Article | Published:

Rare and low-frequency coding variants alter human adult height

Nature volume 542, pages 186190 (09 February 2017) | Download Citation

Abstract

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1–4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1–2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Main

Human height is a highly heritable, polygenic trait1,2. The contribution of common DNA sequence variation to inter-individual differences in adult height has been systematically evaluated through genome-wide association studies (GWAS). This approach has thus far identified 697 independent variants located within 423 loci that together explain around 20% of the heritability of height3. As is typical of complex traits and diseases, most of the alleles that affect height that have been discovered so far are common (with a minor allele frequency (MAF) > 5%) and are mainly located outside coding regions, complicating the identification of the relevant genes or functional variants. Identifying coding variants associated with a complex trait in new or known loci has the potential to help pinpoint causal genes. Furthermore, the extent to which rare (MAF < 1%) and low-frequency (1% < MAF ≤ 5%) coding variants also influence complex traits and diseases remains an open question. Many recent DNA sequencing studies have identified only a few of these variants4,5,6,7,8, but this limited success could be due to their modest sample size9. Some studies have suggested that common sequence variants may explain the majority of the heritable variation in adult height10. It is therefore timely to assess whether and to what extent rare and low-frequency coding variations contribute to the genetic landscape of this model polygenic trait.

In this study, we used an ExomeChip11 to test the association between 241,453 variants (of which 83% are coding variants with a MAF ≤ 5%) and adult height variation in 711,428 individuals (discovery and validation sample sizes were 458,927 and 252,501, respectively). The ExomeChip is a genotyping array designed to query in very large sample sizes coding variants identified by whole-exome DNA sequencing of approximately 12,000 participants. The main goals of our project were to determine whether rare and low-frequency coding variants influence the architecture of a model complex human trait (in this case, adult height) and to discover and characterize new genes and biological pathways implicated in human growth.

Coding variants associated with height

We conducted single-variant meta-analyses in a discovery sample of 458,927 individuals, of whom 381,625 were of European ancestry. We validated our association results in an independent set of 252,501 participants. We first performed standard single-variant association analyses (Extended Data Figs 1, 2, 3 and Supplementary Tables 1–11; technical details of the discovery and validation steps are presented in the Methods). In total, we found 606 independent ExomeChip variants at array-wide significance (P < 2 × 10−7), including 252 non-synonymous or splice-site variants (Methods and Supplementary Table 11). Focusing on non-synonymous or splice-site variants with a MAF < 5%, our single-variant analyses identified 32 rare and 51 low-frequency height-associated variants (Extended Data Tables 1, 2). To our knowledge, these 83 height variants (MAF range of 0.1–4.8%) represent the largest set of validated rare and low-frequency coding variants associated with any complex human trait or disease to date. Among these 83 variants, there are 81 missense, one nonsense (in CCND3), and one essential acceptor splice site (in ARMC5) variants.

We observed a strong inverse relationship between MAF and effect size (Fig. 1). Although power limits our capacity to find rare variants with small effects, we know that common variants with effect sizes comparable to the largest seen in our study would have been easily discovered by prior GWAS, but were not detected. Our results agree with a model based on accumulating theoretical and empirical evidence that suggest that variants with strong phenotypic effects are more likely to be deleterious, and therefore rarer12,13. The largest effect sizes were observed for four rare missense variants, located in the androgen receptor gene AR (NCBI single nucleotide polymorphism (SNP) reference ID: rs137852591; MAF = 0.21%, Pcombined = 2.7 × 10−14), in CRISPLD2 (rs148934412; MAF = 0.08%, Pcombined = 2.4 × 10−20), in IHH (rs142036701, MAF = 0.08%, Pcombined = 1.9 × 10−23), and in STC2 (rs148833559, MAF = 0.1%, Pcombined = 1.2 × 10−30). Carriers of the rare STC2 missense variant are approximately 2.1 cm taller than non-carriers, whereas carriers of the remaining three variants (or hemizygous men that carry a rare X-linked AR allele at rs137852591) are approximately 2 cm shorter than non-carriers. By comparison, the mean effect size of common height alleles is ten times smaller in the same dataset. Across all 83 rare and low-frequency non-synonymous variants, the minor alleles were evenly distributed between height-increasing and height-decreasing effects (48% and 52%, respectively) (Fig. 1 and Extended Data Tables 1, 2).

Figure 1: Variants with a larger effect size on height variation tend to be rarer.
Figure 1

An inverse relationship between the effect size (from the combined ‘discovery and validation’ analysis, in centimetres on the y axis) and the MAF for the height variants (x axis, from 0 to 50%) can be observed. Included in this figure are the 606 height variants with a P < 2 × 10−7.

Coding variants in new and known height loci

Many of the height-associated variants discovered in this study are located near common variants previously associated with height. Of the 83 rare and low-frequency non-synonymous variants, 2 low-frequency missense variants were previously identified (in CYTL1 and IL11)3,14 and 47 fell within 1 Mb of a known height signal; the remaining 34 define new loci. We used conditional analysis of the UK Biobank dataset and confirmed that 38 of these 47 variants were independent of the previously described height SNPs (Supplementary Table 12). We validated the UK Biobank conditional results using an orthogonal imputation-based methodology implemented in the full discovery set (Extended Data Fig. 4 and Supplementary Table 12). In addition, we found a further 85 common variants and one low-frequency synonymous variant (in ACHE) that define novel loci (Supplementary Table 12). Thus, our study identified a total of 120 new height-associated loci (Supplementary Table 11).

We used the UK Biobank dataset to estimate the contribution of the new height variants to heritability, which is h2 ≈ 80% for adult height2. In combination, the 83 rare and low-frequency variants explained 1.7% of the heritability of height. The newly identified novel common variants accounted for another 2.4% and all independent variants, known and novel, together explained 27.4% of heritability. By comparison, the 697 known height-associated SNPs explain 23.3% of height heritability in the same dataset (versus the 4.1% explained by the new height-associated variants identified in this study). We observed a modest positive association between MAF and heritability for each variant (P = 0.012, Extended Data Fig. 5), with each common variant explaining slightly more heritability than rare or low-frequency variants (0.036% versus 0.026%, Extended Data Fig. 5).

Gene-based association results

To increase the power to find rare or low-frequency coding variants associated with height, we performed gene-based analyses (Methods and Supplementary Tables 13–15). After accounting for gene-based signals explained by a single variant driving the association statistics, we identified ten genes with P < 5 × 10−7 that harboured more than one coding variant independently associated with height variation (Supplementary Tables 16, 17). These gene-based results remained significant after conditioning on genotypes at nearby common height-associated variants present on the ExomeChip (Table 1). Using the same gene-based tests in an independent dataset of 59,804 individuals genotyped on the same exome array, we replicated three genes at P < 0.05 (Table 1). Further evidence for replication in these genes was seen at the level of single variants (Supplementary Table 18). From the gene-based results, three genes—CSAD, NOX4, and UGGT2—are outside of the loci found by single-variant analyses and are implicated in human height for the first time to our knowledge.

Table 1: Ten height genes implicated by gene-based testing

Coding variants implicate pathways in skeletal growth

Previous pathway analyses of height loci identified by GWAS have highlighted gene sets related to both general biological processes (such as chromatin modification and regulation of embryonic size) and skeletal-growth-specific pathways (such as chondrocyte biology, extracellular matrix and skeletal development)3. We used two different methods, DEPICT15 and PASCAL16 (see Methods), to perform pathway analyses using the ExomeChip results to test whether coding variants could independently confirm the relevance of these previously highlighted pathways (and further implicate specific genes in these pathways) or identify new pathways. To compare the pathways emerging from coding and non-coding variation, we applied DEPICT separately onto exome-array-wide associated coding variants independent of known GWAS signals and onto non-coding GWAS loci, excluding all novel height-associated genes implicated by coding variants. We identified a total of 496 and 1,623 enriched gene sets, respectively, at a false discovery rate < 1% (Supplementary Tables 19, 20); similar analyses with PASCAL yielded 362 and 278 enriched gene sets, respectively (Supplementary Tables 21, 22). Comparison of the results revealed a high degree of shared biology for coding and non-coding variants (for DEPICT, gene set P values compared between coding and non-coding results had a Pearson’s r = 0.583, P < 2.2 × 10−16; for PASCAL, Pearson’s r = 0.605, P < 2.2 × 10−16). However, some pathways were more strongly enriched for either coding or non-coding genetic variation. In general, coding variants more strongly implicated pathways specific to skeletal growth (such as extracellular matrix and bone growth), whereas GWAS signals highlighted more global biological processes (such as transcription factor binding and embryonic size or lethality) (Extended Data Fig. 6). The two significant gene sets identified by DEPICT and PASCAL that uniquely implicated coding variants were the BCAN protein–protein interaction sub-network and the proteoglycan-binding set. Both of these pathways relate to the biology of proteoglycans, which are proteins (such as aggrecan) that contain glycosaminoglycans (such as chrondroitin sulfate) and that have well established connections to skeletal growth17.

We also investigated which height-associated genes identified by ExomeChip analyses were driving enrichment of pathways such as proteoglycan binding. Using unsupervised clustering analysis, we observed that a cluster of 15 height-associated genes was strongly implicated in a group of correlated pathways that include biology related to proteoglycans and glycosaminoglycans (Fig. 2 and Extended Data Fig. 7). Seven of these 15 genes overlap a previously curated list of 277 genes annotated in OMIM (http://omim.org/) as causing skeletal growth disorders3; genes in this small cluster are enriched for OMIM annotations relative to genes outside the cluster (odds ratio = 27.6, Fisher's exact P = 1.1 × 10−5). As such, the remaining genes in this cluster may harbour variants that cause Mendelian growth disorders. Within this group are genes that are largely uncharacterized (SUSD5), have relevant biochemical functions (GLT8D2, a glycosyltransferase studied mostly in the context of the liver18; LOXL4, a lysyl oxidase expressed in cartilage19), modulate pathways known to affect skeletal growth (FIBIN, SFRP4)20,21 or lead to increased body length when knocked out in mice (SFRP4)22.

Figure 2: Heat map showing subset of DEPICT gene set enrichment results.
Figure 2

The full heat map is available in Extended Data Fig. 7. For any given square, the colour indicates how strongly the corresponding gene (shown on the x axis) is predicted to belong to the reconstituted gene set (y axis). This value is based on the gene’s Z score for gene set inclusion in DEPICT’s reconstituted gene sets, where red indicates a higher Z score and blue indicates a lower one. The proteoglycan-binding pathway (bold) was uniquely implicated by coding variants by DEPICT and PASCAL. To visually reduce redundancy and increase clarity, we chose one representative meta-gene set for each group of highly correlated gene sets, based on affinity propagation clustering (Supplementary Information). Heat map intensity and DEPICT P values correspond to the most significantly enriched gene set within the meta-gene set; meta-gene sets are listed with their database source. Annotations for the genes indicate whether the gene has OMIM annotation as underlying a disorder of skeletal growth (black and grey) and the MAF of the significant ExomeChip (EC) variant (shades of blue; if multiple variants, the lowest-frequency variant was kept). Annotations for the gene sets indicate if the gene set was also found significant for ExomeChip by PASCAL (yellow, orange and grey) and if the gene set was found significant by DEPICT for ExomeChip only or for both ExomeChip and GWAS (purple and green). GO, Gene Ontology; MP, mouse phenotype in the Mouse Genetics Initiative; PPI, protein–protein interaction in the InWeb database.

Functional characterization of rare STC2 variants

To investigate whether the identified rare coding variants affect protein function, we performed in vitro functional analyses of two rare coding variants in a particularly compelling and novel candidate gene, STC2. Overexpression of STC2 diminishes growth in mice by covalent binding to and inhibition of the proteinase PAPP-A, which specifically cleaves insulin growth factor binding protein 4 (IGFBP-4), leading to reduced levels of bioactive insulin-like growth factors23 (Fig. 3a). Although there was no prior genetic evidence implicating STC2 variation in human growth, the PAPPA and IGFBP4 genes have both been implicated in height GWAS3, and rare mutations in PAPPA2 cause severe short stature24, emphasizing the likely relevance of this pathway in humans. The two STC2 height-associated variants are rs148833559 (p.R44L, MAF = 0.096%, Pdiscovery = 5.7 × 10−15) and rs146441603 (p.M86I, MAF = 0.14%, Pdiscovery = 2.1 × 10−5). These rare alleles increase height by an average of 1.9 and 0.9 cm, respectively, suggesting that they both partially impair STC2 activity. In functional studies, STC2 variants with these amino acid substitutions were expressed at similar levels to wild-type STC2, but showed clear, partial defects in binding to PAPP-A and in inhibition of PAPP-A-mediated cleavage of IGFBP-4 (Fig. 3b–d). Thus, the genetic analysis successfully identified rare coding alleles that have demonstrable and predicted functional consequences, strongly confirming the role of these variants and the STC2 gene in human growth.

Figure 3: STC2 mutants p.Arg44Leu (R44L) and p.Met86Ile (M86I) show compromised proteolytic inhibition of PAPP-A.
Figure 3

a, Schematic representation of the role of STC2 in IGF-1 signalling. Partial inactivation of STC2 by height-associated DNA sequence variation could increase bioactive IGF-1 through reduced inhibition of PAPP-A. b, Western blot analysis of recombinant wild-type (WT) STC2 and variants R44L and M86I. c, Covalent complex formation between PAPP-A and wild-type STC2 or variants R44L and M86I. Separately synthesized proteins were analysed by PAPP-A western blotting following incubation for 8 h. In the absence of STC2 (Mock), PAPP-A appears as a single 400-kDa band, indicated by an arrow. Following incubation with wild-type STC2, the majority of PAPP-A is present as the approximately 500-kDa covalent PAPP-A–STC2 complex, indicated by an arrowhead, in which PAPP-A is devoid of proteolytic activity towards IGFBP-4. Under similar conditions, incubation with variants R44L or M86I appeared to cause a lesser degree of covalent complex formation with PAPP-A. The gels are representative of at least three independent experiments. d, PAPP-A proteolytic cleavage of IGFBP-4 following incubation with wild-type STC2 or variants for 1–24 h. Wild-type STC2 causes reduction in PAPP-A activity, with complete inhibition of activity following a 24-h incubation. Both STC2 variants show increased IGFBP-4 cleavage (that is, less inhibition) for all time points analysed. Mean ± s.d. of three independent experiments are shown. One-way repeated measures analysis of variance followed by Dunnett’s post-test showed significant differences between STC2 wild-type and variants R44L (P < 0.001) and M86I (P < 0.01).

Pleiotropic effects

Previous GWAS studies have reported pleiotropic or secondary effects on other phenotypes for many common variants associated with adult height3,25. Using association results from 17 human complex phenotypes for which well-powered meta-analysis results are available, we investigated whether rare and low-frequency height variants are also pleiotropic. We found one rare and five low-frequency missense variants associated with at least one of the other investigated traits at array-wide significance (P < 2 × 10−7) (Extended Data Fig. 8 and Supplementary Table 23). The minor alleles at rs77542162 (ABCA6, MAF = 1.7%) and rs28929474 (SERPINA1, MAF = 1.8%) are associated with increased height and increased levels of low-density lipoprotein (LDL) cholesterol and total cholesterol, whereas the minor allele at rs3208856 in CBLC (MAF = 3.4%) is associated with increased height, high-density lipoprotein (HDL) cholesterol and triglyceride, but decreased LDL cholesterol and total cholesterol levels. The minor allele at rs141845046 (ZBTB7B, MAF = 2.8%) was associated with both increased height and body mass index (BMI). The minor alleles at the other two missense variants associated with shorter stature, rs201226914 in PIEZO1 (MAF = 0.2%) and rs35658696 in PAM (MAF = 4.8%), were associated with decreased glycated haemoglobin (HbA1c) and increased risk of type 2 diabetes (T2D), respectively.

Discussion

We undertook an association study of nearly 200,000 coding variants in 711,428 individuals, and identified 32 rare and 51 low-frequency coding variants associated with adult height. Furthermore, gene-based testing discovered 10 genes that harbour several additional rare or low-frequency variants associated with height, including three genes (CSAD, NOX4 and UGGT2) in loci not previously implicated in height. Given the design of the ExomeChip, which did not consider variants with a MAF < 0.004% (corresponding to approximately one allele in 12,000 participants), our gene-based association results do not rule out the possibility that additional genes with such rarer coding variants also contribute to height variation; deep DNA sequencing in very large sample sizes will be required to address this question. In total, our results highlight 89 genes (10 from gene-based testing and 79 from single-variant analyses (4 genes have 2 independent coding variants)) that are likely to modulate human growth, and 24 alleles segregating in the general population that affect height by more than 1 cm (Table 1 and Extended Data Tables 1, 2). The rare and low-frequency coding variants explain 1.7% of the heritable variation in adult height. When considering all rare, low-frequency and common height-associated variants validated in this study, we can now explain 27.4% of the heritability of height.

Our analyses revealed many coding variants in genes mutated in monogenic skeletal growth disorders, confirming the presence of allelic series (from familial penetrant mutations to mild effect common variants) in the same genes for related growth phenotypes in humans. We used gene-set-enrichment-type analyses to demonstrate the functional connectivity between the genes that harbour coding height variants, highlighting both known and novel biological pathways that regulate height in humans (Fig. 2, Extended Data Fig. 7 and Supplementary Tables 19–22), and implicating genes such as SUSD5, GLT8D2, LOXL4, FIBIN and SFRP4 that have not been previously connected with skeletal growth. Additional noteworthy height candidate genes include NOX4, ADAMTS3, ADAMTS6, PTH1R and IL11RA (Extended Data Tables 1, 2 and Supplementary Tables 17, 24). NOX4, identified through gene-based testing, encodes NADPH oxidase 4, an enzyme that produces reactive oxygen species, a biological pathway not previously implicated in human growth. Nox4−/− mice display higher bone density and a reduced number of osteoclasts, a cell type that is essential for bone repair, maintenance and remodelling12. We also found rare coding variants in ADAMTS3 and ADAMTS6, genes that encode metalloproteinases that belong to the same family as several other human growth syndromic genes (such as ADAMTS2, ADAMTS10 and ADAMTSL2). Moreover, we discovered a rare missense variant in PTH1R that encodes a receptor for parathyroid hormone; parathyroid hormone–PTH1R signalling is important for bone resorption, and mutations in PTH1R cause chondrodysplasia in humans26. Finally, we replicated the association between a low-frequency missense variant in the cytokine gene IL11, but also found a low-frequency missense variant in the gene encoding its receptor, IL11RA. The IL11–IL11RA axis has been shown to play an important role in bone formation in the mouse27,28. Thus, our data confirm that this signalling cascade is also relevant in human growth.

Overall, our findings provide strong evidence that rare and low-frequency coding variants contribute to the genetic architecture of height, a model complex human trait. This conclusion has implications for the prediction of complex human phenotypes in the context of precision medicine initiatives. Although rare, large effect-size variants might not explain most of the heritable disease risk at the population level, they are important for predicting the risk of disease development for the individuals that carry them. Our findings also seem to contrast markedly with results from the recent large-scale T2D association study, which found only six variants with a MAF < 5% (ref. 29.). This apparent difference could be explained simply by the large difference in sample sizes between the two studies (711,428 for height versus 127,145 for T2D). When we consider the fraction of associated variants with a MAF < 5% among all confirmed variants for height and T2D, we find that it is similar (9.7% for height versus 7.1% for T2D). This supports the strong probability that rarer T2D alleles and, more generally, rarer alleles for other polygenic diseases and traits will be uncovered as sample sizes continue to increase.

Methods

Study design and participants

The discovery cohort consisted of 147 studies comprising 458,927 adult individuals of the following ancestries: (1) European descent (n = 381,625); (2) African (n = 27,494); (3) South Asian (n = 29,591); (4) East Asian (n = 8,767); (5) Hispanic (n = 10,776) and (6) Saudi Arabian (n = 695). All participating institutions and coordinating centres approved this project, and informed consent was obtained from all subjects. Discovery meta-analysis was carried out in each ancestry group (except the Saudi Arabian) separately as well as in the All group. Validation was undertaken in individuals of European ancestry only (Supplementary Tables 1–3). Conditional analyses were undertaken only in the European descent group (106 studies, n = 381,625). The SNPs we identify are available from the NCBI dbSNP database of short genetic variations (https://www.ncbi.nlm.nih.gov/projects/SNP/). No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment.

Phenotype

Height (in centimetres) was corrected for age and the genomic principal components (derived from GWAS data, the variants with a MAF > 1% on ExomeChip (http://genome.sph.umich.edu/wiki/Exome_Chip_Design), or ancestry-informative markers available on the ExomeChip), as well as any additional study-specific covariates (for example, recruiting centre), in a linear regression model. For studies with non-related individuals, residuals were calculated separately by sex, whereas for family-based studies sex was included as a covariate in the model. Additionally, residuals for case/control studies were calculated separately. Finally, residuals were subject to inverse normal transformation.

Genotype calling

The majority of studies followed a standardized protocol and performed genotype calling using the designated manufacturer’s software, which was then followed by zCall30. For ten studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the raw intensity data for the samples from seven genotyping centres were assembled into a single project for joint calling11. Study-specific quality-control measures of the genotyped variants was implemented before association analysis (Supplementary Tables 1–2).

Study-level statistical analyses

Individual cohorts were analysed separately for each ancestry population, with either RAREMETALWORKER (http://genome.sph.umich.edu/wiki/RAREMETALWORKER) or RVTEST (http://zhanxw.github.io/rvtests/), to associate inverse normal transformed height data with genotype data taking potential cryptic relatedness (kinship matrix) into account in a linear mixed model. These software are designed to perform score-statistics based rare-variant association analysis, can accommodate both unrelated and related individuals, and provide single-variant results and variance-covariance matrix. The covariance matrix captures linkage disequilibrium relationships between markers within 1 Mb, which is used for gene-level meta-analyses and conditional analyses31. Single-variant analyses were performed for both additive and recessive models (for the alternate allele).

Centralized quality control

The individual study data were investigated for potential existence of ancestry population outliers based on the 1000 Genome Project phase 1 ancestry reference populations. A centralized quality control procedure implemented in EasyQC32 was applied to individual study association summary statistics to identify outlying studies: (1) assessment of possible problems in height transformation; (2) comparison of allele frequency alignment against 1000 Genomes Project phase 1 reference data to pinpoint any potential strand issues; and (3) examination of quantile–quantile plots per study to identify any problems arising from population stratification, cryptic relatedness and genotype biases. We excluded variants if they had a call rate <95%, Hardy–Weinberg equilibrium P < 1 × 10−7, or large allele frequency deviations from reference populations (>0.6 for all ancestry analyses and >0.3 for ancestry-specific population analyses). We also excluded from downstream analyses markers not present on the Illumina ExomeChip array 1.0, variants on the Y chromosome or the mitochondrial genome, indels, multiallelic variants, and problematic variants based on the Blat-based sequence alignment analyses. Meta-analyses were carried out in parallel by two different analysts at two sites.

Single-variant meta-analyses

Discovery analyses

We conducted single-variant meta-analyses in a discovery sample of 458,927 individuals of different ancestries using both additive and recessive genetic models (Extended Data Fig. 1 and Supplementary Tables 1–4). Significance for single-variant analyses was defined at an array-wide level (P < 2 × 10−7, Bonferroni correction for 250,000 variants). The combined additive analyses identified 1,455 unique variants that reached array-wide significance (P < 2 × 10−7), including 578 non-synonymous and splice-site variants (Supplementary Tables 5–7). Under the additive model, we observed a high genomic inflation of the test statistics (for example, a λGC of 2.7 in European ancestry studies for common markers, Extended Data Fig. 2 and Supplementary Table 8), although validation results (see below) and additional sensitivity analyses (see below) suggested that it is consistent with polygenic inheritance as opposed to population stratification, cryptic relatedness, or technical artefacts (Extended Data Fig. 2). The majority of these 1,455 association signals (1,241; 85.3%) were found in the European ancestry meta-analysis (85.5% of the discovery sample size) (Extended Data Fig. 2). Nevertheless, we discovered eight associations within five loci in our all-ancestry analyses that are driven by African studies (including one missense variant in the growth hormone gene GH1 (rs151263636), Extended Data Fig. 3), three height variants found only in African studies, and one rare missense marker associated with height in South Asians only (Supplementary Table 7).

Genomic inflation and confounding

We observed a marked genomic inflation of the test statistics even after adequate control for population stratification (linear mixed model) arising mainly from common markers; λGC in European ancestry was 1.2 and 2.7 for all and common markers, respectively (Extended Data Fig. 2 and Supplementary Table 8). Such inflation is expected for a highly polygenic trait like height, and is consistent with our very large sample size3,33. To confirm this, we applied the recently developed linkage disequilibrium score regression method to our height ExomeChip results34, with the caveats that the method was developed (and tested) with >200,000 common markers available. We restricted our analyses to 15,848 common variants (MAF ≥ 5%) from the European-ancestry meta-analysis, and matched them to pre-computed linkage disequilibrium scores for the European reference dataset34. The intercept of the regression of the χ2 statistics from the height meta-analysis on the linkage disequilibrium score estimates that the inflation in the mean χ2 is due to confounding bias, such as cryptic relatedness or population stratification. The intercept was 1.4 (s.e.m. = 0.07), which is small when compared to the λGC of 2.7. Furthermore, we also confirmed that the linkage disequilibrium score regression intercept is estimated upward because of the small number of variants on the ExomeChip and the selection criteria for these variants (that is, known GWAS hits). The ratio statistic of (intercept − 1)/(mean χ2 − 1) is 0.067 (s.e.m. = 0.012), well within the normal range34, suggesting that most of the inflation (~93%) observed in the height association statistics is due to polygenic effects (Extended Data Fig. 2).

Furthermore, to exclude the possibility that some of the observed associations between height and rare and low-frequency variants could be due to allele calling problems in the smaller studies, we performed a sensitivity meta-analysis with primarily European ancestry studies totalling >5,000 participants. We found very concordant effect sizes, suggesting that smaller studies do not bias our results (Extended Data Fig. 2).

Conditional analyses

The RAREMETAL R package35 and the GCTA v1.24 (ref. 36) software were used to identify independent height association signals across the European descent meta-analysis results. RAREMETAL performs conditional analyses by using covariance matrices in order to distinguish true signals from those driven by linkage disequilibrium at adjacent known variants. First, we identified the lead variants (P < 2 × 10−7) based on a 1-Mb window centred on the most significantly associated variant and performed linkage disequilibrium pruning (r2 < 0.3) to avoid downstream problems in the conditional analyses due to co-linearity. We then conditioned on the linkage disequilibrium-pruned set of lead variants in RAREMETAL and kept new lead signals at P < 2 × 10−7. The process was repeated until no additional signal emerged below the pre-specified P-value threshold. The use of a 1-Mb window in RAREMETAL can obscure dependence between conditional signals in adjacent intervals in regions of extended linkage disequilibrium. To detect such instances, we performed joint analyses using GCTA with the ARIC and UK ExomeChip reference panels, both of which comprise >10,000 individuals of European descent. With the exception of a handful of variants in a few genomic regions with extended linkage disequilibrium (for example, the HLA region on chromosome 6), the two pieces of software identified the same independent signals (at P < 2 × 10−7).

To discover new height variants, we conditioned the height variants found in our ExomeChip study on the previously published GWAS height variants3 using the first release of the UK Biobank imputed dataset and regression methodology implemented in BOLT-LMM37. Because of the difference between the sample size of our discovery set (n = 458,927) and the UK Biobank (first release, n = 120,084), we applied a threshold of Pconditional < 0.05 to declare a height variant as independent in this analysis. We also explored an alternative approach based on approximate conditional analysis36. This latter method (SSimp) relies on summary statistics available from the same cohort, thus we first imputed summary statistics38 for exome variants, using summary statistics from a previous study3. Conversely, we imputed the top variants from this study3 using the summary statistics from the ExomeChip. Subsequently, we calculated effect sizes for each exome variant conditioned on the top variants of this study3 in two ways. First, we conditioned the imputed summary statistics of the exome variant on the summary statistics of the top variants that fell within 5 Mb of the target ExomeChip variant. Second, we conditioned the summary statistics of the ExomeChip variant on the imputed summary statistics of the hits of this study3. We then selected the option that yielded a higher imputation quality. For poorly tagged variants ( < 0.8), we simply used up-sampled HapMap summary statistics for the approximate conditional analysis. Pairwise SNP-by-SNP correlations were estimated from the UK10K data (TwinsUK39 and ALSPAC40 studies, n = 3,781).

Validation of the single-variant discovery results

Several studies, totalling 252,501 independent individuals of European ancestry, became available after the completion of the discovery analyses, and were thus used for validation of our experiment. We validated the single-variant association results in eight studies, totalling 59,804 participants, genotyped on the ExomeChip using RAREMETAL31. We sought additional evidence for association for the top signals in two independent studies in the UK (UK Biobank) and Iceland (deCODE), comprising 120,084 and 72,613 individuals, respectively. We used the same quality control and analytical methodology as described above. Genotyping and study descriptions are provided in Supplementary Tables 1–3. For the combined analysis, we used the inverse-variance-weighted fixed effects meta-analysis method using METAL41. Significant associations were defined as those with a combined meta-analysis (discovery and validation) Pcombined < 2 × 10−7.

We considered 81 variants with suggestive association in the discovery analyses (2 × 10−7 < Pdiscovery ≤ 2 × 10−6). Of those 81 variants, 55 reached significance after combining discovery and replication results based on a Pcombined < 2 × 10−7 (Supplementary Table 9). Furthermore, recessive modelling confirmed seven new independent markers with a Pcombined < 2 × 10−7 (Supplementary Table 10). One of these recessive signals is due to a rare X-linked variant in the AR gene (rs137852591, MAF = 0.21%). Because of its frequency, we only tested hemizygous men (we did not identify homozygous women for the minor allele) so we cannot distinguish between a true recessive mode of inheritance or a sex-specific effect for this variant. To test the independence and integrate all height markers from the discovery and validation phase, we used conditional analyses and GCTA ‘joint’ modelling36 in the combined discovery and validation set. This resulted in the identification of 606 independent height variants, including 252 non-synonymous or splice-site variants (Supplementary Table 11). If we consider only the initial set of lead SNPs with P < 2 × 10−7, we identified 561 independent variants. Of these 561 variants (selected without the validation studies), 560 have concordant direction of effect between the discovery and validation studies, and 548 variants have a Pvalidation < 0.05 (466 variants with Pvalidation < 8.9 × 10−5, Bonferroni correction for 561 tests), suggesting a very low false discovery rate (Supplementary Table 11).

Gene-based association meta-analyses

For the gene-based analyses, we applied two different sets of criteria to select variants, based on coding variant annotation from five prediction algorithms (PolyPhen2 HumDiv and HumVar, LRT, MutationTaster and SIFT)42. The mask labelled ‘broad’ included variants with a MAF < 0.05 that are nonsense, stop-loss, splice site, as well as missense variants that are annotated as damaging by at least one program mentioned above. The mask labelled ‘strict’ included only variants with a MAF < 0.05 that are nonsense, stop-loss, splice-site, as well as missense variants annotated as damaging by all five algorithms. We used two tests for gene-based testing, namely the SKAT43 and VT44 tests. Statistical significance for gene-based tests was set at a Bonferroni-corrected threshold of P < 5 × 10−7 (threshold for 25,000 genes and four tests). The gene-based discovery results were validated (same test and variants, when possible) in the same eight studies genotyped on the ExomeChip (n = 59,804 participants) that were used for the validation of the single-variant results (see above, and Supplementary Tables 1–3). Gene-based conditional analyses were performed in RAREMETAL.

Pleiotropy analyses

We accessed ExomeChip data from GIANT (BMI, waist:hip ratio), GLGC (total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol), IBPC (systolic and diastolic blood pressure), MAGIC (glycaemic traits), REPROGEN (age at menarche and menopause), and DIAGRAM (type 2 diabetes) consortia. For coronary artery disease, we accessed 1000 Genomes Project-imputed GWAS data released by CARDIoGRAMplusC4D45.

Pathway analyses

DEPICT (http://www.broadinstitute.org/mpg/depict/) is a computational framework that uses probabilistically defined reconstituted gene sets to perform gene set enrichment and gene prioritization15. For a description of gene set reconstitution, refer to refs 15, 46. In brief, reconstitution was performed by extending pre-defined gene sets (such as Gene Ontology terms, canonical pathways, protein-protein interaction subnetworks and rodent phenotypes) with genes co-regulated with genes in these pre-defined gene set using large-scale microarray-based transcriptomics data. In order to adapt the gene set enrichment part of DEPICT for ExomeChip data (https://github.com/RebeccaFine/height-ec-depict), we made two principal changes. First, because DEPICT for GWAS incorporates all genes within a given linkage disequilibrium block around each index SNP, we modified DEPICT to take as input only the gene directly impacted by the coding SNP. Second, we adapted the way DEPICT adjusts for confounders (such as gene length) by generating null ExomeChip association results using Swedish ExomeChip data (Malmö Diet and Cancer (MDC), All New Diabetics in Scania (ANDIS), and Scania Diabetes Registry (SDR) cohorts, n = 11,899) and randomly assigning phenotypes from a normal distribution before conducting association analysis (see Supplementary Information). For the gene set enrichment analysis of the ExomeChip data, we used significant non-synonymous variants statistically independent of known GWAS hits (and that were present in the null ExomeChip data; see Supplementary Information for details). For gene set enrichment analysis of the GWAS data, we used all loci with a non-coding index SNP and that did not contain any of the novel ExomeChip genes. In visualizing the analysis, we used affinity propagation clustering47 to group the most similar reconstituted gene sets based on their gene memberships (see Supplementary Information). Within a ‘meta-gene set’, the best P value of any member gene set was used as representative for comparison. DEPICT for ExomeChip was written using the Python programming language and the code can be found at https://github.com/RebeccaFine/height-ec-depict.

We also applied the PASCAL (http://www2.unil.ch/cbg/index.php?title=Pascal) pathway analysis tool16 to association summary statistics for all coding variants. In brief, the method derives gene-based scores (both SUM and MAX statistics) and subsequently tests for the over-representation of high gene scores in predefined biological pathways. We used standard pathway libraries from KEGG, REACTOME and BIOCARTA, and also added dichotomized (Z score > 3) reconstituted gene sets from DEPICT15. To accurately estimate SNP-by-SNP correlations even for rare variants, we used the UK10K data (TwinsUK39 and ALSPAC40 studies, n = 3781). To separate the contribution of regulatory variants from the coding variants, we also applied PASCAL to association summary statistics of only regulatory variants (20 kb upstream, gene body excluded) from a previous study3. In this way, we could classify pathways driven principally by coding, regulatory or mixed signals.

STC2 functional experiments

For the generation of STC2 mutants (R44L and M86I), wild-type STC2 cDNA contained in pcDNA3.1/Myc-His(−) (Invitrogen)23 was used as a template. Mutagenesis was carried out using Quickchange (Stratagene), and all constructs were verified by sequence analysis. Recombinant wild-type STC2 and variants were expressed in human embryonic kidney (HEK) 293T cells (293tsA1609neo, ATCC CRL-3216) maintained in high-glucose DMEM supplemented 10% fetal bovine serum, 2 mM glutamine, nonessential amino acids, and gentamicin. The cells are routinely tested for mycoplasma contamination. Cells (6 × 106) were plated onto 10-cm dishes and transfected 18 h later by calcium phosphate co-precipitation using 10 μg plasmid DNA. Medium was collected 48 h after transfection, cleared by centrifugation, and stored at −20 °C until use. Protein concentrations (58–66 nM) were determined by TRIFMA using antibodies described previously23. PAPP-A was expressed stably in HEK293T cells as previously reported48. Expressed levels of PAPP-A (27.5 nM) were determined by a commercial ELISA (AL-101, Ansh Labs).

Culture supernatants containing wild-type STC2 or variants were adjusted to 58 nM, added an equal volume of culture supernatant containing PAPP-A corresponding to a 2.1-fold molar excess, and incubated at 37 °C. Samples were taken at 1, 2, 4, 6, 8, 16, and 24 h and stored at −20 °C.

Specific proteolytic cleavage of 125I-labeled IGFBP-4 is described in detail elsewhere49. In brief, the PAPP-A–STC2 complex mixtures were diluted (1:190) to a concentration of 72.5 pM PAPP-A and mixed with pre-incubated 125I-IGFBP4 (10 nM) and IGF-1 (100 nM) in 50 mM Tris-HCl, 100 mM NaCl, 1 mM CaCl2. Following 1 h incubation at 37 °C, reactions were terminated by the addition of SDS–PAGE sample buffer supplemented with 25 mM EDTA. Substrate and co-migrating cleavage products were separated by 12% non-reducing SDS–PAGE and visualized by autoradiography using a storage phosphor screen (GE Healthcare) and a Typhoon imaging system (GE Healthcare). Band intensities were quantified using ImageQuant TL 8.1 software (GE Healthcare).

STC2 and covalent complexes between STC2 and PAPP-A were blotted onto PVDF membranes (Millipore) following separation by 3–8% SDS–PAGE. The membranes were blocked with 2% Tween-20, and equilibrated in 50 mM Tris-HCl, 500 mM NaCl, 0.1% Tween-20; pH 9 (TST). For STC2, the membranes were incubated with goat polyclonal anti-STC2 (R&D systems, AF2830) at 0.5 μg ml−1 in TST supplemented with 2% skimmed milk for 1 h at 20 °C. For PAPP-A–STC2 complexes, the membranes were incubated with rabbit polyclonal anti-PAPP-A50 at 0.63 μg ml−1 in TST supplemented with 2% skimmed milk for 16 h at 20 °C. Membranes were washed with TST and subsequently incubated with polyclonal rabbit anti-goat IgG[en rule]horseradish peroxidase (DAKO, P0449) or polyclonal swine anti-rabbit IgG[en rule]horseradish peroxidase (DAKO, P0217), respectively, diluted 1:2,000 in TST supplemented with 2% skimmed milk for 1 h at 20 °C. Following washing with TST, membranes were developed using enhanced chemiluminescence (ECL Prime, GE Healthcare). Images were captured using an ImageQuant LAS 4000 instrument (GE Healthcare).

Data availability

Summary genetic association results are available on the GIANT website (http://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium).

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Acknowledgements

A full list of acknowledgments appears in the Supplementary Information. Part of this work was conducted using the UK Biobank resource.

Author information

Author notes

    • Eirini Marouli
    • , Mariaelisa Graff
    • , Carolina Medina-Gomez
    • , Ken Sin Lo
    • , Andrew R. Wood
    • , Troels R. Kjaer
    • , Rebecca S. Fine
    •  & Yingchang Lu

    These authors contributed equally to this work.

    • Claus Oxvig
    • , Zoltán Kutalik
    • , Fernando Rivadeneira
    • , Ruth J. F. Loos
    • , Timothy M. Frayling
    • , Joel N. Hirschhorn
    • , Panos Deloukas
    •  & Guillaume Lettre

    These authors jointly supervised this work.

Affiliations

  1. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK

    • Eirini Marouli
    • , Kathleen E. Stirrups
    • , Mark J. Caulfield
    • , Stavroula Kanoni
    • , Patricia B. Munroe
    • , Ioanna Ntalla
    • , Helen R. Warren
    •  & Panos Deloukas
  2. Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Mariaelisa Graff
    • , Heather M. Highland
    • , Anne E. Justice
    • , Kristin L. Young
    •  & Angela L. Mazul
  3. Department of Epidemiology, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands

    • Carolina Medina-Gomez
    • , M. Arfan Ikram
    • , André G. Uitterlinden
    • , Cornelia M. van Duijn
    •  & Fernando Rivadeneira
  4. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands

    • Carolina Medina-Gomez
    • , Linda Broer
    • , André G. Uitterlinden
    •  & Fernando Rivadeneira
  5. Montreal Heart Institute, Montreal, Quebec H1T 1C8, Canada

    • Ken Sin Lo
    • , Valérie Turcot
    • , Simon de Denus
    • , John D. Rioux
    • , Jean-Claude Tardif
    •  & Guillaume Lettre
  6. Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK

    • Andrew R. Wood
    • , Katherine S. Ruth
    • , Hanieh Yaghootkar
    •  & Timothy M. Frayling
  7. Department of Molecular Biology and Genetics, Aarhus University, Aarhus, 8000, Denmark

    • Troels R. Kjaer
    •  & Claus Oxvig
  8. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA

    • Rebecca S. Fine
    • , Tõnu Esko
    • , Manuel A. Rivas
    • , Sailaja Vedantam
    • , Daniel I. Chasman
    • , Aniruddh P. Patel
    • , Sekar Kathiresan
    •  & Joel N. Hirschhorn
  9. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Rebecca S. Fine
    •  & Sailaja Vedantam
  10. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, Massachusetts 02115, USA

    • Rebecca S. Fine
    • , Tõnu Esko
    • , Sailaja Vedantam
    •  & Joel N. Hirschhorn
  11. Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee 37203, USA

    • Yingchang Lu
  12. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA

    • Yingchang Lu
    • , Claudia Schurmann
    • , Erwin P. Bottinger
    •  & Ruth J. F. Loos
  13. The Genetics of Obesity and Related Metabolic Traits Program, Ichan School of Medicine at Mount Sinai, New York, New York 10069, USA

    • Yingchang Lu
    • , Claudia Schurmann
    •  & Ruth J. F. Loos
  14. Human Genetics Center, The University of Texas School of Public Health, The University of Texas Graduate School of Biomedical Sciences at Houston, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA

    • Heather M. Highland
  15. Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, 1010, Switzerland

    • Sina Rüeger
    •  & Zoltán Kutalik
  16. Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland

    • Sina Rüeger
    • , David Lamparter
    • , Sven Bergmann
    •  & Zoltán Kutalik
  17. deCODE Genetics/Amgen inc., Reykjavik, 101, Iceland

    • Gudmar Thorleifsson
    • , Valgerdur Steinthorsdottir
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  18. Department of Computational Biology, University of Lausanne, Lausanne, 1011, Switzerland

    • David Lamparter
    •  & Sven Bergmann
  19. Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK

    • Kathleen E. Stirrups
  20. Department of Genetic Epidemiology, University of Regensburg, Regensburg, D-93051, Germany

    • Thomas W. Winkler
    • , Mathias Gorski
    •  & Iris M. Heid
  21. Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia

    • Tõnu Esko
    •  & Andres Metspalu
  22. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK

    • Tugce Karaderi
    • , Anubha Mahajan
    • , Wei Gan
    • , Hidetoshi Kitajima
    • , Andrew P. Morris
    • , Neil Robertson
    • , Lorraine Southam
    • , Mark I. McCarthy
    •  & Cecilia M. Lindgren
  23. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Adam E. Locke
    • , Goncalo Abecasis
    • , Shuang Feng
    • , Anne U. Jackson
    • , Xueling Sim
    • , Heather M. Stringham
    •  & Michael Boehnke
  24. McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, Missouri 63108, USA

    • Adam E. Locke
  25. Department of Cardiovascular Sciences, Univeristy of Leicester, Glenfield Hospital, Leicester LE3 9QP, UK

    • Nicholas G. D. Masca
    • , Christopher P. Nelson
    •  & Nilesh J. Samani
  26. NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK

    • Nicholas G. D. Masca
    • , Christopher P. Nelson
    •  & Nilesh J. Samani
  27. Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA

    • Maggie C. Y. Ng
    • , Poorva Mudgal
    • , Donald W. Bowden
    •  & Amanda J. Cox
  28. Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA

    • Maggie C. Y. Ng
    • , Donald W. Bowden
    •  & Amanda J. Cox
  29. Nuffield Department of Clinical Medicine, Oxford OX3 7BN, UK

    • Manuel A. Rivas
  30. Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California 90502, USA

    • Xiuqing Guo
    • , Yii-Der Ida Chen
    • , Yucheng Jia
    • , Yeheng Liu
    • , Kevin Sandow
    • , Kent D. Taylor
    • , Jie Yao
    •  & Jerome I Rotter
  31. Netherlands Comprehensive Cancer Organisation, Utrecht, 3501 DB, The Netherlands

    • Katja K. Aben
  32. Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands

    • Katja K. Aben
    • , Tessel E. Galesloot
    • , Lambertus A. Kiemeney
    • , Kirsten B. Kluivers
    •  & Sita H. Vermeulen
  33. Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina 27599, USA

    • Linda S. Adair
  34. Centre for Control of Chronic Diseases (CCCD), Dhaka, 1212, Bangladesh

    • Dewan S. Alam
  35. Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, D-85764, Germany

    • Eva Albrecht
    • , Iris M. Heid
    •  & Martina Müller-Nurasyid
  36. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark

    • Kristine H. Allin
    • , Emil V. Appel
    • , Jette Bork-Jensen
    • , Niels Grarup
    • , Torben Hansen
    • , Mette Hollensted
    • , Oluf Pedersen
    • , Henrik Vestergaard
    •  & Tune H. Pers
  37. Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, California 92093, USA

    • Matthew Allison
  38. INSERM U1167, Lille, F-59019, France

    • Philippe Amouyel
  39. Institut Pasteur de Lille, U1167, Lille, F-59019, France

    • Philippe Amouyel
  40. Universite de Lille, U1167 - RID-AGE - Risk factors and molecular determinants of aging-related diseases, Lille, F-59019, France

    • Philippe Amouyel
  41. Department of Epidemiology and Public Health, University of Strasbourg, Strasbourg, F-67085, France

    • Dominique Arveiler
  42. Department of Public Health, University Hospital of Strasbourg, Strasbourg, 67081, France

    • Dominique Arveiler
  43. Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht, The Netherlands

    • Folkert W. Asselbergs
    •  & Jessica van Setten
  44. Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands

    • Folkert W. Asselbergs
  45. Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK

    • Folkert W. Asselbergs
  46. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53201, USA

    • Paul L. Auer
  47. INSERM U1018, Centre de recherche en Épidemiologie et Sante des Populations (CESP), Villejuif, France

    • Beverley Balkau
  48. Department of Nephrology, University Hospital Regensburg, Regensburg, 93042, Germany

    • Bernhard Banas
    • , Carsten A. Böger
    •  & Mathias Gorski
  49. Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, 2100, Denmark

    • Lia E. Bang
  50. Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2730, Denmark

    • Marianne Benn
    • , Pia R. Kamstrup
    • , Sune F. Nielsen
    • , Børge G. Nordestgaard
    •  & Anette Varbo
  51. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark

    • Marianne Benn
    • , Ruth Frikke-Schmidt
    • , Torben Jørgensen
    • , Allan Linneberg
    • , Sune F. Nielsen
    • , Børge G. Nordestgaard
    • , Anne Tybjaerg-Hansen
    •  & Anette Varbo
  52. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Lawrence F. Bielak
    • , Min A. Jhun
    • , Sharon L. R. Kardia
    • , Jennifer A. Smith
    •  & Wei Zhao
  53. IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany

    • Matthias Blüher
    • , Peter Kovacs
    •  & Michael Stumvoll
  54. University of Leipzig, Department of Medicine, Leipzig, 04103, Germany

    • Matthias Blüher
    •  & Michael Stumvoll
  55. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, 14558, Germany

    • Heiner Boeing
  56. School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA

    • Eric Boerwinkle
    •  & Megan L. Grove
  57. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA

    • Eric Boerwinkle
  58. Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Lori L. Bonnycastle
    •  & Francis S. Collins
  59. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

    • Michiel L. Bots
    •  & Paul I. W. de Bakker
  60. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA

    • Donald W. Bowden
  61. Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, 7100, Denmark

    • Ivan Brandslund
  62. Institute of Regional Health Research, University of Southern Denmark, Odense, 5000, Denmark

    • Ivan Brandslund
  63. MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London & NIHR Biomedical Research Centre for Mental Health at the Maudsley, London, SE5 8AF, UK

    • Gerome Breen
  64. Marshfield Clinic Research Foundation, Marshfield, Wisconsin 54449, USA

    • Murray H. Brilliant
    • , Peggy L. Peissig
    •  & Thomas N. Person
  65. Department of Medicine, University of Washington, Seattle, Washington 98195, USA

    • Amber A. Burt
    • , Gail P. Jarvik
    •  & Eric B. Larson
  66. MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK

    • Adam S. Butterworth
    • , Rajiv Chowdhury
    • , John Danesh
    • , Emanuele Di Angelantonio
    • , Joanna M. M. Howson
    • , Praveen Surendran
    •  & Robin Young
  67. NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK

    • Adam S. Butterworth
    • , John Danesh
    •  & Emanuele Di Angelantonio
  68. The Sigfried and Janet Weis Center for Research, Danville, Pennsylvania 17822, USA

    • David J. Carey
    • , Helena Kuivaniemi
    •  & Gerard Tromp
  69. NIHR Barts Cardiovascular Research Unit, Barts and The London School of Medicine & Dentistry, Queen Mary University, London EC1M 6BQ, UK

    • Mark J. Caulfield
    • , Patricia B. Munroe
    •  & Helen R. Warren
  70. Department of Cardiology, London North West Healthcare NHS Trust, Ealing Hospital, Middlesex UB1 3HW, UK

    • John C. Chambers
    • , Jaspal S. Kooner
    •  & Weihua Zhang
  71. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK

    • John C. Chambers
    • , Evangelos Evangelou
    • , Marcelo P. Segura-Lepe
    •  & Weihua Zhang
  72. Imperial College Healthcare NHS Trust, London W12 0HS, UK

    • John C. Chambers
    •  & Jaspal S. Kooner
  73. Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA

    • Daniel I. Chasman
  74. Division of Preventive Medicine, Brigham and Women's and Harvard Medical School, Boston, Massachusetts 02215, USA

    • Daniel I. Chasman
    • , Audrey Y. Chu
    •  & Paul M. Ridker
  75. Harvard Medical School, Boston, Massachusetts 02115, USA

    • Daniel I. Chasman
    • , Aniruddh P. Patel
    • , Paul M. Ridker
    •  & Sekar Kathiresan
  76. Medical Department, Lillebaelt Hospital, Vejle, 7100, Denmark

    • Cramer Christensen
  77. NHLBI Framingham Heart Study, Framingham, Massachusetts 01702, USA

    • Audrey Y. Chu
    •  & Nancy L. Heard-Costa
  78. Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, 34100, Italy

    • Massimiliano Cocca
  79. Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK

    • James P. Cook
    •  & Andrew P. Morris
  80. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK

    • Janie Corley
    • , Gail Davies
    • , Ian J. Deary
    •  & John M. Starr
  81. Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK

    • Janie Corley
    • , Gail Davies
    • , Ian J. Deary
    •  & Alison Pattie
  82. Department of Human Genetics, Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands

    • Jordi Corominas Galbany
  83. Menzies Health Institute Queensland, Griffith University, Southport, Queensland, Australia

    • Amanda J. Cox
  84. Diamantina Institute, University of Qeensland, Brisbane, Queensland, 4072, Australia

    • Gabriel Cuellar-Partida
  85. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 4006, Australia

    • Gabriel Cuellar-Partida
    • , Scott D. Gordon
    • , Nicholas G. Martin
    •  & Yadav Sapkota
  86. Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK

    • John Danesh
    • , Gaëlle Marenne
    • , Lorraine Southam
    •  & Eleftheria Zeggini
  87. British Heart Foundation, Cambridge Centre of Excellence, Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK

    • John Danesh
  88. Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands

    • Paul I. W. de Bakker
  89. Department of Vascular Surgery, Division of Surgical Specialties, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands

    • Gert J. de Borst
  90. Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, H3T 1J4, Canada

    • Simon de Denus
  91. Department of Clinical Chemistry and Haematology, Division of Laboratory and Pharmacy, University Medical Center Utrecht, Utrecht, 3508 GA, The Netherlands

    • Mark C. H. de Groot
  92. Utrecht Institute for Pharmaceutical Sciences, Division Pharmacoepidemiology & Clinical Pharmacology, Utrecht University, Utrecht, 3508 TB, The Netherlands

    • Mark C. H. de Groot
  93. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands

    • Renée de Mutsert
    • , Ruifang Li-Gao
    •  & Dennis O. Mook-Kanamori
  94. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, 17671, Greece

    • George Dedoussis
    •  & Aliki-Eleni Farmaki
  95. Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55454, USA

    • Ellen W. Demerath
  96. Department of Ophthalmology, Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands

    • Anneke I. den Hollander
    •  & Carel B. Hoyng
  97. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK

    • Joe G. Dennis
    • , Douglas F. Easton
    •  & Deborah J. Thompson
  98. Institute of Cardiovascular Science, University College London, London WC1E 6JF, UK

    • Fotios Drenos
  99. MRC Integrative Epidemiology Unit, School of Social & Community Medicine, University of Bristol, Bristol BS8 2BN, UK

    • Fotios Drenos
  100. Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, Washington 98109, USA

    • Mengmeng Du
  101. Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York, New York 10017, USA

    • Mengmeng Du
  102. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK

    • Alison M. Dunning
    • , Douglas F. Easton
    • , Ailith Pirie
    •  & Jonathan P. Tyrer
  103. Department of Medicine, Oulu University Hospital, Oulu, 90029, Finland

    • Tapani Ebeling
  104. Research Unit of Internal Medicine, University of Oulu, Oulu, FI-90014, Finland

    • Tapani Ebeling
  105. Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee 37203, USA

    • Todd L. Edwards
    •  & Ayush Giri
  106. Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Patrick T. Ellinor
    • , Jose C. Florez
    • , Paul L. Huang
    • , Steven A. Lubitz
    • , Alisa K. Manning
    • , Aniruddh P. Patel
    • , Gina M. Peloso
    • , Suthesh Sivapalaratnam
    •  & Sekar Kathiresan
  107. Medical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts 02141, USA

    • Patrick T. Ellinor
    • , Jose C. Florez
    • , Steven A. Lubitz
    • , Alisa K. Manning
    •  & Gina M. Peloso
  108. Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK

    • Paul Elliott
  109. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece

    • Evangelos Evangelou
  110. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan 48104, USA

    • Jessica D. Faul
  111. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, StLouis, Missouri 63108, USA

    • Mary F. Feitosa
    •  & Ingrid B. Borecki
  112. CNR Institute of Clinical Physiology, Pisa, Italy

    • Ele Ferrannini
  113. Department of Clinical & Experimental Medicine, University of Pisa, Pisa, Italy

    • Ele Ferrannini
  114. Research Center on Epidemiology and Preventive Medicine, Department of Clinical and Experimental Medicine, University of Insubria, Varese, 21100, Italy

    • Marco M. Ferrario
  115. Toulouse University School of Medicine, Toulouse, TSA 50032 31059, France

    • Jean Ferrieres
  116. Department of Medicine, Harvard University Medical School, Boston, Massachusetts 02115, USA

    • Jose C. Florez
    •  & Alisa K. Manning
  117. University of Glasgow, Glasgow G12 8QQ, UK

    • Ian Ford
    • , Chris J. Packard
    • , Sandosh Padmanabhan
    • , Naveed Sattar
    •  & Robin Young
  118. Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA

    • Myriam Fornage
    •  & Li-An Lin
  119. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, SE-20502, Sweden

    • Paul W. Franks
    • , Frida Renström
    •  & Tibor V. Varga
  120. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA

    • Paul W. Franks
  121. Department of Public Health and Clinical Medicine, Unit of Medicine, Umeå University, Umeå, 901 87, Sweden

    • Paul W. Franks
  122. Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, 2100, Denmark

    • Ruth Frikke-Schmidt
    •  & Anne Tybjaerg-Hansen
  123. Department of Medical Sciences, University of Trieste, Trieste, 34137, Italy

    • Ilaria Gandin
    • , Paolo Gasparini
    • , Giorgia Girotto
    •  & Anna Morgan
  124. Division of Experimental Genetics, Sidra Medical and Research Center, Doha, 26999, Qatar

    • Paolo Gasparini
    • , Giorgia Girotto
    •  & Diego Vozzi
  125. Geriatrics, Department of Public Health, Uppsala University, Uppsala, 751 85, Sweden

    • Vilmantas Giedraitis
  126. Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Penny Gordon-Larsen
    •  & Kathleen Mullan Harris
  127. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Penny Gordon-Larsen
  128. Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland

    • Vilmundur Gudnason
    • , Albert Vernon Smith
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  129. Icelandic Heart Association, Kopavogur, 201, Iceland

    • Vilmundur Gudnason
    •  & Albert Vernon Smith
  130. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, 751 41, Sweden

    • Stefan Gustafsson
    •  & Erik Ingelsson
  131. Department of Sociology, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Kathleen Mullan Harris
  132. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Tamara B. Harris
  133. University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK

    • Andrew T. Hattersley
  134. MRCHGU, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK

    • Caroline Hayward
    • , Jonathan Marten
    •  & Veronique Vitart
  135. Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, North Carolina 27708, USA

    • Liang He
  136. Department of Public Health, University of Helsinki, Helsinki, FI-00014, Finland

    • Liang He
    • , Kauko Heikkilä
    •  & Jaakko Kaprio
  137. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland

    • Kauko Heikkilä
    •  & Jaakko Kaprio
  138. Department of Pediatrics, Haukeland University Hospital, Bergen, 5021, Norway

    • Øyvind Helgeland
    •  & Pål R. Njølstad
  139. KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, 5020, Norway

    • Øyvind Helgeland
    • , Stefan Johansson
    •  & Pål R. Njølstad
  140. Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, 33521, Finland

    • Jussi Hernesniemi
  141. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33520, Finland

    • Jussi Hernesniemi
    • , Terho Lehtimäki
    •  & Leo-Pekka Lyytikäinen
  142. Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, 33014, Finland

    • Jussi Hernesniemi
    • , Terho Lehtimäki
    •  & Leo-Pekka Lyytikäinen
  143. Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, 3002, Australia

    • Alex W. Hewitt
  144. Centre for Ophthalmology and Vision Science, Lions Eye Institute, University of Western Australia, Perth, Western Australia, 6009, Australia

    • Alex W. Hewitt
    •  & David A. Mackey
  145. Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, 7000, Australia

    • Alex W. Hewitt
  146. Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK

    • Lynne J. Hocking
  147. Musculoskeletal Research Programme, Division of Applied Medicine, University of Aberdeen, Aberdeen, AB25 2ZD, UK

    • Lynne J. Hocking
  148. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, NTNU, Norwegian University of Science and Technology, Trondheim, 7600, Norway

    • Oddgeir L. Holmen
  149. AMC, Department of Vascular Medicine, Amsterdam, 1105 AZ, The Netherlands

    • G. Kees Hovingh
  150. HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, 7600, Norway

    • Kristian Hveem
  151. Department of Neurology, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands

    • M. Arfan Ikram
  152. Department of Radiology, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands

    • M. Arfan Ikram
  153. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California 94305, USA

    • Erik Ingelsson
  154. Department of Public Health & Clinical Medicine, Umeå University, Umeå, SE-90185, Sweden

    • Jan-Håkan Jansson
    •  & Olov Rolandsson
  155. Research Unit Skellefteå, Skellefteå, SE-93141, Sweden

    • Jan-Håkan Jansson
  156. Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA

    • Gail P. Jarvik
  157. The Copenhagen City Heart Study, Frederiksberg Hospital, Frederiksberg, 2000, Denmark

    • Gorm B. Jensen
  158. Department of Preventive Medicine, Keck School of Medicine of the University of California, Los Angeles, California 90089, USA

    • Xuejuan Jiang
  159. USC Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine of the University of Southern California, Los Angeles, California 90089, USA

    • Xuejuan Jiang
    •  & Rohit Varma
  160. Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, 5021, Norway

    • Stefan Johansson
  161. National Institute of Public Health, University of Southern Denmark, Copenhagen, 1353, Denmark

    • Marit E. Jørgensen
  162. Steno Diabetes Center, Gentofte, 2820, Denmark

    • Marit E. Jørgensen
  163. Aalborg University, Aalborg, DK-9000, Denmark

    • Torben Jørgensen
  164. Research Center for Prevention and Health, Capital Region of Denmark, Glostrup, DK-2600, Denmark

    • Torben Jørgensen
    • , Allan Linneberg
    •  & Betina H. Thuesen
  165. National Institute for Health and Welfare, Helsinki, FI-00271, Finland

    • Pekka Jousilahti
    • , Jaakko Kaprio
    • , Jukka Kontto
    • , Jaana Lindström
    • , Satu Männistö
    • , Markus Perola
    • , Veikko Salomaa
    •  & Jaakko Tuomilehto
  166. Department of Cardiology, Leiden University Medical Center, Leiden, 2333, The Netherlands

    • J. Wouter Jukema
    •  & Stella Trompet
  167. The Interuniversity Cardiology Institute of the Netherlands, Utrecht, 2333, The Netherlands

    • J. Wouter Jukema
  168. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Bratati Kahali
    • , Elizabeth K. Speliotes
    • , Wei Zhou
    •  & Cristen J. Willer
  169. Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Bratati Kahali
    • , Elizabeth K. Speliotes
    • , Wei Zhou
    •  & Cristen J. Willer
  170. Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Bratati Kahali
    •  & Elizabeth K. Speliotes
  171. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands

    • René S. Kahn
    •  & Roel A. Ophoff
  172. Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, 33014, Finland

    • Mika Kähönen
  173. Echinos Medical Centre, Echinos, Greece

    • Maria Karaleftheri
  174. Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK

    • Fredrik Karpe
    • , Matt Neville
    • , Katharine R. Owen
    • , Neil Robertson
    •  & Mark I. McCarthy
  175. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford OX3 7LE, UK

    • Fredrik Karpe
    • , Matt Neville
    • , Katharine R. Owen
    •  & Mark I. McCarthy
  176. UKCRC Centre of Excellence for Public Health Research, Queens University Belfast, Belfast BT12 6BJ, UK

    • Frank Kee
  177. Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, 1066 CX, The Netherlands

    • Renske Keeman
    •  & Marjanka K. Schmidt
  178. Department of Restorative Dentistry, Periodontology and Endodontology, University Medicine Greifswald, Greifswald, 17475, Germany

    • Thomas Kocher
  179. Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland

    • Pirjo Komulainen
    • , Timo A. Lakka
    •  & Rainer Rauramaa
  180. National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK

    • Jaspal S. Kooner
  181. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA

    • Charles Kooperberg
    •  & Alex P. Reiner
  182. German Center for Diabetes Research, München-Neuherberg, 85764, Germany

    • Jennifer Kriebel
    • , Karina Meidtner
    •  & Matthias B. Schulze
  183. Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, D-85764, Germany

    • Jennifer Kriebel
  184. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, D-85764, Germany

    • Jennifer Kriebel
  185. Department of Psychiatry, and Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, Western Cape, 7505, South Africa

    • Helena Kuivaniemi
  186. CHU Nantes, Service de Génétique Médicale, Nantes, 44093, France

    • Sébastien Küry
  187. Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland

    • Johanna Kuusisto
    •  & Markku Laakso
  188. Institute for Maternal and Child Health - IRCCS ‘Burlo Garofolo’, Trieste, 34137, Italy

    • Martina La Bianca
    • , Antonietta Robino
    •  & Sheila Ulivi
  189. Institute of Biomedicine & Physiology, University of Eastern Finland, Kuopio, 70210, Finland

    • Timo A. Lakka
  190. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Ethan M. Lange
    • , Leslie A. Lange
    • , Karen L. Mohlke
    •  & Ying Wu
  191. Department of Biostatistical Sciences and Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA

    • Carl D. Langefeld
  192. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge CB2 0QQ, UK

    • Claudia Langenberg
    • , Jian'an Luan
    • , John R. B. Perry
    • , Robert A. Scott
    • , Nicholas J. Wareham
    • , Sara M. Willems
    •  & Jing Hua Zhao
  193. Group Health Research Institute, Seattle, Washington 98101, USA

    • Eric B. Larson
  194. Department of Health Services, University of Washington, Seattle, Washington 98101, USA

    • Eric B. Larson
  195. Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 407, Taiwan

    • I-Te Lee
  196. School of Medicine, National Yang-Ming University, Taipei 112, Taiwan

    • I-Te Lee
  197. School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan

    • I-Te Lee
  198. Division of Preventive Medicine University of Alabama at Birmingham, Birmingham, Alabama 35205, USA

    • Cora E. Lewis
  199. Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China, Shanghai, 200031, China

    • Huaixing Li
    • , Xu Lin
    • , Feijie Wang
    • , Yiqin Wang
    • , Pang Yao
    •  & He Zheng
  200. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, California 94304, USA

    • Jin Li
  201. Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA

    • Honghuang Lin
  202. Uppsala University, Uppsala, 75185, Sweden

    • Lars Lind
  203. Department of Experimental Medicine, Rigshospitalet, Copenhagen, DK-2200, Denmark

    • Allan Linneberg
  204. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA

    • Yongmei Liu
  205. Division of Health Sciences, Warwick Medical School, Warwick University, Coventry CV4 7AL, UK

    • Artitaya Lophatananon
  206. Department of Psychiatry, Washington University, Saint Louis, Missouri 63110, USA

    • Pamela A. F. Madden
  207. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, 14558, Germany

    • Karina Meidtner
    •  & Matthias B. Schulze
  208. Westmead Millennium Institute of Medical Research, Centre for Vision Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, 2022, Australia

    • Paul Mitchell
  209. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands

    • Dennis O. Mook-Kanamori
  210. Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9AG, UK

    • Andrew D. Morris
    • , Ozren Polasek
    •  & Igor Rudan
  211. Department of Medicine I, Ludwig-Maximilians-Universität, Munich, 81377, Germany

    • Martina Müller-Nurasyid
  212. DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, 80802, Germany

    • Martina Müller-Nurasyid
  213. Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, Maryland 20892, USA

    • Mike A. Nalls
  214. DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, 17475, Germany

    • Matthias Nauck
  215. Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, 17475, Germany

    • Matthias Nauck
  216. Department of Cardiology, Heart Center, Tampere University Hospital and School of Medicine, University of Tampere, Tampere, 33521, Finland

    • Kjell Nikus
  217. Program in Personalized Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA

    • Jeffrey R. O'Connel
    • , James A. Perry
    •  & Laura M. Yerges-Armstrong
  218. Department of Medicine, Tampere University Hospital, Tampere, 33521, Finland

    • Heikki Oksa
  219. Center for Neurobehavioral Genetics, UCLA, Los Angeles, California 90095, USA

    • Loes M. Olde Loohuis
    •  & Roel A. Ophoff
  220. Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK

    • Colin N. A. Palmer
  221. Laboratory of Clinical Chemistry and Hematology, Division Laboratories and Pharmacy, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands

    • Gerard Pasterkamp
  222. Laboratory of Experimental Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands

    • Gerard Pasterkamp
    •  & Sander W. van der Laan
  223. School of Women’s and Infants’ Health, The University of Western Australia, Perth, Western Australia, 6009, Australia

    • Craig E. Pennell
    •  & Carol A. Wang
  224. University of Helsinki, Institute for Molecular Medicine (FIMM) and Diabetes and Obesity Research Program, Helsinki, FI00014, Finland

    • Markus Perola
  225. University of Tartu, Estonian Genome Center, Tartu, Estonia, Tartu, 51010, Estonia

    • Markus Perola
  226. School of Medicine, University of Split, Split, 21000, Croatia

    • Ozren Polasek
  227. Center for Neurogenomics and Cognitive Research, Department Complex Trait Genetics, VU University, Amsterdam, 1081 HV, The Netherlands

    • Danielle Posthuma
  228. Neuroscience Campus Amsterdam, Department Clinical Genetics, VU Medical Center, Amsterdam, 1081 HV, The Netherlands

    • Danielle Posthuma
  229. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521, Finland

    • Olli T. Raitakari
  230. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland

    • Olli T. Raitakari
  231. Centre for Non-Communicable Diseases, Karachi, Pakistan

    • Asif Rasheed
    •  & Danish Saleheen
  232. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210, Finland

    • Rainer Rauramaa
  233. MRL, Merck & Co., Inc., Genetics and Pharmacogenomics, Boston, Massachusetts 02115, USA

    • Dermot F. Reilly
  234. Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA

    • Alex P. Reiner
  235. Department of Biobank Research, Umeå University, Umeå, SE-90187, Sweden

    • Frida Renström
  236. Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA

    • Paul M. Ridker
  237. Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, H3T 1J4, Canada

    • John D. Rioux
    • , Jean-Claude Tardif
    •  & Guillaume Lettre
  238. Department of Public Health and Clinical Medicine, Unit of Family Medicine, Umeå University, Umeå, 90185, Sweden

    • Olov Rolandsson
  239. Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Danish Saleheen
    •  & Wei Zhao
  240. Division of Epidemiology & Community Health University of Minnesota, Minneapolis, Minnesota 55454, USA

    • Pamela J. Schreiner
  241. Duke University, Durham, North Carolina 27703, USA

    • Svati Shah
  242. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore

    • Xueling Sim
  243. Departement of Haematology, University of Cambridge, Cambridge CB2 OPT, UK

    • Suthesh Sivapalaratnam
  244. Department of Vascular Medicine, AMC, Amsterdam, 1105 AZ, The Netherlands

    • Suthesh Sivapalaratnam
  245. Department of Twin Research and Genetic Epidemiology, Kingís College London, London SE1 7EH, UK

    • Kerrin S. Small
    •  & Timothy D. Spector
  246. Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh EH8 9JZ, UK

    • John M. Starr
  247. Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007MB, The Netherlands

    • Leen M. ‘t Hart
    •  & Natasja M. van Schoor
  248. Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands

    • Leen M. ‘t Hart
  249. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands

    • Leen M. ‘t Hart
  250. College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF14 4EP, UK

    • Katherine E. Tansey
  251. MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK

    • Katherine E. Tansey
  252. Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany

    • Alexander Teumer
  253. Center for Pediatric Research, Department for Women’s and Child Health, University of Leipzig, Leipzig, 04103, Germany

    • Anke Tönjes
  254. Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, Western Cape, 7505, South Africa

    • Gerard Tromp
  255. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2333, The Netherlands

    • Stella Trompet
  256. Anogia Medical Centre, Anogia, Greece

    • Emmanouil Tsafantakis
  257. Centre for Vascular Prevention, Danube-University Krems, Krems, 3500, Austria

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

    • Jaakko Tuomilehto
  259. Diabetes Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    • Jaakko Tuomilehto
  260. Department of Psychiatry, Dalhousie University, Halifax B3H 4R2, Canada

    • Rudolf Uher
  261. University of Amsterdam, Department of Brain & Cognition, Amsterdam, 1018 WS, The Netherlands

    • Andries R. Van Der Leij
  262. Department of Obstetrics and Gynecology, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee 37203, USA

    • Digna R. Velez Edwards
  263. MRL, Merck & Co., Inc., Cardiometabolic Disease, Kenilworth, New Jersey 07033, USA

    • Thomas F. Vogt
  264. Institute of Cellular Medicine, The Medical School, Newcastle University, Newcastle NE2 4HH, UK

    • Mark Walker
  265. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA

    • Shuai Wang
  266. Departments of Epidemiology & Medicine, Diabetes Translational Research Center, Fairbanks School of Public Health & School of Medicine, Indiana University, Indiana 46202, USA

    • Jennifer Wessel
  267. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi 39216, USA

    • James G. Wilson
  268. Danish Diabetes Academy, Odense, 5000, Denmark

    • Daniel R. Witte
  269. Department of Public Health, Aarhus University, Aarhus, 8000, Denmark

    • Daniel R. Witte
  270. Memorial University, Faculty of Medicine, Discipline of Genetics, St. John’s, Newfoundland A1B 3V6, Canada

    • Michael O. Woods
  271. GlaxoSmithKlein, King of Prussia, Pennsylvania 19406, USA

    • Laura M. Yerges-Armstrong
  272. Department of Clinical Sciences, Quantitative Biomedical Research Center, Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA

    • Xiaowei Zhan
  273. Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Cristen J. Willer
  274. Department of Public Health Sciences, Institute for Personalized Medicine, the Pennsylvania State University College of Medicine, Hershey, Pennsylvania 17033, USA

    • Dajiang J. Liu
  275. Department of Epidemiology and Carolina Center of Genome Sciences, Chapel Hill, North Carolina 27514, USA

    • Kari E. North
  276. Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA

    • Nancy L. Heard-Costa
  277. Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2200, Denmark

    • Tune H. Pers
  278. Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK

    • Cecilia M. Lindgren
  279. The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, New York 10069, USA

    • Ruth J. F. Loos
  280. Departments of Pediatrics and Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Joel N. Hirschhorn
  281. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    • Panos Deloukas

Consortia

  1. The EPIC-InterAct Consortium

  2. CHD Exome+ Consortium

  3. ExomeBP Consortium

  4. T2D-Genes Consortium

  5. GoT2D Genes Consortium

  6. Global Lipids Genetics Consortium

  7. ReproGen Consortium

  8. MAGIC Investigators

    A list of members and their affiliations appears in the Supplementary Information.

Authors

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Contributions

Writing group (wrote and edited manuscript): P.D., T.M.F., M.Gr., J.N.H., G.L., K.S.L., Y.Lu., E.M., C.M.-G., F.Ri. All authors contributed and discussed the results, and commented on the manuscript. Data preparation group (checked and prepared data from contributing cohorts for meta-analyses and replication): T.Es., M.Gr., H.M.H., A.E.J., T.Ka., K.S.L., A.E.L., Y.Lu., E.M., N.G.D.M., C. M.-G., P.Mu., M.C.Y.N., M.A.R., C.S., K.St., V.T., S.V., T.W.W., K.L.Y. This work was done under the auspices of the GIANT, CHARGE, BBMRI, UK ExomeChip, and GOT2D consortia. Height meta-analyses (discovery and replication, single-variant and gene-based): P.D., T.M.F., M.Gr., J.N.H., G.L., D.J.L., K.S.L., Y.Lu, E.M., C.M.-G., F.Ri., A.R.W. UK Biobank-based integration of height association signals group and heritability analyses: P.D., T.M.F., G.L., Z.K., K.S.L., E.M., S.R., A.R.W. Pleiotropy working group: G.A., M. Bo., J.P.C., P.D., F.D., J.C.F., H.M.H., S. Kat., C.M.L., D.J.L., R.J.F.L., A.Ma., E.M., M.I.M., P.B.M., G.M.P., J.R.B.P., K.S.R., C.J.W. Biological and clinical enrichment and pathway analyses: R.S.F., J.N.H., Z.K., D.L., G.L., K.S.L., T.H.P. Functional characterization of STC2: T.R.K., C.O.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Joel N. Hirschhorn or Panos Deloukas or Guillaume Lettre.

Reviewer Information Nature thanks J. Barrett, D. Hinds and D. Hunter for their contribution to the peer review of this work.

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https://doi.org/10.1038/nature21039

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