[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 34.236.216.93. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Figure 1.
Association of Damaging Lipoprotein Lipase Gene (LPL) Mutations With Circulating Lipid Concentrations
Association of Damaging Lipoprotein Lipase Gene (LPL) Mutations With Circulating Lipid Concentrations

Beta coefficients reflective of the difference in lipid concentrations between carriers of a damaging LPL mutation and noncarriers were derived from linear regression models that included adjustment for age, age squared, sex, cohort, and the first 5 principal components of ancestry. Principal components of ancestry were based on observed genotypic differences across subpopulations (eg, race or ethnicity) in the overall study. Inclusion of principal components as covariates in linear regression analyses increases statistical power for true relationships and minimizes confounding by ancestry.27 Fixed-effects meta-analysis was used to combine results across cohorts (P for heterogeneity > .50 for each lipid phenotype). The number of participants from each study cohort with lipid fraction values available is displayed. HDL indicates high-density lipoprotein; IQR, interquartile range; and LDL, low-density lipoprotein. To convert cholesterol to millimoles per liter, multiply by 0.0259; triglycerides to millimoles per liter, multiply by 0.0113.

Figure 2.
Association of Damaging Lipoprotein Lipase Gene (LPL) Mutations With Coronary Artery Disease (CAD) Among 46 891 Individuals in 11 Studies
Association of Damaging Lipoprotein Lipase Gene (LPL) Mutations With Coronary Artery Disease (CAD) Among 46 891 Individuals in 11 Studies

In each study, the relationship of rare damaging mutations in LPL with risk of CAD was determined. P values for association tests and confidence intervals were determined using exact methods. A meta-analysis across studies was performed using 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 0. An odds ratio in the Jackson Heart Study (JHS) cohort was not available (NA) owing to absence of identified carriers of a damaging LPL mutation. ATVB indicates Atherosclerosis, Thrombosis, and Vascular Biology Italian Study; DiscovEHR, DiscovEHR project of the Regeneron Genetics Center and Geisinger Health System; ESP-EOMI, Exome Sequencing Project Early-Onset Myocardial Infarction study; Leicester, Leicester Myocardial Infarction study; North German, North German Myocardial Infarction study; OHS, Ottawa Heart Study; PROCARDIS, Precocious Coronary Artery Disease study; PROMIS, Pakistan Risk of Myocardial Infarction Study; REGICOR, Registre Gironí del COR (Gerona Heart Registry) study; South German, South German Myocardial Infarction study; and dashed line, overall meta-analysis effect estimate.

Table 1.  
Baseline Characteristics of the Myocardial Infarction Genetics Consortium and Early-Onset CAD DiscovEHR Study Participants
Baseline Characteristics of the Myocardial Infarction Genetics Consortium and Early-Onset CAD DiscovEHR Study Participants
Table 2.  
Association of Damaging Lipoprotein Lipase Gene (LPL) Mutations With CAD by Rare Variant Class in the Myocardial Infarction Genetics Consortium Studies and Early-Onset CAD DiscovEHR Study
Association of Damaging Lipoprotein Lipase Gene (LPL) Mutations With CAD by Rare Variant Class in the Myocardial Infarction Genetics Consortium Studies and Early-Onset CAD DiscovEHR Study
1.
Eckel  RH.  Lipoprotein lipase: a multifunctional enzyme relevant to common metabolic diseases.  N Engl J Med. 1989;320(16):1060-1068.PubMedGoogle ScholarCrossref
2.
Brunzell  JD, Deeb  SS. Familial lipoprotein lipase deficiency, apo CII deficiency and hepatic lipase deficiency. In: Scriver  CR, Beaudet  AI, Sly  WS, Valle  D, eds.  The Metabolic and Molecular Basis of Inherited Disease. 8th ed. New York, NY: McGraw-Hill; 2000:2789-2816.
3.
Nordestgaard  BG, Stender  S, Kjeldsen  K.  Reduced atherogenesis in cholesterol-fed diabetic rabbits: giant lipoproteins do not enter the arterial wall.  Arteriosclerosis. 1988;8(4):421-428.PubMedGoogle ScholarCrossref
4.
Nordestgaard  BG.  Triglyceride-rich lipoproteins and atherosclerotic cardiovascular disease: new insights from epidemiology, genetics, and biology.  Circ Res. 2016;118(4):547-563.PubMedGoogle ScholarCrossref
5.
Nordestgaard  BG, Abildgaard  S, Wittrup  HH, Steffensen  R, Jensen  G, Tybjaerg-Hansen  A.  Heterozygous lipoprotein lipase deficiency: frequency in the general population, effect on plasma lipid levels, and risk of ischemic heart disease.  Circulation. 1997;96(6):1737-1744.PubMedGoogle ScholarCrossref
6.
Atherosclerosis, Thrombosis, and Vascular Biology Italian Study Group.  No evidence of association between prothrombotic gene polymorphisms and the development of acute myocardial infarction at a young age.  Circulation. 2003;107(8):1117-1122.PubMedGoogle ScholarCrossref
7.
Do  R, Stitziel  NO, Won  HH,  et al; NHLBI Exome Sequencing Project.  Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction.  Nature. 2015;518(7537):102-106.PubMedGoogle ScholarCrossref
8.
Taylor  HA  Jr, Wilson  JG, Jones  DW,  et al.  Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study.  Ethn Dis. 2005;15(4)(suppl 6):S6-S4, 17.PubMedGoogle Scholar
9.
Crosby  J, Peloso  GM, Auer  PL,  et al; TG and HDL Working Group of the Exome Sequencing Project, National Heart, Lung, and Blood Institute.  Loss-of-function mutations in APOC3, triglycerides, and coronary disease.  N Engl J Med. 2014;371(1):22-31.PubMedGoogle ScholarCrossref
10.
McPherson  R, Pertsemlidis  A, Kavaslar  N,  et al.  A common allele on chromosome 9 associated with coronary heart disease.  Science. 2007;316(5830):1488-1491.PubMedGoogle ScholarCrossref
11.
Clarke  R, Peden  JF, Hopewell  JC,  et al; PROCARDIS Consortium.  Genetic variants associated with Lp(a) lipoprotein level and coronary disease.  N Engl J Med. 2009;361(26):2518-2528.PubMedGoogle ScholarCrossref
12.
Saleheen  D, Zaidi  M, Rasheed  A,  et al.  The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia.  Eur J Epidemiol. 2009;24(6):329-338.PubMedGoogle ScholarCrossref
13.
Sentí  M, Tomás  M, Marrugat  J, Elosua  R; REGICOR Investigators.  Paraoxonase1-192 polymorphism modulates the nonfatal myocardial infarction risk associated with decreased HDLs.  Arterioscler Thromb Vasc Biol. 2001;21(3):415-420.PubMedGoogle ScholarCrossref
14.
Samani  NJ, Erdmann  J, Hall  AS,  et al; WTCCC and the Cardiogenics Consortium.  Genomewide association analysis of coronary artery disease.  N Engl J Med. 2007;357(5):443-453.PubMedGoogle ScholarCrossref
15.
Stitziel  NO, Stirrups  KE, Masca  NG,  et al; Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators.  Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease.  N Engl J Med. 2016;374(12):1134-1144.PubMedGoogle ScholarCrossref
16.
Dewey  FE, Gusarova  V, O’Dushlaine  C,  et al.  Inactivating variants in ANGPTL4 and risk of coronary artery disease.  N Engl J Med. 2016;374(12):1123-1133.PubMedGoogle ScholarCrossref
17.
Baigent  C, Keech  A, Kearney  PM,  et al; Cholesterol Treatment Trialists’ (CTT) Collaborators.  Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins.  Lancet. 2005;366(9493):1267-1278.PubMedGoogle ScholarCrossref
18.
Stitziel  NO, Won  HH, Morrison  AC,  et al; Myocardial Infarction Genetics Consortium Investigators.  Inactivating mutations in NPC1L1 and protection from coronary heart disease.  N Engl J Med. 2014;371(22):2072-2082.PubMedGoogle ScholarCrossref
19.
Benn  M, Watts  GF, Tybjærg-Hansen  A, Nordestgaard  BG.  Mutations causative of familial hypercholesterolaemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217.  Eur Heart J. 2016;37(17):1384-1394.PubMedGoogle ScholarCrossref
20.
Jørgensen  AB, Frikke-Schmidt  R, West  AS, Grande  P, Nordestgaard  BG, Tybjærg-Hansen  A.  Genetically elevated non-fasting triglycerides and calculated remnant cholesterol as causal risk factors for myocardial infarction.  Eur Heart J. 2013;34(24):1826-1833.PubMedGoogle ScholarCrossref
21.
McKenna  A, Hanna  M, Banks  E,  et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.  Genome Res. 2010;20(9):1297-1303.PubMedGoogle ScholarCrossref
22.
Landrum  MJ, Lee  JM, Riley  GR,  et al.  ClinVar: public archive of relationships among sequence variation and human phenotype.  Nucleic Acids Res. 2014;42(database issue):D980-D985.PubMedGoogle ScholarCrossref
23.
Purcell  SM, Moran  JL, Fromer  M,  et al.  A polygenic burden of rare disruptive mutations in schizophrenia.  Nature. 2014;506(7487):185-190.PubMedGoogle ScholarCrossref
24.
McLaren  W, Pritchard  B, Rios  D, Chen  Y, Flicek  P, Cunningham  F.  Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor.  Bioinformatics. 2010;26(16):2069-2070.PubMedGoogle ScholarCrossref
25.
Lek  M, Karczewski  KJ, Minikel  EV,  et al; Exome Aggregation Consortium.  Analysis of protein-coding genetic variation in 60,706 humans.  Nature. 2016;536(7616):285-291.PubMedGoogle ScholarCrossref
26.
Liu  X, Wu  C, Li  C, Boerwinkle  E.  dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs.  Hum Mutat. 2016;37(3):235-241.PubMedGoogle ScholarCrossref
27.
Price  AL, Patterson  NJ, Plenge  RM, Weinblatt  ME, Shadick  NA, Reich  D.  Principal components analysis corrects for stratification in genome-wide association studies.  Nat Genet. 2006;38(8):904-909.PubMedGoogle ScholarCrossref
28.
Nioi  P, Sigurdsson  A, Thorleifsson  G,  et al.  Variant ASGR1 associated with a reduced risk of coronary artery disease.  N Engl J Med. 2016;374(22):2131-2141.PubMedGoogle ScholarCrossref
29.
Yang  J, Ferreira  T, Morris  AP,  et al; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits.  Nat Genet. 2012;44(4):369-375, S1-S3.PubMedGoogle ScholarCrossref
30.
Liu  DJ, Peloso  GM, Zhan  X,  et al.  Meta-analysis of gene-level tests for rare variant association.  Nat Genet. 2014;46(2):200-204.PubMedGoogle ScholarCrossref
31.
Burgess  S, Butterworth  A, Thompson  SG.  Mendelian randomization analysis with multiple genetic variants using summarized data.  Genet Epidemiol. 2013;37(7):658-665.PubMedGoogle ScholarCrossref
32.
Zuk  O, Schaffner  SF, Samocha  K,  et al.  Searching for missing heritability: designing rare variant association studies.  Proc Natl Acad Sci U S A. 2014;111(4):E455-E464.PubMedGoogle ScholarCrossref
33.
Nordestgaard  BG, Wootton  R, Lewis  B.  Selective retention of VLDL, IDL, and LDL in the arterial intima of genetically hyperlipidemic rabbits in vivo: molecular size as a determinant of fractional loss from the intima-inner media.  Arterioscler Thromb Vasc Biol. 1995;15(4):534-542.PubMedGoogle ScholarCrossref
34.
Jensen  MK, Rimm  EB, Rader  D,  et al.  S447X variant of the lipoprotein lipase gene, lipids, and risk of coronary heart disease in 3 prospective cohort studies.  Am Heart J. 2009;157(2):384-390.PubMedGoogle ScholarCrossref
35.
Thomsen  M, Varbo  A, Tybjærg-Hansen  A, Nordestgaard  BG.  Low nonfasting triglycerides and reduced all-cause mortality: a mendelian randomization study.  Clin Chem. 2014;60(5):737-746.PubMedGoogle ScholarCrossref
36.
Jørgensen  AB, Frikke-Schmidt  R, Nordestgaard  BG, Tybjærg-Hansen  A.  Loss-of-function mutations in APOC3 and risk of ischemic vascular disease.  N Engl J Med. 2014;371(1):32-41.PubMedGoogle ScholarCrossref
37.
Miesenböck  G, Hölzl  B, Föger  B,  et al.  Heterozygous lipoprotein lipase deficiency due to a missense mutation as the cause of impaired triglyceride tolerance with multiple lipoprotein abnormalities.  J Clin Invest. 1993;91(2):448-455.PubMedGoogle ScholarCrossref
Original Investigation
March 7, 2017

Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease

Author Affiliations
  • 1Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
  • 2Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston
  • 3Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston
  • 4Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
  • 5Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
  • 6Regeneron Genetics Center, Tarrytown, New Jersey
  • 7Department of Public Health Sciences, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, Pennsylvania
  • 8Department of Medicine, Washington University School of Medicine, St Louis, Missouri
  • 9Department of Genetics, Washington University School of Medicine, St Louis, Missouri
  • 10McDonnell Genome Institute, Washington University School of Medicine, St Louis, Missouri
  • 11Department of Biomedical Sciences, Humanitas University, Milan, Italy
  • 12Humanitas Clinical and Research Center, Milan, Italy
  • 13Ospedale Niguarda, Milano, Italy
  • 14Department of Medicine, University of Mississippi Medical Center, Jackson
  • 15Munich Heart Alliance, München, Germany
  • 16Deutsches Herzzentrum München, Technische Universität München, Deutsches Zentrum für Herz-Kreislauf-Forschung, München, Germany
  • 17Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson
  • 18NIHR Leicester Cardiovascular Biomedical Research Unit, Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
  • 19Leeds Institute of Cardiovascular and Metabolic Medicine, Leeds University, Leeds, United Kingdom
  • 20Geisinger Health System, Danville, Pennsylvania
  • 21Cardiovascular Epidemiology and Genetics, Hospital del Mar Research Institute, Barcelona, Spain
  • 22University of Ottawa Heart Institute, Ottawa, Ontario, Canada
  • 23Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
  • 24Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
  • 25Department of Genetics, University of Pennsylvania, Philadelphia
  • 26Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
  • 27Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
  • 28NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
  • 29Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 30Division of Cardiology, Azienda Ospedaliero–Universitaria di Parma, Parma, Italy
  • 31Associazione per lo Studio Della Trombosi in Cardiologia, Pavia, Italy
  • 32Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor
  • 33Department of Human Genetics, University of Michigan, Ann Arbor
  • 34Department of Internal Medicine, University of Michigan, Ann Arbor
  • 35Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor
JAMA. 2017;317(9):937-946. doi:10.1001/jama.2017.0972
Key Points

Question  Do heterozygous carriers of a damaging mutation in the gene encoding lipoprotein lipase have increased odds of coronary artery disease?

Findings  In this cross-sectional study of coronary artery disease case-control studies, gene sequencing identified a damaging mutation in the lipoprotein lipase gene in 188 of 46 891 individuals (0.4%). These mutations were associated with an increase of 19.6 mg/dL in plasma triglycerides and an increased presence of coronary artery disease.

Meaning  The presence of rare damaging mutations in the lipoprotein lipase gene was significantly associated with higher triglyceride levels and presence of coronary artery disease; however, further research is needed to assess whether this association is causal, including possible mechanisms by which heterozygous lipoprotein lipase deficiency could lead to coronary artery disease.

Abstract

Importance  The activity of lipoprotein lipase (LPL) is the rate-determining step in clearing triglyceride-rich lipoproteins from the circulation. Mutations that damage the LPL gene (LPL) lead to lifelong deficiency in enzymatic activity and can provide insight into the relationship of LPL to human disease.

Objective  To determine whether rare and/or common variants in LPL are associated with early-onset coronary artery disease (CAD).

Design, Setting, and Participants  In a cross-sectional study, LPL was sequenced in 10 CAD case-control cohorts of the multinational Myocardial Infarction Genetics Consortium and a nested CAD case-control cohort of the Geisinger Health System DiscovEHR cohort between 2010 and 2015. Common variants were genotyped in up to 305 699 individuals of the Global Lipids Genetics Consortium and up to 120 600 individuals of the CARDIoGRAM Exome Consortium between 2012 and 2014. Study-specific estimates were pooled via meta-analysis.

Exposures  Rare damaging mutations in LPL included loss-of-function variants and missense variants annotated as pathogenic in a human genetics database or predicted to be damaging by computer prediction algorithms trained to identify mutations that impair protein function. Common variants in the LPL gene region included those independently associated with circulating triglyceride levels.

Main Outcomes and Measures  Circulating lipid levels and CAD.

Results  Among 46 891 individuals with LPL gene sequencing data available, the mean (SD) age was 50 (12.6) years and 51% were female. A total of 188 participants (0.40%; 95% CI, 0.35%-0.46%) carried a damaging mutation in LPL, including 105 of 32 646 control participants (0.32%) and 83 of 14 245 participants with early-onset CAD (0.58%). Compared with 46 703 noncarriers, the 188 heterozygous carriers of an LPL damaging mutation displayed higher plasma triglyceride levels (19.6 mg/dL; 95% CI, 4.6-34.6 mg/dL) and higher odds of CAD (odds ratio = 1.84; 95% CI, 1.35-2.51; P < .001). An analysis of 6 common LPL variants resulted in an odds ratio for CAD of 1.51 (95% CI, 1.39-1.64; P = 1.1 × 10−22) per 1-SD increase in triglycerides.

Conclusions and Relevance  The presence of rare damaging mutations in LPL was significantly associated with higher triglyceride levels and presence of coronary artery disease. However, further research is needed to assess whether there are causal mechanisms by which heterozygous lipoprotein lipase deficiency could lead to coronary artery disease.

Introduction

The enzymatic activity of lipoprotein lipase (LPL) serves as the rate-determining step in the postprandial clearance of circulating triglyceride-rich lipoproteins.1 Homozygous LPL deficiency, known as familial chylomicronemia syndrome, is associated with marked elevations in chylomicrons, severe hypertriglyceridemia, and recurrent pancreatitis.2 However, an increased risk of coronary artery disease (CAD) in this condition has not been observed, potentially because the large circulating chylomicrons are unable to penetrate the arterial wall.3,4 By contrast, in heterozygous LPL deficiency, the attenuated capacity for lipolysis leads to a buildup of circulating chylomicron remnants and intermediate-density lipoproteins that are rich in both triglycerides and cholesterol. A study of 9 such individuals suggested an increased risk of CAD,5 but this association has not been confirmed.

In this study, the LPL gene (LPL; RefSeq NM_000237 .2) was sequenced to test the hypothesis that rare damaging mutations leading to heterozygous LPL deficiency are associated with differences in circulating lipid levels as well as higher odds of early-onset CAD. In addition, to provide complementary evidence, independent common variants (allele frequency >1%) in the LPL gene region were also tested for association with CAD.

Methods
Study Populations

Gene sequencing of LPL was performed in participants of 10 previously described CAD case-control cohorts (eTable 1 in the Supplement). Studies included the Atherosclerosis, Thrombosis, and Vascular Biology Italian Study6; the Exome Sequencing Project Early-Onset Myocardial Infarction study7; a nested case-control of the Jackson Heart Study8; the South German Myocardial Infarction study9; the Ottawa Heart Study10; the Precocious Coronary Artery Disease study11; the Pakistan Risk of Myocardial Infarction Study12; the Registre Gironí del COR (Gerona Heart Registry) study13; the Leicester Myocardial Infarction study14; and the North German Myocardial Infarction study.15 Clinical data were assessed in each study. The majority of CAD cases in this analysis (97.5%) were ascertained with onset at an early age (defined as ≤50 years in men and ≤60 years in women). Written informed consent was obtained from all participants of contributing studies, each of which received ethical approval from respective institutional review boards. Approval for this analysis was obtained from the institutional review board of Partners HealthCare.

Replication of the observed associations with regard to lipid levels and CAD was performed via analysis of the previously described DiscovEHR study.16 DiscovEHR study participants were recruited as part of the MyCode Community Health Initiative of the Geisinger Health System and Regeneron Genetics Center. The present analysis was restricted to early-onset CAD cases and CAD-free controls (aged <55 years for men or <65 years for women for both cases and controls). Median values for serially measured laboratory and anthropometric traits were calculated for all individuals with 2 or more measurements in the electronic health record (EHR) following removal of likely spurious values that were more than 3 SDs from the intraindividual median value. Participants were considered to have CAD if they had a history of coronary revascularization in the EHR, or history of acute coronary syndrome, ischemic heart disease, or exertional angina (International Classification of Diseases, Ninth Revision codes 410*, 411*, 412*, 413*, and 414*) with angiographic evidence of obstructive coronary atherosclerosis (>50% stenosis in ≥1 major epicardial vessel from catheterization report). The CAD-free controls were defined as individuals without any case criteria or any single encounter or problem list diagnosis code indicating CAD.

Across all studies, the effect of lipid-lowering therapy in individuals reporting use at the time of lipid measurement was taken into account by dividing the measured total cholesterol and low-density lipoprotein cholesterol (LDL-C) by 0.8 and 0.7, respectively.16-19 Because remnant cholesterol was not measured in study cohorts, values were estimated according to the following formula: remnant cholesterol = total cholesterol minus high-density lipoprotein cholesterol minus LDL-C.20

To extend the analysis to common variants in LPL, summary statistics of 2 large genome-wide association studies were analyzed. The effect of common LPL variants on circulating triglyceride levels was used as a proxy for influence on LPL activity. The relationship of common LPL variants with triglyceride levels was assessed in an analysis of up to 305 699 individuals from 73 cohorts of the Global Lipids Genetics Consortium genotyped using the Illumina HumanExome BeadChip between 2012 and 2014. These same variants were subsequently linked to CAD in up to 120 600 individuals also genotyped between 2012 and 2014 in the previously reported CARDIoGRAM Exome Consortium study.15

Gene Sequencing

Whole-exome sequencing of the Myocardial Infarction Genetics Consortium participants was performed between 2010 and 2015 at the Broad Institute as previously described.7 In brief, sequence data of all participants were aligned to a human reference genome build GRCh37.p13 using the Burrows-Wheeler Aligner algorithm. Aligned nonduplicate reads were locally realigned and base qualities were recalibrated using Genome Analysis Toolkit software.21 Variants were jointly called using Genome Analysis Toolkit HaplotypeCaller software. The sensitivity of the selected variant quality score recalibration threshold was 99.6% for single-nucleotide polymorphisms and 95% for insertion or deletion variants as empirically assessed using HapMap controls with known genotypes included in the genotyping call set. LPL sequence data from the Geisinger Health System DiscovEHR participants were extracted from exome sequences generated at the Regeneron Genetics Center between 2014 and 2015 as previously described.16

Damaging LPL Variant Ascertainment

The positions of genetic variants were based on the complementary DNA reference sequence for LPL (RefSeq NM_000237 .2). Rare LPL variants (minor allele frequency <1%) were annotated with respect to the following 3 classes in a sequential fashion: (1) loss-of-function variants, ie, single base changes that introduce a stop codon leading to premature truncation of a protein (nonsense), insertions or deletions (indels) of DNA that disrupt the translated protein’s amino acid sequence beyond the variant site (frameshift), or point mutations at sites of pre–messenger RNA splicing that alter the splicing process (splice-site); (2) variants annotated as pathogenic in ClinVar, a publicly available archive of genetic variations associated with clinical phenotypes22; and (3) missense variants predicted to be damaging or possibly damaging by each of 5 computer prediction algorithms (LRT score, MutationTaster, PolyPhen-2 HumDiv, PolyPhen-2 HumVar, and SIFT) as performed previously.7,23 Software used to annotate observed variants included Variant Effect Predictor version 81 and its associated Loss-of-Function Transcript Effect Estimator (LOFTEE) plugin,24,25 and the dbNSFP database version 3.0b1.26

Statistical Analysis

The association of rare damaging LPL mutations with lipid phenotypes in the Myocardial Infarction Genetics Consortium and the DiscovEHR studies was estimated using linear regression with adjustment for age, age squared, sex, study cohort, and the first 5 principal components of ancestry. Principal components of ancestry were based on observed genotypic differences across subpopulations (eg, race or ethnicity) in the overall study. Inclusion of principal components as covariates in linear regression analyses increases statistical power for true relationships and minimizes confounding by ancestry.27 The association of LPL mutations with odds of CAD was determined via meta-analysis using Cochran-Mantel-Haenszel statistics for stratified 2-by-2 tables without continuity correction as implemented previously.9,18,28

Common variants (allele frequency >1%) at the LPL locus independently associated with circulating triglyceride levels were ascertained via analysis of the Global Lipids Genetics Consortium cohorts. The association of variants with inverse normal transformed residuals of natural log of triglyceride levels was determined in a model adjusted for age, age squared, sex, and up to 4 principal components of ancestry. For any given genetic locus, such as LPL, multiple variants may be associated with circulating triglyceride levels in an independent fashion. Sequential forward selection provides a statistical framework to identify such independent variants.29,30 The relationship of all genetic variants in the LPL locus with triglyceride levels was first determined. This analysis was then repeated using regression conditional on the most strongly associated variant, continuing the process until the top result was no longer significant at a prespecified threshold of P < 5 × 10−8 (to represent genome-wide significance). To aid in interpretability, the beta coefficients derived from this analysis were converted into units of milligrams per deciliter using data from the National Health and Nutrition Examination Survey from 2005 through 2012, in which a similar transformation was used (substituting self-reported race for principal components of ancestry) to yield a conversion factor of 60.7-mg/dL change in triglyceride level per 1-unit change in inverse normal transformed values.

These same common LPL variants were linked to CAD using summary-level test statistics in the previously reported CARDIoGRAM Exome Consortium study.15 The cumulative association of these variants with odds of CAD was determined, standardized per genetic 1-SD increase in triglyceride levels. Explicitly, if x is the association of each variant with the outcome of interest, and y the association of each variant with triglyceride levels, then the estimated association of a 1-SD increase in triglycerides mediated by LPL locus variants is calculated as a fixed-effects meta-analysis of x/y for all variants. This method is mathematically equivalent to a previously reported approach.31

Analyses were performed using R version 3.2.2 software (The R Foundation). All reported P values were 2-tailed, with P < .05 used as a threshold for statistical significance unless otherwise specified.

Results

Gene sequencing of LPL was performed in 22 533 participants of the Myocardial Infarction Genetics Consortium, including 12 395 controls and 10 138 cases with CAD (Table 1). A total of 123 loss-of-function or missense variants in LPL with minor allele frequency less than 1% were identified. Of these 123 variants, 52 were classified as damaging (Table 2). Eight of these 123 variants led to loss of function, including 5 premature stop (nonsense) codons, 2 splice acceptor or donor variants, and 1 frameshift mutation. Only about 25% of missense variants in any given gene have a strongly damaging effect on protein function32; additional annotation algorithms were thus needed for the 115 missense variants. Six were previously deemed pathogenic based on ClinVar annotation. In addition, 38 of the 109 remaining missense variants were predicted to be damaging by each of 5 computer prediction algorithms. Because any individual damaging mutation was rare (eTable 2 in the Supplement), the 52 damaging variants were aggregated for subsequent analyses of phenotypic consequences.

A total of 97 individuals in the Myocardial Infarction Genetics Consortium cohorts carried one of the 52 damaging LPL mutations, including 60 cases (0.59%; 95% CI, 0.46% to 0.77%) and 37 controls (0.30%; 95% CI, 0.21% to 0.42%) (eTable 3 in the Supplement). Circulating lipid levels were available in 16 200 participants (72%), including 72 of 97 mutation carriers (74%). Median triglyceride levels were 183 mg/dL (interquartile range, 135-274 mg/dL) in LPL mutation carriers vs 147 mg/dL (interquartile range, 99-217 mg/dL) in noncarriers (to convert to micromoles per liter, multiply by 0.0113). In an adjusted linear regression model, circulating triglyceride levels were 25.6 mg/dL (95% CI, −2.5 to 53.5 mg/dL) higher in mutation carriers as compared with noncarriers, although there was no significant association (P = .07) (Figure 1 and eTable 4 in the Supplement). Furthermore, mutation carriers were at increased odds of having clinical hypertriglyceridemia (triglyceride levels ≥150 mg/dL) (odds ratio = 1.88; 95% CI, 1.13 to 3.20; P = .02).

The presence of a rare damaging LPL mutation was associated with an odds ratio for CAD of 1.96 (95% CI, 1.30-2.96; P = .001) in a combined analysis of the Myocardial Infarction Genetics Consortium studies (Figure 2). This association was most pronounced in those with a loss-of-function mutation in LPL (Table 2). Within the subgroup of 2592 CAD cases and 5341 controls free of CAD with an observed LDL-C level lower than 130 mg/dL (to convert to millimoles per liter, multiply by 0.0259), an increased odds of CAD among carriers of a damaging LPL mutation remained apparent (odds ratio = 2.15; 95% CI, 1.14-4.06; P = .02).

Independent replication of the increased circulating triglyceride levels and CAD was performed in 24 358 individuals from the Geisinger Health System DiscovEHR cohort (Table 1). This cohort included 4107 individuals with early-onset CAD (age <55 years in men or <65 years in women) as ascertained based on medical records as well as 20 251 CAD-free controls. Ninety-one individuals were heterozygous carriers of a damaging LPL mutation, including 23 individuals with CAD (0.56%; 95% CI, 0.36% to 0.85%) and 68 CAD-free controls (0.34%; 95% CI, 0.26% to 0.43%). Circulating triglyceride levels were 17.2 mg/dL (95% CI, −0.5 to 34.9 mg/dL; P = .06) higher in mutation carriers as compared with noncarriers (Figure 1 and Table 2). The mutation carriers had increased odds of early-onset CAD (odds ratio = 1.67; 95% CI, 1.04 to 2.69; P = .03).

In a combined analysis of the Myocardial Infarction Genetics Consortium and DiscovEHR cohorts, among 46 891 individuals with LPL gene sequencing data available, the mean (SD) age was 50 (12.6) years and 51% were female. A damaging LPL mutation was present in 188 of 46 891 individuals (0.40%; 95% CI, 0.35% to 0.46%), including 105 of 32 646 control participants (0.32%) and 83 of 14 245 participants with early-onset CAD (0.58%). A meta-analysis of the association with lipid levels demonstrated that compared with 46 703 noncarriers, the 188 heterozygous carriers of an LPL damaging mutation displayed higher plasma triglyceride levels; these mutations were associated with a circulating triglycerides increase of 19.6 mg/dL (95% CI, 4.6 to 34.6 mg/dL), a high-density lipoprotein cholesterol decrease of 3.6 mg/dL (95% CI, −5.7 to −1.5 mg/dL), and a remnant cholesterol increase of 5.6 mg/dL (95% CI, 2.3 to 9.0 mg/dL) (Figure 1). These beta coefficients can be interpreted to suggest, for example, that an individual with a damaging LPL mutation would be predicted to have a 19.6-mg/dL higher level of circulating triglycerides as compared with an individual without such a mutation after correction for potential confounding related to age, sex, study cohort, and ancestry. These mutations were additionally associated with increased odds of early-onset CAD (odds ratio = 1.84; 95% CI, 1.35 to 2.51; P < .001) (Figure 2).

Beyond rare damaging mutations, common variants at the LPL locus were analyzed to assess for a similar link to triglyceride levels and CAD. In an analysis of up to 305 699 individuals, 6 common variants (minor allele frequency ranging from 1%-29%) were robustly (P < 5 × 10−8) and independently associated with plasma triglyceride levels. The minor (less common) alleles of 4 of these variants were associated with decreased triglyceride levels, suggesting gain of lipoprotein lipase activity, and 2 were linked to increased triglyceride levels, consistent with decreased activity. In an analysis of up to 120 600 individuals of CAD case-control studies, each of these variants was confirmed to be associated with odds of CAD (P < .002 for each) with the expected directionality. A roughly linear relationship was noted in this data set between association with triglyceride levels and odds of CAD (eFigure in the Supplement). A weighted analysis that combined these 6 variants demonstrated an odds ratio for CAD of 1.51 (95% CI, 1.39-1.64; P = 1.1 × 10−22) per 1-SD increase in triglycerides mediated by LPL locus variants.

Discussion

The protein-coding exons of LPL were sequenced in 46 891 individuals from an international collaboration of CAD case-control cohorts and patients of a large health care organization. In this study, approximately 0.40% of individuals carried a rare damaging mutation in LPL. These carriers had increased circulating triglyceride levels (19.6 mg/dL) and an odds ratio of 1.84 for early-onset CAD. An analysis using common variants in LPL similarly demonstrated a significant association with CAD.

These results permit several conclusions. First, heterozygous LPL deficiency was associated with the presence of early-onset CAD. By identifying 188 carriers of a rare damaging mutation, an association with higher levels of triglycerides and remnant cholesterol and lower levels of high-density lipoprotein cholesterol was established along with an odds ratio for early-onset CAD of 1.84. This susceptibility to CAD may be due to impaired lipolysis of triglyceride-rich lipoproteins. Triglyceride-rich lipoproteins penetrate directly into the arterial wall and are selectively retained in the intima, thus promoting the development of cholesterol-rich foam cells and an inflammatory response that accelerate atherosclerosis.33

Second, a complementary common variant analysis involving 6 independent LPL variants confirmed the association of genetic variation in LPL with CAD. In an analysis in more than 300 000 individuals, each common variant’s association with triglyceride levels was used as a proxy for influence on LPL activity. Association of these same variants with CAD in more than 120 000 individuals demonstrated an odds ratio for CAD of 1.51 per 1-SD increase in triglyceride levels associated with common LPL locus variants. These findings confirm and extend previous common variant studies that have suggested similar trends.15,34,35

Third, these data add to considerable recent genetic evidence that beyond LDL-C, LPL and its endogenous regulation—via facilitator (apolipoprotein A5 [APOA5]) and inhibitor (apolipoprotein C3 [APOC3], angiopoietin-like 4 [ANGPTL4]) proteins—represent an important determinant of human atherosclerosis. Similar approaches have been used to demonstrate that damaging mutations in APOA5 are associated with a significant increase in odds of CAD.7,20 By contrast, rare inactivating mutations in APOC3 and ANGPTL4 confer substantial vascular protection.9,15,16,36 Ongoing research will seek to clarify the mechanistic interactions between these proteins. However, in each case, CAD risk is likely to be affected by lifelong alterations in LPL activity. Whether therapy to alter this pathway will decrease risk of CAD remains unknown.

A key strength of the present analysis is that LPL was sequenced in a large number of individuals to analyze the entire spectrum of damaging mutations, each of which was rare in the population. Second, concordant results were demonstrated between CAD case-control studies of the Myocardial Infarction Genetics Consortium and the DiscovEHR study participants from the Geisinger Health System, in whom CAD status was ascertained based on EHRs. This reinforces the potential utility of ongoing efforts such as the UK Biobank and the All of Us Research Program (a cohort study within the Precision Medicine Initiative), which will facilitate large-scale interrogations of genetic variants as they relate to human disease.

Several limitations should be acknowledged. The approach to annotating rare missense variants in LPL using prediction algorithms and the ClinVar database has been previously validated and is fully reproducible.7,23 However, because functional validation of each variant was not performed, this method may have led to misclassification in some cases. Second, because the effect of LPL activity on regulation of circulating triglyceride levels is most pronounced following a meal,37 the degree of triglyceride level elevation among mutation carriers would likely have been greater if postprandial triglyceride levels were available. Third, this study assessed the association of LPL mutations with susceptibility to early-onset CAD; effect estimates might differ among individuals with later onset of disease. Fourth, levels of both triglycerides and calculated remnant cholesterol, the primary lipid components of triglyceride-rich lipoproteins, were increased in individuals harboring an LPL mutation. Because the level of remnant cholesterol was estimated and not directly measured in the present analysis, additional research is needed to determine the relative contributions of these components to human CAD.

Conclusions

The presence of rare damaging mutations in LPL was significantly associated with higher triglyceride levels and presence of CAD. However, further research is needed to assess whether there are causal mechanisms by which heterozygous LPL deficiency could lead to CAD.

Back to top
Article Information

Corresponding Author: Sekar Kathiresan, MD, Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St, CPZN 5.252, Boston, MA 02114 (skathiresan1@mgh.harvard.edu).

Author Contributions: Drs Khera and Kathiresan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Khera and Won contributed equally to this work.

Concept and design: Khera, Won, Lander, Ardissino, Abecasis, Kathiresan.

Acquisition, analysis, or interpretation of data: Khera, Won, Peloso, O’Dushlaine, Liu, Stitziel, Natarajan, Nomura, Emdin, Gupta, Borecki, Asselta, Duga, Merlini, Correa, Kessler, Wilson, Bown, Hall, Braund, Carey, Murray, Kirchner, Leader, Lavage, Manus, Hartzel, Samani, Schunkert, Marrugat, Elosua, McPherson, Farrall, Watkins, Rader, Danesh, Gabriel, Willer, Saleheen, Dewey.

Drafting of the manuscript: Khera, Won, Natarajan, Nomura, Gupta, Saleheen, Kathiresan.

Critical revision of the manuscript for important intellectual content: Khera, Won, Peloso, O’Dushlaine, Liu, Stitziel, Emdin, Borecki, Asselta, Duga, Merlini, Correa, Kessler, Wilson, Bown, Hall, Braund, Carey, Murray, Kirchner, Leader, Lavage, Manus, Hartzel, Samani, Schunkert, Marrugat, Elosua, McPherson, Farrall, Watkins, Lander, Rader, Danesh, Ardissino, Gabriel, Willer, Abecasis, Dewey, Kathiresan.

Statistical analysis: Khera, Won, Peloso, O’Dushlaine, Liu, Stitziel, Natarajan, Nomura, Emdin, Borecki, Lander, Willer, Abecasis, Saleheen.

Obtained funding: Khera, Correa, Wilson, Bown, Hall, Carey, Samani, Marrugat, Elosua, McPherson, Lander, Gabriel, Saleheen.

Administrative, technical, or material support: Gupta, Borecki, Correa, Kessler, Bown, Hall, Braund, Carey, Kirchner, Leader, Lavage, Manus, Hartzel, Marrugat, McPherson, Watkins, Danesh, Gabriel, Dewey, Kathiresan.

Supervision: Khera, Gupta, Wilson, Bown, Carey, Schunkert, Elosua, McPherson, Ardissino, Willer, Dewey, Kathiresan.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Khera reported receiving consulting fees from Merck and Co and Amarin Pharmaceuticals. Dr Stitziel reported receiving a research grant from AstraZeneca and consulting fees from Regeneron and Aegerion Pharmaceuticals. Dr Murray reported serving on an advisory board for InVitae and as a consultant for Merck. Dr Marrugat reported receiving personal fees from Gendiag SL. Dr Elosua reported serving on an advisory board for Gendiag SL. Dr Rader reported received consulting fees from Aegerion Pharmaceuticals, Alnylam Pharmaceuticals, Eli Lilly and Co, Pfizer, Sanofi, and Novartis; being an inventor on a patent related to lomitapide that is owned by the University of Pennsylvania and licensed to Aegerion Pharmaceuticals; and being a cofounder of Vascular Strategies and Staten Biotechnology. Dr Danesh reported receiving personal fees and nonfinancial support from Merck Sharp & Dohme UK Atherosclerosis, Novartis Cardiovascular and Metabolic Advisory Board, Pfizer Population Research Advisory Panel, and Sanofi Advisory Board; nonfinancial support from diaDexus; and grants from the British Heart Foundation, Bupa Foundation, diaDexus, European Research Council, European Union, Evelyn Trust, Fogarty International Center, GlaxoSmithKline, Merck, National Heart, Lung, and Blood Institute, National Institute for Health Research, National Institute of Neurological Disorders and Stroke, NHS Blood and Transplant, Novartis, Pfizer, UK Medical Research Council, Wellcome Trust, and UK Biobank. Dr Ardissino reported receiving speaker fees from AstraZeneca, Boehringer Ingelheim, Johnson & Johnson, Bayer, Daiichi Sankyo, GlaxoSmithKline, Eli Lilly and Co, Boston Scientific, Bristol-Myers Squibb, Menarini Group, Novartis, and Sanofi-Aventis; and research grants from GlaxoSmithKline, Eli Lilly and Co, Pfizer, and Novartis. Dr Abecasis reported serving as a consultant to Regeneron Pharmaceuticals. Dr Saleheen reported receiving grants from Pfizer and the National Institutes of Health. Dr Dewey reported being a stockholder of Regeneron Pharmaceuticals and Personalis. Dr Kathiresan reported receiving research grants from Bayer Healthcare, Aegerion Pharmaceuticals, and Regeneron Pharmaceuticals; serving on scientific advisory boards for Regeneron, Catabasis Pharmaceuticals, Merck, Celera, and Genomics PLC; serving as a consultant to Bayer, Merck, Novartis, Sanofi, AstraZeneca, Alnylam Pharmaceuticals, Eli Lilly and Co, Leerink Partners, Noble Insights, Ionis, Quest Diagnostics, and Genomics PLC; and holding equity in Catabasis Pharmaceuticals and San Therapeutics. No other disclosures were reported.

Funding/Support: Dr Khera was supported by an ACCF/Merck Cardiovascular Research Fellowship from the American College of Cardiology Foundation and Merck, a John S. Ladue Memorial Fellowship at Harvard Medical School, and KL2/Catalyst Medical Research Investigator Training award TR001100 from Harvard Catalyst funded by the National Institutes of Health. Dr Won was supported by grant 2016R1C1B2007920 from the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning. Dr Peloso was supported by grant K01HL125751 from the National Heart, Lung, and Blood Institute. Dr Stitziel was supported by grants K08 HL114642 and R01 HL131961 from the National Institutes of Health. Drs Carey, Murray, and Kirchner and Messrs Leader, Lavage, Manus, and Hartzel were supported by the Regeneron Genetics Center. Dr McPherson was supported by grants from the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Canada, and the Canada Foundation for Innovation. Dr Farrall was supported by core award 090532/Z/09/Z and the Institutional Strategic Support Fund from the Wellcome Trust and grant RE/13/1/30181 from the British Heart Foundation Centre of Research Excellence. Dr Lander was supported by grant 5U54HG003067 from the National Institutes of Health. Dr Danesh was supported by grants from the UK Medical Research Council, the British Heart Foundation, the UK National Institute for Health Research, and the European Commission Framework Programme. Dr Willer was supported by grants HL109946 and HL127564 from the National Heart, Lung, and Blood Institute. Dr Abecasis was supported by a grant from the National Institutes of Health. Dr Kathiresan was supported by a research scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, and grant R01 HL127564 from the National Heart, Lung, and Blood Institute. Field work, genotyping, and standard clinical chemistry assays in the Pakistan Risk of Myocardial Infarction Study (PROMIS) were principally supported by grants awarded to the University of Cambridge from the British Heart Foundation, UK Medical Research Council, Wellcome Trust, EU Framework 6–funded Bloodomics Integrated Project, Pfizer, Novartis, and Merck. Additional support for PROMIS was provided by the UK Medical Research Council (MR/L003120/1), British Heart Foundation (RG/13/13/30194), UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834), and European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The Jackson Heart Study was supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, and HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. The Registre Gironí del COR (Gerona Heart Registry; REGICOR) study was supported by the Spanish Ministry of Economy and Innovation through the Carlos III Health Institute (Red Investigación Cardiovascular RD12/0042, PI09/90506), by the European Funds for Development (ERDF-FEDER), and by the Catalan Research and Technology Innovation Interdepartmental Commission (2014SGR240). Samples for the cohort in the Leicester Myocardial Infarction study were collected as part of projects funded by the British Heart Foundation (British Heart Foundation Family Heart Study, RG2000010; UK Aneurysm Growth Study, CS/14/2/30841) and the National Institute for Health Research (NIHR Leicester Cardiovascular Biomedical Research Unit Biomedical Research Informatics Centre for Cardiovascular Science, IS_BRU_0211_20033). The South German Myocardial Infarction study was supported by the German Federal Ministry of Education and Research in the context of the e:Med program (e:AtheroSysMed) and the FP7 European Union project CVgenes@target (261123). Additional grants were received from the Fondation Leducq (CADgenomics: Understanding Coronary Artery Disease Genes, 12CVD02). This study was also supported through the Deutsche Forschungsgemeinschaft cluster of excellence “Inflammation at Interfaces” and SFB 1123. The Atherosclerosis, Thrombosis, and Vascular Biology Italian Study (ATVB) was supported by grant RFPS-2007-3-644382 from the Italian Ministry of Health and by Programma di ricerca Regione-Università 2010-2012 Area 1 Strategic Programmes from the Regione Emilia-Romagna. Funding for the Exome Sequencing Project Early-Onset Myocardial Infarction study was provided by grants RC2 HL103010 (HeartGO), RC2 HL102923 (LungGO), and RC2 HL102924 (WHISP) from the National Heart, Lung, and Blood Institute. Exome sequencing was performed through grants RC2 HL102925 (BroadGO) and RC2 HL102926 (SeattleGO) from the National Heart, Lung, and Blood Institute. Exome sequencing in ATVB, the Precocious Coronary Artery Disease study (PROCARDIS), the Ottawa Heart Study, PROMIS, South German Myocardial Infarction study, and the Jackson Heart Study was supported by grant 5U54HG003067 from the National Institutes of Health (Drs Lander and Gabriel). The DiscovEHR study was supported in part by the Regeneron Genetics Center.

Role of the Funder/Sponsor: For the analysis of the Myocardial Infarction Genetics Consortium, the Global Lipid Genetics Consortium, and the CARDIoGRAM Exome Consortium studies, the funders of the individual study cohorts had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The DiscovEHR study was funded in part by the Regeneron Genetics Center, and employees of Regeneron Pharmaceuticals were involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Regeneron Pharmaceuticals did not have the power to veto a decision to submit the study for publication. Investigators associated with each study had access to the data, and Drs Khera and Kathiresan were responsible for the final decision to submit the manuscript for publication.

Group Information: The Myocardial Infarction Genetics Consortium, DiscovEHR Study Group, CARDIoGRAM Exome Consortium, and Global Lipids Genetics Consortium members are listed in the eAppendix in the Supplement.

Disclaimer: The views expressed in this article 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, the Harvard Catalyst, or the US Department of Health and Human Services.

Additional Contributions: We thank the MyCode Community Health Initiative participants for their permission to use their health and genomics information in the DiscovEHR collaboration.

References
1.
Eckel  RH.  Lipoprotein lipase: a multifunctional enzyme relevant to common metabolic diseases.  N Engl J Med. 1989;320(16):1060-1068.PubMedGoogle ScholarCrossref
2.
Brunzell  JD, Deeb  SS. Familial lipoprotein lipase deficiency, apo CII deficiency and hepatic lipase deficiency. In: Scriver  CR, Beaudet  AI, Sly  WS, Valle  D, eds.  The Metabolic and Molecular Basis of Inherited Disease. 8th ed. New York, NY: McGraw-Hill; 2000:2789-2816.
3.
Nordestgaard  BG, Stender  S, Kjeldsen  K.  Reduced atherogenesis in cholesterol-fed diabetic rabbits: giant lipoproteins do not enter the arterial wall.  Arteriosclerosis. 1988;8(4):421-428.PubMedGoogle ScholarCrossref
4.
Nordestgaard  BG.  Triglyceride-rich lipoproteins and atherosclerotic cardiovascular disease: new insights from epidemiology, genetics, and biology.  Circ Res. 2016;118(4):547-563.PubMedGoogle ScholarCrossref
5.
Nordestgaard  BG, Abildgaard  S, Wittrup  HH, Steffensen  R, Jensen  G, Tybjaerg-Hansen  A.  Heterozygous lipoprotein lipase deficiency: frequency in the general population, effect on plasma lipid levels, and risk of ischemic heart disease.  Circulation. 1997;96(6):1737-1744.PubMedGoogle ScholarCrossref
6.
Atherosclerosis, Thrombosis, and Vascular Biology Italian Study Group.  No evidence of association between prothrombotic gene polymorphisms and the development of acute myocardial infarction at a young age.  Circulation. 2003;107(8):1117-1122.PubMedGoogle ScholarCrossref
7.
Do  R, Stitziel  NO, Won  HH,  et al; NHLBI Exome Sequencing Project.  Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction.  Nature. 2015;518(7537):102-106.PubMedGoogle ScholarCrossref
8.
Taylor  HA  Jr, Wilson  JG, Jones  DW,  et al.  Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study.  Ethn Dis. 2005;15(4)(suppl 6):S6-S4, 17.PubMedGoogle Scholar
9.
Crosby  J, Peloso  GM, Auer  PL,  et al; TG and HDL Working Group of the Exome Sequencing Project, National Heart, Lung, and Blood Institute.  Loss-of-function mutations in APOC3, triglycerides, and coronary disease.  N Engl J Med. 2014;371(1):22-31.PubMedGoogle ScholarCrossref
10.
McPherson  R, Pertsemlidis  A, Kavaslar  N,  et al.  A common allele on chromosome 9 associated with coronary heart disease.  Science. 2007;316(5830):1488-1491.PubMedGoogle ScholarCrossref
11.
Clarke  R, Peden  JF, Hopewell  JC,  et al; PROCARDIS Consortium.  Genetic variants associated with Lp(a) lipoprotein level and coronary disease.  N Engl J Med. 2009;361(26):2518-2528.PubMedGoogle ScholarCrossref
12.
Saleheen  D, Zaidi  M, Rasheed  A,  et al.  The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia.  Eur J Epidemiol. 2009;24(6):329-338.PubMedGoogle ScholarCrossref
13.
Sentí  M, Tomás  M, Marrugat  J, Elosua  R; REGICOR Investigators.  Paraoxonase1-192 polymorphism modulates the nonfatal myocardial infarction risk associated with decreased HDLs.  Arterioscler Thromb Vasc Biol. 2001;21(3):415-420.PubMedGoogle ScholarCrossref
14.
Samani  NJ, Erdmann  J, Hall  AS,  et al; WTCCC and the Cardiogenics Consortium.  Genomewide association analysis of coronary artery disease.  N Engl J Med. 2007;357(5):443-453.PubMedGoogle ScholarCrossref
15.
Stitziel  NO, Stirrups  KE, Masca  NG,  et al; Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators.  Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease.  N Engl J Med. 2016;374(12):1134-1144.PubMedGoogle ScholarCrossref
16.
Dewey  FE, Gusarova  V, O’Dushlaine  C,  et al.  Inactivating variants in ANGPTL4 and risk of coronary artery disease.  N Engl J Med. 2016;374(12):1123-1133.PubMedGoogle ScholarCrossref
17.
Baigent  C, Keech  A, Kearney  PM,  et al; Cholesterol Treatment Trialists’ (CTT) Collaborators.  Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins.  Lancet. 2005;366(9493):1267-1278.PubMedGoogle ScholarCrossref
18.
Stitziel  NO, Won  HH, Morrison  AC,  et al; Myocardial Infarction Genetics Consortium Investigators.  Inactivating mutations in NPC1L1 and protection from coronary heart disease.  N Engl J Med. 2014;371(22):2072-2082.PubMedGoogle ScholarCrossref
19.
Benn  M, Watts  GF, Tybjærg-Hansen  A, Nordestgaard  BG.  Mutations causative of familial hypercholesterolaemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217.  Eur Heart J. 2016;37(17):1384-1394.PubMedGoogle ScholarCrossref
20.
Jørgensen  AB, Frikke-Schmidt  R, West  AS, Grande  P, Nordestgaard  BG, Tybjærg-Hansen  A.  Genetically elevated non-fasting triglycerides and calculated remnant cholesterol as causal risk factors for myocardial infarction.  Eur Heart J. 2013;34(24):1826-1833.PubMedGoogle ScholarCrossref
21.
McKenna  A, Hanna  M, Banks  E,  et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.  Genome Res. 2010;20(9):1297-1303.PubMedGoogle ScholarCrossref
22.
Landrum  MJ, Lee  JM, Riley  GR,  et al.  ClinVar: public archive of relationships among sequence variation and human phenotype.  Nucleic Acids Res. 2014;42(database issue):D980-D985.PubMedGoogle ScholarCrossref
23.
Purcell  SM, Moran  JL, Fromer  M,  et al.  A polygenic burden of rare disruptive mutations in schizophrenia.  Nature. 2014;506(7487):185-190.PubMedGoogle ScholarCrossref
24.
McLaren  W, Pritchard  B, Rios  D, Chen  Y, Flicek  P, Cunningham  F.  Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor.  Bioinformatics. 2010;26(16):2069-2070.PubMedGoogle ScholarCrossref
25.
Lek  M, Karczewski  KJ, Minikel  EV,  et al; Exome Aggregation Consortium.  Analysis of protein-coding genetic variation in 60,706 humans.  Nature. 2016;536(7616):285-291.PubMedGoogle ScholarCrossref
26.
Liu  X, Wu  C, Li  C, Boerwinkle  E.  dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs.  Hum Mutat. 2016;37(3):235-241.PubMedGoogle ScholarCrossref
27.
Price  AL, Patterson  NJ, Plenge  RM, Weinblatt  ME, Shadick  NA, Reich  D.  Principal components analysis corrects for stratification in genome-wide association studies.  Nat Genet. 2006;38(8):904-909.PubMedGoogle ScholarCrossref
28.
Nioi  P, Sigurdsson  A, Thorleifsson  G,  et al.  Variant ASGR1 associated with a reduced risk of coronary artery disease.  N Engl J Med. 2016;374(22):2131-2141.PubMedGoogle ScholarCrossref
29.
Yang  J, Ferreira  T, Morris  AP,  et al; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits.  Nat Genet. 2012;44(4):369-375, S1-S3.PubMedGoogle ScholarCrossref
30.
Liu  DJ, Peloso  GM, Zhan  X,  et al.  Meta-analysis of gene-level tests for rare variant association.  Nat Genet. 2014;46(2):200-204.PubMedGoogle ScholarCrossref
31.
Burgess  S, Butterworth  A, Thompson  SG.  Mendelian randomization analysis with multiple genetic variants using summarized data.  Genet Epidemiol. 2013;37(7):658-665.PubMedGoogle ScholarCrossref
32.
Zuk  O, Schaffner  SF, Samocha  K,  et al.  Searching for missing heritability: designing rare variant association studies.  Proc Natl Acad Sci U S A. 2014;111(4):E455-E464.PubMedGoogle ScholarCrossref
33.
Nordestgaard  BG, Wootton  R, Lewis  B.  Selective retention of VLDL, IDL, and LDL in the arterial intima of genetically hyperlipidemic rabbits in vivo: molecular size as a determinant of fractional loss from the intima-inner media.  Arterioscler Thromb Vasc Biol. 1995;15(4):534-542.PubMedGoogle ScholarCrossref
34.
Jensen  MK, Rimm  EB, Rader  D,  et al.  S447X variant of the lipoprotein lipase gene, lipids, and risk of coronary heart disease in 3 prospective cohort studies.  Am Heart J. 2009;157(2):384-390.PubMedGoogle ScholarCrossref
35.
Thomsen  M, Varbo  A, Tybjærg-Hansen  A, Nordestgaard  BG.  Low nonfasting triglycerides and reduced all-cause mortality: a mendelian randomization study.  Clin Chem. 2014;60(5):737-746.PubMedGoogle ScholarCrossref
36.
Jørgensen  AB, Frikke-Schmidt  R, Nordestgaard  BG, Tybjærg-Hansen  A.  Loss-of-function mutations in APOC3 and risk of ischemic vascular disease.  N Engl J Med. 2014;371(1):32-41.PubMedGoogle ScholarCrossref
37.
Miesenböck  G, Hölzl  B, Föger  B,  et al.  Heterozygous lipoprotein lipase deficiency due to a missense mutation as the cause of impaired triglyceride tolerance with multiple lipoprotein abnormalities.  J Clin Invest. 1993;91(2):448-455.PubMedGoogle ScholarCrossref
×