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Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

Nature Communicationsvolume 10, Article number: 376 (2019) | Download Citation

Abstract

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

Introduction

Circulating levels of blood lipids are strongly linked to the risk of atherosclerotic cardiovascular disease. Regular physical activity (PA) improves blood lipid profile by increasing the levels of high-density lipoprotein cholesterol (HDL-C) and decreasing the levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG)1. However, there is individual variation in the response of blood lipids to PA, and twin studies suggest that some of this variation may be due to genetic differences2. The genes responsible for this variability remain unknown.

More than 500 genetic loci have been found to be associated with blood levels of HDL-C, LDL-C, or TG in published genome-wide association studies (GWAS)3,4,5,6,7,8,9,10,11,12. At present, it is not known whether any of these main effect associations are modified by PA. Understanding whether the impact of lipid loci can be modified by PA is important because it may give additional insight into biological mechanisms and identify subpopulations in whom PA is particularly beneficial.

Here, we report results from a genome-wide meta-analysis of gene–PA interactions on blood lipid levels in up to 120,979 adults of European, African, Asian, Hispanic, or Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We show that four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, are associated with circulating lipid levels through interaction with PA. None of these four loci have been identified in published main effect GWAS of lipid levels. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of PA interactions in the genetic contribution to blood lipid levels.

Results

Genome-wide interaction analyses in up to 250,564 individuals

We assessed effects of gene–PA interactions on serum HDL-C, LDL-C, and TG levels in 86 cohorts participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Gene-Lifestyle Interactions Working Group13. PA was harmonized across participating studies by categorizing it into a dichotomous variable. The participants were defined as inactive if their reported weekly energy expenditure in moderate-to-vigorous intensity leisure-time or commuting PA was less than 225 metabolic equivalent (MET) minutes per week (corresponding to approximately 1 h of moderate-intensity PA), while all other participants were defined as physically active (Supplementary Data 1).

The analyses were performed in two stages. Stage 1 consisted of genome-wide meta-analyses of linear regression results from 42 cohorts, including 120,979 individuals of European [n = 84,902], African [n = 20,487], Asian [n = 6403], Hispanic [n = 4749], or Brazilian [n = 4438] ancestry (Supplementary Tables 1 and 2; Supplementary Data 2; Supplementary Note 1). All variants that reached two-sided P < 1 × 10−6 in the Stage 1 multi-ancestry meta-analyses or ancestry-specific meta-analyses were taken forward to linear regression analyses in Stage 2, which included 44 cohorts and 131,012 individuals of European [n = 107,617], African [n = 5384], Asian [n = 6590], or Hispanic [n = 11,421] ancestry (Supplementary Tables 3 and 4; Supplementary Data 3; Supplementary Note 2). The summary statistics from Stage 1 and Stage 2 were subsequently meta-analyzed to identify lipid loci whose effects are modified by PA.

We identified lipid loci interacting with PA by three different approaches applied to the meta-analysis of Stage 1 and Stage 2: (i) we screened for genome-wide significant SNP × PA-interaction effects (PINT < 5 × 10−8); (ii) we screened for genome-wide significant 2 degree of freedom (2df) joint test of SNP main effect and SNP × PA interaction14 (PJOINT < 5 × 10−8); and (iii) we screened all previously known lipid loci3,4,5,6,7,8,9,10,11,12 for significant SNP × PA-interaction effects, Bonferroni-correcting for the number of independent variants tested (r2 < 0.1 within 1 Mb distance; PINT = 0.05/501 = 1.0 × 10−4).

PA modifies the effect of four loci on lipid levels

Three novel loci (>1 Mb distance and r2 < 0.1 with any previously identified lipid locus) were identified: in CLASP1 (rs2862183, PINT = 8 × 10−9), near LHX1 (rs295849, PINT = 3 × 10−8), and in SNTA1 (rs141588480, PINT = 2 × 10−8), which showed a genome-wide significant SNP × PA interaction on HDL-C in all ancestries combined (Table 1, Figs. 14). Higher levels of PA enhanced the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci. A novel locus in CNTNAP2 (rs190748049) was genome-wide significant in the joint test of SNP main effect and SNP × PA interaction (PJOINT = 4 × 10−8) and showed moderate evidence of SNP × PA interaction (PINT = 2 × 10−6) in the meta-analysis of LDL-C in all ancestries combined (Table 1, Fig. 5). The LDL-C-increasing effect of the CNTNAP2 locus was attenuated in the physically active group as compared to the inactive group. None of these four loci have been identified in previous main effect GWAS of lipid levels.

Table 1 Lipid loci identified through interaction with physical activity (PINT < 5 × 10−8) or through joint test for SNP main effect and SNP × physical activity interaction (PJOINT < 5 × 10−8)
Fig. 1
Fig. 1

Genome-wide results for interaction with physical activity on HDL cholesterol levels. The P values are two-sided and were obtained by a meta-analysis of linear regression model results (n up to 250,564). Three loci, in/near CLASP1, LHX1, and SNTA1, reached genome-wide significance (P < 5 × 10−8) as indicated in the plot

Fig. 2
Fig. 2

Interaction of rs2862183 in CLASP1 with physical activity on HDL cholesterol levels. The beta and 95% confidence intervals in the forest plot (a) is shown for the rs2862183 × physical activity interaction term, i.e., it indicates the increase in logarithmically transformed HDL cholesterol levels in the active group as compared to the inactive group per each T allele of rs2862183. The −log10(P value) in the association plot (b) is also shown for the rs2862183 × physical activity interaction term. The P values are two-sided and were obtained by a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org)

Fig. 3
Fig. 3

Interaction of rs295849 near LHX1 with physical activity on HDL cholesterol levels. The beta and 95% confidence intervals in the forest plot (a) is shown for the rs295849 × physical activity interaction term, i.e., it indicates the increase in logarithmically transformed HDL cholesterol levels in the active group as compared to the inactive group per each G allele of rs295849. The −log10 (P value) in the association plot (b) is also shown for the rs295849 × physical activity interaction term. The P values are two-sided and were obtained by a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org)

Fig. 4
Fig. 4

Interaction of rs141588480 in SNTA1 with physical activity on HDL cholesterol levels. The beta and 95% confidence intervals in the forest plot (a) is shown for the rs141588480 × physical activity interaction term, i.e., it indicates the increase in logarithmically transformed HDL cholesterol levels in the active group as compared to the inactive group per each insertion of rs141588480. The –log10 (p value) in the association plot (b) is also shown for the rs141588480 × physical activity interaction term. While the rs141588480 variant was identified in African-ancestry individuals in Stage 1, the variant did not pass QC filters in the Stage 2 African-ancestry cohorts, due to insufficient sample sizes of these cohorts. The P values are two-sided and were obtained by a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org)

Fig. 5
Fig. 5

Interaction of rs190748049 variant in CNTNAP2 with physical activity on LDL cholesterol levels. The rs190748049 variant was genome-wide significant in the joint test for SNP main effect and SNP × physical activity interaction and reached P = 2 × 10−6 for the SNP × physical activity interaction term alone. The beta and 95% confidence intervals in the forest plot (a) is shown for the SNP × physical activity interaction term, i.e., it indicates the decrease in LDL cholesterol levels in the active group as compared to the inactive group per each T allele of rs190748049. The −log10 (P value) in the association plot (b) is also for the SNP × physical activity interaction term. The P values are two-sided and were obtained using a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org)

No interaction between known main effect lipid loci and PA

Of the previously known 260 main effect loci for HDL-C, 202 for LDL-C, and 185 for TG3,4,5,6,7,8,9,10,11,12, none reached the Bonferroni-corrected threshold (two-sided PINT = 1.0 × 10−4) for SNP × PA interaction alone (Supplementary Data 4-6). We also found no significant interaction between a combined score of all published European-ancestry loci for HDL-C, LDL-C, or TG with PA (Supplementary Datas 79) using our European-ancestry summary results (two-sided PHDL-C = 0.14, PLDL-C = 0.77, and PTG = 0.86, respectively), suggesting that the beneficial effect of PA on lipid levels may be independent of genetic risk15.

Potential functional roles of the loci interacting with PA

While the mechanisms underlying the beneficial effect of PA on circulating lipid levels are not fully understood, it is thought that the changes in plasma lipid levels are primarily due to an improvement in the ability of skeletal muscle to utilize lipids for energy due to enhanced enzymatic activities in the muscle16,17. Of the four loci we found to interact with PA, three, in CLASP1, near LHX1, and in SNTA1, harbor genes that may play a role in muscle function18,19 and lipid metabolism20,21.

The lead variant rs2862183 (minor allele frequency (MAF) 22%) in the CLASP1 locus which interacts with PA on HDL-C levels is an intronic SNP in CLASP1 that encodes a microtubule-associated protein (Fig. 2). The rs2862183 SNP is associated with CLASP1 expression in esophagus muscularis (P = 3 × 10−5) and is in strong linkage disequilibrium (r2 > 0.79) with rs13403769 variant that shows the strongest association with CLASP1 expression in the region (P = 7 × 10−7). Another potent causal candidate gene in this locus is the nearby GLI2 gene which has been found to play a role in skeletal myogenesis18 and the conversion of glucose to lipids in mouse adipose tissue20 by inhibiting hedgehog signaling.

The rs295849 (MAF 38%) variant near LHX1 interacts with PA on HDL-C levels. However, the more likely causal candidate gene in this locus is acetyl-CoA carboxylase (ACACA), which plays a crucial role in fatty acid metabolism21 (Fig. 3). Rare acetyl-CoA carboxylase deficiency has been linked to hypotonic myopathy, severe brain damage, and poor growth22.

The lead variant in the SNTA1 locus (rs141588480) interacts with PA on HDL-C and is an insertion only found in individuals of African (MAF 6%) or Hispanic (MAF 1%) ancestry. The rs141588480 insertion is in the SNTA1 gene that encodes the syntrophin alpha 1 protein, located at the neuromuscular junction and altering intracellular calcium ion levels in muscle tissue (Fig. 4). Snta1-null mice exhibit differences in muscle regeneration after a cardiotoxin injection19. Two weeks following the injection into mouse tibialis anterior, the muscle showed hypertrophy, decreased contractile force, and neuromuscular junction dysfunction. Furthermore, exercise endurance of the mice was impaired in the early phase of muscle regeneration19. In humans, SNTA1 mutations have been linked to the long-QT syndrome23.

The fourth locus interacting with PA is CNTNAP2, with the lead variant (rs190748049) intronic and no other genes nearby (Fig. 5). The rs190748049 variant is most common in African-ancestry (MAF 8%), less frequent in European-ancestry (MAF 2%), and absent in Asian- and Hispanic-ancestry populations. The protein coded by the CNTNAP2 gene, contactin-associated protein like-2, is a member of the neurexin protein family. The protein is located at the juxtaparanodes of myelinated axons where it may have an important role in the differentiation of the axon into specific functional subdomains. Mice with a Cntnap2 knockout are used as an animal model of autism and show altered phasic inhibition and a decreased number of interneurons24. Human CNTNAP2 variants have been associated with risk of autism and related behavioral disorders25.

Joint test of SNP main effect and SNP × PA interaction

We found 101 additional loci that reached genome-wide significance in the 2df joint test of SNP main effect and SNP × PA interaction on HDL-C, LDL-C, or TG. However, none of these loci showed evidence of SNP × PA interaction (PINT > 0.001) (Supplementary Data 10). All 101 main effect-driven loci have been identified in previous GWAS of lipid levels3,4,5,6,7,8,9,10,11,12.

Discussion

In this genome-wide study of up to 250,564 adults from diverse ancestries, we found evidence of interaction with PA for four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2. Higher levels of PA enhanced the HDL cholesterol-increasing effects of CLASP1, LHX1, and SNTA1 loci and attenuated the LDL cholesterol-increasing effect of the CNTNAP2 locus. None of these four loci have been identified in previous main effect GWAS for lipid levels3,4,5,6,7,8,9,10,11,12.

The loci in/near CLASP1, LHX1, and SNTA1 harbor genes linked to muscle function18,19 and lipid metabolism20,21. More specifically, the GLI2 gene within the CLASP1 locus has been found to play a role in myogenesis18 as well as in the conversion of glucose to lipids in adipose tissue20; the ACACA gene within the LHX1 locus plays a crucial role in fatty acid metabolism21 and has been connected to hypotonic myopathy22; and the SNTA1 gene is linked to muscle regeneration19. These functions may relate to differences in the ability of skeletal muscle to use lipids as an energy source, which may modify the beneficial impact of PA on blood lipid levels16,17.

The inclusion of diverse ancestries in the present meta-analyses allowed us to identify two loci that would have been missed in meta-analyses of European-ancestry individuals alone. In particular, the lead variant (rs141588480) in the SNTA1 locus is only polymorphic in African and Hispanic ancestries, and the lead variant (rs190748049) in the CNTNAP2 locus is four times more frequent in African-ancestry than in European-ancestry. Our findings highlight the importance of multi-ancestry investigations of gene-lifestyle interactions to identify novel loci.

We did not find additional novel loci when jointly testing for SNP main effect and interaction with PA. While 101 loci reached genome-wide significance in the joint test on HDL-C, LDL-C, or TG, all of these loci have been identified in previous GWAS of lipid levels3,4,5,6,7,8,9,10,11,12, and none of them showed evidence of SNP × PA interaction. The 2df joint test bolsters the power to detect novel loci when both main and an interaction effect are present14. The lack of novel loci identified by the 2df test suggests that the loci showing the strongest SNP × PA interaction on lipid levels are not the same loci that show a strong main effect on lipid levels.

In summary, we identified four loci containing SNPs that enhance the beneficial effect of PA on lipid levels. The identification of the SNTA1 and CNTNAP2 loci interacting with PA was made possible by the inclusion of diverse ancestries in the analyses. The gene regions that harbor loci interacting with PA involve pathways targeting muscle function and lipid metabolism. Our findings elucidate the role and underlying mechanisms of PA interactions in the genetic regulation of blood lipid levels.

Methods

Study design

The present study collected summary data from 86 participating cohorts and no individual-level data were exchanged. For each of the participating cohorts, the appropriate ethics review board approved the data collection and all participants provided informed consent.

We included men and women 18–80 years of age and of European, African, Asian, Hispanic, or Brazilian ancestry. The meta-analyses were performed in two stages13. Stage 1 meta-analyses included 42 studies with a total of 120,979 individuals of European (n = 84,902), African (n = 20,487), Asian (n = 6403), Hispanic (n = 4749), or Brazilian ancestry (n = 4438) (Supplementary Table 1; Supplementary Data 2; Supplementary Note 1). Stage 2 meta-analyses included 44 studies with a total of 131,012 individuals of European (n = 107,617), African (n = 5384), Asian (n = 6590), or Hispanic (n = 11,421) ancestry (Supplementary Table 3; Supplementary Data 3; Supplementary Note 2). Studies participating in Stage 1 meta-analyses carried out genome-wide analyses, whereas studies participating in Stage 2 only performed analyses for 17,711 variants that reached P < 10−6 in the Stage 1 meta-analyses and were observed in at least two different Stage 1 studies with a pooled sample size > 4000. The Stage 1 and Stage 2 meta-analyses were performed in all ancestries combined and in each ancestry separately.

Outcome traits: LDL-C, HDL-C, and TG

The levels of LDL-C were either directly assayed or derived using the Friedewald equation (if TG ≤ 400 mg dl−1 and fasting ≥ 8 h). We adjusted LDL-C levels for lipid-lowering drug use if statin use was reported or if unspecified lipid-lowering drug use was listed after 1994, when statin use became common. For directly assayed LDL-C, we divided the LDL-C value by 0.7. If LDL-C was derived using the Friedewald equation, we first adjusted total cholesterol for statin use (total cholesterol divided by 0.8) before the usual calculation. If study samples were from individuals who were nonfasting, we did not include either TG or calculated LDL-C in the present analyses. The HDL-C and TG variables were natural log-transformed, while LDL-C was not transformed.

PA variable

The participating studies used a variety of ways to assess and quantify PA (Supplementary Data 1). To harmonize the PA variable across all participating studies, we coded a dichotomous variable, inactive vs. active, that could be applied in a relatively uniform way in all studies, and that would be congruent with previous findings on SNP × PA interactions26,27,28 and the relationship between PA and disease outcomes29. Inactive individuals were defined as those with <225 MET-min per week of moderate-to-vigorous leisure-time or commuting PA (n = 84,495; 34% of all participants) (Supplementary Data 1). We considered all other participants as physically active. In studies where MET-min per week measures of PA were not available, we defined inactive individuals as those engaging in ≤1 h/week of moderate-intensity leisure-time PA or commuting PA. In studies with PA measures that were not comparable to either MET-min or hours/week of PA, we defined the inactive group using a percentage cut-off, where individuals in the lowest 25% of PA levels were defined as inactive and all other individuals as active.

Genotyping and imputation

Genotyping was performed by each participating study using Illumina or Affymetrix arrays. Imputation was conducted on the cosmopolitan reference panel from the 1000 Genomes Project Phase I Integrated Release Version 3 Haplotypes (2010–2011 data freeze, 2012-03-14 haplotypes). Only autosomal variants were considered. Specific details of each participating study’s genotyping platform and imputation software are described in Supplementary Tables 2 and 4.

Quality control

The participating studies excluded variants with MAF < 1%. We performed QC for all study-specific results using the EasyQC package in R30. For each study-specific results file, we filtered out genetic variants for which the product of minor allele count (MAC) in the inactive and active strata and imputation quality [min(MACINACTIVE,MACACTIVE) × imputation quality] did not reach 20. This removed unstable study-specific results that reflected small sample size, low MAC, or low-imputation quality. In addition, we excluded all variants with imputation quality measure <0.5. To identify issues with relatedness, we examined QQ plots and genomic control inflation lambdas in each study-specific results file as well as in the meta-analysis results files. To identify issues with allele frequencies, we compared the allele frequencies in each study file against ancestry-specific allele frequencies in the 1000 Genomes reference panel. To identify issues with trait transformation, we plotted the median standard error against the maximal sample size in each study. The summary statistics for all beta-coefficients, standard errors, and P values were visually compared to observe discrepancies. Any issues that were found during the QC were resolved by contacting the analysts from the participating studies. Additional details about QC in the context of interactions, including examples, may be found elsewhere13.

Analysis methods

All participating studies used the following model to test for interaction:

$$E\left[ Y \right] = \beta _0 + \beta _E \ast PA + \beta _G \ast G + \beta _{{\mathrm{INT}}} \ast G \ast PA + {\boldsymbol{\beta }}_{\boldsymbol{c}} \ast {\boldsymbol{C}}{,}$$

where Y is the HDL-C, LDL-C, or TG value, PA is the PA variable with 0 or 1 coding for active or inactive group, and G is the dosage of the imputed genetic variant coded additively from 0 to 2. The C is the vector of covariates which included age, sex, study center (for multi-center studies), and genome-wide principal components. From this model, the studies provided the estimated genetic main effect (βG), estimated interaction effect (βGE), and a robust estimate of the covariance between βG and βGE. Using these estimates, we performed inverse variance-weighted meta-analyses for the SNP × PA interaction term alone, and 2df joint meta-analyses of the SNP effect and SNP × PA interaction combined by the method of Manning et al.14, using the METAL meta-analysis software. We applied genomic control correction twice in Stage 1, first for study-specific GWAS results and again for meta-analysis results, whereas genomic control correction was not applied to the Stage 2 results as interaction testing was only performed at select variants. We considered a variant that reached two-sided P < 5 × 10−8 in the meta-analysis for the interaction term alone or in the joint test of SNP main effect and SNP × PA interaction, either in the ancestry-specific analyses or in all ancestries combined, as genome-wide significant. The loci were defined as independent if the distance between the lead variants was >1 Mb.

Combined PA-interaction effect of all known lipid loci

To identify all published SNPs associated with HDL-C, LDL-C, or TG, we extended the previous curated list of lipid loci by Davis et al.4 by searching PubMed and Google Scholar databases and screening the GWAS Catalog. After LD pruning by r2 < 0.1 in the 1000 Genomes European-ancestry reference panel, 260 independent loci remained associated with HDL cholesterol, 202 with LDL cholesterol, and 185 with TG (Supplementary Datas 79). To approximate the combined PA interaction of all known European-ancestry loci associated with HDL-C, LDL-C, or TG, we calculated their combined interaction effect as the weighted sum of the individual SNP coefficients in our genome-wide summary results for European-ancestry. This approach has been described previously in detail by Dastani et al.31 and incorporated in the package “gtx” in R. We did not weigh the loci by their main effect estimates from the discovery GWAS data.

Examining the functional roles of loci interacting with PA

We examined published associations of the identified lipid loci with other complex traits in genome-wide association studies by using the GWAS Catalog of the European Bioinformatics Institute and the National Human Genome Research Institute. We extracted all published genetic associations with r2 > 0.5 and distance < 500 kb from the identified lipid-associated lead SNPs32. We also studied the cis-associations of the lead SNPs with all genes within ±1 Mb distance using the GTEx portal33. We excluded findings where our lead SNP was not in strong LD (r2 > 0.5) with the peak SNP associated with the same gene transcript.

Data availability

The meta-analysis summary results are available for download on the CHARGE dbGaP website under accession phs000930.

Additional information

Journal peer review information: Nature Communications thanks David Meyre and the other anonymous Reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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

A full list of consortium members appears at the end of the paper.

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Acknowledgments

The present work was largely supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). The full list of acknowledgments appears in the Supplementary Notes 3 and 4.

Author information

Affiliations

  1. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark

    • Tuomas O. Kilpeläinen
    • , Hermina Jakupović
    •  & Chiamaka Vivian Nwuba
  2. Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA

    • Tuomas O. Kilpeläinen
  3. Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, 20892, MD, USA

    • Amy R. Bentley
    •  & Charles N. Rotimi
  4. Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands

    • Raymond Noordam
    •  & Diana van Heemst
  5. Division of Biostatistics, Washington University School of Medicine, St. Louis, 63110, MO, USA

    • Yun Ju Sung
    • , Karen Schwander
    • , Lisa de las Fuentes
    • , Chi Charles Gu
    •  & Dabeeru C. Rao
  6. Department of Genetic Epidemiology, University of Regensburg, Regensburg, 93051, Germany

    • Thomas W. Winkler
  7. Preventive Medicine, Brigham and Women’s Hospital, Boston, 02215, MA, USA

    • Daniel I. Chasman
    • , Franco Giulianini
    • , Lynda M. Rose
    •  & Paul M. Ridker
  8. Harvard Medical School, Boston, 02131, MA, USA

    • Daniel I. Chasman
    •  & Paul M. Ridker
  9. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, 02114, MA, USA

    • Alisa Manning
  10. Department of Medicine, Harvard Medical School, Boston, 02115, MA, USA

    • Alisa Manning
    •  & Tamar Sofer
  11. Clinical Pharmacology, William Harvey Research Instititute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK

    • Ioanna Ntalla
    •  & Patricia B. Munroe
  12. Department of Epidemiology, Harvard School of Public Health, Boston, 02115, MA, USA

    • Hugues Aschard
  13. Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, 75015, France

    • Hugues Aschard
  14. Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA

    • Michael R. Brown
    • , Zhe Wang
    • , Eric Boerwinkle
    • , Paul S. de Vries
    •  & Alanna C. Morrison
  15. Cardiovascular Division, Department of Medicine, Washington University, St. Louis, 63110, MO, USA

    • Lisa de las Fuentes
  16. Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, 27514, NC, USA

    • Nora Franceschini
    • , Mariaelisa Graff
    •  & Kari E. North
  17. The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, 90502, CA, USA

    • Xiuqing Guo
    • , Yii-Der Ida Chen
    • , Kent D. Taylor
    • , Jie Yao
    •  & Jerome I. Rotter
  18. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands

    • Dina Vojinovic
    • , Najaf Amin
    • , Ayşe Demirkan
    • , Mohsen Ghanbari
    • , M. Arfan Ikram
    • , Trudy Voortman
    •  & Cornelia M. van Duijn
  19. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, 35294, AL, USA

    • Stella Aslibekyan
  20. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, 63108, MO, USA

    • Mary F. Feitosa
    • , Aldi T. Kraja
    •  & Michael A. Province
  21. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA

    • Minjung Kho
    • , Lawrence F. Bielak
    • , Patricia A. Peyser
    • , Jennifer A. Smith
    • , Wei Zhao
    •  & Sharon L. R. Kardia
  22. Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, 39213, MS, USA

    • Solomon K. Musani
    •  & Mario Sims
  23. Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, 77030, TX, USA

    • Melissa Richard
    •  & Myriam Fornage
  24. Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, 02115, MA, USA

    • Heming Wang
    • , Brian Cade
    • , Tamar Sofer
    •  & Susan Redline
  25. Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, 98101, WA, USA

    • Traci M. Bartz
  26. Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK

    • Archie Campbell
    • , Sarah E. Harris
    •  & David J. Porteous
  27. Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, 138672, Singapore

    • Rajkumar Dorajoo
    •  & Jianjun Liu
  28. Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA

    • Virginia Fisher
    • , Ching-Ti Liu
    •  & L. Adrienne Cupples
  29. Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, 96020220, RS, Brazil

    • Fernando P. Hartwig
    •  & Bernardo L. Horta
  30. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK

    • Fernando P. Hartwig
  31. Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, 01246903, SP, Brazil

    • Andrea R. V. R. Horimoto
    • , Jose E. Krieger
    •  & Alexandre C. Pereira
  32. Epidemiology and Biostatistics, University of Giorgia at Athens College of Public Health, Athens, 30602, GA, USA

    • Changwei Li
  33. Public Health Sciences, Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, 27157, NC, USA

    • Kurt K. Lohman
  34. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK

    • Jonathan Marten
    •  & Caroline Hayward
  35. Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore

    • Xueling Sim
    • , Jin Fang Chai
    • , Woon-Puay Koh
    • , Ei-Ei Khaing Nang
    • , E. Shyong Tai
    •  & Rob M. van Dam
  36. Icelandic Heart Association, 201, Kopavogur, Iceland

    • Albert V. Smith
    •  & Vilmundur Gudnason
  37. Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA

    • Albert V. Smith
  38. Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, 21224, MD, USA

    • Salman M. Tajuddin
    •  & Michele K. Evans
  39. Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia

    • Maris Alver
    • , Reedik Mägi
    • , Andres Metspalu
    •  & Tõnu Esko
  40. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands

    • Marzyeh Amini
    • , Ilja M. Nolte
    • , Peter J. van der Most
    • , Behrooz Z. Alizadeh
    • , H. Marike Boezen
    •  & Harold Snieder
  41. CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, 59000, France

    • Mathilde Boissel
    • , Mickaël Canouil
    •  & Philippe Froguel
  42. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, 17177, Sweden

    • Xu Chen
    • , Nancy L. Pedersen
    •  & Patrik K. E. Magnusson
  43. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA

    • Jasmin Divers
    • , Fang-Chi Hsu
    •  & Carl D. Langefeld
  44. Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK

    • Evangelos Evangelou
    • , Raha Pazoki
    •  & Paul Elliott
  45. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece

    • Evangelos Evangelou
  46. Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA

    • Chuan Gao
  47. Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK

    • Sarah E. Harris
    • , David J. Porteous
    • , John M. Starr
    •  & Ian J. Deary
  48. Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China

    • Meian He
    • , Caizheng Yu
    •  & Tangchun Wu
  49. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, MI, USA

    • Anne U. Jackson
    • , Heather M. Stringham
    •  & Michael Boehnke
  50. MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0QQ, UK

    • Jing Hua Zhao
    • , Claudia Langenberg
    • , Robert A. Scott
    •  & Nicholas J. Wareham
  51. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Brigitte Kühnel
    • , Melanie Waldenberger
    •  & Christian Gieger
  52. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Brigitte Kühnel
    • , Annette Peters
    •  & Melanie Waldenberger
  53. Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177, Sweden

    • Federica Laguzzi
    • , Karin Leander
    •  & Ulf de Faire
  54. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33014, Finland

    • Leo-Pekka Lyytikäinen
    •  & Terho Lehtimäki
  55. Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland

    • Leo-Pekka Lyytikäinen
    •  & Terho Lehtimäki
  56. Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland

    • Rainer Rauramaa
    • , Pirjo Komulainen
    •  & Timo A. Lakka
  57. College of Medicine, Biological Sciences and Psychology, Health Sciences, The Infant Mortality and Morbidity Studies (TIMMS), Leicester, LE1 7RH, UK

    • Muhammad Riaz
  58. Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, Trieste, 34137, Italy

    • Antonietta Robino
    • , Maria Pina Concas
    •  & Paolo Gasparini
  59. Department of Computational Biology, University of Lausanne, Lausanne, 1015, Switzerland

    • Rico Rueedi
  60. Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland

    • Rico Rueedi
    •  & Zoltán Kutalik
  61. Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, 1628655, Japan

    • Fumihiko Takeuchi
    •  & Norihiro Kato
  62. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, 20502, Sweden

    • Tibor V. Varga
    • , Alaitz Poveda
    •  & Paul W. Franks
  63. University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, 9700 RB, The Netherlands

    • Niek Verweij
    • , M. Yldau van der Ende
    •  & Pim van der Harst
  64. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, 48104, MI, USA

    • Erin B. Ware
    • , Jessica D. Faul
    • , Jennifer A. Smith
    •  & David R. Weir
  65. Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, 37203, TN, USA

    • Wanqing Wen
    • , Xiao-Ou Shu
    •  & Wei Zheng
  66. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA

    • Xiaoyin Li
    • , Jingjing Liang
    •  & Xiaofeng Zhu
  67. Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA

    • Lisa R. Yanek
    •  & Diane M. Becker
  68. Dean’s Office, University of Kentucky College of Public Health, Lexington, 40536, KY, USA

    • Donna K. Arnett
  69. Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030, TX, USA

    • Eric Boerwinkle
  70. Department of Medical Sciences, University of Trieste, Trieste, 34137, Italy

    • Marco Brumat
    •  & Paolo Gasparini
  71. Ninewells Hospital & Medical School, University of Dundee, Dundee, DD1 9SY, Scotland, UK

    • John Connell
  72. Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300 RC, Netherlands

    • Renée de Mutsert
    •  & Dennis O. Mook-Kanamori
  73. Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, 11600, Sri Lanka

    • H. Janaka de Silva
  74. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA

    • Jingzhong Ding
    •  & Stephen B. Kritchevsky
  75. Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, 02860, RI, USA

    • Charles B. Eaton
  76. Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, 91120, Israel

    • Yechiel Friedlander
  77. Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Austin, Austin, 78712, TX, USA

    • Kelley P. Gabriel
  78. Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, 91778-99191, Iran

    • Mohsen Ghanbari
  79. Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100006, China

    • Dongfeng Gu
  80. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, 20892, MD, USA

    • Tamara B. Harris
    •  & Lenore J. Launer
  81. Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, 70112, LA, USA

    • Jiang He
    •  & Tanika N. Kelly
  82. Medicine, Tulane University School of Medicine, New Orleans, 70112, LA, USA

    • Jiang He
  83. Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, 70211, Finland

    • Sami Heikkinen
    •  & Timo A. Lakka
  84. Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland

    • Sami Heikkinen
    • , Johanna Kuusisto
    •  & Markku Laakso
  85. Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore

    • Chew-Kiat Heng
  86. Khoo Teck Puat—National University Children’s Medical Institute, National University Health System, Singapore, 119228, Singapore

    • Chew-Kiat Heng
  87. Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, 84132, UT, USA

    • Steven C. Hunt
  88. Department of Genetic Medicine, Weill Cornell Medicine, Doha, 24144, Qatar

    • Steven C. Hunt
  89. Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, 3015 GD, The Netherlands

    • M. Arfan Ikram
  90. Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, 68167, Germany

    • Jost B. Jonas
  91. Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China

    • Jost B. Jonas
    •  & Ya X. Wang
  92. Health Services and Systems Research, Duke-NUS Medical School, Singapore, 169857, Singapore

    • Woon-Puay Koh
    •  & E. Shyong Tai
  93. Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, 1010, Switzerland

    • Zoltán Kutalik
  94. Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, 98101, WA, USA

    • Rozenn N. Lemaitre
  95. Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, School of Medicine, Birmingham, 35294, AL, USA

    • Cora E. Lewis
  96. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore

    • Jianjun Liu
  97. Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, 22908, VA, USA

    • Ani Manichaikul
    •  & Stephen S. Rich
  98. Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Thomas Meitinger
  99. Institute of Human Genetics, Technische Universität München, Munich, 80333, Germany

    • Thomas Meitinger
  100. Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, 1081 HV, The Netherlands

    • Yuri Milaneschi
    •  & Brenda W. J. H. Penninx
  101. Department of Genetics, University of North Carolina, Chapel Hill, 27514, NC, USA

    • Karen L. Mohlke
  102. Geriatrics, Medicine, University of Mississippi, Jackson, 39216, MS, USA

    • Thomas H. Mosley Jr.
  103. The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, AB25 2ZD, UK

    • Alison D. Murray
  104. Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, 20892, MD, USA

    • Mike A. Nalls
  105. Data Tecnica International, Glen Echo, 20812, MD, USA

    • Mike A. Nalls
  106. Department of Cardiovascular Sciences, University of Leicester, Leicester, LE3 9PQ, UK

    • Christopher P. Nelson
    •  & Nilesh J. Samani
  107. NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QD, UK

    • Christopher P. Nelson
    •  & Nilesh J. Samani
  108. Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, 98101, WA, USA

    • Sotoodehnia Nona
  109. Department of Epidemiology, University of Colorado Denver, Aurora, 80045, CO, USA

    • Jill M. Norris
  110. Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, 21201, MD, USA

    • Jeff O’Connell
  111. Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, 21201, MD, USA

    • Jeff O’Connell
  112. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA

    • Nicholette D. Palmer
    •  & Donald W. Bowden
  113. Epidemiology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA

    • George J. Papanicolau
  114. DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, 85764, Germany

    • Annette Peters
  115. Department of Public Health, Department of Medicine, University of Split, Split, 21000, Croatia

    • Ozren Polasek
  116. Psychiatric Hospital “Sveti Ivan”, Zagreb, 10000, Croatia

    • Ozren Polasek
  117. Gen-Info Ltd., 10000, Zagreb, Croatia

    • Ozren Polasek
  118. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521, Finland

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

    • Olli T. Raitakari
  120. Institute for Human Genetics, Department of Epidemiology and Biostatistics, University of California, San Francisco, 94143, CA, USA

    • Neil Risch
  121. Department of Epidemiology and Medicine, University of Iowa, Iowa City, 52242, IA, USA

    • Jennifer G. Robinson
  122. Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH16 4UX, UK

    • Igor Rudan
  123. Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, 55454, MN, USA

    • Pamela J. Schreiner
  124. Kaiser Permanente Washington, Health Research Institute, Seattle, 98101, WA, USA

    • Stephen S. Sidney
    • , Barbara Sternfeld
    •  & Bruce M. Psaty
  125. Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, EH8 9JZ, UK

    • John M. Starr
  126. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany

    • Konstantin Strauch
  127. Institute of Medical Informatics Biometry and Epidemiology, Ludwig-Maximilians-Universitat Munchen, Munich, 81377, Germany

    • Konstantin Strauch
  128. Department of Genetics, Stanford University, Stanford, 94305, CA, USA

    • Hua Tang
  129. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, 55455, MN, USA

    • Michael Y. Tsai
  130. Public Health Solutions, National Institute for Health and Welfare, Helsinki, 00271, Finland

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

    • Jaakko Tuomilehto
  132. Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands

    • André G. Uitterlinden
  133. Department of Public Health & Clinical Medicine, Umeå University, Umeå, 90185, Västerbotten, Sweden

    • Patrik Wennberg
    •  & Paul W. Franks
  134. Jackson Heart Study, School of Public Health, Jackson State University, Jackson, 39213, MS, USA

    • Gregory Wilson
  135. State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200000, China

    • Yong-Bing Xiang
  136. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, 15261, PA, USA

    • Jian-Min Yuan
  137. Division of Cancer Control and Population Sciences, UPMC Hillman Cancer, University of Pittsburgh, Pittsburgh, 15232, PA, USA

    • Jian-Min Yuan
  138. Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, 21224, MD, USA

    • Alan B. Zonderman
  139. Psychology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK

    • Ian J. Deary
  140. MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK

    • Paul Elliott
  141. Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, 02142, MA, USA

    • Tõnu Esko
  142. Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA

    • Barry I. Freedman
  143. Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK

    • Philippe Froguel
  144. German Center for Diabetes Research (DZD e.V.), Neuherberg, 85764, Germany

    • Christian Gieger
  145. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210, Finland

    • Timo A. Lakka
  146. Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands

    • Albertine J. Oldehinkel
  147. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands

    • Lude Franke
    • , Morris Swertz
    • , Cisca Wijmenga
    •  & Pim van der Harst
  148. Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, 1105 AZ, The Netherlands

    • Pim van der Harst
  149. Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands

    • Bruce H. R. Wolffenbuttel
    •  & Jana V. Van Vliet-Ostaptchouk
  150. Internal Medicine, Department of Medicine, Lausanne University Hospital, Lausanne, 1011, Switzerland

    • Peter Vollenweider
  151. Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA

    • Lynne E. Wagenknecht
  152. Harvard T. H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, 02115, MA, USA

    • Paul W. Franks
  153. OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK

    • Paul W. Franks
  154. Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland

    • Vilmundur Gudnason
  155. Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, 27157, NC, USA

    • Yongmei Liu
  156. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore

    • E. Shyong Tai
    •  & Rob M. van Dam
  157. Cardiology, Medicine, University of Mississippi Medical Center, Jackson, 39216, MS, USA

    • Ervin R. Fox
  158. Public Health and Primary Care, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands

    • Dennis O. Mook-Kanamori
  159. Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, 98109, WA, USA

    • Charles B. Kooperberg
  160. Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, 90032, CA, USA

    • W. James Gauderman
  161. Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, 98101, WA, USA

    • Bruce M. Psaty
  162. Department of Biostatistics, University of Washington, Seattle, 98105, WA, USA

    • Kenneth Rice
  163. NIHR Barts Cardiovascular Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK

    • Patricia B. Munroe
  164. NHLBI Framingham Heart Study, Framingham, 01702, MA, USA

    • L. Adrienne Cupples
  165. Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, 10029, NY, USA

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

    • Ruth J. F. Loos
  167. Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands

    • Gerjan Navis
  168. Department of Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands

    • Marianne Rots

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Consortia

  1. Lifelines Cohort Study

Contributions

T.O.K., K. Schwander., D.C.R., and R.J.F.L. conceived and designed the study. The members of the writing group were T.O.K., A.R.B., R.N., Y.J.S., K.Schwander., T. Winkler, H.J., D.I.C., A. Manning., I.N., B.M.P., K.R., P.B.M., M.F., L.A.C., C.N.R., A.C.M., D.C.R., and R.J.F.L. The genome-wide association results were provided by A.R.B., R.N., Y.J.S., K.Strauch, T. Winkler, D.I.C., A. Manning., I.N., H.A., M.R.B., L.d.l.F., N.F., X.G., D.V., S.A., M.F.F., M.K., S.K.M., M. Richard, H.W., Z.W., T.M.B., L.F.B., A.C., R.D., V.F., F.P.H., A.R.V.R.H., C. Li, K.K.L., J.M., X.S., A.V.S., S.M.T., M. Alver., M. Amini, M. Boissel, J.F.C., X.C., J. Divers, E.E., C. Gao, M. Graff, S.E.H., M.H., F.C.H., A.U.J., J.H.Z., A.T.K., B.K., F.L., L.P.L., I.M.N., R. Rauramaa., M. Riaz, A.R., R. Rueedi, H.M.S., F.T., P.J.v.d.M., T.V.V., N.V., E.B.W., W.W., X.L., L.R.Y., N.A., D.K.A., E.B., M. Brumat, B.C., M.C., Y.D.I.C., M.P.C., J.C., R.d.M., H.J.d.S., P.S.d.V., A.D., J. Ding, C.B.E., J.D.F., Y.F., K.P.G., M. Ghanbari, F.G., C.C.G., D.G., T.B.H., J.H., S.H., C.K.H., S.C.H., A.I., J.B.J., W.P.K., P.K., J.E.K., S.B.K., Z.K., J.K., C.D.L., C. Langenberg, L.J.L., K.L., R.N.L., C.E.L., J. Liang, J. Liu, R.M., A. Manichaikul, T.M., A. Metspalu, Y.M., K.L.M., T.H.M., A.D.M., M.A.N., E.E.K.N., C.P.N., S.N., J.M.N., J.O., N.D.P., G.J.P., R.P., N.L.P., A. Peters, P.A.P., O.P., D.J.P., A. Poveda, O.T.R., S.S.R., N.R., J.G.R., L.M.R., I.R., P.J.S., R.A.S., S.S.S., M.S., J.A.S., H.S., T.S., J.M.S., B.S., K.St., H.T., K.D.T., M.Y.T., J.T., A.G.U., M.Y.v.d.E., D.v.H., T.V., M.W., P.W., G.W., Y.B.X., J.Y., C.Y., J.M.Y., W. Zhao, A.B.Z., D.M.B., M. Boehnke, D.W.B., U.d.F., I.J.D., P.E., T.E., B.I.F., P.F., P.G., C. Gieger, N.K., M.L., T.A.L., T.L., P.K.E.M., A.J.O., B.W.J.H.P., N.J.S., X.O.S., P.v.d.H., J.V.V.V.O., P.V., L.E.W., Y.X.W., N.J.W., D.R.W., T. Wu, W. Zheng, X.Z., M.K.E., P.W.F., V.G., C.H., B.L.H., T.N.K., Y.L., K.E.N., A.C.P., P.M.R., E.S.T., R.M.v.D., E.R.F., S.L.R.K., C.T.L., D.O.M.K., M.A.P., S.R., C.M.v.D., J.I.R., C.B.K., W.J.G., B.M.P., K.R., P.B.M., M.F., L.A.C., C.N.R., A.C.M., D.C.R., and R.J.F.L.; The meta-analyses were performed by T.O.K. and H.J.; The combined physical activity interaction effects of all known lipid loci were examined by T.O.K. and H.J.; T.O.K. and C.V.N. collected look-up information in GWAS studies for other traits; T.O.K. and C.V.N. carried out the eQTL look-ups. All authors reviewed and approved the final manuscript.

Competing interests

Bruce M. Psaty serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Brenda W.J.H. Penninx has received research funding (nonrelated to the work reported here) from Jansen Research and Boehringer Ingelheim. Mike A. Nalls’ participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. Dr. Nalls also consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare among others, and has a Commercial affiliation with Data Technica International, Glen Echo, MD, USA. Jost B. Jonas serves as a consultant for Mundipharma Co. (Cambridge, UK), patent holder with Biocompatibles UK Ltd. (Franham, Surrey, UK) (Title: Treatment of eye diseases using encapsulated cells encoding and secreting neuroprotective factor and/or anti-angiogenic factor; Patent number: 20120263794), and is patent applicant with University of Heidelberg (Heidelberg, Germany) (Title: Agents for use in the therapeutic or prophylactic treatment of myopia or hyperopia; Europäische Patentanmeldung 15,000 771.4). Paul W. Franks has been a paid consultant in the design of a personalized Nutrition trial (PREDICT) as part of a private-public partnership at Kings College London, UK, and has received research support from several pharmaceutical Companies as part of European Union Innovative Medicines Initiative (IMI) Projects. Terho Lehtimäki is employed by Fimlab Ltd. Ozren Polasek is employed by Gen-info Ltd. The remaining authors declare no competing interests.

Corresponding authors

Correspondence to Tuomas O. Kilpeläinen or Dabeeru C. Rao or Ruth J. F. Loos.

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DOI

https://doi.org/10.1038/s41467-018-08008-w

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