Abstract
We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Asselbergs, F.W. et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am. J. Hum. Genet. 91, 823–838 (2012).
Willer, C.J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).
Albrechtsen, A. et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013).
Peloso, G.M. et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94, 223–232 (2014).
Surakka, I. et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015).
Tang, C.S. et al. Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat. Commun. 6, 10206 (2015).
Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).
Burkhardt, R. et al. Trib1 is a lipid- and myocardial infarction-associated gene that regulates hepatic lipogenesis and VLDL production in mice. J. Clin. Invest. 120, 4410–4414 (2010).
Voight, B.F. et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380, 572–580 (2012).
Do, R. et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45, 1345–1352 (2013).
Lu, X. et al. Exome chip meta-analysis identifies novel loci and East Asian–specific coding variants contributing to lipid levels and coronary artery disease. Nat. Genet. http://dx.doi.org/10.1038/ng.3978 (2017).
Feng, S., Liu, D., Zhan, X., Wing, M.K. & Abecasis, G.R. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30, 2828–2829 (2014).
Liu, D.J. et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).
Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
Holmen, O.L. et al. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat. Genet. 46, 345–351 (2014).
Shen, X. et al. Identification of genes affecting apolipoprotein B secretion following siRNA-mediated gene knockdown in primary human hepatocytes. Atherosclerosis 222, 154–157 (2012).
Baxter, E.J. et al. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet 365, 1054–1061 (2005).
James, C. et al. A unique clonal JAK2 mutation leading to constitutive signalling causes polycythaemia vera. Nature 434, 1144–1148 (2005).
Kralovics, R. et al. A gain-of-function mutation of JAK2 in myeloproliferative disorders. N. Engl. J. Med. 352, 1779–1790 (2005).
Levine, R.L. et al. Activating mutation in the tyrosine kinase JAK2 in polycythemia vera, essential thrombocythemia, and myeloid metaplasia with myelofibrosis. Cancer Cell 7, 387–397 (2005).
Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
Jaiswal, S. et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N. Engl. J. Med. 377, 111–121 (2017).
Mullally, A. et al. Physiological Jak2V617F expression causes a lethal myeloproliferative neoplasm with differential effects on hematopoietic stem and progenitor cells. Cancer Cell 17, 584–596 (2010).
Lellek, H. et al. Purification and molecular cloning of a novel essential component of the apolipoprotein B mRNA editing enzyme-complex. J. Biol. Chem. 275, 19848–19856 (2000).
Mehta, A., Kinter, M.T., Sherman, N.E. & Driscoll, D.M. Molecular cloning of apobec-1 complementation factor, a novel RNA-binding protein involved in the editing of apolipoprotein B mRNA. Mol. Cell. Biol. 20, 1846–1854 (2000).
Galloway, C.A., Ashton, J., Sparks, J.D., Mooney, R.A. & Smith, H.C. Metabolic regulation of APOBEC-1 complementation factor trafficking in mouse models of obesity and its positive correlation with the expression of ApoB protein in hepatocytes. Biochim. Biophys. Acta 1802, 976–985 (2010).
Galanello, R. & Origa, R. Beta-thalassemia. Orphanet J. Rare Dis. 5, 11 (2010).
Fessas, P., Stamatoyannopoulos, G. & Keys, A. Serum-cholesterol and thalassemia trait. Lancet 1, 1182–1183 (1963).
Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015).
Chen, W. et al. Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration. Proc. Natl. Acad. Sci. USA 107, 7401–7406 (2010).
Neale, B.M. et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc. Natl. Acad. Sci. USA 107, 7395–7400 (2010).
Wang, Y.F. et al. CETP/LPL/LIPC gene polymorphisms and susceptibility to age-related macular degeneration. Sci. Rep. 5, 15711 (2015).
Cheng, C.Y. et al. New loci and coding variants confer risk for age-related macular degeneration in East Asians. Nat. Commun. 6, 6063 (2015).
Momozawa, Y. et al. Low-frequency coding variants in CETP and CFB are associated with susceptibility of exudative age-related macular degeneration in the Japanese population. Hum. Mol. Genet. 25, 5027–5034 (2016).
Lotta, L.A. et al. Association between low-density lipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes: A meta-analysis. J. Am. Med. Assoc. 316, 1383–1391 (2016).
Schmidt, A.F. et al. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study. Lancet Diabetes Endocrinol. 5, 97–105 (2017).
Ference, B.A. et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N. Engl. J. Med. 375, 2144–2153 (2016).
Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Preprint at https://www.biorxiv.org/content/early/2017/05/31/144410.1/ (2017).
Myocardial Infarction Genetics & CARDIoGRAM Exome Consortia Investigators. et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134–1144 (2016).
Goldstein, J.I. et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).
Grove, M.L. et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One 8, e68095 (2013).
Tennessen, J.A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).
1000 Genomes Project Consortium. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Zhan, X. & Liu, D.J. SEQMINER: an R-package to facilitate the functional interpretation of sequence-based associations. Genet. Epidemiol. 39, 619–623 (2015).
Fritsche, L.G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2016).
Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
Gaulton, K.J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).
Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).
The UK Biobank. Genotyping and quality control of UK Biobank, a large-scale, extensively phenotyped prospective resource http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf (2015).
Speliotes, E.K. et al. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet. 7, e1001324 (2011).
Baber, U. et al. Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults: the BioImage study. J. Am. Coll. Cardiol. 65, 1065–1074 (2015).
Acknowledgements
D.J.L. is partially supported by R01HG008983 from the National Human Genome Research Institute of the National Institute of Health, and R21DA040177 and R01DA037904 from the National Institute of Drug Abuse of the National Institute of Health. G.M.P. is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health award K01HL125751. A.P.P. is supported by a research fellowship from the Stanley J. Sarnoff Cardiovascular Research Foundation. H. Tada is supported by a grant from the Japanese Circulation Society to study in the United States. The research was supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility and ERC grant 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F. E.K.S. is supported by NIH grants R01 DK106621 and R01 DK107904, the University of Michigan Biological Sciences Scholars Program, and the University of Michigan Department of Internal Medicine. T.D.S. is supported by an ERC Advanced Principal Investigator award. A.P.M. is supported as a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant no. WT098017). Y.E.C. is supported by HL117491 and HL129778 from the NIH. S.K.G. is supported by HL122684 from the NIH. P.L.A. is supported by NHLBI R21 HL121422-02 from the NIH. C.L., N.J.W., and R.A.S. acknowledge funding from the Medical Research Council, UK (MC_UU_12015/1). J.D. is supported as a British Heart Foundation Professor, European Research Council Senior Investigator, and National Institute for Health Research (NIHR) Senior Investigator. C.J.W. is supported by HL094535 and HL109946 from the NIH. S. Kathiresan is supported by a research scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, and R01 HL127564 and R33 HL120781 from the NIH.
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health or the US Department of Health and Human Services.
This research has been conducted using the UK Biobank resource, application 7089. Funding support for participating studies in the meta-analysis can be found in the Supplementary Note.
Author information
Authors and Affiliations
Consortia
Contributions
All authors contributed to and approved the results and comments on the manuscript. Writing: C.J.W., D.J.L., G.M.P., G.A., P.D., X.L., and S. Kathiresan. Study supervision: S. Kathiresan. Primary analysis: D.J.L. and G.M.P. Secondary analysis: A.V.K., A. Mahajan, Charge Diabetes Working Group, C.M.M., C.E., D.J.R., D.F.R., D.P., E.K.S., E.M.S., GOLD Consortium, J.B.M., J. Wessel, L.G.F., M.O.G., M.I.M., M. Boehnke, N. Stitziel, R.S.S., S. Somayajula, VA Million Veteran Program, and X.L. Functional characterization: A.R.T., C.A.C., H. Yu, K.M., N.W., and X.W. Contribution to study-specific analysis: A.S.B., A.C.A., A.C.M., A.D., A.-E.F., A.K.M., A. Langsted, A. Linneberg, A. Malarstig, A. Manichaikul, A. Maschio, A. Metspalu, A. Mulas, A.P., A.P.M., A.P.P., A.P.R., A.R., A.T.-H., A.U.J., A.V., A.V.S., A.Y.C., B.G.N., B.H.S., B.M.P., C.C., C.G., C.H., C.J.O'D., C.J.W., C.L., C.K., C.M.B., C.M.S., C.N.A.P., C.P., D. Alam, D. Arveiler, D.C.M.L., D.I.C., D.J.L., D.K., D.M.R., D.S., E.B., E.d.A., E.M., E.P.B., EPIC-CVD Consortium, The EPIC-InterAct Consortium, E.Z., F.B., F.C., F.G., F. Karpe, F. Kee, F.R., G.B.J., G. Davies, G. Dedoussis, G.E., G.M.P., G.P., H.A.K., H.G., H.M.S., H.R.W., H. Tada, H. Tang, H. Yaghootkar, H.Z., I.B., I.F., I.J.D., I.R., J.C.B., J.B.-J., J.C.C., J.C.D., J.D., J.D.R., J.F., J.G.W., J.H., J.I.R., J.J., J.K., J.M.C., J.M.M.H., J.M.J., J.M.O., J.M.S., J.B.N., J.N.H., J.S.K., J.-C.T., J.T., J.V., J. Weinstock, J.W.J., K.D.T., K.E.S., K.H., K.K., K.S., K.S.S., L.A.C., L.A.L., L.E.B., L.G., L.J.L., L.S., M. Benn, M. Brown, M.J.C., M.-P.D., M.E.G., M.E.J., M. Ferrario, M.F.F., M. Fornage, M.-R.J., M.J.N., M.L., M.L.G., M.M.-N., M.O.-M., M.P., M.W., M.X., M.Z., N.G., N.G.D.M., N.J.S., N.J.W., N.P., N.R.R., N.R.v.Z., N. Sattar, N.S.Z., O.L.H., O.M., O. Pedersen, O. Polasek, P.A., P.B.M., P.D., P.E.W., P.F., P.L.A., P. Mäntyselkä, P.M.R., P. Muntendam, P.R.K., P. Sever, P.S.T., P. Surendran, P.W.F., P.W.F.W., R.A.S., R.C., R.F.-S., R.J.F.L., R. Magi, R. Mehran, R.R., R.Y., S.P., S.F.N., S.J., S. Kanoni, S. Kathiresan, S.K.G., S.M.D., S. Sanna, S. Sivapalaratnam, S.S.R., S.T., T.B.H., T.D.S., T. Ebeling, T.E.-I.C., T. Esko, T.H., T.L.A., T. Lakka, T. Lauritzen, T.M.F., T.V.V., U.B., V.F., V.G., V.S., W.G., W. Zhang, W. Zhou, X.S., Y.E.C., Y.H., Y.-D.I.C., Y.L., Y. Zhang, and Y. Zhou.
Corresponding authors
Ethics declarations
Competing interests
S. Kathiresan has received grant support from Bayer Healthcare and Amarin; holds equity in San Therapeutics and Catabasis; and has received personal fees for participation in scientific advisory boards for Bayer Healthcare, Catabasis, Regeneron Genetics Center, Merck, Celera, Genomics PLC, Novartis, Sanofi, AstraZeneca, Alnylam, Eli Lilly Company, Leerink Partners, Noble Insights, and Ionis Pharmaceuticals. All other authors have no relationships relevant to the contents of this paper to disclose. Merck authors are employees of Merck Sharp Dohme Corp., New Jersey, USA.
Additional information
A full list of members and affiliations appears in the Supplementary Note
A full list of members and affiliations appears in the Supplementary Note
A full list of members and affiliations appears in the Supplementary Note
A full list of members and affiliations appears in the Supplementary Note
A full list of members and affiliations appears in the Supplementary Note
Integrated supplementary information
Supplementary Figure 2 Manhattan plot of single-variant association analysis P values for LDL-C.
The novel coding variants are labeled in the plot.
Supplementary Figure 3 Manhattan plot of single-variant association analysis P values for HDL-C.
The novel coding variants are labeled in the plot.
Supplementary Figure 4 Manhattan plot of single-variant association analysis P values for triglycerides.
The novel coding variants are labeled in the plot.
Supplementary Figure 5 Manhattan plot of single-variant association analysis P values for total cholesterol.
The novel coding variants are labeled in the plot.
Supplementary Figure 8 mRNA and protein levels of recombinantly expressed wild-type A1CF and p.Gly398Ser variant.
(a) mRNA levels of recombinantly expressed wild-type A1CF and p.Gly398Ser variant are similar in both Huh7 wild-type and A1CF knockout cells (labeled as A1CF KO). (b) and (c) APOB100 ELISA (b) and Western Blot (c) measuring cellular APOB100 levels in Huh7 wild-type and A1CF knockout cells. The bars of mean value and error bars of SD are showed in (a) and (b) from experiments with replicates, N=3 for a, N=4 for b. Statistically significant differences are marked (*p<0.05, **p<0.01).
Supplementary Figure 9 Knock-in mice for the A1CF p.Gly398Ser mutation.
(A) The sequences of the wild-type (WT) and A1cf p.Gly398Ser (KI) alleles. For the WT allele, the guide RNA protospacer is underlined, the PAM is in bold, and the base specifically affected by the A1cf p.Gly398Ser mutation is in red. For the KI allele, the altered bases are underlined, and the base specifically affected by the A1cf p.Gly398Ser mutation is in red. (B) The electropherogram is from a mouse heterozygous for the WT and KI alleles. The base specifically affected by the A1cf p.Gly398Ser mutation is shaded. (C) Plasma triglyceride levels of colony mates of the C57BL/6J background (N = 9 wild-type mice and 8 homozygous knock-in mice). Data are displayed as means and s.e.m. The P-value was calculated with the Mann-Whitney U test. (D) FPLC lipoprotein profiles for triglycerides or cholesterol in pooled plasma samples from WT and KI mice.
Supplementary Figure 10 Association of loss-of-function variants in HBB with hematologic traits and blood lipids.
Hematologic estimates are from an exome chip analysis of 24,814 individuals*. Lipid estimates are derived from fixed effects meta-analysis of estimates from the Global Lipids Genetics Consortium and Myocardial Infarction Genetics Consortium.
* Auer, P.L. et al. Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits. Nat Genet 46, 629-34 (2014).
Supplementary Figure 11 Association of HBB loss-of-function variants with coronary artery disease.
The association of loss of function variants with coronary artery disease was estimated using logistic regression with adjustment for age, sex and principal components of ancestry.
Supplementary Figure 13 Association of PCSK9 p.R46L with risk for type 2 diabetes.
In each study, the relationship of PCSK9 p.R46L with risk of type 2 diabetes was obtained. P-values for association tests and confidence intervals were determined using exact methods. A meta-analysis across studies was performed with the use of the Cochran–Mantel–Haenszel statistics for stratified 2-by-2 tables. This method combines score statistics and is particularly useful when some observed odds ratios are zero.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–14 and Supplementary Note
Supplementary Table 1
Studies contributing to meta-analysis
Supplementary Table 2
Descriptive statistics for lipid levels across contributing studies.
Supplementary Table 3
Genotyping and analysis methods across contributing studies
Supplementary Table 4
Variant site distribution by alternative allele frequency and annotations
Supplementary Table 5
Forty new loci where non-protein-altering variants are associated with lipid levels
Supplementary Table 6
Reason for non-coding variants on array
Supplementary Table 7
Association results in current study for 175 previously reported GWAS variants
Supplementary Table 8
Association analysis of novel lipid loci in samples of European American, African American, South Asian, and Hispanic ancestries.
Supplementary Table 9
Gene-level association results
Supplementary Table 10
Replication results for 75 novel primary associations
Supplementary Table 11
Variance explained by known and independently associated SNPs
Supplementary Table 12
Association Results for 444 independently associated variants with lipid traits
Supplementary Table 13
Loci where protein-altering variant is top signal or protein-altering variant explains the GWAS signal
Supplementary Table 14
59 loci where there's a protein-altering variant that is either the top signal, explains the signal or is independent.
Supplementary Table 15
Association results for null mutations with p < 0.001
Supplementary Table 16
HDL-C variants and risk for age-related macular degeneration (AMD)
Supplementary Table 17
DNA variants in CETP robustly associate with HDL-C and risk for AMD
Supplementary Table 18
Thirty studies from populations of European ancestry contributing to PCSK9 p.R46L on risk of T2D
Supplementary Table 19
Association of LDL-C variants with coronary artery disease (CAD) and type 2 diabetes (T2D)
Supplementary Table 20
Definitions of outcomes in UK Biobank PheWAS
Supplementary Table 21
sgRNA sequences for functional follow-up experiments
Rights and permissions
About this article
Cite this article
Liu, D., Peloso, G., Yu, H. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet 49, 1758–1766 (2017). https://doi.org/10.1038/ng.3977
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.3977
This article is cited by
-
Suppression of IL-1β promotes beneficial accumulation of fibroblast-like cells in atherosclerotic plaques in clonal hematopoiesis
Nature Cardiovascular Research (2024)
-
Cross-ancestry genetic architecture and prediction for cholesterol traits
Human Genetics (2024)
-
The relationship between NAFLD and retinol-binding protein 4 - an updated systematic review and meta-analysis
Lipids in Health and Disease (2023)
-
PNPLA3 rs738409 risk genotype decouples TyG index from HOMA2-IR and intrahepatic lipid content
Cardiovascular Diabetology (2023)
-
Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods
Scientific Reports (2023)
