.2 rs2029641 4 167440421 4q32.3 A G 0.52 0.01 0.006 0.11 0.01 0.002 4.35×10 −7 0.01 0.009 0.15 0.01 0.002 4.27×10 −8 0 rs17769849 4 180899465 4q34.3 T C 0.37 -0.01 0.006 0.08 -0.01 0.002 7.70×10 −7 -0.02 0.009 0.04 -0.01 0.002 2.67×10 −8 0 rs32015 5 66697639 5q12.3 A G 0.46 -0.02 0.006 2.60×10 −3 -0.01 0.002 6.09×10 −6 -0.02 0.009 0.08 -0.01 0.002 1.97×10 −8 0 rs1265093 6 31107187 6p21.33 A G 0.31 0.02 0.007 6.60×10 −3 0.01 0.003 1.70×10 −7 0.02 0.009 0.06 0.01 0.002 7.67×10 −10 0 rs1549864 7 10390011 7p21.3 C G 0.72 -0.03 0.006 1.90×10 −6 -0.01 0.003 6.47×10 −5 -0.02 0.015 0.21 -0.01 0.002 1.52×10 −8 75.2 rs4723134 7 32255322 7p14.3 T C 0.24 -0.01 0.007 0.04 -0.01 0.003 1.06×10 −6 -0.02 0.013 0.12 -0.01 0.003 3.66×10 −8 0 rs11773714 7 142523468 7q34 A C 0.22 0.03 0.007 7.60×10 −5 0.01 0.003 8.16×10 −6 -0.01 0.015 0.61 0.01 0.003 4.60×10 −8 66.1 rs3101268 8 106086274 8q22.3 A G 0.88 -0.03 0.014 0.05 -0.02 0.005 6.99×10 −6 -0.03 0.010 1.10×10 −3 -0.03 0.005 3.65×10 −8 0 rs11186505 10 92982908 10q23.32 A G 0.25 0.01 0.007 0.26 0.01 0.003 3.87×10 −8 0.02 0.018 0.31 0.01 0.002 2.10×10 −8 0 rs7159376 14 47921389 14q21.3 A C 0.54 0.02 0.006 6.80×10 −5 0.01 0.002 2.23×10 −5 0.01 0.009 0.36 0.01 0.002 4.11×10 −8 56 rs6060752 20 34597962 20q11.23 A G 0.07 0.04 0.014 0.01 0.03 0.006 3.36×10 −7 0.04 0.015 0.013 0.03 0.005 5.26×10 −10 0 Note: CHR, chromosome; POS, genomic position based on GRCH37 genome assembly; EA, effect allele; OA, other allele; EAF, effect allele frequency; Beta, regression coefficient; SE, standard error of Beta; Het I 2 , heterogeneity test I square; P 1, the P value of ReproGen Consortium; P 2, the P value of UKB sample; P 3, the P value of BBJ sample; P , the P value calculated in meta-analyses; Het I 2 , heterogeneity test I square. European-ancestry specific loci By comprising the results from European ancestry-specific meta-analysis and trans-ancestry meta-analysis, 214 loci were significant both in the European ancestry-specific meta-analysis and trans-ancestry meta-analysis. 15 loci are only significant in European ancestry-specific meta-analysis. Among them, 4 are novel European-ancestry specific loci (2q14.1, 2q31.2, 6p25.1 and 10q21.3) (Table 1 ). The lead SNPs at 2q14.1 2q31.2 and 6p25.1 were not found in the BBJ study. The lead SNP rs12220144 at 10q21.3 was not significant in the trans-ancestry meta-analysis ( P =2.31×10 −7 ), and had a negative effect in BBJ and a positive effect in the two European samples. In short, a total of 256 loci, including 241 trans-ancestry loci and 15 European-ancestry specific loci were identified in this study. Among them, 21 loci, including 17 trans-ancestry loci and 4 European-ancestry specific loci are novel. Genetic correlations and causal inference LDSC analyses revealed that AAM were genetically correlated with 67 complex traits and diseases, including BMI ( r g =-0.19, P= 6.11×10 −31 ), impedance of whole body ( r g =-0.13, P =4.93×10 −20 ) and age at first live birth ( r g =0.08, P =1.31×10 −6 ), etc ( Supplementary Table 3 ). The genetic correlations were consistent with observational associations from epidemiological studies ( 5 ) ( 14 ). We further explored the causal relationship between AAM and the above 67 genetic correlated traits through CTG-VL ( Supplementary Table 3 ). In GSMR analysis, at the stringent significance threshold of Bonferroni correction ( P adj <0.05/67/2=3.73×10 −4 ), when AAM is the exposure (GSMR1), we observed 45 significant causal associations ( Supplementary Table 3 ). When AAM is the outcome (GSMR2), we observed 9 significant causal associations ( Supplementary Table 3 ). The 9 traits were still significant in GSMR1, indicating that the association between AAM and the 9 traits is bidirectional. In GSMR1, AAM had a causal effect with the remaining 36 ( 45 – 9 ) significant traits ( P <3.73×10 −4 , P HEIDI <0.1), among which impedance had the largest causal effect value: left arm impedance Beta=13.32, P =2.75×10 −180 , whole body impedance Beta=21.85, P =2.05×10 −166 (Table 7). In addition, we also found that trunk fat mass ( P GSMR1 =0.39, P GSMR2 =5.83×10 −6 ) was only significant in GSMR2 analysis, indicating that trunk fat mass would affect AAM. To avoid the horizontal pleiotropic effect, we used the recently developed LCV method to verify the significant causal association identified above. We further explored the causal relationship between AAM and the above 67 genetically related traits and three ocular diseases by CTG-VL. LCV found 13 significant associations, and all GCP values were positive, indicating that the genetic component of AAM had a certain impact on other traits, as shown in Table 3 . The trait with the largest GCP value was ‘forced vital capacity’ (GCP=0.63, P =0.02). There is no significant genetic causal effect between AAM and BMI (GCP=-0.08, P =0.62). While significant causal association was observed between AAM and other fat distribution traits, including impedance of left arm (GCP=0.40, P <1.0×10 −7 ), impedance of right arm (GCP=0.41, P <1.0×10 −7 ), left leg fat percentage (GCP=0.24, P= 0.01), and right leg fat percentage (GCP=0.23, P= 0.02). Significant causal associations were also observed between AAM and forced vital capacity (FVC) (GCP=0.64, P=0.02), blood pressure medication (GCP=0.54, P =0.04), age at first live birth (GCP=0.51, P =0.03), etc. After FDR adjustment, these 13 associations remained significant. Table 3 The results of latent casual variable analysis Phenotype r g SE GCP(SE) P P FDR Forced vital capacity (FVC) 0.07 0.015 0.63(0.27) 0.02 0.026 Medication for cholesterol, blood pressure or diabetes: None of the above 0.10 0.018 0.57(0.25) 0.02 0.026 Medication for cholesterol, blood pressure or diabetes: Blood pressure medication -0.10 0.017 0.54(0.28) 0.04 0.026 Age at first live birth 0.08 0.017 0.51(0.27) 0.03 0.033 Vascular/heart problems diagnosed by doctor: None of the above 0.08 0.013 0.48(0.23) 9.89×10 −3 0.026 Vascular/heart problems diagnosed by doctor: High blood pressure -0.08 0.014 0.47(0.24) 0.02 0.026 Non-cancer illness code, self-reported: hypertension -0.08 0.013 0.48(0.24) 0.01 0.026 Impedance of arm (right) 0.12 0.013 0.41(0.17) <1×10 −7 <6.50×10 −7 Impedance of arm (left) 0.12 0.013 0.40(0.16) <1×10 −7 <6.50×10 −7 Age at last live birth 0.08 0.020 0.38(0.22) 0.03 0.033 Leg fat percentage (left) -0.11 0.014 0.24(0.13) 0.01 0.026 Illnesses of siblings: None of the above (group 1) 0.12 0.020 0.24(0.14) 0.04 0.040 Leg fat percentage (right) -0.10 0.014 0.23(0.15) 0.02 0.026 Note: Significant genetic causal relationship between age at menarche and the correlated traits; r g , regression coefficients from age at menarche to the phenotypes; SE, standard error of r g ; GCP (SE): genetic causality proportion (standard error of GCP); P , the P value of GCP. P FDR , P value adjusted by FDR. Group 1: Heart disease, Stroke, High blood pressure, Chronic bronchitis/emphysema, Alzheimer's disease/dementia, Diabetes. Combined with GSMR and LCV analysis, it was found that all the 13 traits identified in LCV were significant in GSMR1, indicating strong evidence for causal association between AAM and these 13 traits. In addition, leg fat percentage and arm impedance in body composition were significant in both GSMR1 and LCV. Notably, "right arm impedance" was significant in all three analyses (beta GSMR1 =12.61 P =3.86×10 −174 , beta GSMR2 =-0.001 P =9.52×10 −6 , GCP=0.41 P <1×10 −7 ), and the effect was most significant in GSMR1, indicating that arm impedance would increase by 12.61 units for each year of AAM delay. In GSMR analysis, AAM was causality associated with age of first sexual intercourse (beta GSMR1 =0.46, P GSMR1 =1.92×10 −30 ; beta GSMR2 <-0.01, P GSMR2 =0.83), while in LCV analysis, the results were only suggestive (GCP=0.41, P =0.05). Candidate gene prioritization By integrating data from five sources, we prioritized 33 candidate genes at 21 novel loci (Table 4 ). Some candidate genes are highly expressed in endocrine-related tissues. For example, ZNF512 expresses in ovary and endometrium ( 37 ). Some of these loci were screened for more than one gene. For example, 10 candidate genes were identified at 6p21.33, including cis-eQTL target genes CCHCR1 , HLA-C , HCG27 , POU5F1 , TCF19 and XXBacBPG299F13.17 , HLA-C , HCG27 , XXbacBPG299F13.17 and HLA-B , as well as CCHCR1 and PSORS1C2 containing missense coding SNPs. Some of the genes show more than one line of supporting evidence. For example, PDE11A at 2q31.2 is not only the cis-eQTL target gene of the lead SNP rs12408576 within this locus, but also contains a missense coding SNP (rs12408576). Table 4 Prioritized candidate genes at the identified novel loci Locus Lead SNP Candidate genes 2p23.3 rs1881395 ZNF512 (N) 2q14.1 rs2002446 DPP10 (N) 2q22.3 rs6730140 RN5S106 (N) 2q31.2 rs17400325 PDE11A (N, M) 3p12.3 rs7429712 MIR4444-1 (N) 4p16.3 rs1203863 RNF4 (N, Q) 4q24 rs1027013 BANK1 (N), RP11-10L12.2 (X) 4q32.2 rs13126656 AC106860.1 (N) 4q32.3 rs2029641 RP11-217C7.1 (N) 4q34.3 rs17769849 RP11-404J23.1 (N) 5q12.3 rs32015 RP11-434D9.1 (N) 6p25.1 rs2326574 RNase_MRP (N), RP11-428J1.4 (Q) 6p21.33 rs1265093 PSORS1C1 (N), CCHCR1 (M, Q), HLA-C (Q, X), HCG27 (Q, X), POU5F1 (Q), TCF19 (Q), XXbacBPG299F13.17 (Q), PSORS1C2 (M), XXbac-BPG248L24.12 (X), HLA-B (X) 7p21.3 rs1549864 U3 (N) 7p14.3 rs4723134 PDE1C (N) 7q34 rs11773714 TRBV30 (N, Q, S) 8q22.3 rs3101268 RP11-127H5.1 (N) 10q21.3 rs12220144 TET1 (N), SLC25A16 (Q) 10q23.32 rs11186505 PCGF5 (N) 14q21.3 rs7159376 MDGA2 (N) 20q11.23 rs6060752 C20orf152 (N) Note: Genes were prioritized as following: gene nearest to the novel lead SNP (N); gene containing a missense SNP (M); gene with mRNA levels in association with one or more SNPs (cis-eQTL, Q); gene prioritized by summary mendelian randomization (SMR) analysis (S); and gene prioritized by Summary-MultiXcan (S-MultiXcan) analysis (X). Enrichment analysis We next explored particular tissues and cells in which the expression of genes residing in AAM-associated loci was enriched. A total of 5 tissues were identified in trans-ancestry meta-analysis (Table 5 ). Four of the five tissues were related to the nervous system, including the hypothalamus middle ( P =1.92×10 −4 ), hypothalamo hypophyseal system ( P =1.92×10 −4 ), neurosecretory systems ( P =1.92×10 −4 ), and the hypothalamus ( P =1.84×10 −3 ). The fifth tissue was the retina in the sensory organ ( P =4.25×10 −4 ). Table 5 Enriched tissues of DEPICT MeSH term MeSH first level term MeSH second level term P P FDR <5% A08.186.211.730.317.357.352 Hypothalamus Middle Nervous System 1.92×10 −4 Yes A08.186.211.730.317.357.352.435 Hypothalamo Hypophyseal System Nervous System 1.92×10 −4 Yes A08.713 Neurosecretory Systems Nervous System 1.92×10 −4 Yes A09.371.729 Retina Sense Organs 4.25×10 −4 Yes A08.186.211.730.317.357 Hypothalamus Nervous System 1.84×10 −3 Yes 51 gene sets were identified by the gene set enrichment analysis ( Supplementary Table 4 ). The most significant gene set was the MP:0002773 ‘decreased circulating luteinizing hormone level’ ( P =2.45×10 −6 ). Other significant gene sets include ‘positive regulation of insulin-like growth factor receptor signaling pathway’, ‘decreased uterus weight’, ‘abnormal diencephalon morphology’, ‘decreased growth hormone level’ and ‘decreased circulating insulin-like growth factor I level’ etc. These enriched gene sets may be associated with female development and menarche. Discussion In the present study, aiming to identify AAM-associated genetic loci, we performed a European ancestry-specific meta-analysis and a trans-ancestry GWAS meta-analysis by combing the summary statistics of three large GWAS samples of AAM. We identified a total of 21 novel AAM associated loci, including 4 European ancestry-specific loci. There have been examples in recent studies suggesting that causal genes are distinct from the nearest genes ( 33 , 38 ), so we integrated five complementary methods to select candidate genes. The candidate genes we identified may play an important role in the development of some diseases and cancers. For example, hereditary breast cancer and ovarian cancer pathogenic variants were found in PDE11A ( 34 ). RNF4 interacts also with the androgen receptor (AR) functioning as a coactivator ( 39 ), and AR-mediated androgen actions are important for normal female fertility ( 40 ). AR knockout mouse models have identified that AR function is required for full functionality in follicle health, development and ovulation through both intra-ovarian and neuroendocrine mechanisms ( 40 ). Methylation quantitative trait locus 6p21.33 (near PSORS1C1 ) was reported associated with reproductive traits and diseases ( 41 ). In the enrichment analysis, 4 tissues in the nervous system are identified. Besides, we also identified gene sets such as ‘decreased circulating luteinizing hormone level’ and ‘decreased circulating insulin-like growth factor I level’, etc. The hypothalamus and pituitary gland are key structure in the hypothalamic-pituitary-gonadal (HPG) axis--they play an important role in sexual maturation during puberty ( 42 ). The activation of the HPG axis during this developmental period involves: 1) the release of the gonadotropin-releasing hormone (GnRH) from the hypothalamus; 2) the release of gonadotropins, luteinizing hormone (LH) and follicle stimulating hormone (FSH) from the pituitary gland; and 3) the release of sex steroids from maturing gonads, so that the operation of a drive from the hypothalamus and pituitary gland indicated ( 43 ). LH is a heterodimeric glycoprotein hormone released from the anterior pituitary gland in response to GnRH stimulation from the hypothalamus ( 44 ). One of the important roles of LH is to regulate ovarian function ( 45 ). LH plays a key role in follicular maturation, ovulation, luteal development and maintenance ( 44 , 45 ). Lower LH levels can affect ovulation, which can affect menarche and pregnancy ( 44 ). Besides, the role that insulin and IGF-1 play in controlling the hypothalamus and pituitary and their role in regulating puberty and nutritional control of reproduction has been studied extensively ( 46 ). Study has shown IGF-I acts at different levels of the HPG axis; it exerts paracrine effects at the ovary and stimulates GnRH at the hypothalamic-pituitary level ( 47 ). In addition, the expression of genes residing in AAM-associated loci was enriched in the retina. In terms of the ocular system, sex hormones are recognized for their critical role in regulating important body functions that affect the eyes( 48 ). Sex hormones can have a neuroprotective action on the retina and modulate ocular blood flow ( 49 ). Oestrogen is abundant in the mammalian eye ( 50 ). Some studies are suggestive of a modest protective effect of estrogen exposure on the eye health of women, and estrogen may confer antioxidative protection against cataractogenesis ( 51 , 52 ). It is unsurprising therefore that oestrogen levels may impact vision through its effects on the eye, from the ocular surface to the retina. Younger AAM augments estrogen exposure and lower the risk of cataracts in advanced age ( 53 ), and was also associated with a protective effect regarding nuclear sclerosis ( 54 ). In conclusion, both AAM and sense organs are derived from common genetic factors. In genetic correlations and causal inference analysis, we identified significant genetic correlations between AAM and BMI, which was consistent with previous study ( 14 ). While further causality inference demonstrates that there was no significant causal relationship between AAM and BMI. However, we observed causal relationship between AAM and body composition traits, including leg fat percentage and impedance of arm. Impedance can be used to indirectly estimate body composition such as fat mass and fat free mass ( 55 ). Analysis indicated evidence of an effect of younger AAM on lower impedance, lower total body water, lower fat free mass, and higher fat mass. The inconsistent casual associations may be because BMI is a complex composite of fat mass, lean mass, bone mass and other soft tissues. Future studies are needed to further investigate the potential effects of AAM on body composition. MR has previously been used to explore effects of age at menarche on cardiometabolic health outcomes (included systolic and diastolic blood pressure), but AAM was not clearly or inconsistent associated with any other cardiovascular risk factor ( 56 ) ( 57 ). It therefore remains unclear whether the previously reported associations between AAM and health outcomes reflect a causal effect. However, the causal relationship between younger AAM and higher blood pressure was obvious in our study. The causal relationship between AAM and cardiovascular diseases such as blood pressure is also consistent with epidemiological studies ( 58 , 59 ). We also observed a causal effect of AAM on age at first live birth. Early AAM is also associated with early marriage, which may have particularly important implications for age at first live birth ( 60 ). A phenome-wide Mendelian randomization study results of AAM with 17,893 health-related traits suggest that younger age at menarche has potential effects on a broad range of health-related traits. Follow-up analysis indicated imprecise evidence of an effect of younger AAM on several outcomes, including lower lung function, confidence intervals were wide and often included the null ( 61 ). Therefore, larger study populations are needed to re-examine these relationships. The analyses of genetic correlation and casual effect are relatively rough in this study, and the results are only suggestive for AAM-related diseases and traits. We will further explore the causal association of AAM with some diseases and traits. There are some limitations in our study. Firstly, we combined the summary statistics using fixed-effects model, which does not account for genetic heterogeneity that may arise from multiple race/ethnic populations. However, the random-effects model that is more robust to genetic heterogeneity is over-conservative ( 62 ). We noticed that it is a convention to perform fixed-effects model analysis even with multiple ethnic participants ( 63 , 64 ). In addition, the dataset we used in the genetic correlations and causal inference analyses contains both females and males. We expected that choosing to use a dataset that includes both males and females would have attenuated the association signal between AAM and correlated traits in this study, thus rendering our results conservative ( 65 ). Therefore, future MR studies may be warranted to verify our results in female only samples (when such samples are available). In summary, by performing European ancestry-specific and trans-ancestry meta-analyses for AAM, we generated an atlas of candidate SNPs and genes involved in AAM determination and its shared regulation with multiple cardiometabolic traits. Our findings may further elucidate the mechanisms that determine the timing of AAM and underlie its links to disease risk. Declarations Acknowledgements We acknowledge the Reproductive Genetics Consortium, the Biobank Japan project and UK Biobank Cohort for releasing the age at menarche GWAS summary results. Authors' contributions GJF and YFP designed the study. QX and GJF collected the data. GJF and QX analyzed the data. JJN, SSY, BXH, XTW, HZ, LZ and QGZ performed the literature search. GJF drafted the early version of the manuscript. YFP and LZ jointly supervised the study. All authors were involved in writing the paper and had final approval of the submitted and published versions. Funding YFP and LZ are benefited from national natural science foundation of China (31771417 and 31571291), a project funded by the Priority Academic Program Development (PAPD) of Jiangsu higher education institutions. The numerical calculations in this paper have been done on the supercomputing system of the National Supercomputing Center in Changsha. Competing interests On behalf of all authors, the corresponding authors state that there is no conflict of interest. Ethics approval and consent to participate Not applicable. Availability of data and materials Genome-wide summary statistics from the three samples are available to download from the following websites: (i) the ReproGen Consortium ( http://www.reprogen.org/ ); (ii) the UK Biobank ( https://www.dropbox .com/s/wabrpmebxrj3xwq/2714.gwas.imputed_v3.female.tsv.bgz?dl=0 -O 2714.gwas.imputed_v3.female.tsv.bgz); (iii) the BBJ sample ( https://humandbs.biosciencedbc.jp/en/ ) under data set identifier hum0014.v9.Men.v1and hum0014.v9.MP.v1. Consent for publication Not applicable. References Gill D, Sheehan NA, Wielscher M, Shrine N, Amaral AFS, Thompson JR, et al. Age at menarche and lung function: a Mendelian randomization study. Eur J Epidemiol. 2017;32(8):701–10. Anderson CA, Duffy DL, Martin NG, Visscher PM. Estimation of variance components for age at menarche in twin families. Behav Genet. 2007;37(5):668–77. Pierce MB, Leon DA. Age at menarche and adult BMI in the Aberdeen children of the 1950s cohort study. Am J Clin Nutr. 2005;82(4):733–9. 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Age at menarche and osteoporosis: A Mendelian randomization study. Bone. 2018;117:91–7. Supplementary Files Supplementarytables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-955340","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":56049999,"identity":"704461e7-3b85-45b9-8b67-a1ad1c7825bd","order_by":0,"name":"Gui-Juan Feng","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gui-Juan","middleName":"","lastName":"Feng","suffix":""},{"id":56050000,"identity":"5145b845-2507-4213-9012-de7907d82d9e","order_by":1,"name":"Qian Xu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xu","suffix":""},{"id":56050001,"identity":"b15badd8-0586-451b-bcc1-f7dea4d7b068","order_by":2,"name":"Jing-Jing Ni","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing-Jing","middleName":"","lastName":"Ni","suffix":""},{"id":56050002,"identity":"348fac2c-572c-4ecf-9314-09c592085f9b","order_by":3,"name":"Shan-Shan Yang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shan-Shan","middleName":"","lastName":"Yang","suffix":""},{"id":56050003,"identity":"7dec5232-e877-4642-8265-52ef8f12027b","order_by":4,"name":"Bai-Xue Han","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bai-Xue","middleName":"","lastName":"Han","suffix":""},{"id":56050004,"identity":"e2d72935-0171-47ba-910e-224a0fc62bda","order_by":5,"name":"Xin-Tong Wei","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin-Tong","middleName":"","lastName":"Wei","suffix":""},{"id":56050005,"identity":"1070f707-b4b9-4e22-bd3d-74ec95d456a9","order_by":6,"name":"Hong Zhang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":56050006,"identity":"86526885-3798-4d9f-acd0-334f0fcdd5c5","order_by":7,"name":"Qi-Gang Zhao","email":"","orcid":"https://orcid.org/0000-0002-6950-3963","institution":"Medical college of soochow university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi-Gang","middleName":"","lastName":"Zhao","suffix":""},{"id":56050007,"identity":"f38970e0-e3fc-48b1-a315-97d4cffd24cd","order_by":8,"name":"Lei Zhang","email":"","orcid":"","institution":"center for genetic epidemiology and genomics,soochow university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":56050008,"identity":"967e550c-b9fc-4a3b-9ca2-d5eddc44761b","order_by":9,"name":"Yu-Fang Pei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYJCCAx8YGBgbgAwJorUcnEGyFmYekrQY3G4+eNjmz2HZ/gbmg7d5GOzyCGu5cyzhcG7bYeMZB9iSrXkYkosJa7mRY3A4t+FwYsMBHjNpHoYDiQ2EteR/OGzx53Di/AP834jVksNwmIHtcOKGAzxsxGmRvHPM4GBvW7rxxsNsxpZzDJIJa+G73fz4w48/1rLzjjc/vPGmwo6wFoUbMBYz2J2E1AOB/AwiFI2CUTAKRsEIBwAKE0MrSDb5TQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9157-0759","institution":"Soochow University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yu-Fang","middleName":"","lastName":"Pei","suffix":""}],"badges":[],"createdAt":"2021-10-04 11:39:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-955340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-955340/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14420999,"identity":"86b7a25b-38ec-4ba3-8ad0-f0146c57d30d","added_by":"auto","created_at":"2021-10-11 17:38:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39052,"visible":true,"origin":"","legend":"Flow chart of the study.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-955340/v1/29522bc49d585132c8f8eee1.png"},{"id":14421002,"identity":"350a06a8-9726-4e5f-af69-bf98e9b679fd","added_by":"auto","created_at":"2021-10-11 17:38:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7731,"visible":true,"origin":"","legend":"Manhattan plot of the European ancestry-specific meta-analysis for AAM. The x-axis represents the genomic position on chromosome and the y-axis represents –log10 (P value). For ease of presentation, the y axis is truncated at 10-80. Dotted line represents genome-wide significance (GWS, 5.0×10–8) level. Identified loci at the GWS level are plotted in green.","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-955340/v1/9a50601ef2d8f4ead962b461.png"},{"id":14421261,"identity":"417ca28b-a0e2-4a11-9f5e-9612e6de6a90","added_by":"auto","created_at":"2021-10-11 17:41:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7799,"visible":true,"origin":"","legend":"Manhattan plot of trans-ancestry meta-analysis for AAM. The x-axis represents the genomic position on chromosome and the y-axis represents –log10 (P value). For ease of presentation, the y axis is truncated at 10-80. Dotted line represents genome-wide significance (GWS, 5.0×10–8) level. Identified loci at the GWS level are plotted in green.","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-955340/v1/3a035c8fc0e931c7f5a00a70.png"},{"id":14421262,"identity":"04f3abb3-0856-4efa-89f1-4b449adc80e7","added_by":"auto","created_at":"2021-10-11 17:41:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":592884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-955340/v1/4da32838-1e67-4818-ab2a-a601e4113025.pdf"},{"id":14421000,"identity":"8f15fc7f-69ab-40d5-b845-cb0fdc050a32","added_by":"auto","created_at":"2021-10-11 17:38:50","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":52365,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-955340/v1/a369ed410877ea00ae24a480.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eJoint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge at menarche (AAM), defined as the age of first menstrual bleeding, is a commonly reported marker of pubertal timing in females (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). AAM is determined by the overall duration of endocrine-tissue sex hormone exposure level (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and has a series of clinical outcomes through the women's life. On one hand, earlier AAM is considered to be a risk factor for certain diseases, including obesity (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), type 2 diabetes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), cardiovascular diseases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), breast cancer (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and ovarian cancer (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). On the other hand, later AAM may be associated with an increased risk of Alzheimer's disease (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and stroke (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), as well as lower fertility (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAAM is a highly heritable trait, with estimated heritability up to 50% in previous twin and family studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). To date, hundreds of variants associated with AAM have been identified by a number of genome-wide association studies (GWASs) and their meta-analyses (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Nonetheless, variants identified by the largest GWAS meta-analyses to date only explain 7.4% of the total phenotypic variation, far less than the total 50% of heritability (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Therefore, the majority of the genetic components underlying AAM remains to be discovered. The unidentified 'missing' heritability is probably carried by genetic variants with minor phenotypic effects that escaped detection by studies with insufficient sample size and statistical power (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and enlarged sample is warranted to uncover them. To maximize power, it is critical to make use of meta-analyses of multiple independent GWASs whose increasing sample size boosts the statistical power for detecting modest associations.\u003c/p\u003e \u003cp\u003eIn the present study, aiming to identify more association signals that contribute to AAM, we conduct a joint GWAS meta-analysis of AAM in 438,089 participants by integrating 3 GWASs, namely the Reproductive Genetics (ReproGen) Consortium (N=182,416), the UK Biobank (UKB, N=188,644) and the Biobank Japan (BBJ, N=67,029). A series of bioinformatical analyses are then followed to explore in-depth annotations at the associated loci.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eSummary statistics of 3 large GWASs were included in this study, 2 of which are of European population and the third one is of East Asian population. The study design is shown in Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In brief, we conducted two joint analyses. First, the European ancestry-specific meta-analysis was performed to combine results from the ReproGen and the UKB samples. Second, the BBJ was included for the trans-ancestry meta-analysis. The purpose of the second analysis was to identify more loci across populations by a maximal sample size. With European-specific meta-analysis results, we performed a series of follow-up analyses that are sensitive to linkage disequilibrium (LD) pattern, including LD score regression (LDSC) analysis, genetic correlation analysis, Generalized Summary data-based Mendelian Randomization (GSMR) analysis and latent causal variable (LCV) analysis. Functional enrichment and candidate gene prioritization were conducted with trans-ancestry meta-analysis results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations\u003c/h2\u003e \u003cp\u003eThe first study is the ReproGen study, which is a GWAS meta-analysis of 182,416 women of European descent from 58 samples (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In brief, genome-wide SNPs were genotyped by genotyping arrays, and were imputed into the HapMap Phase II CEU build 35 or 36 reference panel. The second study is the UKB sample, which included 188,644 women of European descent (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). All participants were genotyped by the UK BiLEVE Axiom array or UKB Axiom array, and were imputed into UK10K haplotype, 1000 Genomes project phase 3 and Haplotype Reference Consortium (HRC) reference panels (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Subjects who had a self-reported gender inconsistent with the genetic gender, who were genotyped but not imputed or who withdraw their consents were removed. The last sample is the BBJ study, which is a single GWAS of 67,029 women of East Asian descent (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). BBJ participants had DNA genotyped on more than 950,000 variants using either (a) a combination of Illumina Human OmniExpress BeadChip and Infinium HumanExome BeadChip or (b) Infinium OmniExpressExome BeadChip alone. Variants overlapping across these two sets of genotyping arrays were extracted. After quality control (QC), genome-wide genotypes were imputed into the 1000 Genomes Project Phase 3 reference panel (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll participants had provided written informed consent and each study had its research protocol approved by the corresponding local ethics committee or Institutional Review Boards (IRB). No new IRB approval was required. Summary statistics for the 3 studies were downloaded from their respective websites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData quality control\u003c/h2\u003e \u003cp\u003eWe excluded the variants which were duplicated, poorly imputed, or without allele frequency information, only common or less common (minor allele frequency, MAF\u0026gt;1%) SNPs from each individual study were included into analyses (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). There were 2,441,815 genetic variants in the Reprogen released summary data. After QC, there were 2,400,657 variants remained. There were 13,788,288 genetic variants in the UKB released summary data. After removing X-chromosome variants, monomorphic site and duplicated variants (i.e., multiple variants correspond to one identifier), 13,752,112 variants are left. These 13,752,112 variants were matched with 2,400,657 variants in the Reprogen summary data. After removing variants that had intermediate locus incompatibility (i.e., A/G and A/T polymorphisms) in both studies, a total of 2,378,813 variants were eligible for European ancestry-specific meta-analysis. The BBJ study included 9,296,729 genetic variants, of which 15,851 duplicates were removed. The remaining 9,280,878 variables were matched with the 2,378,813 variants in the European ancestry-specific analysis, and then variants with incompatible alleles were excluded. Finally, 2,090,939 genetic variants were present in all three studies, which were used for trans-ancestry meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis\u003c/h2\u003e \u003cp\u003eSummary statistics from each GWAS sample were combined by an inverse-variance weighted fixed-effects model implemented in METAL (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In the European ancestry-specific meta-analysis, the ReproGen and the UKBB studies were meta-analyzed. In the trans-ancestry meta-analysis, all 3 GWASs were meta-analyzed. The potential heterogeneity effects of each variant were assessed using \u003cem\u003eQ\u003c/em\u003e statistics and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e index in European populations and in all-ancestry populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of novel loci\u003c/h2\u003e \u003cp\u003eGenome-wide significance (GWS) level was set at 5.0\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e. An independent locus was defined as a genomic region of 500kb on either side of the variant showing the strongest association signal. We declared an association locus to be novel if it contained GWS SNPs that were at least 500kb outside from any reported loci and the novel lead SNP was not in linkage disequilibrium (LD, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.1) with previously reported signals. We further compared the results of the European ancestry-specific analysis and the trans-ancestry meta-analysis to investigate the ancestry specificity of the identified loci. A European-ancestry specific locus was defined if this locus was: (a) identified only in European ancestry-specific meta-analysis; or (b) identified in both meta-analyses, but was significantly heterogeneous (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026ge;50%) in trans-ancestry meta-analysis and was not heterogeneous in European ancestry-specific analysis. For the overlapped loci in two analyses (a same locus with different lead SNP), we picked the most significant SNP in the two analyses as the lead SNP at this locus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic architecture\u003c/h2\u003e \u003cp\u003eThe LDSC method was applied to the European ancestry specific meta-analysis results to estimate the amount of genomic inflation due to confounding factors such as population stratification and cryptic relatedness (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). LDSC takes GWAS summary statistics as input and partitions overall inflated association statistic into one part attributable to polygenic architecture and the other part due to population stratification and cryptic relatedness. The relative contribution of confounding factors was measured by attenuation ratio (AR), which is defined as (intercept-1)/(mean χ\u003csup\u003e2\u003c/sup\u003e-1), where intercept and mean χ\u003csup\u003e2\u003c/sup\u003e are estimates of confounding and the overall association inflation, respectively (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlations and causal inference\u003c/h2\u003e \u003cp\u003eTo evaluate genetic interplay between AAM and other complex traits/diseases, genetic correlation analyses were performed with a LDSC based web tool Complex-Traits Genetics Virtual Lab (CTG-VL) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). A total of 1,387 traits were available at the CTG-VL database. Genetic correlation between AAM and each of these 1,387 traits was estimated. Significance threshold was set at a Bonferroni corrected significance level 3.60ⅹ10\u003csup\u003e\u0026minus;5\u003c/sup\u003e, e.g., 0.05/1387.\u003c/p\u003e \u003cp\u003eTo further investigate the causal relationship for significant genetic correlations, we performed GSMR to evaluate the causal associations between AAM and correlated traits. GSMR can account for LD between the variants, and is more powerful than existing summary data-based MR methods (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). GWS SNPs show no evidence of horizontal pleiotropy (HEIDI-outlier test \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e\u0026lt;0.01) and independent of each other (LD \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.05 and window size=1 Mb) were selected as instrumental variables (IVs). We switched the exposure and outcome to explore the reverse causation in GSMR analysis.\u003c/p\u003e \u003cp\u003eGSMR may suffer from horizontal pleiotropy even after correction. To resolve this problem, a recently developed LCV model was used to validate the significant results of GSMR analysis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The LCV method estimates a genetic causality proportion (GCP) for each pair of traits being evaluated, which represents the proportion of genetic components of one trait that influence the other trait. A positive GCP value indicates that a proportion of the genetic component of AAM affects the other trait, while a negative GCP value means that the proportion of genetic component of the other trait affects AAM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCandidate gene prioritization\u003c/h2\u003e \u003cp\u003eWe incorporated the following five sources of information to prioritize candidate genes at the identified loci: (i) being the gene nearest the novel lead SNP; (ii) being the target gene for any local expression quantitative trait loci (cis-eQTL); (iii) containing a novel GWS missense SNP; (iv) being prioritized by summary data based mendelian randomization (SMR) analysis and (v) being prioritized by Summary-MultiXcan (S-MultiXcan) analysis.\u003c/p\u003e \u003cp\u003eHaploReg was used to identify the nearest gene of the lead SNP (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The Ensembl Variant Effect Predictor (VEP) was used to look over whether there is a missense mutation at the location of the GWS SNPs (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Gene expression information from seven tissues (brain hypothalamus, breast mammary tissue, ovary, pituitary, uterus, vagina and whole blood) linked to AAM were included for cis-eQTL, SMR and S-MultiXcan analyses.\u003c/p\u003e \u003cp\u003eWe analyzed all novel lead SNPs for their cis-eQTL activity. Briefly, the cis-eQTL was defined as an association signal from SNPs located within 100 kb upstream and downstream of the target gene (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Using a Bonferroni correction, we set the significance threshold to be 0.05/N\u003csub\u003etissue\u003c/sub\u003e/N\u003csub\u003eSNPs\u003c/sub\u003e. Cis-eQTLs are accessed from the web portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genehopper.de/qtlizer\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSMR is another prioritization method developed by Zhu and his colleagues (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). SMR integrates GWAS summary results with data from eQTL studies to identify genes whose expression levels are associated with trait because of causal or pleiotropy effects (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Using SMR analysis, we examined for pleiotropy between GWAS signal and cis-eQTL for genes within 1 megabase (Mb) of the sentinel SNP to identify a possible causal relationship between gene expression and AAM. Only probes with eQTL \u003cem\u003eP\u0026lt;\u003c/em\u003e5.0\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e were included in the SMR analysis. HEIDI test \u003cem\u003eP\u003c/em\u003e-values\u0026lt;0.05 were taken to indicate significant heterogeneity.\u003c/p\u003e \u003cp\u003eS-MultiXcan is a summary result-based extension that leverages the substantial sharing of eQTLs across tissues and contexts to improve the ability to identify potential target genes (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We applied MultiXcan to 7 tissues from the CTG-VL. The total number of gene-tissue pairs were used when determining the Bonferroni corrected significance threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eTissue/cell type enrichment analysis and gene set enrichment analysis were performed using DEPICT(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). DEPICT is an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes and tissues/cell types where genes from associated loci are highly expressed. Results with FDR\u0026lt;0.05 was considered significant enrichment. We used all suggestive significant SNPs (\u003cem\u003eP\u003c/em\u003e\u0026lt;1\u0026times;10\u003csup\u003e\u0026minus;5\u003c/sup\u003e) from trans- ancestry meta-analysis as input.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEuropean ancestry-specific meta-analysis\u003c/h2\u003e \u003cp\u003eA total of 371,060 participants and 2,378,813 SNPs passed the QC in the European ancestry-specific GWAS meta-analysis. The LDSC estimates genome-wide heritability to be 10.54% for AAM. The distribution of genome-wide test statistics demonstrated significant inflation (genomic control inflation factor λ\u003csub\u003eGC\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.53). Results of LDSC analysis showed that 94.0% of the inflation in the mean χ\u003csup\u003e2\u003c/sup\u003e statistic is from polygenic architecture rather than from population stratification (LD score intercept=1.05\u0026plusmn;0.01 SE, mean χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.83). Manhattan plot of the GWAS meta-analyses results is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e6,342 SNPs are significant at the GWS level, encompassing into 229 distinct loci. Of these, 14 loci contained 86 GWS variants that were at least 500kb outside from any reported loci. Lead SNPs in 2 of the 14 loci are in LD with previously reported signals (rs13094336 \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e=0.53 with rs6445624, rs2764272 \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e=0.17 with rs6931884) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). We excluded 31 GWS variants located within these 2 loci and declared the remaining 12 loci contained 55 GWS variants to be novel (\u003cb\u003eSupplementary Table 1\u003c/b\u003e). These 12 novel loci are independent of each other (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.01). All the 12 lead SNPs have consistent effect directions in the ReproGen sample and the UKB sample. 4 of the 12 lead SNPs show significant heterogeneity (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026gt;50%), including rs13126656, rs1549864, rs11773714 and rs12220144 (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eNovel loci identified in European ancestry specific analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eReprogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eUKB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c19\" namest=\"c16\"\u003e \u003cp\u003eMeta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eHet\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers2002446\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e116473865\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2q14.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.11\u003c/b\u003e\u003c/p\u003e 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colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e4.86\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers17400325\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e178565913\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2q31.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e6.5\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.62\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e4.35\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e4.70\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e8.15\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.74\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1027013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102904490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4q24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.80\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.93\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e6.86\u0026times;10\u003csup\u003e\u0026minus;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers13126656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.59\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.98\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1265093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31107187\u003c/p\u003e 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colname=\"c15\"\u003e \u003cp\u003e1.70\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e4.58\u0026times;10\u003csup\u003e\u0026minus;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers2326574\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5041470\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c1\"\u003e \u003cp\u003ers11773714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142523468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7q34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.60\u0026times;10\u003csup\u003e\u0026minus;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e8.16\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.84\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers12220144\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e70364884\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10q21.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.6\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;4\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.31\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e4.77\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e70.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11186505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92982908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10q23.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.87\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e3.41\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6060752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34597962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20q11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.36\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.27\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003eNote: CHR, chromosome; POS, genomic position based on GRCH37 genome assembly; EA, effect allele; OA, other allele; EAF, effect allele frequency; Beta, regression coefficient; SE, standard error of Beta; Het\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, heterogeneity test \u003cem\u003eI\u003c/em\u003e square; \u003cem\u003eP\u003c/em\u003e1, the \u003cem\u003eP\u003c/em\u003e value of ReproGen Consortium; \u003cem\u003eP\u003c/em\u003e2, the \u003cem\u003eP\u003c/em\u003e value of UKB sample; \u003cem\u003eP\u003c/em\u003e, the \u003cem\u003eP\u003c/em\u003e value calculated in meta-analyses. The European-ancestry specific loci are shown with bold font.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTrans-ancestry meta-analysis\u003c/h2\u003e \u003cp\u003eAfter systematic QC, association statistics for 2,100,684 autosomal SNPs were combined across all three studies. Manhattan plot of the GWAS meta-analyses are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results contain 6,370 SNPs significant at the GWS level, encompassing 239 distinct loci. By removing reported loci, we identified 93 novel SNPs mapping to 17 independent loci (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These 17 novel loci are independent of each other (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.01). 16 of 17 lead SNPs have consistent association direction in all three samples, except for rs11773714 at 7q34 (beta\u003csub\u003eReproGen\u003c/sub\u003e=0.027, beta\u003csub\u003eUKB\u003c/sub\u003e=0.013 and beta\u003csub\u003eBBJ=\u003c/sub\u003e-0.007). 4 of the 17 novel lead SNPs show significant heterogeneity (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026gt;50%), included rs7429712, rs1549864, rs11773714, and rs7159376 (\u003cb\u003eSupplementary Table 2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrans-ancestry GWAS meta-analysis identified 17 novel loci associated with AAM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eReprogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eUKB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eBBJ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c20\" namest=\"c17\"\u003e \u003cp\u003eMeta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eHet\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1881395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27838549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2p23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.30\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.80\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.51\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6730140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148460036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2q22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.70\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.81\u0026times;10\u003csup\u003e\u0026minus;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.45\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7429712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75265159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3p12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e 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colname=\"c18\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.65\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11186505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92982908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10q23.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.87\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e2.10\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7159376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47921389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14q21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.80\u0026times;10\u003csup\u003e\u0026minus;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.23\u0026times;10\u003csup\u003e\u0026minus;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e4.11\u0026times;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6060752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34597962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20q11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.36\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e5.26\u0026times;10\u003csup\u003e\u0026minus;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"20\"\u003eNote: CHR, chromosome; POS, genomic position based on GRCH37 genome assembly; EA, effect allele; OA, other allele; EAF, effect allele frequency; Beta, regression coefficient; SE, standard error of Beta; Het\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, heterogeneity test \u003cem\u003eI\u003c/em\u003e square; \u003cem\u003eP\u003c/em\u003e1, the \u003cem\u003eP\u003c/em\u003e value of ReproGen Consortium; \u003cem\u003eP\u003c/em\u003e2, the \u003cem\u003eP\u003c/em\u003e value of UKB sample; \u003cem\u003eP\u003c/em\u003e3, the \u003cem\u003eP\u003c/em\u003e value of BBJ sample; \u003cem\u003eP\u003c/em\u003e, the \u003cem\u003eP\u003c/em\u003e value calculated in meta-analyses; Het\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, heterogeneity test \u003cem\u003eI\u003c/em\u003e square.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEuropean-ancestry specific loci\u003c/h2\u003e \u003cp\u003eBy comprising the results from European ancestry-specific meta-analysis and trans-ancestry meta-analysis, 214 loci were significant both in the European ancestry-specific meta-analysis and trans-ancestry meta-analysis. 15 loci are only significant in European ancestry-specific meta-analysis. Among them, 4 are novel European-ancestry specific loci (2q14.1, 2q31.2, 6p25.1 and 10q21.3) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The lead SNPs at 2q14.1 2q31.2 and 6p25.1 were not found in the BBJ study. The lead SNP rs12220144 at 10q21.3 was not significant in the trans-ancestry meta-analysis (\u003cem\u003eP\u003c/em\u003e=2.31\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e), and had a negative effect in BBJ and a positive effect in the two European samples. In short, a total of 256 loci, including 241 trans-ancestry loci and 15 European-ancestry specific loci were identified in this study. Among them, 21 loci, including 17 trans-ancestry loci and 4 European-ancestry specific loci are novel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlations and causal inference\u003c/h2\u003e \u003cp\u003eLDSC analyses revealed that AAM were genetically correlated with 67 complex traits and diseases, including BMI (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=-0.19, \u003cem\u003eP=\u003c/em\u003e6.11\u0026times;10\u003csup\u003e\u0026minus;31\u003c/sup\u003e), impedance of whole body (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=-0.13, \u003cem\u003eP\u003c/em\u003e=4.93\u0026times;10\u003csup\u003e\u0026minus;20\u003c/sup\u003e) and age at first live birth (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=0.08, \u003cem\u003eP\u003c/em\u003e=1.31\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e), etc (\u003cb\u003eSupplementary Table 3\u003c/b\u003e). The genetic correlations were consistent with observational associations from epidemiological studies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe further explored the causal relationship between AAM and the above 67 genetic correlated traits through CTG-VL (\u003cb\u003eSupplementary Table 3\u003c/b\u003e). In GSMR analysis, at the stringent significance threshold of Bonferroni correction (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05/67/2=3.73\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), when AAM is the exposure (GSMR1), we observed 45 significant causal associations (\u003cb\u003eSupplementary Table 3\u003c/b\u003e). When AAM is the outcome (GSMR2), we observed 9 significant causal associations (\u003cb\u003eSupplementary Table 3\u003c/b\u003e). The 9 traits were still significant in GSMR1, indicating that the association between AAM and the 9 traits is bidirectional. In GSMR1, AAM had a causal effect with the remaining 36 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) significant traits (\u003cem\u003eP\u003c/em\u003e\u0026lt;3.73\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e\u0026lt;0.1), among which impedance had the largest causal effect value: left arm impedance Beta=13.32, \u003cem\u003eP\u003c/em\u003e=2.75\u0026times;10\u003csup\u003e\u0026minus;180\u003c/sup\u003e, whole body impedance Beta=21.85, \u003cem\u003eP\u003c/em\u003e=2.05\u0026times;10\u003csup\u003e\u0026minus;166\u003c/sup\u003e (Table 7). In addition, we also found that trunk fat mass (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eGSMR1\u003c/sub\u003e=0.39, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eGSMR2\u003c/sub\u003e=5.83\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e) was only significant in GSMR2 analysis, indicating that trunk fat mass would affect AAM.\u003c/p\u003e \u003cp\u003eTo avoid the horizontal pleiotropic effect, we used the recently developed LCV method to verify the significant causal association identified above. We further explored the causal relationship between AAM and the above 67 genetically related traits and three ocular diseases by CTG-VL. LCV found 13 significant associations, and all GCP values were positive, indicating that the genetic component of AAM had a certain impact on other traits, as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The trait with the largest GCP value was \u0026lsquo;forced vital capacity\u0026rsquo; (GCP=0.63, \u003cem\u003eP\u003c/em\u003e=0.02). There is no significant genetic causal effect between AAM and BMI (GCP=-0.08, \u003cem\u003eP\u003c/em\u003e=0.62). While significant causal association was observed between AAM and other fat distribution traits, including impedance of left arm (GCP=0.40, \u003cem\u003eP\u003c/em\u003e\u0026lt;1.0\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e), impedance of right arm (GCP=0.41, \u003cem\u003eP\u003c/em\u003e\u0026lt;1.0\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e), left leg fat percentage (GCP=0.24, \u003cem\u003eP=\u003c/em\u003e0.01), and right leg fat percentage (GCP=0.23, \u003cem\u003eP=\u003c/em\u003e0.02). Significant causal associations were also observed between AAM and forced vital capacity (FVC) (GCP=0.64, P=0.02), blood pressure medication (GCP=0.54, \u003cem\u003eP\u003c/em\u003e=0.04), age at first live birth (GCP=0.51, \u003cem\u003eP\u003c/em\u003e=0.03), etc. After FDR adjustment, these 13 associations remained significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of latent casual variable analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003csub\u003eg\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCP(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForced vital capacity (FVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication for cholesterol, blood pressure or diabetes: None of the above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication for cholesterol, blood pressure or diabetes: Blood pressure medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular/heart problems diagnosed by doctor: None of the above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.89\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular/heart problems diagnosed by doctor: High blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-cancer illness code, self-reported: hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpedance of arm (right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;1\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;6.50\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpedance of arm (left)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;1\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;6.50\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeg fat percentage (left)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllnesses of siblings: None of the above (group 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeg fat percentage (right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Significant genetic causal relationship between age at menarche and the correlated traits; r\u003csub\u003eg\u003c/sub\u003e, regression coefficients from age at menarche to the phenotypes; SE, standard error of r\u003csub\u003eg\u003c/sub\u003e; GCP (SE): genetic causality proportion (standard error of GCP); \u003cem\u003eP\u003c/em\u003e, the \u003cem\u003eP\u003c/em\u003e value of GCP. \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e value adjusted by FDR. Group 1: Heart disease, Stroke, High blood pressure, Chronic bronchitis/emphysema, Alzheimer's disease/dementia, Diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCombined with GSMR and LCV analysis, it was found that all the 13 traits identified in LCV were significant in GSMR1, indicating strong evidence for causal association between AAM and these 13 traits. In addition, leg fat percentage and arm impedance in body composition were significant in both GSMR1 and LCV. Notably, \"right arm impedance\" was significant in all three analyses (beta\u003csub\u003eGSMR1\u003c/sub\u003e=12.61 \u003cem\u003eP\u003c/em\u003e=3.86\u0026times;10\u003csup\u003e\u0026minus;174\u003c/sup\u003e, beta\u003csub\u003eGSMR2\u003c/sub\u003e=-0.001 \u003cem\u003eP\u003c/em\u003e=9.52\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e, GCP=0.41 \u003cem\u003eP\u003c/em\u003e\u0026lt;1\u0026times;10\u003csup\u003e\u0026minus;7\u003c/sup\u003e), and the effect was most significant in GSMR1, indicating that arm impedance would increase by 12.61 units for each year of AAM delay. In GSMR analysis, AAM was causality associated with age of first sexual intercourse (beta\u003csub\u003eGSMR1\u003c/sub\u003e=0.46, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eGSMR1\u003c/sub\u003e=1.92\u0026times;10\u003csup\u003e\u0026minus;30\u003c/sup\u003e; beta\u003csub\u003eGSMR2\u003c/sub\u003e\u0026lt;-0.01, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eGSMR2\u003c/sub\u003e=0.83), while in LCV analysis, the results were only suggestive (GCP=0.41, \u003cem\u003eP\u003c/em\u003e=0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCandidate gene prioritization\u003c/h2\u003e \u003cp\u003eBy integrating data from five sources, we prioritized 33 candidate genes at 21 novel loci (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Some candidate genes are highly expressed in endocrine-related tissues. For example, \u003cem\u003eZNF512\u003c/em\u003e expresses in ovary and endometrium (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Some of these loci were screened for more than one gene. For example, 10 candidate genes were identified at 6p21.33, including cis-eQTL target genes \u003cem\u003eCCHCR1\u003c/em\u003e, \u003cem\u003eHLA-C\u003c/em\u003e, \u003cem\u003eHCG27\u003c/em\u003e, \u003cem\u003ePOU5F1\u003c/em\u003e, \u003cem\u003eTCF19\u003c/em\u003e and \u003cem\u003eXXBacBPG299F13.17\u003c/em\u003e, \u003cem\u003eHLA-C\u003c/em\u003e, \u003cem\u003eHCG27\u003c/em\u003e, \u003cem\u003eXXbacBPG299F13.17\u003c/em\u003e and \u003cem\u003eHLA-B\u003c/em\u003e, as well as \u003cem\u003eCCHCR1\u003c/em\u003e and \u003cem\u003ePSORS1C2\u003c/em\u003e containing missense coding SNPs. Some of the genes show more than one line of supporting evidence. For example, \u003cem\u003ePDE11A\u003c/em\u003e at 2q31.2 is not only the cis-eQTL target gene of the lead SNP rs12408576 within this locus, but also contains a missense coding SNP (rs12408576).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrioritized candidate genes at the identified novel loci\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCandidate genes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2p23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1881395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZNF512\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2q14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2002446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDPP10\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2q22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6730140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRN5S106\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2q31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers17400325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePDE11A\u003c/em\u003e (N, M)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3p12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7429712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMIR4444-1\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4p16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1203863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRNF4\u003c/em\u003e (N, Q)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4q24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1027013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBANK1\u003c/em\u003e (N), \u003cem\u003eRP11-10L12.2\u003c/em\u003e (X)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4q32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers13126656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAC106860.1\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4q32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2029641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRP11-217C7.1\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4q34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers17769849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRP11-404J23.1\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5q12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers32015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRP11-434D9.1\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6p25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2326574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRNase_MRP\u003c/em\u003e (N), \u003cem\u003eRP11-428J1.4\u003c/em\u003e (Q)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6p21.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1265093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePSORS1C1\u003c/em\u003e (N), \u003cem\u003eCCHCR1\u003c/em\u003e (M, Q), \u003cem\u003eHLA-C\u003c/em\u003e (Q, X),\u003c/p\u003e \u003cp\u003e\u003cem\u003eHCG27\u003c/em\u003e (Q, X), \u003cem\u003ePOU5F1\u003c/em\u003e (Q), \u003cem\u003eTCF19\u003c/em\u003e (Q), \u003cem\u003eXXbacBPG299F13.17\u003c/em\u003e (Q), \u003cem\u003ePSORS1C2\u003c/em\u003e (M), \u003cem\u003eXXbac-BPG248L24.12\u003c/em\u003e (X), \u003cem\u003eHLA-B\u003c/em\u003e (X)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7p21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1549864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eU3\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7p14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers4723134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePDE1C\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7q34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11773714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTRBV30\u003c/em\u003e (N, Q, S)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8q22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers3101268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRP11-127H5.1\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10q21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers12220144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTET1\u003c/em\u003e (N), \u003cem\u003eSLC25A16\u003c/em\u003e (Q)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10q23.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11186505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePCGF5\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14q21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7159376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMDGA2\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20q11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6060752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eC20orf152\u003c/em\u003e (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Genes were prioritized as following: gene nearest to the novel lead SNP (N); gene containing a missense SNP (M); gene with mRNA levels in association with one or more SNPs (cis-eQTL, Q); gene prioritized by summary mendelian randomization (SMR) analysis (S); and gene prioritized by Summary-MultiXcan (S-MultiXcan) analysis (X).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eWe next explored particular tissues and cells in which the expression of genes residing in AAM-associated loci was enriched. A total of 5 tissues were identified in trans-ancestry meta-analysis (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Four of the five tissues were related to the nervous system, including the hypothalamus middle (\u003cem\u003eP\u003c/em\u003e=1.92\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), hypothalamo hypophyseal system (\u003cem\u003eP\u003c/em\u003e=1.92\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), neurosecretory systems (\u003cem\u003eP\u003c/em\u003e=1.92\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and the hypothalamus (\u003cem\u003eP\u003c/em\u003e=1.84\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e). The fifth tissue was the retina in the sensory organ (\u003cem\u003eP\u003c/em\u003e=4.25\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnriched tissues of DEPICT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeSH term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeSH first level term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeSH second level term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;5%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA08.186.211.730.317.357.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothalamus Middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA08.186.211.730.317.357.352.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothalamo Hypophyseal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA08.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurosecretory Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA09.371.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSense Organs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA08.186.211.730.317.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u0026times;10\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e51 gene sets were identified by the gene set enrichment analysis (\u003cb\u003eSupplementary Table 4\u003c/b\u003e). The most significant gene set was the MP:0002773 \u0026lsquo;decreased circulating luteinizing hormone level\u0026rsquo; (\u003cem\u003eP\u003c/em\u003e=2.45\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e). Other significant gene sets include \u0026lsquo;positive regulation of insulin-like growth factor receptor signaling pathway\u0026rsquo;, \u0026lsquo;decreased uterus weight\u0026rsquo;, \u0026lsquo;abnormal diencephalon morphology\u0026rsquo;, \u0026lsquo;decreased growth hormone level\u0026rsquo; and \u0026lsquo;decreased circulating insulin-like growth factor I level\u0026rsquo; etc. These enriched gene sets may be associated with female development and menarche.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, aiming to identify AAM-associated genetic loci, we performed a European ancestry-specific meta-analysis and a trans-ancestry GWAS meta-analysis by combing the summary statistics of three large GWAS samples of AAM. We identified a total of 21 novel AAM associated loci, including 4 European ancestry-specific loci.\u003c/p\u003e \u003cp\u003eThere have been examples in recent studies suggesting that causal genes are distinct from the nearest genes (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), so we integrated five complementary methods to select candidate genes. The candidate genes we identified may play an important role in the development of some diseases and cancers. For example, hereditary breast cancer and ovarian cancer pathogenic variants were found in \u003cem\u003ePDE11A\u003c/em\u003e (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). \u003cem\u003eRNF4\u003c/em\u003e interacts also with the androgen receptor (AR) functioning as a coactivator (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and AR-mediated androgen actions are important for normal female fertility (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). AR knockout mouse models have identified that AR function is required for full functionality in follicle health, development and ovulation through both intra-ovarian and neuroendocrine mechanisms (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Methylation quantitative trait locus 6p21.33 (near \u003cem\u003ePSORS1C1\u003c/em\u003e) was reported associated with reproductive traits and diseases (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the enrichment analysis, 4 tissues in the nervous system are identified. Besides, we also identified gene sets such as \u0026lsquo;decreased circulating luteinizing hormone level\u0026rsquo; and \u0026lsquo;decreased circulating insulin-like growth factor I level\u0026rsquo;, etc. The hypothalamus and pituitary gland are key structure in the hypothalamic-pituitary-gonadal (HPG) axis--they play an important role in sexual maturation during puberty (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The activation of the HPG axis during this developmental period involves: 1) the release of the gonadotropin-releasing hormone (GnRH) from the hypothalamus; 2) the release of gonadotropins, luteinizing hormone (LH) and follicle stimulating hormone (FSH) from the pituitary gland; and 3) the release of sex steroids from maturing gonads, so that the operation of a drive from the hypothalamus and pituitary gland indicated (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). LH is a heterodimeric glycoprotein hormone released from the anterior pituitary gland in response to GnRH stimulation from the hypothalamus (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). One of the important roles of LH is to regulate ovarian function (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). LH plays a key role in follicular maturation, ovulation, luteal development and maintenance (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Lower LH levels can affect ovulation, which can affect menarche and pregnancy (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Besides, the role that insulin and IGF-1 play in controlling the hypothalamus and pituitary and their role in regulating puberty and nutritional control of reproduction has been studied extensively (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Study has shown IGF-I acts at different levels of the HPG axis; it exerts paracrine effects at the ovary and stimulates GnRH at the hypothalamic-pituitary level (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the expression of genes residing in AAM-associated loci was enriched in the retina. In terms of the ocular system, sex hormones are recognized for their critical role in regulating important body functions that affect the eyes(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Sex hormones can have a neuroprotective action on the retina and modulate ocular blood flow (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Oestrogen is abundant in the mammalian eye (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Some studies are suggestive of a modest protective effect of estrogen exposure on the eye health of women, and estrogen may confer antioxidative protection against cataractogenesis (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). It is unsurprising therefore that oestrogen levels may impact vision through its effects on the eye, from the ocular surface to the retina. Younger AAM augments estrogen exposure and lower the risk of cataracts in advanced age (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), and was also associated with a protective effect regarding nuclear sclerosis (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). In conclusion, both AAM and sense organs are derived from common genetic factors.\u003c/p\u003e \u003cp\u003eIn genetic correlations and causal inference analysis, we identified significant genetic correlations between AAM and BMI, which was consistent with previous study (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). While further causality inference demonstrates that there was no significant causal relationship between AAM and BMI. However, we observed causal relationship between AAM and body composition traits, including leg fat percentage and impedance of arm. Impedance can be used to indirectly estimate body composition such as fat mass and fat free mass (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Analysis indicated evidence of an effect of younger AAM on lower impedance, lower total body water, lower fat free mass, and higher fat mass. The inconsistent casual associations may be because BMI is a complex composite of fat mass, lean mass, bone mass and other soft tissues. Future studies are needed to further investigate the potential effects of AAM on body composition.\u003c/p\u003e \u003cp\u003eMR has previously been used to explore effects of age at menarche on cardiometabolic health outcomes (included systolic and diastolic blood pressure), but AAM was not clearly or inconsistent associated with any other cardiovascular risk factor (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). It therefore remains unclear whether the previously reported associations between AAM and health outcomes reflect a causal effect. However, the causal relationship between younger AAM and higher blood pressure was obvious in our study. The causal relationship between AAM and cardiovascular diseases such as blood pressure is also consistent with epidemiological studies (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). We also observed a causal effect of AAM on age at first live birth. Early AAM is also associated with early marriage, which may have particularly important implications for age at first live birth (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). A phenome-wide Mendelian randomization study results of AAM with 17,893 health-related traits suggest that younger age at menarche has potential effects on a broad range of health-related traits. Follow-up analysis indicated imprecise evidence of an effect of younger AAM on several outcomes, including lower lung function, confidence intervals were wide and often included the null (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Therefore, larger study populations are needed to re-examine these relationships. The analyses of genetic correlation and casual effect are relatively rough in this study, and the results are only suggestive for AAM-related diseases and traits. We will further explore the causal association of AAM with some diseases and traits.\u003c/p\u003e \u003cp\u003eThere are some limitations in our study. Firstly, we combined the summary statistics using fixed-effects model, which does not account for genetic heterogeneity that may arise from multiple race/ethnic populations. However, the random-effects model that is more robust to genetic heterogeneity is over-conservative (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). We noticed that it is a convention to perform fixed-effects model analysis even with multiple ethnic participants (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). In addition, the dataset we used in the genetic correlations and causal inference analyses contains both females and males. We expected that choosing to use a dataset that includes both males and females would have attenuated the association signal between AAM and correlated traits in this study, thus rendering our results conservative (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Therefore, future MR studies may be warranted to verify our results in female only samples (when such samples are available).\u003c/p\u003e \u003cp\u003eIn summary, by performing European ancestry-specific and trans-ancestry meta-analyses for AAM, we generated an atlas of candidate SNPs and genes involved in AAM determination and its shared regulation with multiple cardiometabolic traits. Our findings may further elucidate the mechanisms that determine the timing of AAM and underlie its links to disease risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Reproductive Genetics Consortium, the Biobank Japan project and UK Biobank Cohort for releasing the age at menarche GWAS summary results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGJF and YFP designed the study. QX and GJF collected the data. GJF and QX analyzed the data. JJN, SSY, BXH, XTW, HZ, LZ and QGZ performed the literature search. GJF drafted the early version of the manuscript. YFP and LZ jointly supervised the study. All authors were involved in writing the paper and had final approval of the submitted and published versions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYFP and LZ are benefited from national natural science foundation of China (31771417 and 31571291), a project funded by the Priority Academic Program Development (PAPD) of Jiangsu higher education institutions. The numerical calculations in this paper have been done on the supercomputing system of the National Supercomputing Center in Changsha.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding authors state that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome-wide summary statistics from the three samples are available to download from the following websites: (i) the ReproGen Consortium (\u003ca\u003ehttp://www.reprogen.org/\u003c/a\u003e); (ii) the UK Biobank (\u003ca\u003ehttps://www.dropbox\u003c/a\u003e .com/s/wabrpmebxrj3xwq/2714.gwas.imputed_v3.female.tsv.bgz?dl=0 -O 2714.gwas.imputed_v3.female.tsv.bgz); (iii) the BBJ sample (\u003ca\u003ehttps://humandbs.biosciencedbc.jp/en/\u003c/a\u003e) under data set identifier hum0014.v9.Men.v1and hum0014.v9.MP.v1.\u003cbr\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGill D, Sheehan NA, Wielscher M, Shrine N, Amaral AFS, Thompson JR, et al. 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Bone. 2018;117:91\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Joint Genome-wide association studies, Age at menarche, Functional analysis","lastPublishedDoi":"10.21203/rs.3.rs-955340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-955340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAge at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 participants from 3 genome-wide association studies of European and East Asian ancestries. Twenty-one novel genomic loci were identified at the genome-wide significance level. Besides, we observed significant genetic correlations between AAM and 67 complex traits, and the highest genetic correlation was observed between AAM and body mass index (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=-0.19, \u003cem\u003eP\u003c/em\u003e=6.11×10\u003csup\u003e−31\u003c/sup\u003e). Latent causal variable analyses demonstrate that there is a genetically causal effect of AAM on high blood pressure (GCP=0.47, \u003cem\u003eP\u003c/em\u003e=0.02), forced vital capacity (GCP=0.63, \u003cem\u003eP\u003c/em\u003e=0.02), age at first live birth (GCP=0.51, \u003cem\u003eP\u003c/em\u003e=0.03), impedance of right arm (GCP=0.41, \u003cem\u003eP\u003c/em\u003e\u0026lt;1×10\u003csup\u003e-7\u003c/sup\u003e) and right leg fat percentage (GCP=-0.10, \u003cem\u003eP\u003c/em\u003e=0.02), etc. Enrichment analysis identified 5 enriched tissues and 51 enriched gene sets. Four of the five enriched tissues were related to the nervous system, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems and hypothalamus. The fifth tissue was the retina in the sensory organ. The most significant gene set was the ‘decreased circulating luteinizing hormone level’ (\u003cem\u003eP\u003c/em\u003e=2.45×10\u003csup\u003e-6\u003c/sup\u003e). Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.\u003c/p\u003e","manuscriptTitle":"Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-11 17:38:49","doi":"10.21203/rs.3.rs-955340/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":"8745c391-aec5-4bbf-8aa1-9fc4825bcaf3","owner":[],"postedDate":"October 11th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7769741,"name":"Internal Medicine"},{"id":7769742,"name":"Molecular Biology"}],"tags":[],"updatedAt":"2021-10-28T19:19:35+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-11 17:38:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-955340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-955340","identity":"rs-955340","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}