Abstract
Myocardial infarction (MI), a leading cause of death around the world, displays a complex pattern of inheritance1,2. When MI occurs early in life, genetic inheritance is a major component to risk1. Previously, rare mutations in low-density lipoprotein (LDL) genes have been shown to contribute to MI risk in individual families3,4,5,6,7,8, whereas common variants at more than 45 loci have been associated with MI risk in the population9,10,11,12,13,14,15. Here we evaluate how rare mutations contribute to early-onset MI risk in the population. We sequenced the protein-coding regions of 9,793 genomes from patients with MI at an early age (≤50 years in males and ≤60 years in females) along with MI-free controls. We identified two genes in which rare coding-sequence mutations were more frequent in MI cases versus controls at exome-wide significance. At low-density lipoprotein receptor (LDLR), carriers of rare non-synonymous mutations were at 4.2-fold increased risk for MI; carriers of null alleles at LDLR were at even higher risk (13-fold difference). Approximately 2% of early MI cases harbour a rare, damaging mutation in LDLR; this estimate is similar to one made more than 40 years ago using an analysis of total cholesterol16. Among controls, about 1 in 217 carried an LDLR coding-sequence mutation and had plasma LDL cholesterol > 190 mg dl−1. At apolipoprotein A-V (APOA5), carriers of rare non-synonymous mutations were at 2.2-fold increased risk for MI. When compared with non-carriers, LDLR mutation carriers had higher plasma LDL cholesterol, whereas APOA5 mutation carriers had higher plasma triglycerides. Recent evidence has connected MI risk with coding-sequence mutations at two genes functionally related to APOA5, namely lipoprotein lipase15,17 and apolipoprotein C-III (refs 18, 19). Combined, these observations suggest that, as well as LDL cholesterol, disordered metabolism of triglyceride-rich lipoproteins contributes to MI risk.
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Data deposits
DNA sequences have been deposited with the NIH dbGAP repository under accession numbers phs000279 and phs000814.
References
Marenberg, M. E., Risch, N., Berkman, L. F., Floderus, B. & de Faire, U. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 330, 1041–1046 (1994)
Lloyd-Jones, D. M. et al. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. J. Am. Med. Assoc. 291, 2204–2211 (2004)
Lehrman, M. A. et al. Mutation in LDL receptor: Alu–Alu recombination deletes exons encoding transmembrane and cytoplasmic domains. Science 227, 140–146 (1985)
Brown, M. S. & Goldstein, J. L. A receptor-mediated pathway for cholesterol homeostasis. Science 232, 34–47 (1986)
Soria, L. F. et al. Association between a specific apolipoprotein B mutation and familial defective apolipoprotein B-100. Proc. Natl Acad. Sci. USA 86, 587–591 (1989)
Garcia, C. K. et al. Autosomal recessive hypercholesterolemia caused by mutations in a putative LDL receptor adaptor protein. Science 292, 1394–1398 (2001)
Berge, K. E. et al. Accumulation of dietary cholesterol in sitosterolemia caused by mutations in adjacent ABC transporters. Science 290, 1771–1775 (2000)
Abifadel, M. et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature Genet. 34, 154–156 (2003)
McPherson, R. et al. A common allele on chromosome 9 associated with coronary heart disease. Science 316, 1488–1491 (2007)
Samani, N. J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007)
Helgadottir, A. et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316, 1491–1493 (2007)
Kathiresan, S. et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nature Genet. 41, 334–341 (2009)
Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nature Genet. 43, 333–338 (2011)
Coronary Artery Disease (C4D) Genetics Consortium A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nature Genet. 43, 339–344 (2011)
The CARDIoGRAMplusC4D Consortium et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nature Genet. 45, 25–33 (2013)
Goldstein, J. L., Schrott, H. G., Hazzard, W. R., Bierman, E. L. & Motulsky, A. G. Hyperlipidemia in coronary heart disease. II. Genetic analysis of lipid levels in 176 families and delineation of a new inherited disorder, combined hyperlipidemia. J. Clin. Invest. 52, 1544–1568 (1973)
Varbo, A. et al. Remnant cholesterol as a causal risk factor for ischemic heart disease. J. Am. Coll. Cardiol. 61, 427–436 (2013)
The TG and HDL Working Group of the Exome Sequencing Project et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N. Engl. J. Med. 371, 22–31 (2014)
Jørgensen, A. B., Frikke-Schmidt, R., Nordestgaard, B. G. & Tybjaerg-Hansen, A. Loss-of-function mutations in APOC3 and risk of ischemic vascular disease. N. Engl. J. Med. 371, 32–41 (2014)
Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nature Biotechnol. 27, 182–189 (2009)
Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008)
Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014)
Leigh, S. E., Foster, A. H., Whittall, R. A., Hubbart, C. S. & Humphries, S. E. Update and analysis of the University College London low density lipoprotein receptor familial hypercholesterolemia database. Ann. Hum. Genet. 72, 485–498 (2008)
Pennacchio, L. A. et al. An apolipoprotein influencing triglycerides in humans and mice revealed by comparative sequencing. Science 294, 169–173 (2001)
Triglyceride Coronary Disease Genetics Consortium and Emerging Risk Factors Collaboration et al. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies. Lancet 375, 1634–1639 (2010)
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010)
Do, R. et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nature Genet. 45, 1345–1352 (2013)
Pollin, T. I. et al. A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 322, 1702–1705 (2008)
Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012)
Antman, E. et al. Myocardial infarction redefined—a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J. Am. Coll. Cardiol. 36, 959–969 (2000)
Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011)
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011)
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w (1118); iso-2; iso-3. Fly 6, 80–92 (2012)
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010)
Sunyaev, S. et al. Prediction of deleterious human alleles. Hum. Mol. Genet. 10, 591–597 (2001)
1000 Genomes Projects Consortium et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010)
Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009)
Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genet. 39, 906–913 (2007)
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genet. 38, 904–909 (2006)
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007)
Stephens, M., Sloan, J. S., Robertson, P. D., Scheet, P. & Nickerson, D. A. Automating sequence-based detection and genotyping of SNPs from diploid samples. Nature Genet. 38, 375–381 (2006)
Gordon, D., Abajian, C. & Green, P. Consed: a graphical tool for sequence finishing. Genome Res. 8, 195–202 (1998)
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)
Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012)
Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nature Genet. 42, 348–354 (2010)
Kryukov, G. V., Shpunt, A., Stamatoyannopoulos, J. A. & Sunyaev, S. R. Power of deep, all-exon resequencing for discovery of human trait genes. Proc. Natl Acad. Sci. USA 106, 3871–3876 (2009)
Acknowledgements
The authors wish to acknowledge the support of the National Heart, Lung, and Blood Institute (NHLBI) and the National Human Genome Research Institute (NHGRI) of the US National Institutes of Health (NIH) and the contributions of the research institutions, study investigators, field staff and study participants in creating this resource for biomedical research. Funding for the exome sequencing project (ESP) was provided by NHLBI grants RC2 HL-103010 (HeartGO), RC2 HL-102923 (LungGO) and RC2 HL-102924 (WHISP). Exome sequencing was performed through NHLBI grants RC2 HL-102925 (BroadGO) and RC2 HL-102926 (SeattleGO). Exome sequencing in the ATVB, PROCARDIS, and Ottawa studies was supported by NHGRI 5U54HG003067-11 to E.S.L. and S.G. Cleveland Clinic GeneBank was supported by NIH grants P01 HL076491 and P01 HL098055. S.K. is supported by a Research Scholar award from the Massachusetts General Hospital (MGH), the Howard Goodman Fellowship from MGH, the Donovan Family Foundation, R01HL107816, and a grant from Fondation Leducq. R.D. is supported by a Banting Fellowship from the Canadian Institutes of Health Research. N.O.S. is supported, in part, by a career development award from the NIH/NHLBI K08HL114642 and by The Foundation for Barnes-Jewish Hospital. N.O.S. was supported by award number T32HL007604 from the NHLBI. G.M.P was supported by award number T32HL007208 from the NHLBI. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI, NHGRI, or NIH. The Italian ATVB Study was supported by a grant from RFPS-2007-3-644382. A full listing of acknowledgements is provided in the Supplementary Information.
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R.Do, N.O.S., H.-H.W., A.B.J., and A.K. carried out the primary data analyses. R.Do, N.O.S., L.A.L., G.M.P., P.L.A., J.E.R., B.M.P., D.M.H., J.G.W., S.S.R., M.J.B., R.P.T., L.A.C., S.L.H., H.A., J.A.S., S.C., C.S.C., C.K., R.D.J., E.B., G.R.A., S.M.S., D.S.S., D.A.N., S.R.S., C.J.O., D. Altshuler, S.G., and S.K. contributed to the design and conduct of the discovery exome sequencing study. S.G., D.N.F., and M.A.D. enabled the exome sequencing, variant calling, and annotation. R.Do, N.O.S., H.-H.W., A.B.J., S.D., P.A.M., M.F., A.G., I.G., R.A., D.G., N.M., O.O., R.R., A.F.R.S., D.S., J.D., S.E.E., S.S., G.K.H., J.J.K., N.J.S., H.S., J.E., S.H.S., W.E.K., C.T.J., R.A.H., O.Z, E.H., W.M., M.N., J.W., A.H., R.C., D.F.R, W.Y., M.E.K., J.H., A.D.J., M.L., G.L.B., M.G., Y.L., T.L.A., G.H., E.M.L., A.R.F., H.A.T., M.A.R., P.D., D.J.R., M.P.R., J.H., W.W.H.T., A.P.R., D. Ardissino, D. Altshuler, R.M., A.T.-H., H.W., and S.K. contributed to the design and conduct of the imputation-based validation, genotyping-based validation, and/or the re-sequencing based validation study. S.S. supervised the analysis of exome sequencing data and power analysis. R.Do, N.O.S., H.-H.W., S.D., P.A.M., M.F., A.G., R.A., E.S.L., R.M., H.W., D. Ardissino, S.G., and S.K. contributed to the design and conduct of the replication exome sequencing study. S.G., E.S.L., S.K., D.A.N., and D. Altshuler obtained funding. D. Altshuler, D.A.N., S.S.R., R.D.J., and M.J.B. comprised the executive committee of the NHLBI Exome Sequencing Project. C.J.O. and S.K. led the Early-Onset Myocardial Infarction study team within the NHLBI Exome Sequencing Project. R.Do, N.O.S., H.-H.W. and S.K. wrote the manuscript.
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This file contains a list of acknowledgements, Supplementary Figures 1-32, Supplementary Tables 1-26 and Supplementary References. (PDF 6382 kb)
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Do, R., Stitziel, N., Won, HH. et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102–106 (2015). https://doi.org/10.1038/nature13917
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DOI: https://doi.org/10.1038/nature13917
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