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Exome-wide association study of plasma lipids in >300,000 individuals

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.

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Figure 1: A1CF p.Gly398Ser mutant leads to increased APOB100 secretion.
Figure 2: Association of genetically lowered triglycerides by LPL variants with a range of phenotypes.

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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.

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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

Correspondence to Cristen J Willer or Sekar Kathiresan.

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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 1 Quantile–quantile (QQ) plots of single-variant association analysis P values for each lipid trait.

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 6 Diagram of sequential forward-selection procedure.

Supplementary Figure 7 FPLC profile showing cholesterol content of plasma-lipoprotein fractions from pooled plasma of WT→Ldlr−/− or JAK2V617F→Ldlr−/− recipient mice fed a Western diet for 8 weeks.

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 12 Correlation plot of the effects of HDL and AMD for 168 independent HDL variants.

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 Figure 14 Correlation plot of the effects of 113 independent LDL variants with MAF >1% and T2D.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Note

Life Sciences Reporting Summary

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

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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

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