Genome-wide association study identifies a functional myostatin variant increasing lean mass in humans

Genome-wide association study identifies a functional myostatin variant increasing lean mass in humans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-wide association study identifies a functional myostatin variant increasing lean mass in humans Adalheidur Larusdottir, Unnur Teitsdottir, Vinicius Tragante, and 57 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9238373/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Muscle mass is central to physical function and metabolic health 1 , but can decrease with disease, aging 2 and weight loss interventions 3-5 . As such interventions become more widely used, agents that preserve or increase muscle mass are needed. To identify such therapeutic opportunities, we performed genome-wide association meta-analyses (GWAS) of arm, leg, trunk and total lean mass measured by dual-energy X-ray absorptiometry (DXA), a widely used method to estimate muscle mass. We identified 63 loci, including the rare missense variant in MSTN (p.Ile225Thr, rs143242500), which had the largest effect on leg lean mass (β = 0.28 SD [95% CI: 0.19, 0.37], P = 1.9 × 10 -9 ). MSTN encodes myostatin, a negative regulator of skeletal muscle mass and a therapeutic target for muscle wasting disorders 6 . p.Ile225Thr is the first genome-wide significant association in MSTN in humans with functional consequences and could provide further insight into long-term systemic effects of myostatin inhibition. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Health sciences/Anatomy/Musculoskeletal system/Muscle/Skeletal muscle Biological sciences/Genetics/Functional genomics/Gene expression profiling Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Figures Figure 1 Figure 2 Figure 3 Introduction Muscle mass and strength decline with age and are key predictors of disability onset 2,7 . With aging populations and the expanding use of weight-loss therapies, preserving muscle mass is increasingly important. To explore the genetic contribution to lean mass variation, we conducted GWASs of DXA-derived lean mass in arms, legs, trunk, and total body of approximately 79,000 individuals of European ancestry from Iceland and the UK biobank (UKB) using a weighted genome-wide significance threshold based on predicted variant impact (Supplementary Table 1, Supplementary Fig. 1-4) 8 . The lean mass measurements were adjusted for sex, age, body mass index (BMI) and height, capturing variation in lean mass conditional on overall body size and obesity status. Of the 63 identified loci, the rare MSTN missense variant p.Ile225Thr (rs143242500) had the largest effect size across the DXA measures, with the strongest association observed for leg lean mass (β = 0.28 SD [95% CI: 0.19, 0.37], P = 1.9 × 10 -9 , Table 1, Supplementary Fig. 5). We did not observe a difference in effects between males and females (P Heterogeneity > 0.05, Supplementary Table 1). The variant was most frequent in Finnish and Norwegian populations (1.03% and 0.95% respectively), with lower frequencies in other Europeans (0.17–0.58%) and very rare occurrence in other ancestries (<0.1%; Table 1, Supplementary Figs. 6 and 7). Myostatin is a secreted growth factor which signals through the activin type II B receptor ( ACVR2B ) to suppress muscle hypertrophy 9 . It is expressed primarily in skeletal muscle and to a lesser extent in other tissues such as cardiac muscle and adipose tissue 10 . Extensive animal data 11 , including mouse Mstn knock-out models 12 and pharmacologic inhibition studies 13 , demonstrate that loss of myostatin activity markedly increases muscle mass. In humans, a homozygous loss-of-function mutation in MSTN has been associated with extreme muscular hypertrophy as reported in a single case study 14 . However, common MSTN variants, such as p.Lys153Arg and p.Ala55Thr have shown inconsistent associations with muscle mass and performance across case-control studies 15-17 To validate the association between p.Ile225Thr in MSTN and lean mass, we tested it across other lean mass measurement modalities. In an independent subset of UKB participants with bioelectrical impedance analysis (BIA) measures, the variant was associated with greater leg fat-free mass (β = 0.11 SD [95% CI: 0.06, 0.16], P = 3.6 x 10 -5 , N = 363,056). Similarly, using high-resolution Magnetic Resonance Imaging (MRI) data from the UKB, the variant associated with higher total thigh muscle volume (β = 0.23 SD [95% CI: 0.10, 0.37], P = 7.4 × 10 -4 , N = 51,068). Furthermore, the variant was associated with higher serum creatinine levels, a biochemical marker of muscle mass (effect = 0.12 SD [95% CI: 0.10, 0.15], P = 9.9 x 10 -21 , N = 1,183,832) but not with serum cystatin C levels (effect = 0.02 SD [95% CI: -0.02, 0.07], P = 0.29, N = 456,678) indicating that the creatinine association is unlikely to reflect altered kidney function (Fig. 1) 18 . Without BMI adjustment, effect sizes for DXA, BIA, and MRI measures remained directionally consistent, but slightly reduced (Supplementary Fig. 8). Together, these findings support the hypothesis that the MSTN p.Ile225Thr variant contributes to increased muscle mass in humans. We evaluated the associations of MSTN p.Ile225Thr with a broad set of traits across available cohorts in the deCODE genetics phenotype database (Methods and Supplementary Table 2). We chose traits with reference to myostatin literature, including altered body size and composition, muscle function, and cardiometabolic health. Six associations (birthweight, height, grip strength, creatinine, hypertension, and frequency of stair climbing) remained associated with the variant after correction for multiple testing ( P threshold = 0.05/84 = 6.0 x 10 -4 ; Fig. 1 and 2). The association with increased birthweight (β = 0.13 SD [95% CI: 0.08, 0.17], P = 5.7 x 10 -7 , N = 374,725) is notable, as myostatin mutations associate with increased neonatal growth in animals 20,21 , but this has not been confirmed in humans. The association with increased grip strength (β = 0.09 SD [95% CI: 0.04, 0.13], P = 3.8 x 10 -4 , N = 424,576) aligns with findings of increased muscle mass and strength in Mstn knockout mouse models 22 , although therapeutic inhibition in humans has not resulted in improved strength 23,24 . The variant also associated with reduced risk of primary hypertension (OR = 0.92 [95% CI: 0.89, 0.96], P = 5.1 x 10 -5 , N cases/controls = 882,927/1,409,034), suggesting a cardioprotective mechanism (Fig. 2) 25 . No association was observed with cardiovascular risk traits, such as blood pressure (Supplementary Fig. 9). In rodent models, Mstn deletion has been associated with increased heart size and improved heart function 26-28 . However, we found nominally significant associations with increased risk of dilated cardiomyopathy (OR = 1.28 [95% CI: 1.05, 1.56], P = 0.017, N cases/controls = 7,419/1,583,702, Fig. 2, Supplementary Fig. 10) and non-ischemic heart failure (OR = 1.08 [95% CI: 1.00, 1.16], P=0.042, N cases/controls =80,704/1,563,175, Fig. 2). The potential adverse effect on the heart warrants cautious interpretation but underscore the importance of evaluating cardiovascular outcomes when modulating myostatin activity. Because the association of MSTN p.Ile225Thr with increased lean mass suggests loss of function, we investigated the effects of predicted loss-of-function (pLOF) MSTN variants on the traits explored using carriers in the UKB and NASHBio datasets (combined minor allele frequency (MAF) across datasets = 0.007%; Supplementary Tables 3 and 4). In gene-based meta-analysis burden testing (Fig. 1 29 , these pLOF variants collectively associate with greater lean mass in legs (β = 1.12 SD [95% CI: 0.25, 1.98], P = 0.012) and arms (β = 1.05 SD [95% CI: 0.18, 1.92], P = 0.018). Together, these findings suggest that p.Ile225Thr may act as a loss-of-function allele, reducing myostatin activity and thereby promoting muscle growth. To assess the regulatory consequences of the MSTN p.Ile225Thr variant, we tested its association with gene expression (eQTL), alternative splicing (sQTL), and circulating protein levels (pQTL). Analyses were conducted using mRNA sequencing data from blood (N = 17,848) and adipose tissue (N = 770) in Icelandic individuals, transcriptomic data from the GTEx project 10 and other published sources, and plasma protein measurements (pQTL) using the SomaScan (4,719 proteins, N = 35,559) and Olink (2,925 proteins, N = 47,150) panels 30 . Notably, the missense variant p.Ile225Thr is the sentinel cis-pQTL for MSTN , substantially increasing circulating levels of myostatin as measured by Olink (OID20115, β = 0.84 [95% CI: 0.70, 0.98], P = 7.6 × 10 -32 ; N = 46,446, Supplementary Fig. 11 and 12). In contrast, the variant showed no association with the SomaScan aptamer measurement of myostatin (SeqID.14583-49, P = 0.68). This discrepancy may reflect differences in epitope recognition between assays. Myostatin is synthesized as an inactive precursor protein and activated extracellularly by proteolysis of its propeptide. Because the bioactive myostatin domain is largely inhibited before cleavage 31 , we investigated whether these platforms captured total or bioactive myostatin levels. To address this, we quantified plasma myostatin levels in Icelandic individuals using two alternative immunoassays targeting total (R&D Systems) and bioactive (MSD) myostatin (Supplementary Note and Supplementary Fig. 13-18). The Olink measurements correlated more strongly with total myostatin than with bioactive myostatin (Pearson’s ρ = 0.73, P = 2.0 × 10 -43 and Pearson’s ρ = 0.30, P = 1.5 × 10 -6 respectively, N = 252) whereas the SomaScan measurement showed low correlation with either assay (Pearson’s ρ = 0.12, P = 2.1 × 10 -4 ; Pearson’s ρ = 0.023, P = 0.48; N = 930), indicating that the Olink assay primarily reflects total circulating myostatin. Given that the p.Ile225Thr variant increases lean mass, consistent with reduced myostatin activity, we explored whether carriers had an altered proportion of lower bioactive myostatin levels relative to total levels. Carriers had increased myostatin levels, both total (mean difference = 0.81, Wilcoxon signed-rank P = 1.6 × 10 -46 ) and bioactive (mean difference = 0.49, Wilcoxon signed-rank P = 0.0032) compared to controls (N matched pairs = 395). However, when bioactive myostatin was evaluated relative to total levels, carriers showed a relative reduction in bioactive myostatin (mean difference = -0.32, Wilcoxon signed-rank P = 1.6 × 10 -7 ; ANCOVA mature myostatin level estimate for carriers, -0.57 [95% CI: -1.1, -0.079], P = 0.023, Supplementary Fig. 15, 19 and 20). These results indicate that p.Ile225Thr elevates total circulating myostatin while reducing the relative amount of the bioactive form. We speculate that the substitution of Ile225 with threonine stabilizes the inhibitory complex by forming a stronger hydrogen bond with Cys138 in the propeptide 32,33 without disrupting the protein structure 34 . Such stabilization could reduce proteolytic cleavage, thereby reducing the levels of bioactive myostatin relative to total circulating levels. Among individuals of African ancestry, we observed that the missense variant p.Ala55Thr (MAF= 14%) associated with substantially lower circulating myostatin levels measured with Olink (β = 0.40 SD [95% CI: -0.51, -0.30], P = 9.6 × 10 -14 ; N = 1,505, cohort=UKB) and reduced serum creatinine (β = -0.03 SD [95% CI: -0.04, -0.02], P = 2.2 × 10 -8 ; N = 144,075, cohorts: UKB, NASHBio, MVP; Supplementary Fig. 21 and 22). These results support a role for MSTN in regulating circulating myostatin and muscle mass across human ancestries. To extend these findings beyond myostatin, we also assessed functional annotations of all 63 lean-mass-associated loci. Specifically, we evaluated whether the sentinel variant at each locus was a top coding variant or QTL ir in linkage disequilibrium (LD, r 2 > 0.8) with such variants. Of these, 37 were in LD with coding variants, cis-eQTLs or pQTLs (Fig. 3, Supplementary Tables 1 and 5). Among the lean-mass-associated loci was a common variant upstream of the myostatin receptor gene, ACVR2B (rs1870915-G, MAFin Iceland = 41%, MAF in the UK =46%; Table 1). This variant was associated with lower DXA-derived total lean mass (β = -0.04 SD [95% CI: -0.05, -0.03], P = 3.7 × 10 -13 , N = 78,932) as well as lower circulating myostatin levels (β = –0.04 SD [95% CI: -0.05, -0.03], P = 2.5 × 10 -9 , N = 46,664). The variant was in LD (r 2 > 0.8) with eQTLs for ACVR2B expression B cells, thyroid and lung tissue (Supplementary Table 1, Fig. 3). Together with the MSTN p.Ile225Thr findings, these results provide evidence that both the ligand (myostatin) and its receptor contribute to muscle mass regulation through the MSTN–ACVR2B pathway in humans. Of the 63 sentinel lean mass variants, only nine are in linkage disequilibrium (LD, r 2 >0.01) with previously reported signals (Supplementary Table 1) 35-37 . The missense variant p.Met87Thr in CASQ1 , associated with reduced arm lean mass (β = -0.08 SD [95% CI: -0.11, -0.05], P = 2.36 × 10 -8 ; MAF ICE = 4.5%, MAF UKB = 3.2%). CASQ1 encodes a calcium-buffering protein that regulates Ca 2+ release from the sarcoplasmic reticulum in fast-twitch skeletal fibers 38 . A 3′ UTR variant in TRIM13 was associated with increased lean mass (β = 0.12 SD [95% CI: 0.09, 0.16], P = 8.8 × 10 -13 ; MAF ICE = 1.2%, MAF UKB = 1.8%). TRIM13 stabilizes p53, whichpromotes muscle regeneration in aged mice 39,40 .Numerous associations were observed in genes involved in myofiber physiology, such as in the triad junction ( CASQ1, SPEG, CACNA1S ), the sarcomere ( TTN, MYPN ), and in acetylcholine handling ( SYN2, SLC44A4 ). Overall, the results from the GWAS meta-analyses extend biological insights into lean mass regulation beyond MSTN by highlighting additional genetic contributors to skeletal muscle function. In summary, we identified a rare MSTN variant (p.Ile225Thr) strongly associated with DXA-derived lean mass, with validation across BIA- and MRI-derived measures. The variant increases total circulating myostatin levels, while reducing the relative abundance of bioactive myostatin, supporting a loss-of-function mechanism. Beyond muscle mass, it is associated with increased strength, intrauterine growth, and reduced risk of primary hypertension. This work provides insight into the potential consequences of long-term myostatin inhibition. While partial inhibition may be beneficial, suggestive associations with non-ischemic heart failure and dilated cardiomyopathy underscore the importance of evaluating cardiovascular safety in therapeutic settings. Methods Study design. The study is a hypothesis-free discovery genome-wide association meta-analysis of lean mass measured with dual-energy X-ray absorptiometry, utilizing data from Iceland and the UKB. Multi-omics annotation of the lean mass variants was performed using Icelandic and UKB data on protein levels and gene expression, in addition to publicly available eQTLs. After identifying a strong lean mass variant in MSTN , p.Ile225Thr, found in participants of European ancestry, a clinical phenome lookup was performed using meta-analyzed data from multiple cohorts. Rare-variant burden testing for MSTN was conducted using information on rare loss-of-function variants in the Icelandic dataset, UKB and U.S. cohorts. Ethics. This study was conducted in accordance with the principles of the Declaration of Helsinki. Dataset-specific ethics declarations are provided below. The Icelandic study population. The Icelandic deCODE genetics study is built on phenotypic and biological data obtained from more than 170,000 volunteers participating in multiple research studies conducted in Iceland. DXA measurements of 20,614 Icelanders were obtained from the Landspitali National University Hospital electronic health records (DEXA, Hologic QDR4500/A) and the deCODE study Heilsurannsókn (DEXA, Hologic S/N200547) from the years 1999 to 2017. Participants who donated biological samples gave written informed consent and the National Bioethics Committee approved the study (VSN-15-057 and VSN-15-023) which was conducted in agreement with conditions issued by the Data Protection Authority of Iceland. Personal identities were encrypted by a third-party system (Identity Protection System), approved and monitored by the Data Protection Authority 41 . The UK Biobank study population . UK Biobank (UKB) is a large population-based prospective study comprising approximately 500,000 participants recruited between 2006 and 2010 at 22 assessment centres across the United Kingdom (UK). The participants were aged 40-69 years at recruitment and represent diverse socioeconomic and ethnic backgrounds. UKB collects extensive information on participants, including questionnaire data, physical measurements, and longitudinal follow-up for a wide range of health-related outcomes, with the aim of conducting health-related research for the benefit of the public 42 . Participants provided written informed consent, and the UKB scientific protocol and operational procedures were approved by the North West Research Ethics Committee (REC Reference Number: 06/MRE08/65). DXA whole-body scans from 58,318 UKB participants and MRI abdominal scans (Siemens 1.5T MAGNETOM Aera) from 51,109 UK were obtained from the UKB. All participants included in the meta-analysis were of genetically inferred European ancestry. This research was conducted under the UKB application number 56270. Genotyping . A total of 63,118 Icelanders underwent whole-genome sequencing (WGS) with coverage of over 20x using Illumina GAII, HiSeq, HiSeqX and NovaSeq sequencing instruments, resulting in the identification of approximately 106 million sequence variants 43 . Genotypes were determined using joint calling with the GraphTyper v2.7.1 44 . WGS identified variants were imputed into 173,025 chip-genotyped individuals using long-range phasing. Chip genotyping was performed using Illumina OmniExpress and HumanHap arrays 45,46 . Furthermore, genotype probabilities for first- and second-degree relatives of chip-typed individuals were inferred using genealogical records assembled by deCODE Genetics. We used imputed UKB dataset based on a subset of participants with WGS data (N=150,119) 47 . WGS was performed jointly by deCODE (N=90,667) and the Sanger Institute (59,452) using Illumina NovaSeq sequencing machines to an average coverage of 32.5× (at least 23.5× per individual). WGS identified variants were imputed into the long range phased chip data (N = 431,079). Genotypes were determined using joint calling with the GraphTyper v2.7.1 44 . The first 50,000 participants were chip genotyped using the custom Affimetrix UK BiLEVE Axiom array 48 and the remaining participants using the Affimetrix UK Biobank Axiom array 49 , with 95% of variants shared between arrays. As a part of the discovery DXA lean mass meta-analyses, 431,079 imputed UKB participants of European ancestry were included. For refined assessment of the MSTN p.Ile225Thr missense variant in other phenotypes, we use WGS dataset of 429,193 participants of European ancestry when available 50 . Calculations for p.Ala55Thr (rs1805085) were based on 9,229 UKB participants of African ancestry with WGS data. UK Biobank imaging processing . For UKB imaging data, only 48-60% of DXA scans and 17-79% of MRI scans contained instrument-derived readouts (Supplementary Table 6). Missing image-derived phenotypes (IDPs) were imputed using supervised deep-learning regression models trained directly on raw imaging data 51 . For DXA, paired bone and fat images were pre-processed to remove background signals. The two modalities were then combined into a single three-channel image by assigning the bone map to the red channel and the fat map to the green channel, yielding a standardized composite representation suitable for 2D convolutional models. Abdominal MRI pre-processing followed the previously described deep-learning segmentation and phenotyping pipeline 52 . To generate a computationally efficient 2D representation of volumetric fat- and water-separated Dixon images, mean-intensity projections were computed in both the coronal and sagittal planes. The resulting four projection images (fat-coronal, fat-sagittal, water-coronal, water-sagittal) were merged horizontally and encoded into a single RGB image by assigning the fat projection to the red channel and the water projection to the green channel. This approach preserves major body-composition features while reducing input dimensionality. Predicted IDPs were generated by fitting deep residual convolutional neural networks using these standardized DXA and MRI images as inputs and the corresponding measured IDPs as supervised targets. Both modalities used the same architecture, consisting of stacked 2D residual convolutional blocks followed by two fully connected projection layers. Labeled datasets were randomly partitioned into training and validation subsets using an 80/20 split, and model performance was monitored on the held-out validation set prior to inference in participants with missing IDPs. Genetic association testing. Linear mixed model implemented in BOLT-LMM 53 was applied to test the relationship between sequence variants and quantitative traits. Genetic associations were tested under an additive model, using the expected allele count from imputation as the explanatory variable and normalized phenotypes as response. Prior to the association analyses, phenotypes were adjusted for age using regression, performed separately in males and females. Additional covariates included county of birth for Icelandic data and 20 principal components (PCs) for the UKB data. Instrument-based body composition measurements (DXA, BIA, MRI), grip strength, hip circumference, waist circumference and waist-to-hip ratio were also adjusted for BMI and height in both cohorts consistent with previous work on waist-to-hip ratio adjusted for BMI genetics 19 . Regression residuals were transformed into a standard normal distribution using a rank-based inverse normal transformation. Quantitative measurements were assumed to follow a normal distribution with a mean depending linearly on the expected allele count at each variant and a variance-covariance matrix proportional to the kinship matrix 54 . We used logistic regression, assuming the additive model, to assess the relationship between sequence variants and case-control phenotypes. The binary phenotype was treated as a response, and the expected genotype counts from imputation as covariates. P-values were calculated using a likelihood ratio test. In the Icelandic cohort, thecovariates included sex, county of birth, current age or age at death (first- and second-order terms included), blood sample availability for the individual and an indicator function for the overlap of the lifetime of the individual with the time span of the phenotype collection. In the UKB cohort,covariates included, sex, age and 20 PCs. LD score regression was applied, using the intercept to adjust test statistics for inflation due to cryptic relatedness and population stratification. Meta-analysis. A fixed-effects inverse-variance method was used to combine summary statistics across cohorts based on effect estimates and standard errors. Associations with phenotypes were considered significant based on weighted genome-wide significance threshold determined by variant annotation 8 ; P ≤ 1.3 × 10 -7 for high-impact variants (including stop-gained and loss, frameshift, splice acceptor or donor and initiator codon variants), P ≤ 2.6 × 10 -8 for missense, splice-region variants and in-frame-indels, P ≤ 2.38 × 10 -9 for low-impact variants (including synonymous, 3′ and 5′ UTR, and upstream and downstream variants), P ≤ 4.00 × 10 -10 for deep intronic and intergenic variants, and P ≤ 1.19 × 10 -9 for those in DNase I hypersensitivity sites (DHS). Sentinel variants were identified by selecting, for each megabase (MB), the variant with the lowest weighted genome-wide significant P-value in any of the four DXA lean mass meta-analyses and with imputation information score > 0.8. Independent signals were defined by clumping the variants into linkage disequilibrium (LD) blocks (r 2 > 0.01) and selecting the variant with the lowest weighted P-value per block. LD clumping was performed using the phased Icelandic genotypes and subsequently confirmed using the UK Biobank genotypes, yielding the same set of sentinel loci. We performed conditional analysis across the MSTN locus, confirming that p.Ile225Thr represents the strongest independent signal for lean mass in the region. Heritability. SNP-based heritability (observed scale) was estimated using LD score regression (Supplementary Table 7). These analyses included approximately 1.2 million well-imputed variants, with LD information obtained from precomputed European populations reference LD scores (downloaded from: https://data.broadinstitute.org/alkesgroup/LDSCORE/ eur_w_ld_chr.tar.bz2). Replication analysis. To assess whether the DXA-derived phenotypes captured the same lean mass traits as those derived from UK Biobank bioimpedance analysis (BIA) in the largest published GWAS of lean mass 35 , we performed replication analysis using a proxy DXA phenotype defined as appendicular lean mass (sum of arm and leg lean mass) adjusted for BMI. Of 970 reported appendicular lean-mass associated variants, 234 replicated (consistent direction of effect and P < 0.05), indicating incomplete concordance between BIA- and DXA-derived phenotypes (Supplementary Table 8). Gene burden analysis . For the MSTN gene, we identified 34 carriers of 28 predicted loss-of-function variants (pLOF) using the Ensembl Variant Effect Predictor (VEP). In the model, we included rare variants with minor allele frequency < 0.01% in UK Biobank and NashBio datasets (Supplementary Tables 3 and 4). Variants were analyzed under a burden framework, if when grouped, they exert a shared phenotypic effect. Genotypes were coded as 1 for individuals carrying at least one MSTN pLOF variant and 0 otherwise. Genetic associations were tested under an additive model, with MSTN pLOF carrier status as the explanatory variable and normalized phenotypes as the response. Association testing was performed using a linear mixed model implemented in BOLT-LMM. The number of variant carriers contributing to each phenotype-specific analysis varied depending on phenotype availability (Fig. 1). For example, although 34 pLOF carriers were identified overall, only 5 had dual-energy X-ray absorptiometry (DXA) measurements; therefore, only these five carriers contributed to the lean and fat mass burden analysis (Fig. 1). Myostatin immunoassay measurements . The MSTN p.Ile225Thr variant was associated with increased myostatin levels on the Olink platform (OID20115), whereas no association was observed with the GDF8 specific SomaScan probe SeqId.14583-49 ( P = 0.68). To validate and interpret these findings, circulating myostatin levels were quantified using two independent ELISA platforms: R&D Systems Quantikine GDF-8 ELISA (RD) and Meso Scale Discovery R-PLEX Human GDF-8 assay (MSD). RD measures total myostatin, as samples were subjected to an activation step involving acid denaturation (1N HCl) followed by neutralization (NaOH/Hepes), thereby disrupting protein complexes and releasing myostatin. Subsequently, GDF-8 was detected using a monoclonal antibody specific to mature GDF-8. MSD measures approximately free (active) myostatin and does not include a denaturation step. Samples were drawn from 930 Icelandic participants with available SomaScan and/or Olink measurements, including a subset of 398 MSTN p.Ile225Thr heterozygous carriers with sex- and age-matched (within 5 years) controls. Assays were conducted blinded to genotype. RD samples were generally diluted 1:12; a subset of plates was run at 1:4 dilution or with incorrect buffer, and these deviations were recorded and handled in sensitivity analyses. MSD samples were undiluted due to low circulating concentrations. For MSD measurements, a proportion of samples fell below the formal detection range. These values nevertheless had estimated concentrations that fit the calibration curve, consistent with standard handling in large-scale proteomic pipelines. Values below the fit curve were set to 0.03 pg/mL, corresponding to the lowest reliably observed concentration, and retained in analyses. Sensitivity analyses excluding these samples yielded consistent results. Protein concentrations were log2-transformed prior to analysis. To account for technical and biological covariates, concentrations were adjusted using linear models of the form: log2(protein) ~ age at sampling + sex + plate. Residuals from these models were used for correlation analyses between platforms. Pearson correlations were calculated on adjusted residuals, and Spearman correlations were additionally computed without adjustment. Carrier–control comparisons were performed using matched case–control pairs where available. Differences in protein concentrations and derived ratios were assessed using the Wilcoxon signed-rank test. For analyses comparing free-to-total myostatin, ratios were calculated after covariate adjustment of MSD and RD measurements separately. To assess whether differences in free myostatin were independent of total myostatin levels, linear regression models were fitted: log2(MSD) ~ log2(RD) + genotype + age + sex + plate Multiple sensitivity analyses were conducted, excluding plates with documented buffer or dilution errors, samples measured on alternative instruments, and samples below detection limits. Results were consistent across all sensitivity analyses. Multi-omics data. Regulatory consequences of lean mass variants were evaluated by assessing whether each sentinel variant was in linkage disequilibrium (r 2 ≥ 0.8) with variants associated with gene expression (eQTL), alternative splicing (sQTL), or protein levels (pQTL). RNA sequencing data were obtained from whole blood (N = 17,848) and adipose tissue (N = 770) samples from Icelandic participants. In addition, publicly available eQTL information from the GTEx Portal and other published sources 55-66 was used (Supplementary Table 5). In the Icelandic dataset, gene expression levels were computed based on personalized transcript abundances 67 . Association between genetic variants and gene expression were estimated using linear regression models assuming additive genetic effect, with normally quantile-transformed gene expression estimates as the response. Models were adjusted for sequencing artefacts, demography variables, blood cell composition and principal components (PCs). Gene expression PCs were computed per chromosome using a leave-one-chromosome-out approach. All variants within 1Mb of each gene were tested. Top independent eQTL signals were identified using iterative conditional association analysis, where in each iteration the genotype of the variant with lowest P-value was included as an additional covariate. pQTLs where obtained from Icelandic Somascan measurements (4,719 proteins, N = 35,559) and from UK Biobank Olink data (1,450 proteins, N = 47,150), as previously described 30 . Genes were prioritized if the sentinel variant was associated with (i) a coding variant in a single gene with no additional cis-QTL evidence (cis-eQTL, cis-sQTL, or cis-pQTL) at the locus, or (ii) with a coding variant in a gene supporting cis-QTL evidence for the same gene (Fig. 3). Data from the Genotype-Tissue Expression (GTEx) Project were obtained from the GTEx Portal on 20 November 2024. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Additional datasets. MSTN p.Ile225Thr was tested for association with additional phenotypes in the deCODE genetics phenotype database, which contains extensive medical information on a wide range of diseases and quantitative traits meta-analyzed across multiple cohorts. Quantitative traits were analyzed using the same method as for the DXA measurements. For blood-based traits, models were additionally adjusted for sample availability and for an indicator variable reflecting overlap of individual’s lifetime and the time span of phenotype collection. Binary traits were analyzed using logistic regression, including the same covariates as described above. We visually inspected regional association plots for lean mass and for any significant associations of p.Ile225Thr with other phenotypes to ensure that the signal at p.Ile225Thr was not driven by a stronger neighboring variant for the respective phenotype. Detailed information on phenotype definitions is provided in Supplementary Table 2. Shared haplotype reference panel for the Copenhagen Hospital Biobank/ The Danish Blood Donor Study (CHB/DBDS) -Intermountain (INTMT) and the Hordaland health study (HUSK) cohorts was constructed using a 50,839 jointly whole genome-sequenced samples (average coverage >20x) from Denmark, North America, Iran, the Netherlands, Norway, and Sweden. Joint variant calling was performed using Graphtyper (version 2.7.5) 44 . Whole genome sequencing, chip-genotyping, and the subsequent imputation were performed at deCODE genetics. Danish study population . The Danish data were obtained from the Copenhagen Hospital Biobank - Oral-Cardiometabolic Health (CHB-OCMS) 68 and The Danish Blood Donor Study (DBDS) 69 . The study was approved by the Zealand Region Committee on Health Research Ethics and the Capital Region Data Protection Office (SJ-989 and P-2022-913). All participants were informed of the option to opt out and could withdraw from the study at any time. Genotypes were imputed using the shared haplotype reference panel that includes whole genome sequencing data from 10,828 Danish individuals and was applied to 464,016 DBDS and CHB-OCMS participants genotyped using the Illumina Global Screening Array. For CHB-OCMS and DBDS analyses, additional covariates included 20 principal components and blood sample availability. All participants were of European ancestry. Intermountain study population (US) is a collaboration of 33 hospitals and 385 clinics in Utah and surrounding states and deCODE Genetics aiming to analyze the genomes of 500,000 participants. The dataset was obtained from HerediGene, a general population study, and the INSPIRE Registry Study, which contains data on volunteer subjects, both healthy and diagnosed with a variety of medical conditions. The studies have been approved by the Intermountain Healthcare Institutional Review Board (IRB), and all participants have provided written informed consent. Eligibility criteria for both studies include being 18 years of age or older. A total of 23,551 US participants were whole genome sequenced and included in the shared haplotype reference panel. This panel was imputed into additional 138,006 US participants genotyped with the Illumina Global Screening Array and OmniExpress. The Hordaland Health Study (HUSK) is a community-based study in Western Norway conducted as a collaboration between the University of Bergen, the Norwegian Health Screening Service (now part of the National Institute of Public Health) and the Municipal Health Service in Hordaland (https://husk-en.w.uib.no/) 70 . Approximately 36,000 residents of Hordaland County participated in the studies, with about 18,000 taking part in 1992/93 and 26,000 in 1997/99. Approximately 7,000 of those who participated in the 1992/93 survey also participated in 1997/99. Data from the HUSK are available for researchers after ethical approval and there are currently several active projects. The current study was done as part of the HUSKment project which is approved by the Regional Committee for Medical Research Ethics Western Norway, reference 2018/915. The shared haplotype reference panel was imputed into 35,146 HUSK participants genotyped with the Illumina Global Screening Array and OmniExpress. Nashville Biosciences (NashBio) study population . The Alliance for Genomic Discovery (AGD) is a collaborative initiative involving Amgen deCODE, Nashville Biosciences, Illumina and seven additional pharmaceutical companies, with the aim to sequence 250,000 samples from the BioVU® biobank. BioVU® is a biobank of de-identified DNA samples linked to a longitudinal electronic health record data from the Synthetic Derivative database, created and maintained by Vanderbilt University Medical Center (VUMC, Tennessee, USA) 71,72 . BioVU® includes clinical information from more than 3.6 million patients receiving care at VUMC since 2001. The use of BioVU® data is classified as non-human subjects research by the VUMC Institutional Review Board (IRB), and no study-specific informed consent is required. The overall biobanking program is reviewed annually by the IRB, and individual studies using BioVU® data are submitted for confirmation of non-human subjects’ status. Germline DNA samples from more than 307,000 patients have been collected, of which 250,000 samples were selected for whole genome sequencing. In this study, a total of 124,791 participants were whole genome sequenced with Illumina NovaSeq instrument at deCODE genetics in Iceland. The average genome-wide sequencing coverage was 33.9x (sd 3.5, min:28.7x, max:66.4x). Estonian Biobank (EstBB) is a population-based cohort comprising approximately 210,000 participants with linked genomic and health-related data 73 . Participants provided written informed consent at recruitment for linkage of their electronic health records, enabling the longitudinal follow-up. Health data includes diagnoses coded according to ICD-10, with records obtained from the National Health Insurance Fund Treatment Bills (since 2004), Tartu University Hospital (since 2008), and North Estonia Medical Center (since 2005). The activities of the EstBB are regulated by the Human Genes Research Act, adopted in 2000 specifically for the operations of the EstBB. Individual-level data analysis was conducted under the ethical approval 1.1-12/624 from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data accessed under an approved release application 6-7/GI/1564 from the Estonian Biobank. This work was supported by the Estonian Research Council (grant PUT PRG1911), the Ministry of Education and Research Centres of Excellence grant TK214, and the Estonian Research Council-funded Estonian Center of Genomics/Roadmap II (project number TT17). Additional funding was provided by the European Union’s Horizon Europe research and innovation programme (grant agreement No 101060011). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Data analysis was carried out in part in the High-Performance Computing Center of University of Tartu. The Estonian Biobank provided information on carrier counts for MSTN p.Ile225Thr and on cardiomyopathy phenotypes defined by the Code Consensus (https://code-consensus.netlify.app/). FinnGen is a large public-private genomic research project that collects and analyses genome and health data from 500,000 Finnish biobank donors. We used publicly available GWAS summary statistics from FinnGen, including disease endpoints and quantitative traits, with primary analysis based on Data Release 12 (2024), comprising 500,348 individuals. FinnGen is coordinated by the University of Helsinki and integrates data from Finnish biobanks and national health registries. Detailed description of cohort composition, genotyping, imputation and phenotype definition is provided elsewhere 74 and on the FinnGen documentation website. FinnGen provided information on carrier counts for MSTN p.Ile225Thr and on heart failure phenotypes. The FinnGen study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS/990/2017). The study was conducted under permits from Finnish national health and population data authorities (e.g. THL/2031/6.02.00/2017; Findata THL/2364/14.02/2020). Biobank samples and data were accessed under approved Finnish biobank access decisions (e.g. THL Biobank BB2017_55; Helsinki Biobank HUS/359/2017) and analyzed using FinnGen Data Freeze 12. The Million Veteran Program study population . The Million Veteran Program (MVP) is a large national biobank established by the U.S. Department of Veterans Affairs (VA) to study the genetic basis of health and disease by linking genomic data with longitudinal electronic health records. We used publicly available GWAS summary statistics generated by MVP for cardiometabolic and related phenotypes, as released by the consortium and described previously 75 . The MVP study was approved by the VA Central Institutional Review Board, and all participants provided written informed consent for genetic research. MVP summary statistics were used for secondary analyses only, and no individual-level MVP data were accessed in this study. Genetic Factors for Osteoporosis Consortium (GEFOS). GEFOS is a large international collaboration focusing on genetic factors influencing osteoporosis 76 . GEFOS is a European Union Seventh Framework Package funded project, registered under grant agreement number: FP7-HEALTH- F2-2008-201865-GEFOS. Declarations Code availability We used R version 4.5.2 for the analysis and visualizations using the packages tidyverse (v1.3.0), ggsci (v2.9), ggrepel (v0.8.2), patchwork (v1.3.0), forestploter (v1.1.3), data.table(v1.17.0), gridExtra(v2.3), and gtable(v0.3.6). Variant annotation and downstream analysis utilized publicly available software including Variant Effect Predictor (VEP), Graphtyper(v2), IMPUTE2(v.2.3.1), BOLT-LMM(v2.1.), LeafCutter(v1), Kallisto(v0.46), and gorpipe (v5.24.8). URLs for all software tools are provided below: VEP: https://www.ensembl.org/info/docs/tools/vep/index.html; Graphtyper v.2: https://github.com/DecodeGenetics/graphtyper; IMPUTE2 v.2.3.1: https://mathgen.stats.ox.ac.uk/impute/impute_v2.html; BOLT-LMM v.2.1:, http://www.hsph.harvard.edu/alkes-price/software/; Ensembl v.87: https://www.ensembl.org/index.html; LeafCutter v.1: https://github.com/davidaknowles/leafcutter; kallisto v0.46: https://github.com/pachterlab/kallisto. gorpipe v5.24.8: https://github.com/gorpipe/gor-test-data. Acknowledgements We want to acknowledge the participants and investigators of the FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., Alnylam Pharmaceuticals, Inc., AstraZeneca UK Ltd, Bayer AG, Biogen MA Inc., Boehringer Ingelheim International GmbH, Bristol Myers Squibb Inc. (and Celgene Corporation & Celgene International II Sàrl), Genentech Inc., GlaxoSmithKline Intellectual Property Development Ltd., Johnson&Johnson Innovative Medicine Inc., Maze Therapeutics Inc., Merck Sharp & Dohme LCC, Novartis AG, Pfizer Inc. and Sanofi US Services Inc. Following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank (www.auria.fi/biopankki), THL Biobank (www.thl.fi/biobank), Helsinki Biobank (www.helsinginbiopankki.fi), Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx), Finnish Clinical Biobank Tampere (www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (www.ita-suomenbiopankki.fi/en), Central Finland Biobank (www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (www.veripalvelu.fi/verenluovutus/biopankkitoiminta), Terveystalo Biobank (www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/) and Arctic Biobank (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/northern-finland-birth-cohorts-and-arctic-biobank). All Finnish Biobanks are members of BBMRI.fi infrastructure (https://www.bbmri-eric.eu/national-nodes/finland/). Finnish Biobank Cooperative -FINBB (https://finbb.fi/) is the coordinator of BBMRI-ERIC operations in Finland. The Finnish biobank data can be accessed through the Fingenious ® services (https://site.fingenious.fi/en/) managed by FINBB. Authors contribution Adalheidur E. Larusdottir, Unnur D. Teitsdóttir, and Daniel F. Gudbjartsson designed the study, Frosti Palsson, Anna M. Kristinsdóttir, Grimur H. Eldjarn, Gisli H. Halldorsson, Gudmar Thorleifsson, Vinicius Tragante, Lilja Stefansdottir, Egil Ferkingstad, Felix Vaura, Fanny-Dhelia Pajuste, Samuli Ripatti, Aarno Palotie, Triin Laisk, and Reedik Mägi contributed to the acquisition and analysis of data. Hildur M. Aegisdottir, Unnur Styrkarsdottir, Mariana Bustamante, Thorunn A. Olafsdottir, Asmundur Oddsson, Audunn S. Snaebjarnarson, Erna Valdis Ivarsdottir, and Gudmar Thorleifsson defined phenotypes. R Thomas Lumbers, Sonia Shah, Nick Sunderland, and Jiayue-Clara Jiang provided code consensus for heart failure subtypes, Sigurjon Axel Gudjonsson, and Arnar K.S. Sandholt contributed to bioinformatics analysis. Kirk Knowlton and Lincoln Nadauld contributed to design, data collection and analyses of the Intermountain study. Ole Birger Vesterager Pedersen, Erik Sørensen, Sisse Rye Ostrowski, Henning Bundgaard, Johan Skov Bundgaard, Christian Erikstrup, Christina Mikkelsen, Mie Topholm Bruun, Bitten Aagaard Jensen, and Henrik Ullum contributed to design, data collection, and data analyses of the The DBDS Genetic Consortium. Jannicke Igland, Ole Andreassen and Jan Haavik contributed to design, data collection, and data analyses of the HUSK study. Adalheidur E. Larusdottir and Unnur D. Teitsdóttir analyzed the data, interpreted results and drafted the manuscript with input and supervision from Daniel F. Gudbjartsson, Anna Helgadottir, Rosa B. Thorolfsdottir, Valgerdur Steinthorsdottir, Thorhildur Olafsdottir, Gudmundur Einarsson, Gardar Sveinbjornsson, Hilma Holm, Gudmundur L. Norddahl, Ragnar G. Bjarnason, Patrick Sulem, and Gudmundur Thorgeirsson. All authors reviewed and contributed to the final version of the manuscript. References Schnyder, S. & Handschin, C. Skeletal muscle as an endocrine organ: PGC-1α, myokines and exercise. Bone 80 , 115-125 (2015). Mitchell, W.K. et al. Sarcopenia, dynapenia, and the impact of advancing age on human skeletal muscle size and strength; a quantitative review. in Frontiers in Physiology Vol. 3 JUL (2012). Chaston, T.B., Dixon, J.B. & O'Brien, P.E. Changes in fat-free mass during significant weight loss: A systematic review. in International Journal of Obesity Vol. 31 (2007). Jastreboff, A.M. et al. Tirzepatide Once Weekly for the Treatment of Obesity. New England Journal of Medicine 387 (2022). Wilding, J.P.H. et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. New England Journal of Medicine 384 (2021). Lee, S.J. Targeting the myostatin signaling pathway to treat muscle loss and metabolic dysfunction. J Clin Invest 131 (2021). Cruz-Jentoft, A.J. et al. Sarcopenia: Revised European consensus on definition and diagnosis. in Age and Ageing Vol. 48 (2019). Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat Genet 48 , 314-317 (2016). Zhu, X., Topouzis, S., Liang, L.F. & Stotish, R.L. Myostatin signaling through Smad2, Smad3 and Smad4 is regulated by the inhibitory Smad7 by a negative feedback mechanism. Cytokine 26 (2004). Consortium, G.T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 , 1318-1330 (2020). Nielsen, T.L., Vissing, J. & Krag, T.O. Antimyostatin Treatment in Health and Disease: The Story of Great Expectations and Limited Success. Cells 10 (2021). McPherron, A.C., Lawler, A.M. & Lee, S.J. Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. Nature 387 , 83-90 (1997). Whittemore, L.A. et al. Inhibition of myostatin in adult mice increases skeletal muscle mass and strength. Biochem Biophys Res Commun 300 , 965-71 (2003). Schuelke, M. et al. Myostatin Mutation Associated with Gross Muscle Hypertrophy in a Child. New England Journal of Medicine 350 (2004). Santiago, C. et al. The K153R polymorphism in the myostatin gene and muscle power phenotypes in young, non-athletic men. PLoS One 6 , e16323 (2011). Gonzalez-Freire, M. et al. The K153R variant in the myostatin gene and sarcopenia at the end of the human lifespan. Age (Dordr) 32 , 405-9 (2010). Li, X. et al. The A55T and K153R polymorphisms of MSTN gene are associated with the strength training-induced muscle hypertrophy among Han Chinese men. J Sports Sci 32 , 883-91 (2014). Baxmann, A.C. et al. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol 3 , 348-54 (2008). Pulit, S.L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 28 , 166-174 (2019). Ryan, C.A., Purfield, D.C., Naderi, S. & Berry, D.P. Associations between polymorphisms in the myostatin gene with calving difficulty and carcass merit in cattle. Journal of Animal Science 101 (2023). Osman, N.M., Shafey, H.I., Abdelhafez, M.A., Sallam, A.M. & Mahrous, K.F. Genetic variations in the Myostatin gene affecting growth traits in sheep. Vet World 14 , 475-482 (2021). Camporez, J.P. et al. Anti-myostatin antibody increases muscle mass and strength and improves insulin sensitivity in old mice. Proc Natl Acad Sci U S A 113 , 2212-7 (2016). Becker, C. et al. Myostatin antibody (LY2495655) in older weak fallers: a proof-of-concept, randomised, phase 2 trial. Lancet Diabetes Endocrinol 3 , 948-57 (2015). Muntoni, F. et al. The Clinical Development of Taldefgrobep Alfa: An Anti-Myostatin Adnectin for the Treatment of Duchenne Muscular Dystrophy. Neurol Ther 13 , 183-219 (2024). Shantsila, E., Shantsila, A., Gao, Z., Zheng, Y. & Lip, G.Y.H. High skeletal muscle mass is related to higher blood pressure and left ventricular mass irrespective of adiposity. European Journal of Preventive Cardiology 32 (2025). Rodgers, B.D. et al. Myostatin represses physiological hypertrophy of the heart and excitation-contraction coupling. J Physiol 587 , 4873-86 (2009). Butcher, J.T. et al. Effect of myostatin deletion on cardiac and microvascular function. Physiol Rep 5 (2017). Jackson, M.F., Li, N. & Rodgers, B.D. Myostatin regulates tissue potency and cardiac calcium-handling proteins. Endocrinology 155 , 1771-85 (2014). Dhindsa, R.S. et al. Rare variant associations with plasma protein levels in the UK Biobank. Nature 622 , 339-347 (2023). Eldjarn, G.H. et al. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 622 , 348-358 (2023). Hill, J.J. et al. The myostatin propeptide and the follistatin-related gene are inhibitory binding proteins of myostatin in normal serum. J Biol Chem 277 , 40735-41 (2002). Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381 , eadg7492 (2023). Mazmanian, K., Sargsyan, K., Grauffel, C., Dudev, T. & Lim, C. Preferred Hydrogen-Bonding Partners of Cysteine: Implications for Regulating Cys Functions. J Phys Chem B 120 , 10288-10296 (2016). Cotton, T.R. et al. Structure of the human myostatin precursor and determinants of growth factor latency. EMBO J 37 , 367-383 (2018). Pei, Y.F. et al. The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study. Communications Biology 3 (2020). Zillikens, M.C. et al. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nature Communications 8 , 80 (2017). Hernandez Cordero, A.I. et al. Genome-wide Associations Reveal Human-Mouse Genetic Convergence and Modifiers of Myogenesis, CPNE1 and STC2. The American Journal of Human Genetics 105 , 1222-1236 (2019). Wang, S. et al. Crystal structure of calsequestrin from rabbit skeletal muscle sarcoplasmic reticulum. Nat Struct Biol 5 , 476-83 (1998). Nolt, G.L. et al. Inhibition of p53-MDM2 binding reduces senescent cell abundance and improves the adaptive responses of skeletal muscle from aged mice. GeroScience 46 , 2153-2176 (2024). Joo, H.M. et al. Ret finger protein 2 enhances ionizing radiation-induced apoptosis via degradation of AKT and MDM2. European Journal of Cell Biology 90 , 420-431 (2011). Gulcher, J.R., Kristjansson, K., Gudbjartsson, H. & Stefansson, K. Protection of privacy by third-party encryption in genetic research in Iceland. Eur J Hum Genet 8 , 739-42 (2000). Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12 , e1001779 (2015). Palsson, G. et al. Complete human recombination maps. Nature 639 , 700-707 (2025). Eggertsson, H.P. et al. Graphtyper enables population-scale genotyping using pangenome graphs. Nat Genet 49 , 1654-1660 (2017). Gudbjartsson, D.F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat Genet 47 , 435-44 (2015). Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat Genet 40 , 1068-75 (2008). Halldorsson, B.V. et al. The sequences of 150,119 genomes in the UK Biobank. Nature 607 , 732-740 (2022). Wain, L.V. et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir Med 3 , 769-81 (2015). Welsh, S., Peakman, T., Sheard, S. & Almond, R. Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort. BMC Genomics 18 , 26 (2017). Consortium, U.K.B.W.-G.S. Whole-genome sequencing of 490,640 UK Biobank participants. Nature 645 , 692-701 (2025). Littlejohns, T.J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun 11 , 2624 (2020). Liu, Y. et al. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. Elife 10 (2021). Loh, P.R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47 , 284-90 (2015). Benonisdottir, S. et al. Epigenetic and genetic components of height regulation. Nat Commun 7 , 13490 (2016). Vosa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 53 , 1300-1310 (2021). Strunz, T. et al. A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Sci Rep 8 , 5865 (2018). Ratnapriya, R. et al. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet 51 , 606-610 (2019). Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat Neurosci 20 , 1418-1426 (2017). Hauberg, M.E. et al. Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression. Am J Hum Genet 101 , 157 (2017). Zeller, T. et al. Genetics and beyond--the transcriptome of human monocytes and disease susceptibility. PLoS One 5 , e10693 (2010). Hao, K. et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet 8 , e1003029 (2012). Gillies, C.E. et al. An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome. Am J Hum Genet 103 , 232-244 (2018). Pala, M. et al. Population- and individual-specific regulatory variation in Sardinia. Nat Genet 49 , 700-707 (2017). Franzen, O. et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353 , 827-30 (2016). Lee, M.N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343 , 1246980 (2014). Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 44 , 1084-9 (2012). Bray, N.L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34 , 525-7 (2016). Sorensen, E. et al. Data Resource Profile: The Copenhagen Hospital Biobank (CHB). Int J Epidemiol 50 , 719-720e (2021). Erikstrup, C. et al. Cohort Profile: The Danish Blood Donor Study. Int J Epidemiol 52 , e162-e171 (2023). Refsum, H. et al. The Hordaland Homocysteine Study: a community-based study of homocysteine, its determinants, and associations with disease. J Nutr 136 , 1731S-1740S (2006). Pulley, J., Clayton, E., Bernard, G.R., Roden, D.M. & Masys, D.R. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin Transl Sci 3 , 42-8 (2010). Roden, D.M. et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 84 , 362-9 (2008). Leitsalu, L., Alavere, H., Tammesoo, M.L., Leego, E. & Metspalu, A. Linking a population biobank with national health registries-the estonian experience. J Pers Med 5 , 96-106 (2015). Kurki, M.I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 , 508-518 (2023). Gaziano, J.M. et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol 70 , 214-23 (2016). Morris, J.A. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet 51 , 258-266 (2019). Table Table 1 . The two sentinel DXA lean mass variants in the myostatin pathway: MSTN p.Ile225Thr variant and rs1870915 in ACVR2B . MSTN encodes the ligand in the myostatin pathway, and ACVR2B encodes activin-receptor type 2B, its receptor on muscle cells. The table shows the genomic position in GRCh38, annotation, and the genes within 500 Mb of each variant. The most significant DXA lean mass phenotype for each variant is leg lean mass for MSTN p.Ile225Thr variant and total lean mass for rs1870915 in ACVR2B . Effect allele frequency in the main datasets used in this study is shown. Detailed information on both variants is found in Supplementary Table 1. Information p.Ile225Thr hetero- and homozygous numbers is found in Supplementary fig.5. Information on world-wide allele frequency of p.Ile225Thr is in found in Supplementary fig.6. MVP, Million Veterans Program. Additional Declarations Yes there is potential Competing Interest. Adalheidur E. Larusdottir, Unnur D. Teitsdottir, Vinicius Tragante, Frosti Palsson, Anna M. Kristinsdottir, Grimur H. Eldjarn, Lilja Stefansdottir, Asmundur Oddsson, Hildur M. Aegisdottir, Unnur Styrkarsdottir, Mariana Bustamante, Thorunn A. Olafsdottir, Audunn S. Snaebjarnarson, Thorhildur Olafsdottir, Gudmundur Einarsson, Sigurjon A. Gudjonsson, Gisli H. Halldorsson, Egil Ferkingstad, Arnar K.S. Sandholt, Patrick Sulem, Unnur Thorsteinsdottir, Erna V. Ivarsdottir, Gardar Sveinbjornsson, Rosa B. Thorolfsdottir, Gudmundur L. Norddahl, Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Hilma Holm, Anna Helgadottir, and Daniel F. Gudbjartsson were employed by Amgen deCODE genetics during the course of this work. 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Associations for \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr were tested across meta-analyzed traits (left). DXA lean mass, DXA fat mass, grip strength, hip circumference, waist circumference and waist-to-hip ratio were adjusted for BMI and height. Number of participants in each meta-analysis is shown. Meta-analyses included data from Iceland and UK Biobank, with additional cohorts contributing to specific traits: GEFOS for whole body bone mineral density, NASHBio for weight, and NASHBio, Danish, and Intermountain cohorts for creatinine. For refined assessment of the \u003cem\u003eMSTN \u003c/em\u003ep.Ile225Thr across additional phenotypes, analyses were performed in up to 429,193 whole-genome sequenced UK Biobank participants when available. Comparative association results for the \u003cem\u003eMSTN\u003c/em\u003epLOF burden model are shown (right). In total, 34 carriers of 28 rare \u003cem\u003eMSTN\u003c/em\u003e pLOF variants (minor allele frequency \u0026lt; 0.01% in UK Biobank and NASHBio) were identified. The number of variant carriers contributing to each phenotype analysis varied by data availability. For example, only 5 pLOF carriers had DXA measurements and thus contributed to the lean and fat mass analysis. Details on individual pLOF variants and carrier counts by cohort are provided in Supplementary Tables 3 and 4. Effects are shown in standard deviations (SD). Squares and whiskers display effect sizes and 95% confidence intervals (CI). Carrier counts are shown, with number in parentheses indicating homozygous carriers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9238373/v1/ee35f49bd93c1ef3947cb461.png"},{"id":106404667,"identity":"ee8de008-b417-48a7-be42-bae782eee952","added_by":"auto","created_at":"2026-04-08 09:16:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMSTN \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ep.Ile225Thr with cardiometabolic diseases\u003c/strong\u003e. Associations for \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr were tested across 84 meta-analyzed traits. For refined assessment of \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr, analyses were performed in up to 429,193 whole-genome sequenced UK Biobank participants, when available. Detailed information on cohorts and definitions for secondary phenotypes, including cardiovascular diseases, is listed in Supplementary Table 2. Squares and whiskers display effect sizes as odds ratios (OR) and 95% confidence intervals (CI).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9238373/v1/ce70aea219ef2697641fcef9.png"},{"id":106389577,"identity":"8010be8a-e650-4d22-a6c7-b80186f543bd","added_by":"auto","created_at":"2026-04-08 07:01:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSentinel DXA-derived lean mass variants correlated with coding variants, cis gene expression or protein levels.\u003c/strong\u003e For each variant, information is provided on the closest gene (Loci), chromosome (Chrom), genomic position (Pos) in GRCh38, effect alle (EA) and allele frequencies in Iceland (ICE) and the UK Biobank whole-genome sequenced dataset (UKB, N = 150,119). Regulatory effects were evaluated by testing whether sentinel variants were a QTL or in high linkage disequilibrium (LD; r² ≥ 0.8) with variants associated with gene expression (eQTL), alternative splicing (sQTL), or protein levels (pQTL). RNA sequencing data were obtained from whole blood (N = 17,848) and adipose tissue (N = 770) samples from Icelandic participants. Publicly available eQTL data from other sources were also included (Supplementary Table 5). The tissue showing the strongest association with gene expression, including muscle tissue where applicable, is reported. At each locus, a gene is prioritized if the sentinel variant was associated with (i) a coding variant in a single gene with no additional cis-QTL evidence (cis-eQTL, cis-sQTL, or cis-pQTL) at the locus, or (ii) a coding variant in a gene supporting cis-QTL evidence for the same gene. If a variant is associated with cis-QTL evidence for more than one gene, all such genes are listed. Associations with total fat and lean mass, body mass index, and grip strength are shown. Squares and whiskers display effect sizes and 95% confidence intervals (CI). † marks variants not in LD (r2\u0026gt;0.01) with previously published lean mass variants. Details are found in Supplementary Table 1.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9238373/v1/3de94c78812bd3cac5d91949.png"},{"id":106405969,"identity":"cc2b005b-a49c-40aa-89f1-12a2df8caa92","added_by":"auto","created_at":"2026-04-08 09:29:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2883318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9238373/v1/6493750b-aedc-44dd-ba2b-b816175bfb36.pdf"},{"id":106389573,"identity":"22de28a9-314f-4ba9-8fc0-4b5e50aca4d1","added_by":"auto","created_at":"2026-04-08 07:00:59","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":834377,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9238373/v1/9fa1a9452a5f3e0e8622e191.xlsx"},{"id":106389578,"identity":"f137d65a-d83b-4b2d-ab04-0b3f8fc998af","added_by":"auto","created_at":"2026-04-08 07:01:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8293747,"visible":true,"origin":"","legend":"Supplementary data","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-9238373/v1/dc10c27f5c28cf5aec36445e.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nAdalheidur E. Larusdottir, Unnur D. Teitsdottir, Vinicius Tragante, Frosti Palsson, Anna M. Kristinsdottir, Grimur H. Eldjarn, Lilja Stefansdottir, Asmundur Oddsson, Hildur M. Aegisdottir, Unnur Styrkarsdottir, Mariana Bustamante, Thorunn A. Olafsdottir, Audunn S. Snaebjarnarson, Thorhildur Olafsdottir, Gudmundur Einarsson, Sigurjon A. Gudjonsson, Gisli H. Halldorsson, Egil Ferkingstad, Arnar K.S. Sandholt, Patrick Sulem, Unnur Thorsteinsdottir, Erna V. Ivarsdottir, Gardar Sveinbjornsson, Rosa B. Thorolfsdottir, Gudmundur L. Norddahl, Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Hilma Holm, Anna Helgadottir, and Daniel F. Gudbjartsson were employed by Amgen deCODE genetics during the course of this work.","formattedTitle":"Genome-wide association study identifies a functional myostatin variant increasing lean mass in humans","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMuscle mass and strength decline with age and are key predictors of disability onset\u003csup\u003e2,7\u003c/sup\u003e. With aging populations and the expanding use of weight-loss therapies, preserving muscle mass is increasingly important.\u0026nbsp;To explore the genetic contribution to lean mass variation, we conducted GWASs of DXA-derived\u0026nbsp;lean mass in arms, legs, trunk, and total body\u0026nbsp;of approximately\u0026nbsp;79,000 individuals of European ancestry from Iceland and the UK biobank (UKB) using a weighted genome-wide significance threshold based on predicted variant impact (Supplementary Table 1, Supplementary Fig. 1-4)\u003csup\u003e8\u003c/sup\u003e.\u0026nbsp;The lean mass measurements were adjusted for sex, age, body mass index (BMI) and height, capturing variation in lean mass conditional on overall body size and obesity status. Of the\u0026nbsp;63 identified loci, the rare \u003cem\u003eMSTN\u003c/em\u003e missense variant p.Ile225Thr (rs143242500) had the largest effect size across the DXA measures, with the strongest association observed for leg lean mass (β\u0026nbsp;=\u0026nbsp;0.28 SD [95% CI: 0.19, 0.37],\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 1.9 × 10\u003csup\u003e-9\u003c/sup\u003e, Table 1, Supplementary Fig. 5). We did not observe a difference in effects between males and females (P\u003csub\u003eHeterogeneity\u003c/sub\u003e \u0026gt; 0.05, Supplementary Table 1). The variant was most frequent in Finnish and Norwegian populations (1.03% and 0.95% respectively), with lower frequencies in other Europeans (0.17–0.58%) and very rare occurrence in other ancestries (\u0026lt;0.1%; Table 1, Supplementary Figs. 6 and 7).\u003c/p\u003e\n\u003cp\u003eMyostatin is a secreted growth factor which signals through the activin type II B receptor (\u003cem\u003eACVR2B\u003c/em\u003e) to suppress muscle hypertrophy\u003csup\u003e9\u003c/sup\u003e. It is expressed primarily in skeletal muscle and to a lesser extent in other tissues such as cardiac muscle and adipose tissue\u003csup\u003e10\u003c/sup\u003e. Extensive animal data\u003csup\u003e11\u003c/sup\u003e, including mouse \u003cem\u003eMstn\u003c/em\u003e knock-out models\u003csup\u003e12\u003c/sup\u003e and pharmacologic inhibition studies\u003csup\u003e13\u003c/sup\u003e, demonstrate that loss of myostatin activity markedly increases muscle mass. In humans, a homozygous loss-of-function mutation in \u003cem\u003eMSTN\u003c/em\u003e has been associated with extreme muscular hypertrophy as reported in a single case study\u003csup\u003e14\u003c/sup\u003e. However, common \u003cem\u003eMSTN\u003c/em\u003e variants, such as p.Lys153Arg and p.Ala55Thr have shown inconsistent associations with muscle mass and performance across case-control studies \u003csup\u003e15-17\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the association between p.Ile225Thr in \u003cem\u003eMSTN\u003c/em\u003e and lean mass, we tested it across other lean mass measurement modalities. In an independent subset of UKB participants with bioelectrical impedance analysis (BIA) measures, the variant was associated with greater leg fat-free mass (β = 0.11 SD [95% CI: 0.06, 0.16], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 3.6 x 10\u003csup\u003e-5\u003c/sup\u003e, N = 363,056). Similarly, using high-resolution Magnetic Resonance Imaging (MRI) data from the UKB, the variant associated with higher total thigh muscle volume (β = 0.23 SD [95% CI: 0.10, 0.37], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 7.4 × 10\u003csup\u003e-4\u003c/sup\u003e, N = 51,068). Furthermore, the variant was associated with higher serum creatinine levels, a biochemical marker of muscle mass (effect = 0.12 SD [95% CI: 0.10, 0.15], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 9.9 x 10\u003csup\u003e-21\u003c/sup\u003e, N = 1,183,832) but not with serum cystatin C levels (effect = 0.02 SD [95% CI: -0.02, 0.07], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.29, N = 456,678) indicating that the creatinine association is unlikely to reflect altered kidney function\u0026nbsp;(Fig. 1)\u003csup\u003e18\u003c/sup\u003e. Without BMI adjustment, effect sizes for DXA, BIA, and MRI measures remained directionally consistent, but slightly reduced (Supplementary Fig. 8). Together, these findings support the hypothesis that\u0026nbsp;the \u003cem\u003eMSTN\u0026nbsp;\u003c/em\u003ep.Ile225Thr variant contributes to increased muscle mass in humans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe evaluated the associations of \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr with a broad set of traits across available cohorts in the deCODE genetics phenotype database (Methods and Supplementary Table 2). We chose traits with reference to myostatin literature, including altered body size and composition, muscle function, and cardiometabolic health. Six associations (birthweight, height, grip strength, creatinine, hypertension, and frequency of stair climbing) remained associated with the variant after correction for multiple testing (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ethreshold\u003c/sub\u003e = 0.05/84 = 6.0 x 10\u003csup\u003e-4\u003c/sup\u003e; Fig. 1 and 2). The association with increased birthweight (β = 0.13 SD [95% CI: 0.08, 0.17], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 5.7 x 10\u003csup\u003e-7\u003c/sup\u003e, N = 374,725) is notable, as myostatin mutations associate with increased neonatal growth in animals\u003csup\u003e20,21\u003c/sup\u003e, but this has not been confirmed in humans. The association with increased grip strength (β = 0.09 SD [95% CI: 0.04, 0.13], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 3.8 x 10\u003csup\u003e-4\u003c/sup\u003e, N = 424,576) aligns with findings of increased muscle mass and strength in\u003cem\u003e\u0026nbsp;Mstn\u003c/em\u003e knockout mouse models\u003csup\u003e22\u003c/sup\u003e, although therapeutic inhibition in humans has not resulted in improved strength\u003csup\u003e23,24\u003c/sup\u003e. The variant also associated with reduced risk of primary hypertension (OR = 0.92 [95% CI: 0.89, 0.96], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 5.1 x 10\u003csup\u003e-5\u003c/sup\u003e, N\u003csub\u003ecases/controls\u003c/sub\u003e = 882,927/1,409,034), suggesting a cardioprotective mechanism (Fig. 2)\u003csup\u003e25\u003c/sup\u003e. No association was observed with cardiovascular risk traits, such as blood pressure (Supplementary Fig. 9). In rodent models, \u003cem\u003eMstn\u003c/em\u003e deletion has been associated with increased heart size and improved heart function\u003csup\u003e26-28\u003c/sup\u003e. However, we found nominally significant associations with increased risk of dilated cardiomyopathy (OR = 1.28 [95% CI: 1.05, 1.56], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.017, N\u003csub\u003ecases/controls\u003c/sub\u003e = 7,419/1,583,702, Fig. 2, Supplementary Fig. 10) and non-ischemic heart failure (OR = 1.08 [95% CI: 1.00, 1.16], P=0.042, N\u003csub\u003ecases/controls\u003c/sub\u003e=80,704/1,563,175, Fig. 2). The potential adverse effect on the heart warrants cautious interpretation but underscore the importance of evaluating cardiovascular outcomes when modulating myostatin activity.\u003c/p\u003e\n\u003cp\u003eBecause the association of \u003cem\u003eMSTN\u0026nbsp;\u003c/em\u003ep.Ile225Thr with increased lean mass suggests loss of function, we investigated the effects of predicted loss-of-function (pLOF) \u003cem\u003eMSTN\u003c/em\u003e variants on the traits explored using carriers in the UKB and NASHBio datasets (combined minor allele frequency (MAF) across datasets = 0.007%; Supplementary Tables 3 and 4). In gene-based meta-analysis burden testing (Fig. 1 \u003csup\u003e29\u003c/sup\u003e, these pLOF variants collectively associate with greater lean mass in legs (β = 1.12 SD [95% CI: 0.25, 1.98], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.012) and arms (β = 1.05 SD [95% CI: 0.18, 1.92], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.018). Together, these findings suggest that p.Ile225Thr may act as a loss-of-function allele, reducing myostatin activity and thereby promoting muscle growth.\u003c/p\u003e\n\u003cp\u003eTo assess the regulatory consequences of the MSTN p.Ile225Thr variant, we tested its association with gene expression (eQTL), alternative splicing (sQTL), and circulating protein levels (pQTL). Analyses were conducted using mRNA sequencing data from blood (N = 17,848) and adipose tissue (N = 770) in Icelandic individuals, transcriptomic data from the GTEx project\u003csup\u003e10\u003c/sup\u003e and other published sources, and plasma protein measurements (pQTL) using the SomaScan (4,719 proteins, N = 35,559) and Olink (2,925 proteins, N = 47,150) panels\u003csup\u003e30\u003c/sup\u003e. Notably, the missense variant p.Ile225Thr is the sentinel cis-pQTL for \u003cem\u003eMSTN\u003c/em\u003e, substantially increasing circulating levels of myostatin as measured by Olink (OID20115, β = 0.84 [95% CI: 0.70, 0.98], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 7.6 × 10\u003csup\u003e-32\u003c/sup\u003e; N = 46,446, Supplementary Fig. 11 and 12). In contrast, the variant showed no association with the SomaScan aptamer measurement of myostatin (SeqID.14583-49, \u003cem\u003eP\u003c/em\u003e = 0.68). This discrepancy may reflect differences in epitope recognition between assays. Myostatin is synthesized as an inactive precursor protein and activated extracellularly by proteolysis of its propeptide. Because the bioactive myostatin domain is largely inhibited before cleavage\u003csup\u003e31\u003c/sup\u003e, we investigated whether these platforms captured total or bioactive myostatin levels. To address this, we quantified plasma myostatin levels in Icelandic individuals using two alternative immunoassays targeting total (R\u0026amp;D Systems) and bioactive (MSD) myostatin (Supplementary Note and Supplementary Fig. 13-18). The Olink measurements correlated more strongly with total myostatin than with bioactive myostatin (Pearson’s ρ = 0.73, P = 2.0 × 10\u003csup\u003e-43\u003c/sup\u003e and Pearson’s ρ = 0.30, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 1.5 × 10\u003csup\u003e-6\u003c/sup\u003e respectively, N = 252) whereas the SomaScan measurement showed low correlation with either assay (Pearson’s ρ = 0.12, P = 2.1 × 10\u003csup\u003e-4\u003c/sup\u003e; Pearson’s ρ = 0.023, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.48; N = 930), indicating that the Olink assay primarily reflects total circulating myostatin.\u003c/p\u003e\n\u003cp\u003eGiven that the p.Ile225Thr variant increases lean mass, consistent with reduced myostatin activity, we explored whether carriers had an altered proportion of lower bioactive myostatin levels relative to total levels. Carriers had increased myostatin levels, both total (mean difference = 0.81, Wilcoxon signed-rank \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 1.6 × 10\u003csup\u003e-46\u003c/sup\u003e) and bioactive (mean difference = 0.49, Wilcoxon signed-rank \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0032) compared to controls (N matched pairs = 395). However, when bioactive myostatin was evaluated relative to total levels, carriers showed a relative reduction in bioactive myostatin (mean difference = -0.32, Wilcoxon signed-rank \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 1.6 × 10\u003csup\u003e-7\u003c/sup\u003e; ANCOVA mature myostatin level estimate for carriers, -0.57 [95% CI: -1.1, -0.079], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.023, Supplementary Fig. 15, 19 and 20). These results indicate that p.Ile225Thr elevates total circulating myostatin while reducing the relative amount of the bioactive form. We speculate that the substitution of Ile225 with threonine stabilizes the inhibitory complex by forming a stronger hydrogen bond with Cys138 in the propeptide\u003csup\u003e32,33\u003c/sup\u003e without disrupting the protein structure\u003csup\u003e34\u003c/sup\u003e. Such stabilization could reduce proteolytic cleavage, thereby reducing the levels of bioactive myostatin relative to total circulating levels.\u003c/p\u003e\n\u003cp\u003eAmong individuals of African ancestry, we observed that the missense variant p.Ala55Thr (MAF= 14%) associated with substantially lower circulating myostatin levels measured with Olink (β = 0.40 SD [95% CI:\u0026nbsp;-0.51, -0.30], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 9.6 × 10\u003csup\u003e-14\u003c/sup\u003e; N = 1,505, cohort=UKB) and reduced serum creatinine (β = -0.03 SD [95% CI:\u0026nbsp;-0.04, -0.02], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 2.2 × 10\u003csup\u003e-8\u003c/sup\u003e; N = 144,075, cohorts: UKB, NASHBio, MVP; Supplementary Fig. 21 and 22). These results support a role for \u003cem\u003eMSTN\u003c/em\u003e in regulating circulating myostatin and muscle mass across human ancestries.\u003c/p\u003e\n\u003cp\u003eTo extend these findings beyond myostatin, we also assessed functional annotations of all 63 lean-mass-associated loci. Specifically, we evaluated whether the sentinel variant at each locus was a top coding variant or QTL ir in linkage disequilibrium (LD, r\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.8) with such variants. Of these, 37 were in LD with coding variants, cis-eQTLs or pQTLs (Fig. 3, Supplementary Tables 1 and 5). Among the lean-mass-associated loci was a common variant upstream of the myostatin receptor gene, \u003cem\u003eACVR2B\u003c/em\u003e (rs1870915-G, MAFin Iceland = 41%, MAF in the UK =46%; Table 1). This variant was associated with lower DXA-derived total lean mass (β = -0.04 SD [95% CI: -0.05, -0.03], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 3.7 × 10\u003csup\u003e-13\u003c/sup\u003e, N = 78,932) as well as lower circulating myostatin levels (β = –0.04 SD [95% CI: -0.05, -0.03], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 2.5 × 10\u003csup\u003e-9\u003c/sup\u003e, N = 46,664). The variant was in LD (r\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.8) with eQTLs for \u003cem\u003eACVR2B\u003c/em\u003e expression B cells, thyroid and lung tissue (Supplementary Table 1, Fig. 3). Together with the \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr findings, these results provide evidence that both the ligand (myostatin) and its receptor contribute to muscle mass regulation through the \u003cem\u003eMSTN–ACVR2B\u0026nbsp;\u003c/em\u003epathway in humans.\u003c/p\u003e\n\u003cp\u003eOf the 63 sentinel lean mass variants, only nine are in linkage disequilibrium (LD, r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.01) with previously reported signals (Supplementary Table 1)\u003csup\u003e35-37\u003c/sup\u003e. The missense variant p.Met87Thr in \u003cem\u003eCASQ1\u003c/em\u003e, associated with reduced arm lean mass (β = -0.08 SD [95% CI: -0.11, -0.05], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 2.36 × 10\u003csup\u003e-8\u003c/sup\u003e; MAF\u003csub\u003eICE\u003c/sub\u003e = 4.5%, MAF\u003csub\u003eUKB\u003c/sub\u003e = 3.2%). \u003cem\u003eCASQ1\u003c/em\u003e encodes a calcium-buffering protein that regulates Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003erelease from the sarcoplasmic reticulum in fast-twitch skeletal fibers\u003csup\u003e38\u003c/sup\u003e. A 3′ UTR variant in \u003cem\u003eTRIM13\u003c/em\u003e was associated with increased lean mass (β = 0.12 SD [95% CI: 0.09, 0.16], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 8.8 × 10\u003csup\u003e-13\u003c/sup\u003e; MAF\u003csub\u003eICE\u003c/sub\u003e = 1.2%, MAF\u003csub\u003eUKB\u003c/sub\u003e = 1.8%). \u003cem\u003eTRIM13\u003c/em\u003e stabilizes \u003cem\u003ep53,\u0026nbsp;\u003c/em\u003ewhichpromotes muscle regeneration in aged mice\u003csup\u003e39,40\u003c/sup\u003e.Numerous\u0026nbsp;associations were observed in genes involved in myofiber physiology, such as in the triad junction (\u003cem\u003eCASQ1, SPEG, CACNA1S\u003c/em\u003e), the sarcomere (\u003cem\u003eTTN, MYPN\u003c/em\u003e), and in acetylcholine handling (\u003cem\u003eSYN2, SLC44A4\u003c/em\u003e). Overall, the results from the GWAS meta-analyses extend biological insights into lean mass regulation beyond \u003cem\u003eMSTN\u003c/em\u003e by highlighting additional genetic contributors to skeletal muscle function.\u003c/p\u003e\n\u003cp\u003eIn summary, we identified a rare \u003cem\u003eMSTN\u003c/em\u003e variant (p.Ile225Thr) strongly associated with DXA-derived lean mass, with validation across BIA- and MRI-derived measures. The variant increases total circulating myostatin levels, while reducing the relative abundance of bioactive myostatin, supporting a loss-of-function mechanism. Beyond muscle mass, it is associated with increased strength, intrauterine growth, and reduced risk of primary hypertension. This work provides insight into the potential consequences of long-term myostatin inhibition. While partial inhibition may be beneficial, suggestive associations with non-ischemic heart failure and dilated cardiomyopathy underscore the importance of evaluating cardiovascular safety in therapeutic settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design. \u003c/strong\u003eThe study is a hypothesis-free discovery genome-wide association meta-analysis of lean mass measured with dual-energy X-ray absorptiometry, utilizing data from Iceland and the UKB. Multi-omics annotation of the lean mass variants was performed using Icelandic and UKB data on protein levels and gene expression, in addition to publicly available eQTLs. After identifying a strong lean mass variant in \u003cem\u003eMSTN\u003c/em\u003e, p.Ile225Thr, found in participants of European ancestry, a clinical phenome lookup was performed using meta-analyzed data from multiple cohorts. Rare-variant burden testing for \u003cem\u003eMSTN\u003c/em\u003e was conducted using information on rare loss-of-function variants in the Icelandic dataset, UKB and U.S. cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics. \u003c/strong\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Dataset-specific ethics declarations are provided below. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Icelandic study population. \u003c/strong\u003eThe Icelandic deCODE genetics study is built on phenotypic and biological data obtained from more than 170,000 volunteers participating in multiple research studies conducted in Iceland. DXA measurements of 20,614 Icelanders were obtained from the Landspitali National University Hospital electronic health records (DEXA, Hologic QDR4500/A) and the deCODE study Heilsuranns\u0026oacute;kn (DEXA, Hologic S/N200547) from the years 1999 to 2017. Participants who donated biological samples gave written informed consent and the National Bioethics Committee approved the study (VSN-15-057 and VSN-15-023) which was conducted in agreement with conditions issued by the Data Protection Authority of Iceland. Personal identities were encrypted by a third-party system (Identity Protection System), approved and monitored by the Data Protection Authority\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe UK Biobank study population\u003c/strong\u003e. UK Biobank (UKB) is a large population-based prospective study comprising approximately 500,000 participants recruited between 2006 and 2010 at 22 assessment centres across the United Kingdom (UK). The participants were aged 40-69 years at recruitment and represent diverse socioeconomic and ethnic backgrounds. UKB collects extensive information on participants, including questionnaire data, physical measurements, and longitudinal follow-up for a wide range of health-related outcomes, with the aim of conducting health-related research for the benefit of the public\u003csup\u003e42\u003c/sup\u003e. Participants provided written informed consent, and the UKB scientific protocol and operational procedures were approved by the North West Research Ethics Committee (REC Reference Number: 06/MRE08/65). DXA whole-body scans from 58,318 UKB participants and MRI abdominal scans (Siemens 1.5T MAGNETOM Aera) from 51,109 UK were obtained from the UKB. All participants included in the meta-analysis were of genetically inferred European ancestry. This research was conducted under the UKB application number 56270.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping\u003c/strong\u003e. A total of 63,118 Icelanders underwent whole-genome sequencing (WGS) with coverage of over 20x using Illumina GAII, HiSeq, HiSeqX and NovaSeq sequencing instruments, resulting in the identification of approximately 106 million sequence variants\u003csup\u003e43\u003c/sup\u003e. Genotypes were determined using joint calling with the GraphTyper v2.7.1\u003csup\u003e44\u003c/sup\u003e. WGS identified variants were imputed into 173,025 chip-genotyped individuals using long-range phasing. Chip genotyping was performed using Illumina OmniExpress and HumanHap arrays \u003csup\u003e45,46\u003c/sup\u003e. Furthermore, genotype probabilities for first- and second-degree relatives of chip-typed individuals were inferred using genealogical records assembled by deCODE Genetics.\u003c/p\u003e\n\u003cp\u003eWe used imputed UKB dataset based on a subset of participants with WGS data (N=150,119)\u003csup\u003e47\u003c/sup\u003e. WGS was performed jointly by deCODE (N=90,667) and the Sanger Institute (59,452) using Illumina NovaSeq sequencing machines to an average coverage of 32.5\u0026times; (at least 23.5\u0026times; per individual). WGS identified variants were imputed into the long range phased chip data (N = 431,079). Genotypes were determined using joint calling with the GraphTyper v2.7.1\u003csup\u003e44\u003c/sup\u003e. The first 50,000 participants were chip genotyped using the custom Affimetrix UK BiLEVE Axiom array\u003csup\u003e48\u003c/sup\u003e and the remaining participants using the Affimetrix UK Biobank Axiom array\u003csup\u003e49\u003c/sup\u003e, with 95% of variants shared between arrays.\u003c/p\u003e\n\u003cp\u003eAs a part of the discovery DXA lean mass meta-analyses, 431,079 imputed UKB participants of European ancestry were included. For refined assessment of the \u003cem\u003eMSTN \u003c/em\u003ep.Ile225Thr missense variant in other phenotypes, we use WGS dataset of 429,193 participants of European ancestry when available\u003csup\u003e50\u003c/sup\u003e. Calculations for p.Ala55Thr (rs1805085) were based on 9,229 UKB participants of African ancestry with WGS data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUK Biobank imaging processing\u003c/strong\u003e. For UKB imaging data, only 48-60% of DXA scans and 17-79% of MRI scans contained instrument-derived readouts (Supplementary Table 6). Missing image-derived phenotypes (IDPs) were imputed using supervised deep-learning regression models trained directly on raw imaging data\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor DXA, paired bone and fat images were pre-processed to remove background signals. The two modalities were then combined into a single three-channel image by assigning the bone map to the red channel and the fat map to the green channel, yielding a standardized composite representation suitable for 2D convolutional models.\u003c/p\u003e\n\u003cp\u003eAbdominal MRI pre-processing followed the previously described deep-learning segmentation and phenotyping pipeline\u003csup\u003e52\u003c/sup\u003e. To generate a computationally efficient 2D representation of volumetric fat- and water-separated Dixon images, mean-intensity projections were computed in both the coronal and sagittal planes. The resulting four projection images (fat-coronal, fat-sagittal, water-coronal, water-sagittal) were merged horizontally and encoded into a single RGB image by assigning the fat projection to the red channel and the water projection to the green channel. This approach preserves major body-composition features while reducing input dimensionality.\u003c/p\u003e\n\u003cp\u003ePredicted IDPs were generated by fitting deep residual convolutional neural networks using these standardized DXA and MRI images as inputs and the corresponding measured IDPs as supervised targets. Both modalities used the same architecture, consisting of stacked 2D residual convolutional blocks followed by two fully connected projection layers. Labeled datasets were randomly partitioned into training and validation subsets using an 80/20 split, and model performance was monitored on the held-out validation set prior to inference in participants with missing IDPs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic association testing. \u003c/strong\u003eLinear mixed model implemented in BOLT-LMM\u003csup\u003e53\u003c/sup\u003e was applied to test the relationship between sequence variants and quantitative traits. Genetic associations were tested under an additive model, using the expected allele count from imputation as the explanatory variable and normalized phenotypes as response. Prior to the association analyses, phenotypes were adjusted for age using regression, performed separately in males and females. Additional covariates included county of birth for Icelandic data and 20 principal components (PCs) for the UKB data. Instrument-based body composition measurements (DXA, BIA, MRI), grip strength, hip circumference, waist circumference and waist-to-hip ratio were also adjusted for BMI and height in both cohorts consistent with previous work on waist-to-hip ratio adjusted for BMI genetics\u003csup\u003e19\u003c/sup\u003e. Regression residuals were transformed into a standard normal distribution using a rank-based inverse normal transformation. Quantitative measurements were assumed to follow a normal distribution with a mean depending linearly on the expected allele count at each variant and a variance-covariance matrix proportional to the kinship matrix\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe used logistic regression, assuming the additive model, to assess the relationship between sequence variants and case-control phenotypes. The binary phenotype was treated as a response, and the expected genotype counts from imputation as covariates. P-values were calculated using a likelihood ratio test. In the Icelandic cohort, thecovariates included sex, county of birth, current age or age at death (first- and second-order terms included), blood sample availability for the individual and an indicator function for the overlap of the lifetime of the individual with the time span of the phenotype collection. In the UKB cohort,covariates included, sex, age and 20 PCs.\u003c/p\u003e\n\u003cp\u003eLD score regression was applied, using the intercept to adjust test statistics for inflation due to cryptic relatedness and population stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeta-analysis.\u003c/strong\u003eA fixed-effects inverse-variance method was used to combine summary statistics across cohorts based on effect estimates and standard errors. Associations with phenotypes were considered significant based on weighted genome-wide significance threshold determined by variant annotation\u003csup\u003e8\u003c/sup\u003e; \u003cem\u003eP\u003c/em\u003e \u0026le; 1.3 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e for high-impact variants (including stop-gained and loss, frameshift, splice acceptor or donor and initiator codon variants), \u003cem\u003eP\u003c/em\u003e \u0026le; 2.6 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e for missense, splice-region variants and in-frame-indels, \u003cem\u003eP\u003c/em\u003e \u0026le; 2.38 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e for low-impact variants (including synonymous, 3\u0026prime; and 5\u0026prime; UTR, and upstream and downstream variants), \u003cem\u003eP\u003c/em\u003e \u0026le; 4.00 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e for deep intronic and intergenic variants, and \u003cem\u003eP\u003c/em\u003e \u0026le; 1.19 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e for those in DNase I hypersensitivity sites (DHS).\u003c/p\u003e\n\u003cp\u003eSentinel variants were identified by selecting, for each megabase (MB), the variant with the lowest weighted genome-wide significant P-value in any of the four DXA lean mass meta-analyses and with imputation information score \u0026gt; 0.8. Independent signals were defined by clumping the variants into linkage disequilibrium (LD) blocks (r\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e \u003c/em\u003e\u0026gt; 0.01) and selecting the variant with the lowest weighted P-value per block. LD clumping was performed using the phased Icelandic genotypes and subsequently confirmed using the UK Biobank genotypes, yielding the same set of sentinel loci. We performed conditional analysis across the \u003cem\u003eMSTN\u003c/em\u003e locus, confirming that p.Ile225Thr represents the strongest independent signal for lean mass in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeritability.\u003c/strong\u003e SNP-based heritability (observed scale) was estimated using LD score regression (Supplementary Table 7). These analyses included approximately 1.2 million well-imputed variants, with LD information obtained from precomputed European populations reference LD scores (downloaded from: https://data.broadinstitute.org/alkesgroup/LDSCORE/ eur_w_ld_chr.tar.bz2). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReplication analysis.\u003c/strong\u003e To assess whether the DXA-derived phenotypes captured the same lean mass traits as those derived from UK Biobank bioimpedance analysis (BIA) in the largest published GWAS of lean mass\u003csup\u003e35\u003c/sup\u003e, we performed replication analysis using a proxy DXA phenotype defined as appendicular lean mass (sum of arm and leg lean mass) adjusted for BMI. Of 970 reported appendicular lean-mass associated variants, 234 replicated (consistent direction of effect and P \u0026lt; 0.05), indicating incomplete concordance between BIA- and DXA-derived phenotypes (Supplementary Table 8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene burden analysis\u003c/strong\u003e. For the \u003cem\u003eMSTN\u003c/em\u003e gene, we identified 34 carriers of 28 predicted loss-of-function variants (pLOF) using the Ensembl Variant Effect Predictor (VEP). In the model, we included rare variants with minor allele frequency \u0026lt; 0.01% in UK Biobank and NashBio datasets (Supplementary Tables 3 and 4). Variants were analyzed under a burden framework, if when grouped, they exert a shared phenotypic effect. Genotypes were coded as 1 for individuals carrying at least one \u003cem\u003eMSTN\u003c/em\u003e pLOF variant and 0 otherwise. Genetic associations were tested under an additive model, with \u003cem\u003eMSTN\u003c/em\u003e pLOF carrier status as the explanatory variable and normalized phenotypes as the response. Association testing was performed using a linear mixed model implemented in BOLT-LMM. The number of variant carriers contributing to each phenotype-specific analysis varied depending on phenotype availability (Fig. 1). For example, although 34 pLOF carriers were identified overall, only 5 had dual-energy X-ray absorptiometry (DXA) measurements; therefore, only these five carriers contributed to the lean and fat mass burden analysis (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMyostatin immunoassay measurements\u003c/strong\u003e. The \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr variant was associated with increased myostatin levels on the Olink platform (OID20115), whereas no association was observed with the GDF8 specific SomaScan probe SeqId.14583-49 (\u003cem\u003eP \u003c/em\u003e= 0.68). To validate and interpret these findings, circulating myostatin levels were quantified using two independent ELISA platforms: R\u0026amp;D Systems Quantikine GDF-8 ELISA (RD) and Meso Scale Discovery R-PLEX Human GDF-8 assay (MSD).\u003c/p\u003e\n\u003cp\u003eRD measures total myostatin, as samples were subjected to an activation step involving acid denaturation (1N HCl) followed by neutralization (NaOH/Hepes), thereby disrupting protein complexes and releasing myostatin. Subsequently, GDF-8 was detected using a monoclonal antibody specific to mature GDF-8. MSD measures approximately free (active) myostatin and does not include a denaturation step. Samples were drawn from 930 Icelandic participants with available SomaScan and/or Olink measurements, including a subset of 398 \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr heterozygous carriers with sex- and age-matched (within 5 years) controls.\u003c/p\u003e\n\u003cp\u003eAssays were conducted blinded to genotype. RD samples were generally diluted 1:12; a subset of plates was run at 1:4 dilution or with incorrect buffer, and these deviations were recorded and handled in sensitivity analyses. MSD samples were undiluted due to low circulating concentrations. For MSD measurements, a proportion of samples fell below the formal detection range. These values nevertheless had estimated concentrations that fit the calibration curve, consistent with standard handling in large-scale proteomic pipelines. Values below the fit curve were set to 0.03 pg/mL, corresponding to the lowest reliably observed concentration, and retained in analyses. Sensitivity analyses excluding these samples yielded consistent results.\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eProtein concentrations were log2-transformed prior to analysis. To account for technical and biological covariates, concentrations were adjusted using linear models of the form: log2(protein) ~ age at sampling + sex + plate. Residuals from these models were used for correlation analyses between platforms. Pearson correlations were calculated on adjusted residuals, and Spearman correlations were additionally computed without adjustment.\u003c/p\u003e\n\u003cp\u003eCarrier\u0026ndash;control comparisons were performed using matched case\u0026ndash;control pairs where available. Differences in protein concentrations and derived ratios were assessed using the Wilcoxon signed-rank test. For analyses comparing free-to-total myostatin, ratios were calculated after covariate adjustment of MSD and RD measurements separately. To assess whether differences in free myostatin were independent of total myostatin levels, linear regression models were fitted: log2(MSD) ~ log2(RD) + genotype + age + sex + plate\u003cimg width=\"3\" height=\"19\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAcBAMAAABbmGiFAAAAAXNSR0IArs4c6QAAAA9QTFRFAAAAAAAAZrb/kDoA///bKJL+FAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAFElEQVQYV2NgoBJwFmJggGE8RgIAIxcBAN53B44AAAAASUVORK5CYII=\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e \u003c/p\u003e\n\u003cp\u003eMultiple sensitivity analyses were conducted, excluding plates with documented buffer or dilution errors, samples measured on alternative instruments, and samples below detection limits. Results were consistent across all sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-omics data.\u003c/strong\u003e Regulatory consequences of lean mass variants were evaluated by assessing whether each sentinel variant was in linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e \u0026ge; 0.8) with variants associated with gene expression (eQTL), alternative splicing (sQTL), or protein levels (pQTL). RNA sequencing data were obtained from whole blood (N = 17,848) and adipose tissue (N = 770) samples from Icelandic participants. In addition, publicly available eQTL information from the GTEx Portal and other published sources \u003csup\u003e55-66\u003c/sup\u003e was used (Supplementary Table 5).\u003c/p\u003e\n\u003cp\u003eIn the Icelandic dataset, gene expression levels were computed based on personalized transcript abundances\u003csup\u003e67\u003c/sup\u003e. Association between genetic variants and gene expression were estimated using linear regression models assuming additive genetic effect, with normally quantile-transformed gene expression estimates as the response. Models were adjusted for sequencing artefacts, demography variables, blood cell composition and principal components (PCs). Gene expression PCs were computed per chromosome using a leave-one-chromosome-out approach. All variants within 1Mb of each gene were tested.\u003c/p\u003e\n\u003cp\u003eTop independent eQTL signals were identified using iterative conditional association analysis, where in each iteration the genotype of the variant with lowest P-value was included as an additional covariate. pQTLs where obtained from Icelandic Somascan measurements (4,719 proteins, N = 35,559) and from UK Biobank Olink data (1,450 proteins, N = 47,150), as previously described\u003csup\u003e30\u003c/sup\u003e. Genes were prioritized if the sentinel variant was associated with (i) a coding variant in a single gene with no additional cis-QTL evidence (cis-eQTL, cis-sQTL, or cis-pQTL) at the locus, or (ii) with a coding variant in a gene supporting cis-QTL evidence for the same gene (Fig. 3). Data from the Genotype-Tissue Expression (GTEx) Project were obtained from the GTEx Portal on 20 November 2024. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional datasets. \u003c/strong\u003e\u003cem\u003eMSTN\u003c/em\u003ep.Ile225Thr was tested for association with additional phenotypes in the deCODE genetics phenotype database, which contains extensive medical information on a wide range of diseases and quantitative traits meta-analyzed across multiple cohorts. Quantitative traits were analyzed using the same method as for the DXA measurements. For blood-based traits, models were additionally adjusted for sample availability and for an indicator variable reflecting overlap of individual\u0026rsquo;s lifetime and the time span of phenotype collection. Binary traits were analyzed using logistic regression, including the same covariates as described above. We visually inspected regional association plots for lean mass and for any significant associations of p.Ile225Thr with other phenotypes to ensure that the signal at p.Ile225Thr was not driven by a stronger neighboring variant for the respective phenotype. Detailed information on phenotype definitions is provided in Supplementary Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eShared haplotype reference panel\u003c/em\u003e\u003c/strong\u003e for the Copenhagen Hospital Biobank/ The Danish Blood Donor Study (CHB/DBDS) -Intermountain (INTMT) and the Hordaland health study (HUSK) cohorts was constructed using a 50,839 jointly whole genome-sequenced samples (average coverage \u0026gt;20x) from Denmark, North America, Iran, the Netherlands, Norway, and Sweden. Joint variant calling was performed using Graphtyper (version 2.7.5)\u003csup\u003e44\u003c/sup\u003e. Whole genome sequencing, chip-genotyping, and the subsequent imputation were performed at deCODE genetics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDanish study population\u003c/em\u003e\u003c/strong\u003e. The Danish data were obtained from the Copenhagen Hospital Biobank - Oral-Cardiometabolic Health (CHB-OCMS)\u003csup\u003e68\u003c/sup\u003e and The Danish Blood Donor Study (DBDS)\u003csup\u003e69\u003c/sup\u003e. The study was approved by the Zealand Region Committee on Health Research Ethics and the Capital Region Data Protection Office (SJ-989 and P-2022-913). All participants were informed of the option to opt out and could withdraw from the study at any time. Genotypes were imputed using the shared haplotype reference panel that includes whole genome sequencing data from 10,828 Danish individuals and was applied to 464,016 DBDS and CHB-OCMS participants genotyped using the Illumina Global Screening Array. For CHB-OCMS and DBDS analyses, additional covariates included 20 principal components and blood sample availability. All participants were of European ancestry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIntermountain study population (US)\u003c/em\u003e\u003c/strong\u003e is a collaboration of 33 hospitals and 385 clinics in Utah and surrounding states and deCODE Genetics aiming to analyze the genomes of 500,000 participants. The dataset was obtained from HerediGene, a general population study, and the INSPIRE Registry Study, which contains data on volunteer subjects, both healthy and diagnosed with a variety of medical conditions. The studies have been approved by the Intermountain Healthcare Institutional Review Board (IRB), and all participants have provided written informed consent. Eligibility criteria for both studies include being 18 years of age or older. A total of 23,551 US participants were whole genome sequenced and included in the shared haplotype reference panel. This panel was imputed into additional 138,006 US participants genotyped with the Illumina Global Screening Array and OmniExpress. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Hordaland Health Study (HUSK)\u003c/em\u003e\u003c/strong\u003e is a community-based study in Western Norway conducted as a collaboration between the University of Bergen, the Norwegian Health Screening Service (now part of the National Institute of Public Health) and the Municipal Health Service in Hordaland (https://husk-en.w.uib.no/)\u003csup\u003e70\u003c/sup\u003e. Approximately 36,000 residents of Hordaland County participated in the studies, with about 18,000 taking part in 1992/93 and 26,000 in 1997/99. Approximately 7,000 of those who participated in the 1992/93 survey also participated in 1997/99. Data from the HUSK are available for researchers after ethical approval and there are currently several active projects. The current study was done as part of the HUSKment project which is approved by the Regional Committee for Medical Research Ethics Western Norway, reference 2018/915. The shared haplotype reference panel was imputed into 35,146 HUSK participants genotyped with the Illumina Global Screening Array and OmniExpress. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNashville Biosciences (NashBio) study population\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003e The Alliance for Genomic Discovery (AGD) is a collaborative initiative involving Amgen deCODE, Nashville Biosciences, Illumina and seven additional pharmaceutical companies, with the aim to sequence 250,000 samples from the BioVU\u0026reg; biobank. BioVU\u0026reg; is a biobank of de-identified DNA samples linked to a longitudinal electronic health record data from the Synthetic Derivative database, created and maintained by Vanderbilt University Medical Center (VUMC, Tennessee, USA)\u003csup\u003e71,72\u003c/sup\u003e. BioVU\u0026reg; includes clinical information from more than 3.6 million patients receiving care at VUMC since 2001. \u003c/p\u003e\n\u003cp\u003eThe use of BioVU\u0026reg; data is classified as non-human subjects research by the VUMC Institutional Review Board (IRB), and no study-specific informed consent is required. The overall biobanking program is reviewed annually by the IRB, and individual studies using BioVU\u0026reg; data are submitted for confirmation of non-human subjects\u0026rsquo; status.\u003c/p\u003e\n\u003cp\u003eGermline DNA samples from more than 307,000 patients have been collected, of which 250,000 samples were selected for whole genome sequencing. In this study, a total of 124,791 participants were whole genome sequenced with Illumina NovaSeq instrument at deCODE genetics in Iceland. The average genome-wide sequencing coverage was 33.9x (sd 3.5, min:28.7x, max:66.4x).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEstonian Biobank (EstBB) \u003c/em\u003e\u003c/strong\u003eis a population-based cohort comprising approximately 210,000 participants with linked genomic and health-related data\u003csup\u003e73\u003c/sup\u003e. Participants provided written informed consent at recruitment for linkage of their electronic health records, enabling the longitudinal follow-up. Health data includes diagnoses coded according to ICD-10, with records obtained from the National Health Insurance Fund Treatment Bills (since 2004), Tartu University Hospital (since 2008), and North Estonia Medical Center (since 2005).\u003c/p\u003e\n\u003cp\u003eThe activities of the EstBB are regulated by the Human Genes Research Act, adopted in 2000 specifically for the operations of the EstBB. Individual-level data analysis was conducted under the ethical approval 1.1-12/624 from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data accessed under an approved release application 6-7/GI/1564 from the Estonian Biobank.\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Estonian Research Council (grant PUT PRG1911), the Ministry of Education and Research Centres of Excellence grant TK214, and the Estonian Research Council-funded Estonian Center of Genomics/Roadmap II (project number TT17). Additional funding was provided by the European Union\u0026rsquo;s Horizon Europe research and innovation programme (grant agreement No 101060011). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Data analysis was carried out in part in the High-Performance Computing Center of University of Tartu. The Estonian Biobank provided information on carrier counts for \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr and on cardiomyopathy phenotypes defined by the Code Consensus (https://code-consensus.netlify.app/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFinnGen\u003c/em\u003e\u003c/strong\u003e is a large public-private genomic research project that collects and analyses genome and health data from 500,000 Finnish biobank donors. We used publicly available GWAS summary statistics from FinnGen, including disease endpoints and quantitative traits, with primary analysis based on Data Release 12 (2024), comprising 500,348 individuals. FinnGen is coordinated by the University of Helsinki and integrates data from Finnish biobanks and national health registries. Detailed description of cohort composition, genotyping, imputation and phenotype definition is provided elsewhere\u003csup\u003e74\u003c/sup\u003e and on the FinnGen documentation website. FinnGen provided information on carrier counts for \u003cem\u003eMSTN\u003c/em\u003e p.Ile225Thr and on heart failure phenotypes.\u003c/p\u003e\n\u003cp\u003eThe FinnGen study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS/990/2017). The study was conducted under permits from Finnish national health and population data authorities (e.g. THL/2031/6.02.00/2017; Findata THL/2364/14.02/2020). Biobank samples and data were accessed under approved Finnish biobank access decisions (e.g. THL Biobank BB2017_55; Helsinki Biobank HUS/359/2017) and analyzed using FinnGen Data Freeze 12.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Million Veteran Program study population\u003c/em\u003e\u003c/strong\u003e. The Million Veteran Program (MVP) is a large national biobank established by the U.S. Department of Veterans Affairs (VA) to study the genetic basis of health and disease by linking genomic data with longitudinal electronic health records. We used publicly available GWAS summary statistics generated by MVP for cardiometabolic and related phenotypes, as released by the consortium and described previously\u003csup\u003e75\u003c/sup\u003e. The MVP study was approved by the VA Central Institutional Review Board, and all participants provided written informed consent for genetic research. MVP summary statistics were used for secondary analyses only, and no individual-level MVP data were accessed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenetic Factors for Osteoporosis Consortium (GEFOS). \u003c/em\u003e\u003c/strong\u003eGEFOS is a large international collaboration focusing on genetic factors influencing osteoporosis \u003csup\u003e76\u003c/sup\u003e. GEFOS is a European Union Seventh Framework Package funded project, registered under grant agreement number: FP7-HEALTH- F2-2008-201865-GEFOS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used R version 4.5.2 for the analysis and visualizations using the packages tidyverse (v1.3.0), ggsci (v2.9), ggrepel (v0.8.2), patchwork (v1.3.0), forestploter (v1.1.3), data.table(v1.17.0), gridExtra(v2.3), and gtable(v0.3.6). \u003c/p\u003e\n\u003cp\u003eVariant annotation and downstream analysis utilized publicly available software including Variant Effect Predictor (VEP), Graphtyper(v2), IMPUTE2(v.2.3.1), BOLT-LMM(v2.1.), LeafCutter(v1), Kallisto(v0.46), and gorpipe (v5.24.8). \u003c/p\u003e\n\u003cp\u003eURLs for all software tools are provided below: \u003c/p\u003e\n\u003cp\u003eVEP: https://www.ensembl.org/info/docs/tools/vep/index.html;\u003c/p\u003e\n\u003cp\u003eGraphtyper v.2: https://github.com/DecodeGenetics/graphtyper; \u003c/p\u003e\n\u003cp\u003eIMPUTE2 v.2.3.1: https://mathgen.stats.ox.ac.uk/impute/impute_v2.html; \u003c/p\u003e\n\u003cp\u003eBOLT-LMM v.2.1:, http://www.hsph.harvard.edu/alkes-price/software/;\u003c/p\u003e\n\u003cp\u003eEnsembl v.87: https://www.ensembl.org/index.html; \u003c/p\u003e\n\u003cp\u003eLeafCutter v.1: https://github.com/davidaknowles/leafcutter;\u003c/p\u003e\n\u003cp\u003ekallisto v0.46: https://github.com/pachterlab/kallisto.\u003c/p\u003e\n\u003cp\u003egorpipe v5.24.8: https://github.com/gorpipe/gor-test-data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of the FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., Alnylam Pharmaceuticals, Inc., AstraZeneca UK Ltd, Bayer AG, Biogen MA Inc., Boehringer Ingelheim International GmbH, Bristol Myers Squibb Inc. (and Celgene Corporation \u0026amp; Celgene International II S\u0026agrave;rl), Genentech Inc., GlaxoSmithKline Intellectual Property Development Ltd., Johnson\u0026amp;Johnson Innovative Medicine Inc., Maze Therapeutics Inc., Merck Sharp \u0026amp; Dohme LCC, Novartis AG, Pfizer Inc. and Sanofi US Services Inc. Following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank (www.auria.fi/biopankki), THL Biobank (www.thl.fi/biobank), Helsinki Biobank (www.helsinginbiopankki.fi), Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx), Finnish Clinical Biobank Tampere (www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (www.ita-suomenbiopankki.fi/en), Central Finland Biobank (www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (www.veripalvelu.fi/verenluovutus/biopankkitoiminta), Terveystalo Biobank (www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/) and Arctic Biobank (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/northern-finland-birth-cohorts-and-arctic-biobank). All Finnish Biobanks are members of BBMRI.fi infrastructure (https://www.bbmri-eric.eu/national-nodes/finland/). Finnish Biobank Cooperative -FINBB (https://finbb.fi/) is the coordinator of BBMRI-ERIC operations in Finland. The Finnish biobank data can be accessed through the Fingenious\u003csup\u003e\u0026reg; \u003c/sup\u003eservices (https://site.fingenious.fi/en/) managed by FINBB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eAdalheidur E. Larusdottir, Unnur D. Teitsd\u0026oacute;ttir, and Daniel F. Gudbjartsson\u003csup\u003e \u003c/sup\u003edesigned the study, Frosti Palsson, Anna M. Kristinsd\u0026oacute;ttir, Grimur H. Eldjarn, Gisli H. Halldorsson, Gudmar Thorleifsson, Vinicius Tragante, Lilja Stefansdottir, Egil Ferkingstad, Felix Vaura, Fanny-Dhelia Pajuste, Samuli Ripatti, Aarno Palotie, Triin Laisk, and Reedik M\u0026auml;gi contributed to the acquisition and analysis of data. Hildur M. Aegisdottir, Unnur Styrkarsdottir, Mariana Bustamante, Thorunn A. Olafsdottir, Asmundur Oddsson, Audunn S. Snaebjarnarson, Erna Valdis Ivarsdottir, and Gudmar Thorleifsson defined phenotypes. R Thomas Lumbers, Sonia Shah, Nick Sunderland, and Jiayue-Clara Jiang provided code consensus for heart failure subtypes, Sigurjon Axel Gudjonsson, and Arnar K.S. Sandholt contributed to bioinformatics analysis. Kirk Knowlton and Lincoln Nadauld contributed to design, data collection and analyses of the Intermountain study. Ole Birger Vesterager Pedersen, Erik S\u0026oslash;rensen, Sisse Rye Ostrowski, Henning Bundgaard, Johan Skov Bundgaard, Christian Erikstrup, Christina Mikkelsen, Mie Topholm Bruun, Bitten Aagaard Jensen, and Henrik Ullum contributed to design, data collection, and data analyses of the The DBDS Genetic Consortium. Jannicke Igland, Ole Andreassen and Jan Haavik contributed to design, data collection, and data analyses of the HUSK study. Adalheidur E. Larusdottir and Unnur D. Teitsd\u0026oacute;ttir analyzed the data, interpreted results and drafted the manuscript with input and supervision from Daniel F. Gudbjartsson, Anna Helgadottir, Rosa B. Thorolfsdottir, Valgerdur Steinthorsdottir, Thorhildur Olafsdottir, Gudmundur Einarsson, Gardar Sveinbjornsson, Hilma Holm, Gudmundur L. Norddahl, Ragnar G. Bjarnason, Patrick Sulem, and Gudmundur Thorgeirsson. All authors reviewed and contributed to the final version of the manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSchnyder, S. \u0026amp; Handschin, C. Skeletal muscle as an endocrine organ: PGC-1\u0026alpha;, myokines and exercise. \u003cem\u003eBone\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 115-125 (2015).\u003c/li\u003e\n \u003cli\u003eMitchell, W.K.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Sarcopenia, dynapenia, and the impact of advancing age on human skeletal muscle size and strength; a quantitative review. in \u003cem\u003eFrontiers in Physiology\u0026nbsp;\u003c/em\u003eVol. 3 JUL (2012).\u003c/li\u003e\n \u003cli\u003eChaston, T.B., Dixon, J.B. \u0026amp; O\u0026apos;Brien, P.E. Changes in fat-free mass during significant weight loss: A systematic review. in \u003cem\u003eInternational Journal of Obesity\u0026nbsp;\u003c/em\u003eVol. 31 (2007).\u003c/li\u003e\n \u003cli\u003eJastreboff, A.M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Tirzepatide Once Weekly for the Treatment of Obesity. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e \u003cstrong\u003e387\u003c/strong\u003e(2022).\u003c/li\u003e\n \u003cli\u003eWilding, J.P.H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Once-Weekly Semaglutide in Adults with Overweight or Obesity. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e \u003cstrong\u003e384\u003c/strong\u003e(2021).\u003c/li\u003e\n \u003cli\u003eLee, S.J. Targeting the myostatin signaling pathway to treat muscle loss and metabolic dysfunction. \u003cem\u003eJ Clin Invest\u003c/em\u003e \u003cstrong\u003e131\u003c/strong\u003e(2021).\u003c/li\u003e\n \u003cli\u003eCruz-Jentoft, A.J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Sarcopenia: Revised European consensus on definition and diagnosis. in \u003cem\u003eAge and Ageing\u0026nbsp;\u003c/em\u003eVol. 48 (2019).\u003c/li\u003e\n \u003cli\u003eSveinbjornsson, G.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Weighting sequence variants based on their annotation increases power of whole-genome association studies. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 314-317 (2016).\u003c/li\u003e\n \u003cli\u003eZhu, X., Topouzis, S., Liang, L.F. \u0026amp; Stotish, R.L. Myostatin signaling through Smad2, Smad3 and Smad4 is regulated by the inhibitory Smad7 by a negative feedback mechanism. \u003cem\u003eCytokine\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e(2004).\u003c/li\u003e\n \u003cli\u003eConsortium, G.T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e369\u003c/strong\u003e, 1318-1330 (2020).\u003c/li\u003e\n \u003cli\u003eNielsen, T.L., Vissing, J. \u0026amp; Krag, T.O. Antimyostatin Treatment in Health and Disease: The Story of Great Expectations and Limited Success. \u003cem\u003eCells\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e(2021).\u003c/li\u003e\n \u003cli\u003eMcPherron, A.C., Lawler, A.M. \u0026amp; Lee, S.J. Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e387\u003c/strong\u003e, 83-90 (1997).\u003c/li\u003e\n \u003cli\u003eWhittemore, L.A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Inhibition of myostatin in adult mice increases skeletal muscle mass and strength. \u003cem\u003eBiochem Biophys Res Commun\u003c/em\u003e \u003cstrong\u003e300\u003c/strong\u003e, 965-71 (2003).\u003c/li\u003e\n \u003cli\u003eSchuelke, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Myostatin Mutation Associated with Gross Muscle Hypertrophy in a Child. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e \u003cstrong\u003e350\u003c/strong\u003e(2004).\u003c/li\u003e\n \u003cli\u003eSantiago, C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The K153R polymorphism in the myostatin gene and muscle power phenotypes in young, non-athletic men. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e16323 (2011).\u003c/li\u003e\n \u003cli\u003eGonzalez-Freire, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The K153R variant in the myostatin gene and sarcopenia at the end of the human lifespan. \u003cem\u003eAge (Dordr)\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 405-9 (2010).\u003c/li\u003e\n \u003cli\u003eLi, X.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The A55T and K153R polymorphisms of MSTN gene are associated with the strength training-induced muscle hypertrophy among Han Chinese men. \u003cem\u003eJ Sports Sci\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 883-91 (2014).\u003c/li\u003e\n \u003cli\u003eBaxmann, A.C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 348-54 (2008).\u003c/li\u003e\n \u003cli\u003ePulit, S.L.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. \u003cem\u003eHum Mol Genet\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 166-174 (2019).\u003c/li\u003e\n \u003cli\u003eRyan, C.A., Purfield, D.C., Naderi, S. \u0026amp; Berry, D.P. Associations between polymorphisms in the myostatin gene with calving difficulty and carcass merit in cattle. \u003cem\u003eJournal of Animal Science\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e(2023).\u003c/li\u003e\n \u003cli\u003eOsman, N.M., Shafey, H.I., Abdelhafez, M.A., Sallam, A.M. \u0026amp; Mahrous, K.F. Genetic variations in the Myostatin gene affecting growth traits in sheep. \u003cem\u003eVet World\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 475-482 (2021).\u003c/li\u003e\n \u003cli\u003eCamporez, J.P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Anti-myostatin antibody increases muscle mass and strength and improves insulin sensitivity in old mice. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 2212-7 (2016).\u003c/li\u003e\n \u003cli\u003eBecker, C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Myostatin antibody (LY2495655) in older weak fallers: a proof-of-concept, randomised, phase 2 trial. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 948-57 (2015).\u003c/li\u003e\n \u003cli\u003eMuntoni, F.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The Clinical Development of Taldefgrobep Alfa: An Anti-Myostatin Adnectin for the Treatment of Duchenne Muscular Dystrophy. \u003cem\u003eNeurol Ther\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 183-219 (2024).\u003c/li\u003e\n \u003cli\u003eShantsila, E., Shantsila, A., Gao, Z., Zheng, Y. \u0026amp; Lip, G.Y.H. High skeletal muscle mass is related to higher blood pressure and left ventricular mass irrespective of adiposity. \u003cem\u003eEuropean Journal of Preventive Cardiology\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e(2025).\u003c/li\u003e\n \u003cli\u003eRodgers, B.D.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Myostatin represses physiological hypertrophy of the heart and excitation-contraction coupling. \u003cem\u003eJ Physiol\u003c/em\u003e \u003cstrong\u003e587\u003c/strong\u003e, 4873-86 (2009).\u003c/li\u003e\n \u003cli\u003eButcher, J.T.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Effect of myostatin deletion on cardiac and microvascular function. \u003cem\u003ePhysiol Rep\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e(2017).\u003c/li\u003e\n \u003cli\u003eJackson, M.F., Li, N. \u0026amp; Rodgers, B.D. Myostatin regulates tissue potency and cardiac calcium-handling proteins. \u003cem\u003eEndocrinology\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 1771-85 (2014).\u003c/li\u003e\n \u003cli\u003eDhindsa, R.S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Rare variant associations with plasma protein levels in the UK Biobank. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e622\u003c/strong\u003e, 339-347 (2023).\u003c/li\u003e\n \u003cli\u003eEldjarn, G.H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Large-scale plasma proteomics comparisons through genetics and disease associations. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e622\u003c/strong\u003e, 348-358 (2023).\u003c/li\u003e\n \u003cli\u003eHill, J.J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The myostatin propeptide and the follistatin-related gene are inhibitory binding proteins of myostatin in normal serum. \u003cem\u003eJ Biol Chem\u003c/em\u003e \u003cstrong\u003e277\u003c/strong\u003e, 40735-41 (2002).\u003c/li\u003e\n \u003cli\u003eCheng, J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Accurate proteome-wide missense variant effect prediction with AlphaMissense. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e381\u003c/strong\u003e, eadg7492 (2023).\u003c/li\u003e\n \u003cli\u003eMazmanian, K., Sargsyan, K., Grauffel, C., Dudev, T. \u0026amp; Lim, C. Preferred Hydrogen-Bonding Partners of Cysteine: Implications for Regulating Cys Functions. \u003cem\u003eJ Phys Chem B\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, 10288-10296 (2016).\u003c/li\u003e\n \u003cli\u003eCotton, T.R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Structure of the human myostatin precursor and determinants of growth factor latency. \u003cem\u003eEMBO J\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 367-383 (2018).\u003c/li\u003e\n \u003cli\u003ePei, Y.F.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study. \u003cem\u003eCommunications Biology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e(2020).\u003c/li\u003e\n \u003cli\u003eZillikens, M.C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 80 (2017).\u003c/li\u003e\n \u003cli\u003eHernandez Cordero, A.I.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Genome-wide Associations Reveal Human-Mouse Genetic Convergence and Modifiers of Myogenesis, CPNE1 and STC2. \u003cem\u003eThe American Journal of Human Genetics\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 1222-1236 (2019).\u003c/li\u003e\n \u003cli\u003eWang, S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Crystal structure of calsequestrin from rabbit skeletal muscle sarcoplasmic reticulum. \u003cem\u003eNat Struct Biol\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 476-83 (1998).\u003c/li\u003e\n \u003cli\u003eNolt, G.L.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Inhibition of p53-MDM2 binding reduces senescent cell abundance and improves the adaptive responses of skeletal muscle from aged mice. \u003cem\u003eGeroScience\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 2153-2176 (2024).\u003c/li\u003e\n \u003cli\u003eJoo, H.M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Ret finger protein 2 enhances ionizing radiation-induced apoptosis via degradation of AKT and MDM2. \u003cem\u003eEuropean Journal of Cell Biology\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 420-431 (2011).\u003c/li\u003e\n \u003cli\u003eGulcher, J.R., Kristjansson, K., Gudbjartsson, H. \u0026amp; Stefansson, K. Protection of privacy by third-party encryption in genetic research in Iceland. \u003cem\u003eEur J Hum Genet\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 739-42 (2000).\u003c/li\u003e\n \u003cli\u003eSudlow, C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. \u003cem\u003ePLoS Med\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e1001779 (2015).\u003c/li\u003e\n \u003cli\u003ePalsson, G.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Complete human recombination maps. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e639\u003c/strong\u003e, 700-707 (2025).\u003c/li\u003e\n \u003cli\u003eEggertsson, H.P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Graphtyper enables population-scale genotyping using pangenome graphs. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 1654-1660 (2017).\u003c/li\u003e\n \u003cli\u003eGudbjartsson, D.F.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Large-scale whole-genome sequencing of the Icelandic population. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 435-44 (2015).\u003c/li\u003e\n \u003cli\u003eKong, A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Detection of sharing by descent, long-range phasing and haplotype imputation. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 1068-75 (2008).\u003c/li\u003e\n \u003cli\u003eHalldorsson, B.V.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The sequences of 150,119 genomes in the UK Biobank. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e607\u003c/strong\u003e, 732-740 (2022).\u003c/li\u003e\n \u003cli\u003eWain, L.V.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. \u003cem\u003eLancet Respir Med\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 769-81 (2015).\u003c/li\u003e\n \u003cli\u003eWelsh, S., Peakman, T., Sheard, S. \u0026amp; Almond, R. Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort. \u003cem\u003eBMC Genomics\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 26 (2017).\u003c/li\u003e\n \u003cli\u003eConsortium, U.K.B.W.-G.S. Whole-genome sequencing of 490,640 UK Biobank participants. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e645\u003c/strong\u003e, 692-701 (2025).\u003c/li\u003e\n \u003cli\u003eLittlejohns, T.J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 2624 (2020).\u003c/li\u003e\n \u003cli\u003eLiu, Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. \u003cem\u003eElife\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e(2021).\u003c/li\u003e\n \u003cli\u003eLoh, P.R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Efficient Bayesian mixed-model analysis increases association power in large cohorts. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 284-90 (2015).\u003c/li\u003e\n \u003cli\u003eBenonisdottir, S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Epigenetic and genetic components of height regulation. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 13490 (2016).\u003c/li\u003e\n \u003cli\u003eVosa, U.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 1300-1310 (2021).\u003c/li\u003e\n \u003cli\u003eStrunz, T.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 5865 (2018).\u003c/li\u003e\n \u003cli\u003eRatnapriya, R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 606-610 (2019).\u003c/li\u003e\n \u003cli\u003eNg, B.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e An xQTL map integrates the genetic architecture of the human brain\u0026apos;s transcriptome and epigenome. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1418-1426 (2017).\u003c/li\u003e\n \u003cli\u003eHauberg, M.E.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression. \u003cem\u003eAm J Hum Genet\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 157 (2017).\u003c/li\u003e\n \u003cli\u003eZeller, T.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Genetics and beyond--the transcriptome of human monocytes and disease susceptibility. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e10693 (2010).\u003c/li\u003e\n \u003cli\u003eHao, K.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Lung eQTLs to help reveal the molecular underpinnings of asthma. \u003cem\u003ePLoS Genet\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e1003029 (2012).\u003c/li\u003e\n \u003cli\u003eGillies, C.E.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome. \u003cem\u003eAm J Hum Genet\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 232-244 (2018).\u003c/li\u003e\n \u003cli\u003ePala, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Population- and individual-specific regulatory variation in Sardinia. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 700-707 (2017).\u003c/li\u003e\n \u003cli\u003eFranzen, O.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e353\u003c/strong\u003e, 827-30 (2016).\u003c/li\u003e\n \u003cli\u003eLee, M.N.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Common genetic variants modulate pathogen-sensing responses in human dendritic cells. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e343\u003c/strong\u003e, 1246980 (2014).\u003c/li\u003e\n \u003cli\u003eGrundberg, E.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Mapping cis- and trans-regulatory effects across multiple tissues in twins. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 1084-9 (2012).\u003c/li\u003e\n \u003cli\u003eBray, N.L., Pimentel, H., Melsted, P. \u0026amp; Pachter, L. Near-optimal probabilistic RNA-seq quantification. \u003cem\u003eNat Biotechnol\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 525-7 (2016).\u003c/li\u003e\n \u003cli\u003eSorensen, E.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Data Resource Profile: The Copenhagen Hospital Biobank (CHB). \u003cem\u003eInt J Epidemiol\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 719-720e (2021).\u003c/li\u003e\n \u003cli\u003eErikstrup, C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Cohort Profile: The Danish Blood Donor Study. \u003cem\u003eInt J Epidemiol\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, e162-e171 (2023).\u003c/li\u003e\n \u003cli\u003eRefsum, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The Hordaland Homocysteine Study: a community-based study of homocysteine, its determinants, and associations with disease. \u003cem\u003eJ Nutr\u003c/em\u003e \u003cstrong\u003e136\u003c/strong\u003e, 1731S-1740S (2006).\u003c/li\u003e\n \u003cli\u003ePulley, J., Clayton, E., Bernard, G.R., Roden, D.M. \u0026amp; Masys, D.R. Principles of human subjects protections applied in an opt-out, de-identified biobank. \u003cem\u003eClin Transl Sci\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 42-8 (2010).\u003c/li\u003e\n \u003cli\u003eRoden, D.M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Development of a large-scale de-identified DNA biobank to enable personalized medicine. \u003cem\u003eClin Pharmacol Ther\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 362-9 (2008).\u003c/li\u003e\n \u003cli\u003eLeitsalu, L., Alavere, H., Tammesoo, M.L., Leego, E. \u0026amp; Metspalu, A. Linking a population biobank with national health registries-the estonian experience. \u003cem\u003eJ Pers Med\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 96-106 (2015).\u003c/li\u003e\n \u003cli\u003eKurki, M.I.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e FinnGen provides genetic insights from a well-phenotyped isolated population. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e613\u003c/strong\u003e, 508-518 (2023).\u003c/li\u003e\n \u003cli\u003eGaziano, J.M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Million Veteran Program: A mega-biobank to study genetic influences on health and disease. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 214-23 (2016).\u003c/li\u003e\n \u003cli\u003eMorris, J.A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e An atlas of genetic influences on osteoporosis in humans and mice. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 258-266 (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. \u003cstrong\u003eThe two sentinel DXA lean mass variants in the myostatin pathway: \u003cem\u003eMSTN\u0026nbsp;\u003c/em\u003ep.Ile225Thr variant and rs1870915 in \u003cem\u003eACVR2B\u003c/em\u003e.\u003c/strong\u003e\u003cem\u003eMSTN\u003c/em\u003e encodes the ligand in the myostatin pathway, and \u003cem\u003eACVR2B\u0026nbsp;\u003c/em\u003eencodes\u003cem\u003e\u0026nbsp;\u003c/em\u003eactivin-receptor type 2B, its receptor on muscle cells. The table shows the genomic position in GRCh38, annotation, and the genes within 500 Mb of each variant. The most significant DXA lean mass phenotype for each variant is leg lean mass for \u003cem\u003eMSTN\u0026nbsp;\u003c/em\u003ep.Ile225Thr variant\u003cem\u003e\u0026nbsp;\u003c/em\u003eand total lean mass for rs1870915 in \u003cem\u003eACVR2B\u003c/em\u003e. Effect allele frequency in the main datasets used in this study is shown. Detailed information on both variants is found in Supplementary Table 1. Information p.Ile225Thr hetero- and homozygous numbers is found in Supplementary fig.5. Information on world-wide allele frequency of p.Ile225Thr is in found in Supplementary fig.6. MVP, Million Veterans Program.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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As such interventions become more widely used, agents that preserve or increase muscle mass are needed. To identify such therapeutic opportunities, we performed genome-wide association meta-analyses (GWAS) of arm, leg, trunk and total lean mass measured by dual-energy X-ray absorptiometry (DXA), a widely used method to estimate muscle mass. We identified 63 loci, including the rare missense variant in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMSTN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (p.Ile225Thr, rs143242500), which had the largest effect on leg lean mass (β = 0.28 SD [95% CI: 0.19, 0.37],\u003c/strong\u003e \u003cem\u003e\u003cstrong\u003eP \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e= 1.9 × 10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-9\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e). \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMSTN \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eencodes myostatin, a negative regulator of skeletal muscle mass and a therapeutic target for muscle wasting disorders\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e. p.Ile225Thr is the first genome-wide significant association in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMSTN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in humans with functional consequences and could provide further insight into long-term systemic effects of myostatin inhibition.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Genome-wide association study identifies a functional myostatin variant increasing lean mass in humans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 07:00:51","doi":"10.21203/rs.3.rs-9238373/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ec1a31cb-ea6d-421d-bbf0-167df30eb9e2","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65752509,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"},{"id":65752510,"name":"Health sciences/Anatomy/Musculoskeletal system/Muscle/Skeletal muscle"},{"id":65752511,"name":"Biological sciences/Genetics/Functional genomics/Gene expression profiling"},{"id":65752512,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2026-04-08T07:00:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 07:00:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9238373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9238373","identity":"rs-9238373","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}