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Large meta-analysis of genome-wide association studies identifies five loci for lean body mass

  • Nature Communications 8, Article number: 80 (2017)
  • doi:10.1038/s41467-017-00031-7
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Abstract

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10−8) or suggestively genome wide (p < 2.3 × 10−6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.

Introduction

Lean body mass consists primarily of skeletal muscle, is an important contributor to physical strength, mobility, stamina, and balance1,2,3, and has been a very recent focus of an effort to define “sarcopenia” (loss of muscle tissue) for clinical care and drug development4. The determinants of adult skeletal muscle mass have not been well characterized. It is known, for example, that exercise produces increases in muscle mass, and there is some evidence that protein intake is directly associated with lean mass5. Heavier people have increased muscle mass6, which may be due to the loading effect of increased fat mass or may reflect a common genetic background between muscle and adipose tissue7. With aging, there is a progressive loss of skeletal muscle mass, and a concurrent increase in fatty infiltration and fibrosis of muscle. This loss of muscle mass may reach a critical point at which time functional impairment and even disability occurs8. In fact, the annual healthcare costs of sarcopenia in the United States are estimated to be in excess of 18 billion dollars9.

As estimated from family and twin studies, lean mass is a highly heritable phenotype with heritability estimates of 0.52–0.6010, 11. While there have been previous studies related to the genetic background of BMI and fat mass, few studies have searched for genes associated with lean mass12,13,14,15, 16, 17. To date, no single-nucleotide polymorphisms (SNPs) have been found to be associated at a genome-wide significance level with lean mass (p-values < 5 × 10−8)18, 19. A copy number variation located in the GREM1 gene was reported to be associated with lean body mass in a genome-wide association study (GWAS) of 1627 Chinese20. Guo et al.19 identified in 1627 Chinese and replicated in 2286 European ancestry individuals, a locus near CNTF and GLYAT genes at 11q12.1 in a bivariate GWAS for bone-size phenotypes and appendicular lean mass. Most recently in a study of Japanese women, the PRDM16 gene was suggested to be associated with lean mass21.

With the advent of relatively simple, inexpensive methods of measuring the fat and lean compartments of the body using dual energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA), multiple cohort studies have accumulated phenotypic information on body composition that permits large-scale GWAS to be performed. While whole body lean mass incorporates all of the non-fat soft tissue including the internal organs, appendicular lean mass, estimated by DXA and BIA, may be a better reflection of skeletal muscle mass22,23,24. To identify genetic loci associated with whole body and appendicular lean mass, we performed a large-scale GWAS meta-analysis in over 100,000 participants from 53 studies yielding sufficient power to identify common variants with small to moderate effect sizes.

Results

GWAS meta-analyses for discovery and replication

Descriptions and characteristics of the study populations in the discovery stage and the replication stage are shown in Supplementary Tables 4 and 5 and Supplementary Note 2.8. The age of the participants ranged from 18 to 100 years. In the GWAS discovery set, comprising 38,292 participants for whole body lean mass and 28,330 participants for appendicular lean mass, a substantial excess of low p-values compared to the null distribution was observed after genomic control adjustment of the individual studies prior to meta-analysis: λGC = 1.076 and λGC = 1.075, for whole body and appendicular lean mass, respectively (Supplementary Fig. 1)

Meta-analyses were conducted using the METAL package (www.sph.umich.edu/csg/abecasis/metal/). We used the inverse variance weighting and fixed-effect model approach. Supplementary Table 1 shows the genome-wide significant (GWS) and suggestive (sGWS) results in the discovery set. For whole body lean mass, we observed one GWS result in/near HSD17B11 and 12 sGWS results (in/near VCAN, ADAMTSL3, IRS1, FTO (two SNPs), MOV10, HMCN1, RHOC, FRK, AKR1B1, CALCR, and KLF12). For appendicular lean mass, one result was GWS (intronic SNP in PKIB) and seven were sGWS (in/near VCAN, ADAMTSL3, HSD17B11, IRS1, FRK, TXN, and CTNNA3).

We selected 21 associations (13 for whole body lean mass and 8 for appendicular lean mass; a total of 16 discovery SNPs with 5 SNPs overlapping between the two phenotypes) (Supplementary Table 1) to conduct a replication study in a set of 33 cohorts comprising up to 48,125 participants of European descent for whole body lean mass and 43,258 participants for appendicular lean mass. Both in silico replication and de novo genotyping for replication was conducted. Table 1 shows the results for successfully replicated SNPs in participants of European ancestry, including the discovery phase, replication phase, and the combined results.

Table 1: Results for the successfully replicated SNPs in discovery, replication and combined sample

For whole body lean mass, joint analysis of the discovery and replication cohorts successfully replicated five SNPs in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO (p-values between 1.4 × 10−8 and 1.5 × 10−11 and lower than discovery p-values). Three of these five SNPs (located in/near VCAN, ADAMTSL3, and IRS1) were also successfully replicated (p-values between 5 × 10−8 and 2.9 × 10−10) for appendicular lean mass. None of the eight replicated associations (five for whole body and three appendicular lean mass) had significant heterogeneity at α = 0.00625 (0.05/8, Bonferroni-corrected for eight tests). Only mild heterogeneity was indicated in two whole body lean mass SNPs when using an uncorrected threshold of α = 0.05: FTO (p = 0.018, I2 = 34%) and HSD17B11 (p = 0.04, I2 = 31%). For appendicular lean mass, p-values for heterogeneity were >0.05 for all three replicated SNPs.

Supplementary Table 1 shows the results for all participants including those of non-European descent as well. Results were similar showing low heterogeneity using “transethnic meta-analysis” (MANTRA)25 for inclusion of both cohorts of European ancestry and replication cohorts with Asians or African Americans. Heterogeneity probability values were below 0.5 for all replicated SNPs both for whole body and appendicular lean mass. Furthermore, in this combined analysis, except for the VCAN locus, log10 Bayes’ factors were >6.0 and p-values were smaller than those found in European-only ancestry analysis.

Additional analyses stratified by sex failed to identify significant sex-specific associations or evidence of an interaction between SNPs and sex (Supplementary Table 2 and Supplementary Note 1). Similarly, we also found no evidence for heterogeneity between measurement techniques (BIA vs DXA; Supplementary Table 3 and Supplementary Note 2). Finally, we failed to replicate previously reported candidate genes for lean mass (Supplementary Note 2.4)

Association with other anthropometric phenotypes

We looked for associations between the lead SNPs in the five replicated loci and other reported anthropometric phenotypes from the GIANT Consortium (Supplementary Table 10)26,27,28,29. There were no significant associations (p < 0.05) between the SNP in HSD17B11 and any reported phenotypes. The allele associated with greater lean mass was associated with lower values of various anthropometric traits for the IRS1 (hip circumference (HC), waist circumference (WC)), VCAN (hip ratio, WC adjusted for BMI, waist to hip ratio adjusted for BMI), and the ADAMTSL3 locus (height, hip, and WC with the association becoming more significant for hip and waist adjusted for BMI). The replicated FTO SNP was very significantly associated with BMI (p = 2.7 × 10−144) and significantly associated with HC (p = 9.3 × 10−81) and WC (p = 1.3 × 10−96) in the same direction as the lean mass association (i.e., the higher lean mass allele was associated with higher values of anthropometric traits).

Annotation and enrichment analysis of regulatory elements

Among five replicated SNPs, rs9991501 (HSD17B11 locus) and rs2287926 (VCAN locus) are missense SNPs. rs2287926 was predicted as possibly damaging to the protein structure and/or function by PolyPhen-230. Since the remaining GWAS SNPs are non-coding, to estimate whether these SNPs are located in regulatory elements in specific human tissue/cell types, we performed a tissue-specific regulatory-element enrichment analysis using experimental epigenetic evidence including DNAse hypersensitive sites, histone modifications, and transcription factor-binding sites in human cell lines and tissues from the ENCODE Project and the Epigenetic Roadmap Project. As shown in Table 2, 86 SNPs were in high LD (r2 ≥ 0.8) with the GWAS lead SNP rs2943656 at the IRS1 locus. The SNPs in this locus that were in high LD were enriched in enhancers estimated by ChromHMM31 (permutation p-values <0.05; after multiple testing corrections), especially enriched in fat and brain tissues, but not in skeletal muscle, smooth muscle, blood and gastrointestinal tract tissues in the ENCODE and Roadmap projects. Although the IRS1 locus was not specifically enriched with skeletal muscle tissue enhancers, the lead SNP rs2943656 itself at the IRS1 locus was actually located within a histone mark-identified promoter in an adult skeletal muscle sample and a histone mark-identified enhancer in several muscle samples (such as muscle satellite cultured cells, fetal skeletal muscle, skeletal muscle myoblasts, and skeletal muscle myotubes (Supplementary Figs. 6 and 7)). Based on the position weight matrices (PWMs) score from Chip-seq and other sequencing resources, rs2943656 was found to possibly alter regulatory motifs, including Irf, Foxo, Sox, and Zfp105 in skeletal muscle tissues with a PWM score p-value <4−7(≈6.1 × 10−5)32.

Table 2: Tissue-specific regulatory-element enrichment analyses of the GWAS loci (GWAS SNPs and SNPs in LD with the GWAS SNPs)

For the ADAMTSL3 locus, the GWAS lead SNP rs4842924 was located in a histone mark-identified enhancer in smooth muscle tissues (Supplementary Figs. 11 and 12). The promoter/enhancer enrichment analysis in the ADAMTSL3 locus showed significant enrichments in skeletal muscle, smooth muscle, fat and brain tissues, but not in blood and gastrointestinal tract tissues. At the FTO locus, a few SNPs in high LD with the GWAS lead SNP, rs2287926, were located within a group of enhancers that are not muscle tissue-specific (Supplementary Fig. 13); therefore, no significant enrichment was found in any tissues listed in Table 2.

Expression quantitative trait loci

We queried existing cis-expression quantitative trait loci (eQTLS) analyses on the five replicated GWAS SNPs rs2943656, rs9991501, rs2287926, rs4842924, and rs9936385 with transcripts within 2 Mb of the SNP position in skeletal muscle tissues as well as in subcutaneous adipose, omental adipose, liver tissue, lymphocytes, and primary osteoblasts (obtained from bone biopsies). Rs9936385 was associated with FTO expression in skeletal muscle tissues in the FUSION samples (p = 4.4 × 10−11) (Supplementary Table 10). However, in sequential conditional analysis, upon addition of the lead FTO eQTL SNP (rs11649091, association p-value with FTO gene expression = 5.1 × 10−15), rs9936385 was no longer significantly associated (p = 0.16), whereas rs11649091 remained significantly associated with FTO gene expression (p = 1.9 × 10−5). SNP rs11649091 could not be imputed using our HapMap imputation references; thus, we did not have association results between rs11649091 and lean mass in the present study. The T allele of rs9936385, associated with reduced lean mass in the present study, was significantly associated with lower FTO gene expression levels in the GTEx project (Supplementary Table 9 and Supplementary Fig. 14). For rs9936385, we also examined IRX3 and IRX5 expression in skeletal muscle tissues, as recent reports have implicated GWAS SNPs associated with obesity in intron 2 of the FTO gene as being associated with IRX3 and IRX5 gene expression in brain33 and adipose tissue34. SNP rs9936385 was not significantly associated with IRX3 and IRX5 gene expression in skeletal muscle tissues. For missense SNP rs9991501 in the HSD17B11 locus, a significant association with HSD17B11 gene expression was found in skeletal muscle from the GTEx project (p = 1.4 × 10−4). There were no significant GWAS SNPs in strong LD with this one; thus conditional analyses were not performed.

As shown in Supplementary Table 10, for GWAS lead SNP rs2943656 in the IRS1 locus, significant eQTLs with the IRS1 gene expression in omental (p = 4 × 10−7) and subcutaneous fat tissue (p = 6.44 × 10−6) were found.

Finally we found no evidence for differential expression of our five replicated genes in young vs old muscle biopsies (Supplementary Note 2.7 for methods and results).

Discussion

In this first large-scale GWA meta-analysis study for lean mass that included most of the cohorts worldwide with lean mass phenotypes, we identified and successfully replicated five GWS loci (in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO genes) for whole body lean mass and three of these (in/near VCAN, IRS1, and ADAMTSL3 genes) for appendicular lean mass, both important for sarcopenia diagnosis.

This study contributes to a better understanding of the biology underlying inter-individual variation in muscle mass, since lean body mass consists primarily of muscle mass (especially in the extremities). Genetic determinants of lean body mass cannot be studied specifically by using anthropometric measures such as height, waist circumference, hip circumferences, or BMI, as evidenced by our finding of associations between genetic loci and lean mass that were not observed in results from the GIANT consortium26,27,28,29. Four novel GWS loci for lean mass phenotypes harboring ADAMTSL3, VCAN, HSD17B11, and IRS1 genes have biologic effects supporting their role in skeletal muscle. Although the functional involvement of the ADAMTSL3 gene (a disintegrin-like and metalloprotease domain with thrombospondin type I motifs-like 3) remains unknown, it has been shown to be consistently associated with adult height in large human samples26, including individuals of African ancestry35. The gene is expressed ubiquitously, including in skeletal muscle but the lead GWAS SNP in this locus was not significantly associated with expression of ADAMTSL3 in any of the tissues examined. One might hypothesize that our observed association between this gene and lean mass could be a reflection of an allometric relationship between muscle mass/size and body size36. Since our analyses were adjusted for height, it is plausible that variation in ADAMTSL3 is associated with muscle mass directly. In fact, in a recent study that identified classes of potentially regulatory genomic elements that are enriched in GWAS loci, the height phenotype was almost exclusively enriched in DNAse sites in muscle37. In our study, we observed enrichment of regulatory genomic elements in this locus in both skeletal muscle and smooth muscle, which may not fully support a unique role of this gene in only skeletal muscle tissue.

Versican (VCAN) plays a role in intercellular signaling and in connecting cells with the extracellular matrix, which also is important for the skeletal muscle. Interestingly, VCAN facilitates chondrocyte differentiation and regulates joint morphogenesis38, and therefore has a wider role in the musculoskeletal health. The GWAS SNP in the HSD17B11 locus was significantly associated with the HSD17B11 gene expression in skeletal muscle. Hydroxysteroid (17-beta) dehydrogenase 11 (HSD17B11) functions in steroid biosynthesis, and most relevant to muscle tissue, contributes to androgen catabolic processes. Although not much is known about how variants in this gene associate with sex-hormone related human phenotypes, androgen metabolism is certainly a driver of muscle tissue anabolism and catabolism.

As for the FTO locus, variants in the FTO gene, which is known to regulate postnatal growth in mice39, have been found to be associated with adiposity/obesity40 and related traits, such as BMI41, metabolic syndrome/type 2 diabetes and even with menarche42. Apart from its role in adiposity, in two recent candidate gene studies, SNPs in FTO were found to associate both with DXA-derived fat and lean mass43, and in one, the association for lean mass was only slightly attenuated after fat mass adjustment44, similar to our results using fat mass-adjusted lean mass. Also, FTO knockout mice have been shown to have not only reduced fat mass, but also decrease in lean mass45. Recently, obesity associated non-coding sequences in the FTO gene were found to be functionally connected to the nearby gene, IRX3, by directly interacting with the promoters of this gene33, suggesting obesity SNPs located inside the FTO gene may regulate gene expression other than FTO. Our GWAS lead SNP rs9936385 in the FTO locus was in LD with the obesity GWAS SNP and located in the same haplotype. A denser fine-mapping study using sequencing of the FTO locus with a better resolution will be helpful in narrowing down the FTO region to identify potential causal variant(s). The actual function of the variants and the underlying mechanisms of FTO’s involvement in skeletal muscle biology still need to be further elucidated by in vitro and animal experiments.

The other body composition-related gene that was successfully replicated is the insulin receptor substrate 1 (IRS1), which belongs to the insulin signaling pathway and participates in growth hormone and adipocytokine signaling pathways. Besides being overexpressed in adipocytes, IRS1 is also highly expressed in skeletal muscle13, IRS1 polymorphisms have been associated with fasting insulin-related traits46, adiposity13, and serum triglycerides/HDL cholesterol47. Our GWAS lead SNP rs2943656 was associated with IRS1 gene expression in skeletal muscle obtained from the GTEx project. Interestingly, another SNP, rs2943650, near IRS1 in high LD with our lead SNP (R2 = 0.854) was previously found to be associated with percent body fat in the opposite direction from the association that we find with lean mass13. The body fat percentage decreasing allele in this study was associated with lower IRS1 expression in omental and subcutaneous fat13. Another study reported an association between a SNP in high LD (rs2943641) with IRS-1 protein expression and insulin-induced phosphatidylinositol 3-OH kinase activity in skeletal muscle13. Also rs2943656 was associated with obesity traits in the GIANT Consortium; the allele associated with greater lean mass was inversely associated with obesity traits. Further understanding of the potential functional effects of these variants in the IRS1 locus are needed to determine whether they have pleiotropic and opposite effects on fat and lean tissue. The same is true for variants in the FTO locus for which the allele that was associated with greater lean mass was previously reported to be associated with greater fat mass.

From the current eQTL data analysis, we have no definitive evidence that the non-coding GWAS SNPs (or variants in high LD) functionally influence gene expression of IRS1, HSD17B1, and FTO in skeletal muscle. Use of a larger reference panel for imputation in the GWAS sample, and analysis of larger tissue expression data sets coupled with conditional analysis will help reveal if underlying functional associations exist. It should be emphasized that with the current data we are not able to determine which SNPs may be functional and it is possible that the identified lean mass variants are not driving the associations with expression. Using ENCODE and Epigenetic Roadmap data, we found that the GWAS SNPs (or SNPs in LD with these SNPs) were significantly enriched in the predicted gene regulatory regions (in our case, enhancers) not only in skeletal muscle, but also in smooth muscle, fat and brain tissues. With the complexity of lean mass phenotypes, we cannot rule out the possibility that these genes are involved in regulating lean mass biology via tissues other than skeletal muscle.

Using results from the overall meta-analysis, the percent variance explained by the successfully replicated SNPs was 0.23% and 0.16% for whole body and appendicular lean mass, respectively. Estimates were slightly higher when we used individual level data from the Framingham Study cohorts (percent variance explained of 0.97% and 0.55% for whole body and appendicular lean mass, respectively). This relatively small percentage of explained variance is not dissimilar to other body composition measures such as bone mineral density of the femoral neck, for which 63 SNPs explained 5.8% of the variance48. To estimate the proportion of variance for lean mass explained by all genotyped SNPs across the genome in Framingham Study participants, we applied a GREML model implemented in the GCTA package49 with the assumption that all 550K genotyped SNPs captured ≥80% of the common sequence variance in the Framingham Study participants who are Caucasians of European ancestry. We estimated that the proportion of whole body lean mass and appendicular lean mass variance explained by all genotyped SNPs together was 43.3% (SE: 2.7%) and 44.2% (SE: 2.6%), respectively (after adjustment for age, age2, sex, height and fat mass), suggesting most of the heritability of lean mass was not detected in the current study due to the limitations of study design (only common variants in this study). This percent variance is similar to the 45% of the variance explained for height using this method49.

Because of the substantial sexual dimorphism in body composition with men having higher muscle mass compared with women24, we performed both sex-combined and sex-stratified analyses. We examined potential sex differences in the genetic associations. Lean mass is a highly heritable trait and the heritability is of similar magnitude in both genders24, 50. No formal interaction test between SNP and sex was significant for any of these SNPs. Thus, our findings do not support any substantial sex-specific genetic influence for any of the successfully replicated lean mass SNPs. We cannot rule out the possibility that the reported GWS SNPs are false-positive findings, although the chance of such false positives is extremely low due to the robustness of replication in our well-powered study. There are also other limitations to our study. Because lean mass is correlated with fat mass, we adjusted for fat mass to focus our search on genes contributing to lean mass independent of those regulating fat mass. A potential limitation with this strategy of adjusting for fat mass is that the power to identify genetic signals with a similar impact on lean and fat mass will be reduced. Nevertheless, the FTO signal was found to be significantly associated with lean mass after fat adjustment and the direction of this association was the same as the association with fat mass. Since androgens have a major impact on muscle mass, it is a limitation of the present study that the X chromosome, harboring the androgen receptor gene, was not included in the present meta-analysis. Another potential weakness of this study is our decision to meta-analyze body composition results using two different techniques (BIA and DXA). Nevertheless, the two methods are highly correlated (r = 0.83 for Framingham cohort participants), and by combining them power to detect GWS loci was greatly enhanced.

In conclusion, in this first large-scale meta-analysis of GWAS, five GWS variants in or near the HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO genes were found to be robustly associated with lean body mass. Three of these loci were found to be significantly enriched in enhancers and promoters in muscle cells, suggesting that our signals have a potential functional role in muscle. Our findings shed light on pathophysiological mechanisms underlying lean mass variation and potential complex interrelations between the genetic architecture of muscle mass, fat mass, body height and metabolic disease.

Methods

Study summary

We focused on two phenotypes: (1) whole body lean mass; (2) appendicular lean mass. For these two phenotypes, we performed a genome-wide meta-analysis of the discovery cohorts (Stage I), then meta-analyzed the genotyped discovery SNPs in replication cohorts (Stage II), followed by a combined analysis with discovery and replication cohorts (Supplementary Fig. 1). The total sample size for the combined analysis was just over 100,000 from 53 studies. Our primary results are based on the ~85,000 individuals of European descent in 47 studies, as was pre-specified. Because whole body and appendicular lean mass are correlated with fat mass and height, analyses were adjusted for these potential confounders in addition to sex, age, age2, and other study-specific covariates to focus our search on genes contributing to lean mass independent of those of body height and fat mass.

Study population

The Stage I Discovery sample comprised 38,292 individuals of European ancestry drawn from 20 cohorts with a variety of epidemiological designs and participant characteristics (Supplementary Tables 46 and Supplementary Note 2.8). Whole body lean mass was measured using DXA (10 cohorts, n = 21,074) and BIA (10 cohorts, n = 17,218). Of the 20 cohorts, 15 consisted of male and female subjects, while 2 had male and 3 had female subjects only. In total, the cohorts included 22,705 women and 15,587 men. Appendicular lean mass was estimated in 28,330 subjects from a subset of 15 cohorts (9 using DXA and 6 using BIA).

Subjects from 33 additional studies were used for replication with a total sample size of 63,475 individuals. Of these 63,475, the majority was of European ancestry (n = 47,227 in 27 cohorts), and the remaining 16,248 were of African American, South Asian, or Korean ancestry (Supplementary Table 4). All these 33 cohorts had data for whole body lean mass. Among them, 16 studies had DXA measurements (n = 23,718) and 17 studies had BIA measurements (n = 39,757). Twenty-five cohorts had data for appendicular lean mass in a total of 45,090 individuals (16 cohorts with DXA (n = 23,718) and 9 with BIA (n = 21,372)). Of these, 42,360 individuals (23 cohorts) were of European ancestry and the remaining 2730 of African American and Korean descent. Our a priori aim was to perform replication in cohorts with European subjects only and to explore if adding non-European cohorts would increase power or show evidence of heterogeneity due to ethnicity. The Stage II Replication included cohorts with existing GWAS data that were unavailable at the time of the Stage I Discovery, and cohorts who agreed to undergo de novo genotyping. All studies were approved by their institutional ethics review committees and all participants provided written informed consent.

Lean mass measurements

Lean mass was measured in all cohorts using either DXA or BIA. DXA provides body composition as three materials based on specific X-ray attenuation properties; bone mineral, lipid (triglycerides, phospholipid membranes, etc.) and lipid-free soft tissue. For each pixel on the DXA scan, these three materials are quantified. For the cohorts with DXA measures, the phenotype used for these analyses was the lipid free, soft tissue compartment that is referred to as lean mass, and is the sum of body water, protein, glycerol, and soft tissue mineral mass. Two lean mass phenotypes were used: whole body lean mass and appendicular lean mass. The latter was obtained by considering only pixels in the arms and legs collectively, which has been demonstrated to be a valid measure of skeletal muscle mass51.

Some of the cohorts estimated body composition using BIA, which relies on the geometrical relationship between impedance (Z), length (L), and volume (V) of an electrical conductor. Adapted to the human body, V corresponds to the volume of fat-free mass (FFM) and L to the height of the subject. Z is composed of the pure resistance (R) of the conductor, the FFM, and the reactance (Xc), produced by the capacitance of cellular membranes, tissue interfaces and non-ionic tissues: Z2 = R2 + Xc2. A variety of BIA machines were used by the various cohorts (summarized in Supplementary Table 5), and in some cohorts, the specific resistance and reactance measures were not available because the manufacturers provided only summary output on FFM. For BIA cohorts with specific resistance and reactance measures, we used the validated equation from Kyle et al.52 with an R2 of 0.95 between BIA and DXA to calculate the appendicular lean mass.

Stage 1: genome-wide association analyses in discovery cohorts

Genotyping and imputation

Genome-wide genotyping was done by each study on a variety of platforms following standard manufacturer protocols. Quality control was performed independently for each study. To facilitate meta-analysis, each group performed genotype imputation with IMPUTE53 or MACH54 software using HapMap Phase II release 22 reference panels (CEU or CHB/JPT as appropriate). Overall imputation quality scores for each SNP were obtained from IMPUTE (“proper_info”) or MACH (“rsq_hat”). Details on the genotyping platform used, genotype quality control procedures and software for imputation employed for each study are presented in Supplementary Table 6.

Study-specific genome-wide association analyses with lean mass

In each study, a multiple linear regression model with additive genetic effect was applied to test for phenotype–genotype association using ~2.0 to 2.5 million genotyped and/or imputed autosomal SNPs. Other covariates adjusted in the model included ancestral genetic background, sex, age, age2, height, fat mass measured by the body composition device (kg) and study-specific covariates when appropriate such as clinical center for multi-center cohorts. Adjustment for ancestral background was done within cohorts using principal component analyses as necessary. Furthermore, for family-based studies, including the Framingham Study, ERF, UK-Twins, Old Order Amish Study and the Indiana cohort, familial relatedness was taken into account in the statistical analysis within their cohorts by: (1) linear mixed-effects models that specified fixed genotypic and covariate effects and a random polygenic effect to account for familial correlations (the R Kinship package; http://cran.r-project.org/web/packages/) in the Framingham Study; (2) the GenABEL55 in the ERF and UK-Twins cohorts; (3) the Mixed Model Analysis for Pedigrees (MMAP) program (http://edn.som.umaryland.edu/mmap/index.php) in the Amish cohort; and GWAF, an R package for genome-wide association analyses with family data in the Indiana cohort56.

Meta-analyses

Meta-analyses were conducted using the METAL package (www.sph.umich.edu/csg/abecasis/metal/). We used the inverse variance weighting and fixed-effect model approach. Prior to meta-analysis, we filtered out SNPs with low minor allele frequency, MAF (<1%) and poor imputation quality (proper_info<0.4 for IMPUTE and rsq_hat<0.3) and applied genomic control correction where the genomic control parameter lambda (λGC) was >1.0.

We used quantile–quantile (Q–Q) plots of observed vs. expected –log10 (p-value) to examine the genome-wide distribution of p-values for signs of excessive false-positive results. We generated Manhattan plots to report genome-wide p-values, regional plots for genomic regions within 100 Kb of top hits, and forest plots for meta-analyses and study-specific results of the most significant SNP associations. A threshold of p < 5 × 10−8 was pre-specified as being genome-wide significant (GWS), while a threshold of p < 2.3 × 10−6 was used to select SNPs for a replication study (suggestive genome-wide significant, sGWS).

Stage 2: replication

In each GWS or sGWS locus, we selected the lead SNP with the lowest p-value for replication. In addition, GWS or sGWS SNPs that had low-linkage disequilibrium with the lead SNPs (LD < 0.5) were also selected for replication. Both in silico replication and de novo genotyping for replication was conducted. In silico replication was done in 24 cohorts with GWAS SNP chip genotyping that did not have data available at the time of the initial discovery efforts (Supplementary Table 7). De-novo replication genotyping was done using: KBioScience Allele-Specific Polymorphism (KASP) SNP genotyping system (in OPRA, PEAK25, AGES, CAIFOS, DOPS cohorts), TaqMan (METSIM), Illumina OmniExpress + Illumina Metabochip (PIVUS and ULSAM), or Sequenom’s iPLEX (WHI) (Supplementary Table 8). Samples and SNPs that did not meet the quality control criteria defined by each individual study were excluded. Minimum genotyping quality-control criteria were defined as: SNP call rate > 90% and Hardy–Weinberg equilibrium p > 1 × 10−4.

Meta-analysis of replication and discovery studies

In the replication stage, we meta-analyzed results from: (1) individuals of European descent only (Rep-EUR); and (2) all replication cohorts with multiple ethnicities (Rep-All). Likewise we meta-analyzed results from discovery cohorts and European-descent-only replication cohorts (“Combined EUR”) from discovery cohorts and all replication cohorts (“Combined All”). To investigate and account for potential heterogeneities in allelic effects between studies, we also performed “trans-ethnic meta-analysis” using MANTRA25 in the replication sample that included all ethnic groups (“Rep-All”) and in the combined analysis of the discovery and all ethnic groups in the replication sample (“Combined All”).

A successful replication was considered if: (1) the association p-value in the cumulative-meta-analysis (Combined EUR) was genome-wide significant (p < 5 × 10−8) and less than the discovery meta-analysis p-value; or (2) the association p-value in the meta-analysis of replication-cohorts only (Rep-EUR) was less than p = 0.0024 (a Bonferroni-adjusted threshold at p = 0.05/21 since there were a total of 21 tests performed for whole body and appendicular lean mass in Rep-EUR cohorts during replication). Using the METAL package we also estimated I2 to quantify heterogeneity and p-values to assess statistical significance for a total of eight associations that were replicated in the cumulative-meta-analysis (combined EUR, five SNPs for whole body and three for appendicular lean mass).

To estimate the phenotypic variance explained by the genotyped SNPs in the Framingham Heart Study (FHS), we used a restricted maximum likelihood model implemented in the GCTA (Genome-wide Complex Trait Analysis) tool package57, 58 and adjusted for the same set of covariates included in our GWAS.

Finally, we examined associations between all imputed SNPs in/near five genes (THRH, GLYAT, GREM1, CNTF, and PRDM16 including 60 kB up and downstream of the gene) and lean mass, as these genes have been implicated to have associations with lean mass in previous association studies18,19,20.

Annotation and enrichment analysis of regulatory elements

For coding variants, we predicted their function by PolyPhen-2. For all variants, we annotated potential regulatory functions of our replicated GWAS SNPs and loci based on experimental epigenetic evidence including DNAse hypersensitive sites, histone modifications, and transcription factor-binding sites in human cell lines and tissues from the ENCODE Project and the Epigenetic Roadmap Project. We first selected SNPs in high LD (r2 ≥ 0.8) with GWAS lead SNPs based on the approach of Trynka et al.59 We then identified potential enhancers and promoters in the GWAS loci (GWAS SNPs and SNPs in LD with the GWAS SNPs) across 127 healthy human tissues/normal cell lines available in the ENCODE Project and the Epigenetic Roadmap Project from the HaploReg4 web browser60 using ChromHMM31. To evaluate whether replicated GWAS loci were enriched with regulatory elements in skeletal muscle tissue, we performed a hypergeometric test. Specifically we tested whether estimated tissue-specific promoters and enhancers in a GWAS locus were enriched in eight relevant skeletal muscle tissues/cell lines vs enrichment in non-skeletal muscle tissues (119 tissues/cell lines). The permutation with minimum p-value approach was performed to correct for multiple testing. Permutation p-values <0.05 were considered statistically significant. In addition, we also performed enrichment analyses in smooth muscle tissues/cells, fat tissue, brain, blood cells and gastrointestinal tract tissues. The eight skeletal muscle relevant tissues/cells were excluded when conducting enrichment analyses for other tissue types. The detailed information for tissue types and chromatin state estimation is described in the Supplementary Materials.

cis-eQTL

We conducted cis-eQTL analyses on the five replicated GWS loci, SNPs rs2943656, rs9991501, rs2287926, rs4842924, and rs9936385, with gene expression within 2 Mb of the SNP position. A linear regression model was applied to examine associations between SNP and gene expression. The eQTL analyses were performed in five studies with available human skeletal muscle tissues, including: GTEx61, STRRIDE62, 63, a study with chest wall muscle biopsies from patients who underwent thoracic surgery for lung and cardiac diseases64, the Finland-United States Investigation of NIDDM Genetics (FUSION) Study65, and a study of Pima Indians66. In addition, eQTL analyses were also conducted in studies with other human tissues, including subcutaneous adipose67, 68, omental adipose67, 68, liver tissue67, lymphocytes69, and primary osteoblasts70 (obtained from bone biopsies). These five GWAS SNPs were either genotyped or imputed in each sample. The detailed methods are described in the Supplementary Materials. Multiple testing was corrected by using false discovery rate (FDR q-value <0.05) to account for all pairs of SNP-gene expression analyses in multiple tissues and studies.

Data availability

All relevant data are available from the authors and summary level results are available on dbGaP.

Additional Information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We acknowledge the essential role of the Cohorts for Heart and Aging Research in Genome Epidemiology (CHARGE) Consortium in development and support of this manuscript. CHARGE members include the Netherland’s Rotterdam Study (RS), Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), the NHLBI’s Atherosclerosis Risk in Communities (ARIC) Study, and Iceland’s Age, Gene/Environment Susceptibility (AGES) Reykjavik Study. Age, Gene/Environment Susceptibility Reykjavik Study (AGES-Reykjavik): has been funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, (VSN: 00-063) and the Data Protection Authority. The researchers are indebted to the participants for their willingness to participate in the study. Old Order Amish (OOA): this work was supported by NIH research grants U01 HL72515, U01 GM074518, R01 HL088119, R01 AR046838, and U01 HL084756. Partial funding was also provided by the Mid-Atlantic Nutrition and Obesity Research Center of Maryland (P30 DK072488).). L.M.Y.-A. was supported by F32AR059469 from NIH/NIAMS. M.F. was supported by American Heart Association grant 10SDG2690004. Cardiovascular Health Study (CHS): This CHS research was supported by NHLBI contracts N01-HC- 85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086; N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, N01-HC-85239, and by HHSN268201200036C and NHLBI grants HL080295, HL087652, HL105756, HL103612, HL120393, and HL130114 with additional contribution from NINDS. Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the NIA. See also http://www.chs-nhlbi.org/pi.htm. DNA handling and genotyping at Cedars-Sinai Medical Center was supported in part by the National Center for Research Resources, grant UL1RR033176, and is now at the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124; in addition to the National Institute of Diabetes and Digestive and Kidney Disease grant DK063491 to the Southern California Diabetes Endocrinology Research Center. CoLaus: The CoLaus study received financial contributions from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation (grants 33CSCO-122661, 33CS30-139468, and 33CS30-148401). We thank Vincent Mooser and Gérard Waeber, Co-PIs of the CoLaus study. Special thanks to Yolande Barreau, Mathieu Firmann, Vladimir Mayor, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey, and Sylvie Mermoud for data collection. Data analysis was supervised by Sven Bergmann and Jacques S. Beckmann. The computations for this paper were performed in part at the Vital-IT Center for high-performance computing of the Swiss Institute of Bioinformatics. deCODE Study: The study was funded by deCODE Genetics, ehf. We thank all the participants of this study, the staff of deCODE Genetics core facilities and recruitment center and the densitometry clinic at the University Hospital for their important contributions to this work. The EPIC Study: The EPIC Obesity study is funded by Cancer Research United Kingdom and the Medical Research Council. I.B. acknowledges support from EU FP6 funding (contract no. LSHM-CT-2003-503041) and by the Wellcome Trust (WT098051). Erasmus Rucphen Family (ERF) Study: The study was supported by grants from The Netherlands Organisation for Scientific Research (NWO), Erasmus MC, the Centre for Medical Systems Biology (CMSB), and the European Community’s Seventh Framework Programme (FP7/2007-2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413. We are grateful to all general practitioners for their contributions, to Petra Veraart for her help in genealogy, Jeannette Vergeer for the supervision of the laboratory work and Peter Snijders for his help in data collection. Fenland: The Fenland Study is funded by the Wellcome Trust and the Medical Research Council, as well as by the Support for Science Funding programme and CamStrad. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for help with recruitment. We thank the Fenland Study co-ordination team and the Field Epidemiology team of the MRC Epidemiology Unit for recruitment and clinical testing. Tuomas O. Kilpeläinen was supported by the Danish Council for Independent Research (DFF—1333-00124 and Sapere Aude program grant DFF—1331-00730B). Framingham Osteoporosis Study (FOS)/Framingham Heart Study (FHS): The study was funded by grants from the US National Institute for Arthritis, Musculoskeletal and Skin Diseases and National Institute on Aging (R01 AR 41398 and U24AG051129; D.P.K. and R01AR057118; D.K. D.K. was also supported by FP7-PEOPLE-2012-Marie Curie Career Integration Grants (CIG)). The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health and Boston University School of Medicine were supported by the National Heart, Lung, and Blood Institute’s Framingham Heart Study (N01-HC-25195) and its contract with Affymetrix, Inc. for genotyping services (N02-HL-6-4278). Analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. A portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. eQTL HOb Study: The study was supported by Genome Quebec, Genome Canada and the Canadian Institutes of Health Research (CIHR). Gothenburg Osteoporosis and Obesity Determinants Study (GOOD): The study was funded by the Swedish Research Council, the Swedish Foundation for Strategic Research, The ALF/LUA research grant in Gothenburg, the Lundberg Foundation, the Emil and Vera Cornell Foundation, the Torsten and Ragnar Söderberg’s Foundation, Petrus and Augusta Hedlunds Foundation, the Västra Götaland Foundation, and the Göteborg Medical Society. We would like to thank Dr Tobias A. Knoch, Luc V. de Zeeuw, Anis Abuseiris, and Rob de Graaf as well as their institutions the Erasmus Computing Grid, Rotterdam, The Netherlands, and especially the national German MediGRID and Services@MediGRID part of the German D-Grid, both funded by the German Bundesministerium fuer Forschung und Technology under grants #01 AK 803 A-H and # 01 IG 07015G for access to their grid resources. We also thank Karol Estrada, Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands for advice regarding the grid resources. Health Aging and Body Composition Study (Health ABC): This study was funded by the National Institutes of Aging. This research was supported by NIA contracts N01AG62101, N01AG62103, and N01AG62106. The genome-wide association study was funded by NIA grant 1R01AG032098-01A1 to Wake Forest University Health Sciences and genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. Indiana: We thank the individuals who participated in this study, as well as the study coordinators, without whom this work would not have been possible. This work was supported by National Institutes of Health grants R01 AG 041517 and M01 RR-00750. Genotyping services were provided by CIDR. CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. This research was supported in part by the Intramural Research Program of the NIH, National Library of Medicine. Kora (KORA F3 and KORA F4): The KORA research platform was initiated and financed by the Helmholtz Center Munich, German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Part of this work was financed by the German National Genome Research Network (NGFN-2 and NGFNPlus: 01GS0823). Our research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. The London Life Sciences Population (LOLIPOP): The study was funded by the British Heart Foundation, Wellcome Trust, the Medical Research Council, and Kidney Research UK. The study also receives support from a National Institute for Health Research (NIHR) programme grant. Rotterdam Study (RSI, RSII & RSIII): The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project no. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera, Marjolein Peters, MSc, and Carolina Medina-Gomez, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and Carolina Medina-Gomez, PhD, for the creation and analysis of imputed data. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. We are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. We thank Dr Karol Estrada, Dr Fernando Rivadeneira, Dr Tobias A. Knoch, Anis Abuseiris, and Rob de Graaf (Erasmus MC Rotterdam, The Netherlands) for their help in creating GRIMP, and we thank BigGRID, MediGRID, and Services@MediGRID/D-Grid (funded by the German Bundesministerium fuer Forschung und Technology; grants 01 AK 803 A-H, 01 IG 07015G) for access to their grid computing resources. Rush Memory and Aging Project (MAP): The Memory and Aging Project was supported by National Institute on Aging grants R01AG17917, R01AG15819, and R01AG24480, the Illinois Department of Public Health, the Rush Clinical Translational Science Consortium, and a gift from Ms Marsha Dowd. TwinsUK (TUK): The study was funded by the Wellcome Trust, Arthritis Research UK, and the Chronic Disease Research Foundation. The study also received support from a National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London. We thank the staff and volunteers of the TwinsUK study. The study was also supported by Israel Science Foundation, grant number 994/10. Age, Gene/Environment Susceptibility Reykjavik Study (AGES-Reykjavik) has been funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee (VSN: 00-063) and the Data Protection Authority. The researchers are indebted to the participants for their willingness to participate in the study. Berlin Aging Study II (BASE-II) was supported by the German Federal Ministry of Education and Research (BMBF (grants #16SV5536K, #16SV5537, #16SV5538, and #16SV5837; previously #01UW0808)). Additional contributions (e.g., financial, equipment, logistics, personnel) are made from each of the other participating sites, i.e., the Max Planck Institute for Human Development (MPIB), Max Planck Institute for Molecular Genetics (MPIMG), Charite University Medicine, German Institute for Economic Research (DIW), all located in Berlin, Germany, and University of Lübeck in Lübeck, Germany. B-vitamins in the prevention of osteoporotic fractures (B-PROOF): B-PROOF is supported and funded by The Netherlands Organization for Health Research and Development (ZonMw, grant 6130.0031), the Hague; unrestricted grant from NZO (Dutch Dairy Association), Zoetermeer; Orthica, Almere; NCHA (Netherlands Consortium Healthy Ageing) Leiden/Rotterdam; Ministry of Economic Affairs, Agriculture and Innovation (project KB-15-004-003), the Hague; Wageningen University, Wageningen; VU University Medical Center, Amsterdam; Erasmus Medical Center, Rotterdam. All organizations are based in the Netherlands. We thank Dr Tobias A. Knoch, Anis Abuseiris, Karol Estrada, and Rob de Graaf as well as their institutions the Erasmus Grid Office, Erasmus MC Rotterdam, The Netherlands, and especially the national German MediGRID and Services@MediGRID part of the German D-Grid, both funded by the German Bundesministerium fuer Forschung und Technology (grants #01 AK 803 A-H and #01 IG 07015G) for access to their gird resources. Further, we gratefully thank all participants. Calcium Intake Fracture Outcome Study (CAIFOS): This study was funded by Healthway Health Promotion Foundation of Western Australia, Australasian Menopause Society and the Australian National Health and Medical Research Council Project Grants (254627, 303169, and 572604). We are grateful to the participants of the CAIFOS Study. The salary of Dr Lewis is supported by a National Health and Medical Research Council of Australia Career Development Fellowship. Danish Osteoporosis Study (DOPS): The study was supported by Karen Elise Jensen foundation. Family Heart Study (FamHS): The study was supported by NIH grants R01-HL-117078, R01-HL-087700, and R01-HL-088215 from NHLBI; and R01-DK-089256 and R01-DK-075681 from NIDDK. GenMets (Health 2000): S.R. was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (213506 and 129680), Academy of Finland (251217), the Finnish foundation for Cardiovascular Research and the Sigrid Juselius Foundation. S.M. was supported by grants #136895 and #141005, V.S. by grants #139635 and 129494, and M.P. by grant #269517 from the Academy of Finland and a grant from the Finnish Foundation for Cardiovascular Research. M.P. was supported by the Yrjö Jahnsson Foundation. Helsinki Birth Cohort Study (HBCS): We thank all study participants as well as everybody involved in the HBCS. HBCS has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Samfundet Folkhälsann, Novo Nordisk Foundation, Liv och Hälsa, Finska Läkaresällskapet, Signe and Ane Gyllenberg Foundation, University of Helsinki, European Science Foundation (EUROSTRESS), Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation, Juho Vainio Foundation, and Wellcome Trust (grant number WT089062). Johnston County Study: The Johnston County Osteoarthritis Project is supported in part by cooperative agreements S043, S1734, and S3486 from the Centers for Disease Control and Prevention/Association of Schools of Public Health; the NIAMS Multipurpose Arthritis and Musculoskeletal Disease Center grant 5-P60-AR30701; and the NIAMS Multidisciplinary Clinical Research Center grant 5 P60 AR49465-03. Genotyping services were provided by Algynomics company. Korean Genome Epidemiology Study (KoGES): Korean Genome Epidemiology Study (KoGES): This work was supported by the Research Program funded by the Korea Centers for Disease Control and Prevention (found 2001-347-6111-221, 2002-347-6111-221, 2009-E71007-00, 2010-E71004-00). Kora F3 and Kora F4: The KORA research platform was initiated and financed by the Helmholtz Center Munich, German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Part of this work was financed by the German National Genome Research Network (NGFN-2 and NGFNPlus: 01GS0823). Our research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. LOLIP-REP-IA610: The study was supported by the Wellcome Trust. We thank the participants and research teams involved in LOLIPOP. LOLIP-REP-IA_I: The study was supported by the British Heart Foundation Grant SP/04/002. LOLIP-REP-IA_P: The study was supported by the British Heart Foundation Grant SP/04/002. METSIM: The study was supported by the Academy of Finland, the Finnish Diabetes Research Foundation, the Finnish Cardiovascular Research Foundation, the Strategic Research Funding from the University of Eastern Finland, Kuopio, and the EVO grant 5263 from the Kuopio University Hospital. MrOS Sweden: Financial support was received from the Swedish Research Council (2006-3832), the Swedish Foundation for Strategic Research, the ALF/LUA research grant in Gothenburg, the Lundberg Foundation, the Torsten and Ragnar Söderberg’s Foundation, Petrus and Augusta Hedlunds Foundation, the Västra Götaland Foundation, the Göteborg Medical Society, and the Novo Nordisk foundation. Greta and Johan Kock Foundation, A. Påhlsson Foundation, A. Osterlund Foundation, Malmö University Hospital Research Foundation, Research and Development Council of Region Skåne, Sweden, the Swedish Medical Society. MrOS US: The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provided funding for the MrOS ancillary study “GWAS in MrOS and SOF” under the grant number RC2ARO58973. Osteoporosis Prospective Risk Assessment study (OPRA): This work was supported by grants from the Swedish Research Council (K2009-53X-14691-07-3, K2010-77PK-21362-01-2), FAS (grant 2007-2125), Greta and Johan Kock Foundation, A. Påhlsson Foundation, A. Osterlund Foundation, Malmö University Hospital Research Foundation, Research and Development Council of Region Skåne, Sweden, the Swedish Medical Society. We are thankful to all the women who kindly participated in the study and to the staff at the Clinical and Molecular Osteoporosis Research Unit for helping in recruitment of study individuals. Orkney Complex Disease Study (ORCADES): ORCADES was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276, CZB/4/710), the Royal Society, the MRC Human Genetics Unit, Arthritis Research UK (17539) and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA extractions were performed at the Wellcome Trust Clinical Research Facility in Edinburgh. We acknowledge the invaluable contributions of Lorraine Anderson and the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney. PEAK 25: This work was supported by grants from the Swedish Research Council (K2009-53X-14691-07-3, K2010-77PK-21362-01-2), FAS (grant 2007-2125), Greta and Johan Kock Foundation, A. Påhlsson Foundation, A. Osterlund Foundation, Malmö University Hospital Research Foundation, Research and Development Council of Region Skåne, Sweden, the Swedish Medical Society. We are thankful to all the women who kindly participated in the study and to the staff at the Clinical and Molecular Osteoporosis Research Unit for helping in recruitment of study individuals. Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS): The study was supported by grants from the Swedish research council (projects 2008-2202 and 2005-8214) and ALF/LUA research grants from Uppsala university hospital, Uppsala, Sweden. Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC): The RISC study is supported by European Union Grant QLG1-CT-2001-01252 and AstraZeneca. We thank Merck Research Labs for conducting DNA genotyping on RISC samples.Rotterdam III: Rotterdam Study (RS): See discovery. SHIP and SHIP TREND: This work was supported by SHIP, which is part of the Community Medicine Research Network of the University of Greifswald, Germany, by the Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania and the network “Greifswald Approach to Individualized Medicine (GANI_MED)” funded by the Federal Ministry of Education and Research (03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany, and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the “Center of Knowledge Interchange” program of the Siemens. A.G. and the Cache´ Campus program of the InterSystems GmbH. The SHIP authors are grateful to the contribution of Florian Ernst, Anja Wiechert, and Astrid Petersmann in generating the SNP data and to Mario Stanke for the opportunity to use his Server Cluster for SNP Imputation. Data analyses were further supported by the German Research Foundation (DFG Vo 955/10-1) and the Federal Ministry of Nutrition, Agriculture and Consumer’s Safety. SOF: The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provided funding for the SOF ancillary study “GWAS in MrOS and SOF” under the grant number RC2ARO58973. Uppsala Longitudinal Study of Adult Men (ULSAM): The study was funded by grants from the Swedish research council (projects 2008-2202 and 2005-8214), the Wallenberg foundation, and ALF/LUA research grants from Uppsala university hospital, Uppsala, Sweden. Andrew P. Morris is a Wellcome Trust Senior Fellow in Basic Biomedical Science, grant number WT098017. CROATIA-VIS (VIS): The CROATIA-Vis study was funded by grants from the Medical Research Council (UK) and Republic of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-1080315-0302). We acknowledge the staff of several institutions in Croatia that supported the field work, including but not limited to The University of Split and Zagreb Medical Schools, the Institute for Anthropological Research in Zagreb and Croatian Institute for Public Health. The SNP genotyping for the CROATIA-Vis cohort was performed in the core genotyping laboratory of the Wellcome Trust Clinical Research Facility at the Western General Hospital, Edinburgh, Scotland. Women’s Health Initiative (WHI): The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts N01WH22110, 24152, 32100–2, 32105–6, 32108–9, 32111–13, 32115, 32118–32119, 32122, 42107–26, 42129–32, and 44221. We thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A listing of WHI investigators can be found at https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf. FUSION: This research was supported in part by US National Institutes of Health grants 1-ZIA-HG000024 (to F.S.C.), U01DK062370 (to M.B.), R00DK099240 (to S.C.J.P.), the American Diabetes Association Pathway to Stop Diabetes Grant 1-14-INI-07 (to S.C.J.P.), and Academy of Finland Grants 271961 and 272741 (to M.L.) and 258753 (to H.A.K.). We thank all the subjects for participation and the study personnel for excellent technical assistance. The Pima Indian Study: This study was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases, NIH, USA. Studies of a Targeted Risk Reduction Intervention with Defined Exercise (STRRIDE): This study was supported by the National Heart Lung and Blood Institute of the National Institutes of Health, HL57453 (WEK). Gene expression in old and young muscle biopsies: S.M. and T.G. were supported in part by NIH U24AG051129.

Author information

Author notes

  1. M. Carola Zillikens, Serkalem Demissie, Yi-Hsiang Hsu and Laura M. Yerges-Armstrong contributed equally to this work.

  2. David Karasik, Tamara B. Harris, Claes Ohlsson and Douglas P. Kiel jointly supervised this work.

Affiliations

  1. Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands

    • M. Carola Zillikens
    • , Lisette Stolk
    • , Natalia Campos Obanda
    • , Anke W. Enneman
    • , Karol Estrada
    • , Jacqueline S. L. Kloth
    • , Carolina Medina-Gomez
    • , Fernando Rivadeneira
    •  & André G. Uitterlinden
  2. Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands

    • M. Carola Zillikens
    • , Lisette Stolk
    • , Albert Hofman
    • , Fernando Rivadeneira
    •  & André G. Uitterlinden
  3. Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA

    • Serkalem Demissie
    •  & Yanhua Zhou
  4. Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA

    • Yi-Hsiang Hsu
    • , Wen-Chi Chou
    • , Melina Claussnitzer
    • , David Karasik
    •  & Douglas P. Kiel
  5. Harvard Medical School, Boston, MA, 02115, USA

    • Yi-Hsiang Hsu
    • , Wen-Chi Chou
    • , Melina Claussnitzer
    • , Philip L. De Jager
    • , David Karasik
    •  & Douglas P. Kiel
  6. Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, 02115, USA

    • Yi-Hsiang Hsu
  7. Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA

    • Laura M. Yerges-Armstrong
    • , Mao Fu
    • , Braxton D. Mitchell
    • , Jeffrey R. O’Connell
    •  & Elizabeth A. Streeten
  8. Broad Institute, Cambridge, MA, 02142, USA

    • Wen-Chi Chou
    •  & Melina Claussnitzer
  9. Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel

    • Gregory Livshits
    •  & Ida Malkin
  10. Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, WC2R 2LS, UK

    • Gregory Livshits
    • , Frances M. K. Williams
    •  & Timothy D. Spector
  11. Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands

    • Linda Broer
    • , Najaf Amin
    • , Karol Estrada
    • , Albert Hofman
    • , Carolina Medina-Gomez
    • , Cornelia M. van Duijn
    • , Fernando Rivadeneira
    •  & André G. Uitterlinden
  12. Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland

    • Toby Johnson
    •  & Zoltán Kutalik
  13. Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland

    • Toby Johnson
    •  & Zoltán Kutalik
  14. Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland

    • Toby Johnson
    • , Zoltán Kutalik
    •  & Murielle Bochud
  15. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    • Daniel L. Koller
  16. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK

    • Jian’an Luan
    • , Jing Hua Zhao
    • , Tuomas O. Kilpeläinen
    • , Nicholas J. Wareham
    •  & Ruth J. F. Loos
  17. Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Janina S. Ried
    • , Christian Gieger
    • , Harald Grallert
    • , Annette Peters
    • , H.-Erich Wichmann
    •  & Angela Döring
  18. Icelandic Heart Association, Kopavogur, 201, Iceland

    • Albert V. Smith
    • , Gudny Eiriksdottir
    •  & Vilmundur Gudnason
  19. Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland

    • Albert V. Smith
    • , Gunnar Sigurdsson
    • , Unnur Thorsteinsdottir
    • , Vilmundur Gudnason
    •  & Kari Stefansson
  20. deCODE Genetics, Reykjavik, 101, Iceland

    • Gudmar Thorleifsson
    • , Unnur Styrkarsdottir
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  21. Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden

    • Liesbeth Vandenput
    • , Joel Eriksson
    • , Mattias Lorentzon
    • , Dan Mellström
    •  & Claes Ohlsson
  22. Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK

    • Weihua Zhang
    •  & John C. Chambers
  23. Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK

    • Weihua Zhang
    • , Jagvir Grewal
    • , Joban Sehmi
    • , Sian-Tsung Tan
    • , Arjun Verma
    • , John C. Chambers
    •  & Jaspal S. Kooner
  24. Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany

    • Ali Aghdassi
    •  & Markus M. Lerch
  25. Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden

    • Kristina Åkesson
    •  & Fiona E. McGuigan
  26. Department of Orthopedics, Skåne University Hospital, Malmö, S-205 02, Sweden

    • Kristina Åkesson
  27. Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA

    • Leslie J. Baier
    • , Robert L. Hanson
    • , Wen-Chi Hsueh
    •  & Vicky Ossowski
  28. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK

    • Inês Barroso
  29. NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, CB2 OQQ, UK

    • Inês Barroso
  30. Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge Metabolic Research Laboratories, Cambridge, CB2 OQQ, UK

    • Inês Barroso
  31. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA

    • David A. Bennett
    • , Aron S. Buchman
    •  & Lei Yu
  32. Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Experimental & Integrative Genomics, University of Lübeck, Lübeck, 23562, Germany

    • Lars Bertram
  33. School of Public Health, Faculty of Medicine, Imperial College London, London, W6 8RP, UK

    • Lars Bertram
  34. Centre of Oral Health, Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University of Greifswald, Greifswald, 17489, Germany

    • Rainer Biffar
  35. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA

    • Michael Boehnke
    • , Laura Scott
    •  & Ryan Welch
  36. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA

    • Ingrid B. Borecki
    •  & Mary F. Feitosa
  37. Division of Biostatistics, Washington University School of Medicine, St Louis, MO, 63110, USA

    • Ingrid B. Borecki
  38. Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden

    • Liisa Byberg
    •  & Karl Michaëlsson
  39. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK

    • Harry Campbell
    • , Igor Rudan
    •  & James F. Wilson
  40. Department of Epidemiology Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    • Jane A. Cauley
  41. California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA

    • Peggy M. Cawthon
    • , Steven R. Cummings
    • , Daniel S. Evans
    •  & Gregory J. Tranah
  42. Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland

    • Henna Cederberg
    • , Teemu Kuulasmaa
    • , Johanna Kuusisto
    • , Markku Laakso
    •  & Alena Stančáková
  43. Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA

    • Zhao Chen
    •  & Cynthia A. Thomson
  44. Department of Preventive Medicine, Ajou University School of Medicine, Youngtong-Gu, Suwon, 16499, Korea

    • Nam H. Cho
  45. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea

    • Hyung Jin Choi
    •  & Chan Soo Shin
  46. Department of Internal Medicine, Chungbuk National University Hospital, Cheongju Si, Korea

    • Hyung Jin Choi
  47. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, 02139, USA

    • Melina Claussnitzer
  48. Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany

    • Melina Claussnitzer
    •  & Thomas Meitinger
  49. Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA

    • Melina Claussnitzer
    •  & Douglas P. Kiel
  50. Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA

    • Francis Collins
    •  & Mike Erdos
  51. Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    • Philip L. De Jager
  52. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA

    • Philip L. De Jager
  53. Lipid Clinic at the Interdisciplinary Metabolism Center, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany

    • Ilja Demuth
    •  & Elisabeth Steinhagen-Thiessen
  54. Institute of Medical and Human Genetics, Charité – Universitätsmedizin Berlin, Berlin, 13353, Germany

    • Ilja Demuth
  55. Department of Human Nutrition, Wageningen University, PO Box 17, Wageningen, 6700 AA, The Netherlands

    • Rosalie A. M. Dhonukshe-Rutten
  56. Alan Edwards Centre for Research on Pain, McGill University, Montreal, H3A 0G1, Canada

    • Luda Diatchenko
  57. Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    • Luda Diatchenko
    •  & William Maixner
  58. Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, 00014, Finland

    • Johan G. Eriksson
  59. Unit of General Practice, Helsinki University Central Hospital, Helsinki, 00014, Finland

    • Johan G. Eriksson
  60. Folkhalsan Research Centre, Helsinki, 00250, Finland

    • Johan G. Eriksson
  61. Vasa Central Hospital, Vasa, 65130, Finland

    • Johan G. Eriksson
    •  & Jaakko Tuomilehto
  62. National Institute for Health and Welfare, Helsinki, 00271, Finland

    • Johan G. Eriksson
    • , Antti Jula
    • , Markus Perola
    • , Veikko Salomaa
    •  & Emmi Tikkanen
  63. Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA

    • Melissa Garcia
    • , Lenore J. Launer
    •  & Tamara B. Harris
  64. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Christian Gieger
    • , Harald Grallert
    • , Thomas Illig
    • , Norman Klopp
    •  & Annette Peters
  65. Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Christian Gieger
  66. Institute for Integrative Genome Biology, University of California, Riverside, CA, 92521, USA

    • Thomas Girke
  67. Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA

    • Thomas Girke
    •  & Harald Grallert
  68. Departments of Medicine and Epidemiology, Boston University School of Medicine and Public Health, Boston, MA, 02118, USA

    • Nicole L. Glazer
  69. German Center for Diabetes Research (DZD), Neuherberg, Germany

    • Harald Grallert
  70. CCG Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, 85764, Germany

    • Harald Grallert
  71. CCG Nutrigenomics and Type 2 Diabetes. Helmholtz Zentrum München, Neuherberg, 85764, Germany

    • Harald Grallert
  72. National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK

    • Jagvir Grewal
    • , Joban Sehmi
    • , Sian-Tsung Tan
    •  & Jaspal S. Kooner
  73. Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea

    • Bok-Ghee Han
    •  & Jong-Young Lee
  74. MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK

    • Caroline Hayward
    •  & James F. Wilson
  75. Department of Pharmaceutical Sciences, SUNY Binghamton, Binghamton, NY, 13902, USA

    • Eric P. Hoffman
  76. Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, 17487, Germany

    • Georg Homuth
  77. Department of Exercise and Nutrition Sciences, George Washington University, Washington, DC, 20052, USA

    • Monica J. Hubal
  78. Research Center for Genetic Medicine, Children’s National Medical Center, Washington, DC, 20052, USA

    • Monica J. Hubal
  79. Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA

    • Alan Hubbard
  80. Division of Rheumatology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA

    • Kim M. Huffman
  81. Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark

    • Lise B. Husted
    • , Bente L. Langdahl
    •  & Leif Mosekilde
  82. Department of Human Genetics, Hannover Medical School, Hannover, 30625, Germany

    • Thomas Illig
  83. Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany

    • Thomas Illig
    •  & Norman Klopp
  84. Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden

    • Erik Ingelsson
    • , Lars Lind
    • , Östen Ljunggren
    •  & Håkan Melhus
  85. Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA

    • Erik Ingelsson
  86. Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany

    • Till Ittermann
    • , Sabine Schipf
    •  & Henry Völzke
  87. Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE 405 30, Sweden

    • John-Olov Jansson
  88. Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA

    • Joanne M. Jordan
    •  & Youfang Liu
  89. Department of Clinical Sciences and Orthopaedics, Lund University, Skåne University Hospital SUS, Malmö, 22362, Sweden

    • Magnus Karlsson
  90. Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK

    • Kay-Tee Khaw
    •  & Robert N. Luben
  91. The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen, 2100, Denmark

    • Tuomas O. Kilpeläinen
  92. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Tuomas O. Kilpeläinen
  93. Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland

    • Heikki A. Koistinen
  94. Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland

    • Heikki A. Koistinen
  95. Department of Health, National Institute for Health and Welfare, Helsinki, 00271, Finland

    • Heikki A. Koistinen
  96. Minerva Foundation Institute for Medical Research, Helsinki, 00290, Finland

    • Heikki A. Koistinen
  97. Division of Cardiology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA

    • William E. Kraus
  98. Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

    • Stephen Kritchevsky
  99. Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland

    • Jari Lahti
    •  & Katri Räikkönen
  100. University of California San Francisco, San Francisco, CA, 94143, USA

    • Thomas Lang
  101. School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia

    • Joshua R. Lewis
    •  & Richard L. Prince
  102. Centre for Kidney Research, School of Public Health, University of Sydney, Sydney, 2006, Australia

    • Joshua R. Lewis
  103. Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK

    • Cecilia Lindgren
    •  & Andrew P. Morris
  104. Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, 27517, USA

    • Yongmei Liu
  105. Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany

    • Tian Liu
  106. Max Planck Institute for Human Development, Berlin, 14195, Germany

    • Tian Liu
  107. Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Thomas Meitinger
  108. Buck Institute for Research on Aging, Novato, CA, 94945, USA

    • Simon Melov
  109. Leonard Davis School of Gerontology, University of Southern California, LA, CA, 90089, USA

    • Simon Melov
  110. Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, 21201, USA

    • Braxton D. Mitchell
  111. Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK

    • Andrew P. Morris
  112. Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    • Anne Newman
  113. Oregon Health & Science University, Portland, OR, 97239, USA

    • Carrie M. Nielson
    • , Eric S. Orwoll
    •  & Jian Shen
  114. Department of Clinical Genetics, Erasmus MC, Rotterdam, 300 CA, The Netherlands

    • Ben A. Oostra
  115. Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands

    • Ben A. Oostra
    •  & Cornelia M. van Duijn
  116. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland

    • Aarno Palotie
    • , Markus Perola
    • , Samuli Ripatti
    • , Emmi Tikkanen
    •  & Elisabeth Widen
  117. Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, FI00014, Finland

    • Aarno Palotie
  118. Human Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA

    • Stephan Parker
  119. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    • Munro Peacock
  120. Diabetes and Obesity Research Program, University of Helsinki, Helsinki, FI00014, Finland

    • Markus Perola
  121. Estonian Genome Center, University of Tartu, Tartu, Estonia

    • Markus Perola
  122. Faculty of Medicine, Department of Public Health, University of Split, Split, 21000, Croatia

    • Ozren Polasek
  123. Department of Endocrinology and Diabetes, Sir Charles Gardiner Hospital, Perth, 6009, Australia

    • Richard L. Prince
  124. Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK

    • Stuart H. Ralston
    •  & Emmi Tikkanen
  125. Hjelt Institute, University of Helsinki, Helsinki, Finland

    • Samuli Ripatti
  126. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK

    • Samuli Ripatti
    •  & Nicole Soranzo
  127. Department of Medicine, University of California at Davis, Sacramento, CA, 95817, USA

    • John A. Robbins
  128. Institute for Translational Genomic and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor UCLA Medical Center, Torrance, CA, 90502, USA

    • Jerome I. Rotter
  129. Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, 38163, USA

    • Suzanne Satterfield
  130. Department of Genetics and Genomic Science, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Eric E. Schadt
  131. Department of Endocrinology and Metabolism, Landspitali, The National University Hospital of Iceland, Reykjavik, 101, Iceland

    • Gunnar Sigurdsson
  132. Center for Translational Pain Medicine, Department of Anesthiology, Duke University Medical Center, Durham, NC, 27110, USA

    • Shad Smith
  133. Geriatric Research and Education Clinical Center (GRECC) - Veterans Administration Medical Center, Baltimore, MD, 21201, USA

    • Elizabeth A. Streeten
  134. Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands

    • Karin M. A. Swart
    •  & Natasja M. van Schoor
  135. Department of Medicine, McMaster University Medical Center, Hamilton, ON, Canada, L8N 3Z5

    • Mark A. Tarnopolsky
  136. Department of Pathology, Stony Brook School of Medicine, Stony Brook, NY, 11794, USA

    • Patricia Thompson
  137. Department of Neuroscience and Preventive Medicine, Danube-University Krems, Krems, 3500, Austria

    • Jaakko Tuomilehto
  138. Diabetes Research Group, King Abdulaziz University, Jeddah, 12589, Saudi Arabia

    • Jaakko Tuomilehto
  139. Dasman Diabetes Institute, Dasman, 15462, Kuwait

    • Jaakko Tuomilehto
  140. Department of Medicine and Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, CH-1011, Switzerland

    • Peter Vollenweider
  141. Department of Epidemiology and Environmental Health, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA

    • Jean Wactawski-Wende
  142. Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK

    • Mark Walker
  143. Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK

    • Michael N. Weedon
    •  & Weijia Xie
  144. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, 81377, Germany

    • H.-Erich Wichmann
  145. Institute of Medical Statistics and Epidemiology, Technical University, Munich, 81675, Germany

    • H.-Erich Wichmann
  146. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    • Nicole C. Wright
  147. NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Foundation Trust and Imperial College, London, SW3 6NP, UK

    • John C. Chambers
  148. Imperial College Healthcare NHS Trust, London, W2 1NY, UK

    • John C. Chambers
    •  & Jaspal S. Kooner
  149. Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Angela Döring
  150. Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    • Michael J. Econs
  151. Departments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA

    • Bruce M. Psaty
  152. Kaiser Permanente Washington Health Research Institute, Washington, Seattle, WA, 98101, USA

    • Bruce M. Psaty
  153. Medical Genetics, GlaxoSmithKline, Philadelphia, PA, 19112, USA

    • Dawn Waterworth
  154. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Ruth J. F. Loos
  155. Institute of Child Health and Development, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Ruth J. F. Loos
  156. The Genetics of Obesity and Related Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Ruth J. F. Loos
  157. Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Ruth J. F. Loos
  158. Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, 1311502, Israel

    • David Karasik

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Contributions

Writing group: (Amish) L.M.Y.-A.; (FHS) S.D., Y.-H.H., D.K., D.P.K.; (GOOD) C.O.; (HABC) T.B.H.; (Rotterdam Study II) M.C.Z. Individual study design: (AGES) G.E., M.G., V.G., L.J.L.; (Amish) E.A.S.; (Berlin Aging Study II) L.B., I.D., E.S.-T.; (B-PROOF) R.A.M.D.-R., N.M.v.S.; (CAIFOS) R.L.P.; (CHS) N.L.G., B.M.P.; (CoLaus) P.V., D.W.; (deCODE) G.S., K.S.; (DOPS) B.L.L., L.M.; (EPIC) K.-T.K., R.J.F.L., R.N.L.; (ERF) B.A.O., C.M.v.D.; (Fenland) T.O.K., R.J.F.L., N.J.W.; (FHS) D.K.; (FUSION) M.B., F.S.C., M.R.E., H.A.K., M.L., S.C.J.P., L.J.S., J.T.; (Genmets—controls) A.J., M.P., S.R.; (HABC) S.R.C., T.B.H., S.K., Y.L., A.N., S.S.; (Helskinki Birth Cohort) A.P.; (Johnston County Osteoarthritis Project) L.D., S.S.; (KOGES) N.H.C., J.-Y.L.; (Kora F3/F4) A.D., T.M., J.S.R.; (Lolipop) J.G., J.S.K.; (MrOS Sweden) D.M., C.O.; (MrOS US) D.S.E., C.M.N., E.S Orwoll; (Old and Young Muscle Bx) A.H., S.M., M.T.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) H.C., S.H.R, J.F.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIMA Indians) L.J.B., R.L.H., W.-C.H., V.M.O.; (PIVUS) E.I., K.M.; (RISC) W.X.; (Rotterdam Study III) C.M.-G.; (Rush Memory and Aging Project) D.A.B.; (SHIP 2/SHIP-TREND) S.S.; (SOF) S.RC.; (STRRIDE) E.H., M.H., W.E.K.; (Twins UK) N.S.; (ULSAM) H.M., K.M.; (VIS) O.P., I.R. Data collection: (AGES) G.E., V.G., T.L.; (Amish) B.D.M., E.A.S.; (Berlin Aging Study II) I.D., E.S.-T.; (B-PROOF) A.W.E., K.M.A.S.; (CAIFOS) R.L.P.; (CHS) B.M.P., J.A.R., J.I.R.; (CoLaus) P.V.; (deCODE) G.S., U.S.; (DOPS) L.B.H., B.L.L., L.M.; (EPIC) K.-T.K.; (ERF) B.A.O., C.M.v.D.; (Fenland) N.J.W.; (FHS) D.K.; (FUSION) M.R.E., H.A.K., M.L.; (Genmets—controls) M.P., E.T.; (GOOD) J.-O.J., M.L., C.O.; (HABC) S.R.C., T.B.H., S.K., Y.L., A.N., S.S.; (Helsinki Birth Cohort) J.G.E., A.P., K.R.; (Johnston County Osteoarthritis Project) L.D., S.S.; (KOGES) N.H.C., J.-Y.L.; (KORA F3/KORA F4) T.M., J.S.R.; (Lolipop) J.C.C., J.G., J.S.K., J.S., S.-T.T., A.V.; (METSIM) A.S.; (MrOS Sweden) J.E., M.K., Ö.L., D.M., C.O.; (MrOS US) D.S.E., C.M.N., E.S.O.; (Old and Young Muscle Bx) A.H., S.M., M.T.; (OPRA) K.A., F.E.M.; (ORCADES) S.H.R.; (PEAK 25) K.A., F.E.M.; (PIMA Indians) L.J.B., R.L.H., W.-C.H., V.M.O.; (PIVUS) E.I., L.L., K.M.; (RISC) W.X.; (Rotterdam Study I) L.S.; (Rotterdam Study II) J.S.L.K., F.R., M.C.Z.; (Rotterdam Study III) C.M.-G.; (Rush Memory and Aging Project) D.A.B., L.Y.; (SHIP 2/SHIP-TREND) A.A., G.H., T.I., M.M.L., S.S.; (SOF) S.R.C.; (STRRIDE) E.H., K.M.H., M.H.; (Twins UK) N.S., T.D.S.; (ULSAM) H.M., K.M.; (VIS) C.H., O.P., I.R. Genotyping: (AGES) A.V.S.; (Berlin Aging Study II) L.B.; (CAIFOS) J.R.L., R.L.P.; (CHS) J.I.R.; (deCODE) U.T.; (EPIC) I.B., R.J.F.L., R.N.L., J.H.Z.; (ERF) L.B.; (FamHS) I.B.B., M.F.F.; (Fenland) R.J.F.L., J.L., N.J.W.; (FUSION) M.R.E.; (Genmets—controls) E.T.; (GOOD) J.-O., M.L.; (HABC) S.K.; (Helsinki Birth Cohort) J.L., K.R.; (Johnston County Osteoarthritis Project) L.D., Y.L., W.M.; (KOGES) H.J.C., B.-G.H.; (KORA F3/KORA F4) C.G., H.G., T.I., N.K.; (Lolipop) J.G., J.S.K.; (MrOS Sweden) D.M.; (MrOS US) E.S.O.; (OPRA) K.A., F.E.M.; (PEAK 25) K.A., F.E.M.; (PIVUS) L.L., K.M.; (Rush Memory and Aging Project) A.S.B.; (SHIP 2/SHIP-TREND) R.B.; (Twins UK) I.M.; (ULSAM) H.M., K.M., A.M.; (VIS) I.R. Genotype preparation: (AGES) A.V.S.; (Amish) J.R.O’C., L.M.Y.-A.; (Berlin Aging Study II) L.B.; (CAIFOS) J.R.L., R.L.P.; (CHS) N.L.G., J.I.R.; (deCODE) G.S., U.S., U.T.; (DOPS) L.B.H.; (EPIC) R.N.L., J.H.Z.; (ERF) N.A., L.B., B.A.O., C.M.v.D.; (Fenland) J.L.; (FUSION) M.R.E.; (HABC) S.K.; (Helsinki Birth Cohort) K.R.; (Johnson County Osteoarthritis Project) L.D., J.M.J., Y.L., W.M.; (KOGES) H.J.C., B.-G.H.; (KORA F3/KORA F4) A.D., H.G., T.I.; (Lolipop) J.G., J.S.K.; (MrOS US) E.S.O.; (OPRA) K.A., F.E.M.; (PEAK 25) K.A., F.E.M.; (PIVUS) K.M.; (Rotterdam Study II) J.S.L.K.; (Rush Memory and Aging Project) A.S.B.; (SHIP 2/SHIP-TREND) R.B.; (SOF) S.R.C.; (Twins UK) I.M.; (ULSAM) K.M., A.M.; (VIS) I.R. Phenotype preparation: (AGES) M.G., A.V.S.; (Amish) E.A.S., L.M.Y.-A.; (Berlin Aging Study II) I.D., E.S.-T.; (B-PROOF) A.W.E., K.M.A.S.; (CAIFOS) J.R.L., R.L.P.; (CHS) N.L.G., J.A.R.; (CoLaus) P.V.; (deCODE) G.S., U.S., G.T.; (DOPS) B.L.L., L.M.; (EPIC) K.-T.K., R.J.F.L., R.N.L., J.H.Z.; (ERF) N.A.; (Fenland) T.O.K., R.J.F.L., J.L., N.J.W.; (FHS) D.K.; (FUSION) H.A.K., M.L., R.P.W.; (Genmets—controls) E.T.; (GOOD) J.-O.J., M.L., C.O.; (HABC) S.R.C., S.S.; (Helsinki Birth Cohort) J.G.E., A.P.; (Indiana) M.P.; (Johnston County Osteoarthritis Project) L.D., J.M.J., S.S.; (KOGES) N.H.C., J.-Y.L.; (Lolipop) J.G., J.S.K.; (METSIM) A.S.; (MrOS Sweden) J.E., M.K., Ö.L., D.M., C.O.; (MrOS US) D.S.E., E.S.O.; (Old and Young Muscle Bx) S.M., M.T.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) S.H.R., J.F.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIVUS) L.B., L.L., K.M.; (Rotterdam Study II) J.S.L.K., F.R., M.C.Z.; (Rush Memory and Aging Project) L.Y.; (SHIP 2/SHIP-TREND) G.H., M.M.L., H.V.; (Twins UK) N.S., T.D.S.; (ULSAM) H.M., K.M.; (VIS) C.H., O.P., I.R. Analysis plan development: (Amish) L.M.Y.-A.; (CHS) J.A.C., B.M.P.; (FHS) D.P.K., W.-C.C., S.D., Y.-H.H., D.K.; (FUSION) M.R.E., S.C.J.P., L.J.S.; (GOOD) M.L.; (Johnston County Osteoarthritis) S.S.; (KOGES) N.H.C., J.-Y.L.; (Lolipop) J.G., J.S.K.; (MrOS Sweden) D.M.; (MrOS US) J.S.; (Old and Young Muscle Bx) T.G., A.H., S.M.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) H.C., S.H.R., J.F.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIVUS) K.M.; (Rotterdam Study II) F.R., M.C.Z.; (SOF) S.R.C.; (STRRIDE) W.E.K.; (Twins UK) G.L.; (ULSAM) K.M. Analysis plan review: (AGES) A.V.S.; (Amish) L.M.Y.-A.; (CHS) J.A.C., N.L.G., J.I.R.; (CoLaus) M.B., T.J., D.W.; (EPIC) R.J.F.L.; (ERF) N.A., L.B., B.A.O., C.M.v.D.; (Fenland) T.O.K., R.J.F.L.; (FHS) W.-C.C., S.D., Y.-H.H., D.K.; (FUSION) M.B., F.S.C., M.L., J.T.; (GOOD) M.L.; (HABC) S.R.C.; (Indiana) M.J.E.; (Johnston County Osteoarthritis Project) L.D., J.M.J., S.S.; (KOGES) N.H.C., J.-Y.L.; (Lolipop) J.G., J.S.K.; (MrOS Sweden) D.M.; (MrOS US) P.M.C., D.S.E., J.S.; (Old and Young Muscle Bx) T.G., A.H., S.M.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) H.C., S.H.R., J.F.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIVUS) K.M.; (Rotterdam Study I) L.S.; (Rotterdam Study II) J.S.L.K., F.R., M.C.Z.; (SOF) S.R.C.; (VIS) I.R.; (WHI) J.W.-W. Study Data Analysis: (AGES) A.V.S.; (Amish) L.M.Y.-A.; (Berlin Aging Study II) L.B., T.L.; (B-PROOF) A.W.E., K.M.A.S.; (CAIFOS) J.R.L., R.L.P.; (CHS) N.L.G., J.I.R.; (CoLaus) M.B., Z.K., T.J.; (deCODE) U.S., G.T.; (EPIC) J.H.Z.; (ERF) N.A., L.B.; (FamHS) M.F.F.; (Fenland) J.L.; (FHS) W.-C.C., D.P.K., Y.Z.; (FUSION) R.P.W.; (Genmets—controls) V.S., E.T.; (GOOD) J.-O.J., M.L., C.O., L.V.; (HABC) S.R.C., S.K.; (Helsinki Birth Cohort) J.G.E.; (Indiana) M.J.E.; (Johnston County Osteoarthritis Project) J.M.J.; (KOGES) N.H.C.; (KORA F3/KORA F4) A.P.; (Lolipop) J.C.C., J.G., J.S.K., J.S., S.-T.T., A.V., W.Z.; (METSIM) H.C., T.K., J.K., M.L., A.S.; (MrOS Sweden) D.M.; (MrOS US) P.C., D.S.E., J.S.; (Old and Young Muscle Bx) T.G., A.H., S.M.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) H.C., S.H.R., J.F.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIVUS) K.M.; (RISC) M.W., M.N.W.; (Rotterdam Study III) N.C.-O., K.E.; (Rush Memory and Aging Project) P.L.D.J.; (SHIP 2/SHIP-TREND) G.H., M.M.L.; (SOF) G.J.T.; (STRRIDE) E.H., M.H., W.E.K.; (Twins UK) G.L.; (ULSAM) K.M.; (VIS) I.R. Reviewed/interpreted analyses: (AGES) A.V.S.; (Amish) M.F., B.D.M., J.R.O’C., E.A.S., L.M.Y.-A.; (Berlin Aging Study II) L.B., T.L.; (CAIFOS) J.R.L., R.L.P.; (CHS) B.M.P., J.I.R.; (CoLaus) M.B., Z.K., T.J.; (EPIC) R.J.F.L.; (Fenland) T.O.K., R.J.F.L.; (FHS) W.-C.C., S.D., Y.-H.H., D.K., D.P.K.; (FUSION) M.R.E., S.C.J.P., L.J.S.; (GOOD) M.L.; (HABC) S.R.C.; (Helsinki Birth Cohort) J.G.E., E.W.; (Indiana) M.J.E., D.L.K.; (Johnston County Osteoarthritis Project) L.D., J.M.J., S.S.; (KOGES) N.H.C., H.J.C., B.-G.H., J.-Y.L., C.S.S.; (Lolipop) J.G., J.S.K.; (METSIM) J.K., M.L.; (MrOS Sweden) D.M.; (MrOS US) D.S.E.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) H.C., S.H.R., J.F.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIVUS) L.B., E.I., L.L.; (Rotterdam Study I) A.H., A.G.U.; (Rotterdam Study II) J.S.L.K., F.R., M.C.Z.; (ULSAM) C.L., H.M., A.M.; (VIS) I.R.; (WHI) J.W.-W. Meta-analyses: (Amish) L.M.Y.-A.; (FHS) S.D. Pathway/other analyses: (FHS) W.-C.C., M.C., Y.-H.H. Lean mass study design: (AGES) A.V.S.; (Amish) L.M.Y.-A.; (Berlin Aging Study II) I.D., E.S.-T.; (FHS) Y.-H.H., D.K.; (GOOD) M.L.; (HABC) S.R.C.; (MrOS Sweden) D.M.; (OPRA) K.A., F.E.Mc.G.; (PEAK 25) K.A., F.E.Mc.G.; (PIVUS) K.M.; (Rotterdam Study II) F.R., M.C.Z.; (SHIP 2/SHIP-TREND) H.V.; (STRRIDE) W.E.K.; (Twins UK) N.S.; (ULSAM) K.M.; (VIS) I.R. Manuscript preparation: (Amish) L.M.Y.-A.; (FHS) S.D., Y.-H.H., D.K., D.P.K.; (GOOD) C.O.; (HABC) T.B.H.; (Rotterdam Study II) M.C.Z. Manuscript review: (AGES) G.E., M.G., V.G., T.L., L.J.L., A.V.S.; (Amish) M.F., B.D.M., J.R.O’C., E.A.S., L.M.Y.-A.; (Berlin Aging Study II) L.B.; (CAIFOS) J.R.L., R.L.P.; (CHS) J.A.C., N.L.G., B.M.P., J.A.R., J.I.R.; (CoLaus) M.B., Z.K., T.J., P.V., D.W.; (DOPS) L.B.H., B.L.L., L.M.; (EPIC) R.J.F.L.; (ERF) N.A., L.B., B.A.O., C.M.v.D.; (FamHS) M.F.F.; (Fenland) T.O.K., R.J.F.L.; (FHS) W.-C.C., M.C., S.D., Y.-H.H., D.K., Y.Z.; (FUSION) M.B., H.A.K., S.C.J.P., L.J.S.; (Genmets—controls) M.P., E.T.; (GOOD) J.-O.J., M.L., C.O., L.V.; (HABC) S.R.C., T.B.H., Y.L., A.N., S.S.; (Helsinki Birth Cohort) J.G.E., J.L., A.P., K.R., E.W.; (Indiana) M.J.E., D.L.K.; (KOGES) N.H.C., H.J.C., B.-G.H., J.-Y.L., C.S.S.; (KORA) H.E.W.; (Lolipop) W.Z.; (METSIM) H.C., T.K., J.K., M.L., C.S.S., A.S.; (MrOS Sweden) J.E., M.K., Ö.L., D.M., C.O.; (MrOS US) P.M.C., D.S.E., C.M.N., E.S.O.; (Old and Young Muscle Bx) T.G., A.H., S.M., M.T.; (OPRA) K.A., F.E.Mc.G.; (ORCADES) H.C., S.H.R., J.W.; (PEAK 25) K.A., F.E.Mc.G.; (PIMA Indians) L.J.B., R.L.H., W.-C.H., V.M.O.; (PIVUS) L.B., E.I., L.L., K.M.; (RISC) M.W., M.N.W.; (Rotterdam Study I) A.H., A.G.U.; (Rotterdam Study II) J.S.L.K., F.R.; (Rotterdam Study III) N.C.-O., K.E., C.M.-G.; (Rush Memory and Aging Project) D.A.B., A.S.B, P.L.D.J.; (SHIP 2/SHIP-TREND) H.V.; (SOF) S.R.C., G.J.T.; (STRRIDE) E.P.H., M.H., K.M.H., W.E.K.; (Twins UK) N.S., T.D.S., F.M.K.W.; (ULSAM) C.L., H.M., K.M., A.M.; (VIS) I.R.; (WHI) Z.C., P.T., C.A.T., J.W.-W.

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The authors declare no competing financial interests.

Corresponding author

Correspondence to Douglas P. Kiel.

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