Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease.

BACKGROUND: The discovery of low-frequency coding variants affecting the risk of coronary artery disease has facilitated the identification of therapeutic targets. METHODS: Through DNA genotyping, we tested 54,003 coding-sequence variants covering 13,715 human genes in up to 72,868 patients with coronary artery disease and 120,770 controls who did not have coronary artery disease. Through DNA sequencing, we studied the effects of loss-of-function mutations in selected genes. RESULTS: We confirmed previously observed significant associations between coronary artery disease and low-frequency missense variants in the genes LPA and PCSK9. We also found significant associations between coronary artery disease and low-frequency missense variants in the genes SVEP1 (p.D2702G; minor-allele frequency, 3.60%; odds ratio for disease, 1.14; P=4.2×10(-10)) and ANGPTL4 (p.E40K; minor-allele frequency, 2.01%; odds ratio, 0.86; P=4.0×10(-8)), which encodes angiopoietin-like 4. Through sequencing of ANGPTL4, we identified 9 carriers of loss-of-function mutations among 6924 patients with myocardial infarction, as compared with 19 carriers among 6834 controls (odds ratio, 0.47; P=0.04); carriers of ANGPTL4 loss-of-function alleles had triglyceride levels that were 35% lower than the levels among persons who did not carry a loss-of-function allele (P=0.003). ANGPTL4 inhibits lipoprotein lipase; we therefore searched for mutations in LPL and identified a loss-of-function variant that was associated with an increased risk of coronary artery disease (p.D36N; minor-allele frequency, 1.9%; odds ratio, 1.13; P=2.0×10(-4)) and a gain-of-function variant that was associated with protection from coronary artery disease (p.S447*; minor-allele frequency, 9.9%; odds ratio, 0.94; P=2.5×10(-7)). CONCLUSIONS: We found that carriers of loss-of-function mutations in ANGPTL4 had triglyceride levels that were lower than those among noncarriers; these mutations were also associated with protection from coronary artery disease. (Funded by the National Institutes of Health and others.).Supported by a career development award from the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH) (K08HL114642 to Dr. Stitziel) and by the Foundation for Barnes–Jewish Hospital. Dr. Peloso is supported by the National Heart, Lung, and Blood Institute of the NIH (award number K01HL125751). Dr. Kathiresan is supported by a Research Scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, grants from the NIH (R01HL107816 and R01HL127564), a grant from Fondation Leducq, and an investigator-initiated grant from Merck. Dr. Merlini was supported by a grant from the Italian Ministry of Health (RFPS-2007-3-644382). Drs. Ardissino and Marziliano were supported by Regione Emilia Romagna Area 1 Grants. Drs. Farrall and Watkins acknowledge the support of the Wellcome Trust core award (090532/Z/09/Z), the British Heart Foundation (BHF) Centre of Research Excellence. Dr. Schick is supported in part by a grant from the National Cancer Institute (R25CA094880). Dr. Goel acknowledges EU FP7 & Wellcome Trust Institutional strategic support fund. Dr. Deloukas’s work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Unit, which is supported and funded by the National Institute for Health Research (NIHR). Drs. Webb and Samani are funded by the British Heart Foundation, and Dr. Samani is an NIHR Senior Investigator. Dr. Masca was supported by the NIHR Leicester Cardiovascular Biomedical Research Unit (BRU), and this work forms part of the portfolio of research supported by the BRU. Dr. Won was supported by a postdoctoral award from the American Heart Association (15POST23280019). Dr. McCarthy is a Wellcome Trust Senior Investigator (098381) and an NIHR Senior Investigator. Dr. Danesh is a British Heart Foundation Professor, European Research Council Senior Investigator, and NIHR Senior Investigator. Drs. Erdmann, Webb, Samani, and Schunkert are supported by the FP7 European Union project CVgenes@ target (261123) and the Fondation Leducq (CADgenomics, 12CVD02). Drs. Erdmann and Schunkert are also supported by the German Federal Ministry of Education and Research e:Med program (e:AtheroSysMed and sysINFLAME), and Deutsche Forschungsgemeinschaft cluster of excellence “Inflammation at Interfaces” and SFB 1123. Dr. Kessler received a DZHK Rotation Grant. The analysis was funded, in part, by a Programme Grant from the BHF (RG/14/5/30893 to Dr. Deloukas). Additional funding is listed in the Supplementary Appendix.This is the author accepted manuscript. The final version is available from the Massachusetts Medical Society via http://dx.doi.org/10.1056/NEJMoa150765

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention

The application and development of methods to combine and infer information from genetic epidemiological studies of cardiovascular and other complex traits

This thesis investigates methods to combine and infer information from genetic epidemiological studies. Three issues are explored, each in a distinct and self-contained chapter. Chapter 1 investigates how best to incorporate treatment information in genetic analyses of blood pressure. Different approaches to adjusting for treatment are compared in a number of simulated scenarios, and the approaches that utilise all the observed data are generally shown to perform best. One particular condition, however, causes these approaches to suffer bias. This is where a genetic variant (or some other factor) interacts with treatment. This chapter therefore urges caution in the interpretation of results from these studies, and suggests some possible approaches to identifying existing interactions with treatment. Chapter 2 concerns participant privacy in genome-wide association studies (GWAS). Recent methods claim to be able to infer whether an individual participated in a study, using only aggregate statistics from the study such as allele frequencies. In the past, these statistics have been freely published online. This chapter explores the full implications of these methods, by investigating their true capabilities and limitations. In addition, some modifications are proposed to one particular method, to demonstrate how it can be adapted for use in practice. This work finds that participant identification is possible in ideal conditions, but common characteristics of real studies may prevent any reliable application of these methods in practice. Chapter 3 proposes a new approach to synthesising data between studies. This approach – named “DataSHIELD” – guarantees identical results to an individual-level meta-analysis, while offering greater flexibility than the studylevel meta-analysis. DataSHIELD also potentially circumvents some of the laws that restrict data use, because it does not involve sharing any individual-level data between studies. This chapter outlines the principles underpinning DataSHIELD, and demonstrates its use in a simulated data example

The application and development of methods to combine and infer information from genetic epidemiological studies of cardiovascular and other complex traits

This thesis investigates methods to combine and infer information from genetic epidemiological studies. Three issues are explored, each in a distinct and self-contained chapter. Chapter 1 investigates how best to incorporate treatment information in genetic analyses of blood pressure. Different approaches to adjusting for treatment are compared in a number of simulated scenarios, and the approaches that utilise all the observed data are generally shown to perform best. One particular condition, however, causes these approaches to suffer bias. This is where a genetic variant (or some other factor) interacts with treatment. This chapter therefore urges caution in the interpretation of results from these studies, and suggests some possible approaches to identifying existing interactions with treatment. Chapter 2 concerns participant privacy in genome-wide association studies (GWAS). Recent methods claim to be able to infer whether an individual participated in a study, using only aggregate statistics from the study such as allele frequencies. In the past, these statistics have been freely published online. This chapter explores the full implications of these methods, by investigating their true capabilities and limitations. In addition, some modifications are proposed to one particular method, to demonstrate how it can be adapted for use in practice. This work finds that participant identification is possible in ideal conditions, but common characteristics of real studies may prevent any reliable application of these methods in practice. Chapter 3 proposes a new approach to synthesising data between studies. This approach – named “DataSHIELD” – guarantees identical results to an individual-level meta-analysis, while offering greater flexibility than the studylevel meta-analysis. DataSHIELD also potentially circumvents some of the laws that restrict data use, because it does not involve sharing any individual-level data between studies. This chapter outlines the principles underpinning DataSHIELD, and demonstrates its use in a simulated data example.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Participant identification in genetic association studies: improved methods and practical implications

Background In a recent paper by Homer et al. (Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet 2008;4:e1000167), a method for detecting whether a given individual is a contributor to a particular genomic mixture was proposed. This prompted grave concern about the public dissemination of aggregate statistics from genome-wide association studies. It is of clear scientific importance that such data be shared widely, but the confidentiality of study participants must not be compromised. The issue of what summary genomic data can safely be posted on the web is only addressed satisfactorily when the theoretical underpinnings of the proposed method are clarified and its performance evaluated in terms of dependence on underlying assumptions. Methods The original method raised a number of concerns and several alternatives have since been proposed, including a simple linear regression approach. In our proposed generalized estimating equation approach, we maintain the simplicity of the linear regression model but obtain inferences that are more robust to approximation of the variance/covariance structure and can accommodate linkage disequilibrium. Results We affirm that, in principle, it is possible to determine that a ‘candidate’ individual has participated in a study, given a subset of aggregate statistics from that study. However, the methods depend critically on a number of key factors including: the ancestry of participants in the study; the absolute and relative numbers of cases and controls; and the number of single nucleotide polymorphisms. Conclusions Simple guidelines for publication that are based on a single criterion are therefore unlikely to suffice. In particular, ‘directed’ summary statistics should not be posted openly on the web but could be protected by an internet-based access check as proposed by the P3G_Consortium et al. (Public access to genome-wide data: five views on balancing research with privacy and protection. PLoS Genet 2009;5:e1000665)

Three-dimensional dominant frequency mapping using autoregressive spectral analysis of atrial electrograms of patients in persistent atrial fibrillation

Background: Areas with high frequency activity within the atrium are thought to be ‘drivers’ of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas seems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm. Clinical groups have applied the traditional FFT-based approach to generate the three-dimensional dominant frequency (3D DF) maps during electrophysiology (EP) procedures but literature is restricted on using alternative spectral estimation techniques that can have a better frequency resolution that FFT-based spectral estimation. Methods: Autoregressive (AR) model-based spectral estimation techniques, with emphasis on selection of appropriate sampling rate and AR model order, were implemented to generate high-density 3D DF maps of atrial electrograms (AEGs) in persistent atrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded for 20.478 s-long segments in the left atrium (LA) and exported for analysis, together with their anatomical locations. After the DFs were identified using AR-based spectral estimation, they were colour coded to produce sequential 3D DF maps. These maps were systematically compared with maps found using the Fourier-based approach. Results: 3D DF maps can be obtained using AR-based spectral estimation after AEGs downsampling (DS) and the resulting maps are very similar to those obtained using FFT-based spectral estimation (mean 90.23%). There were no significant differences between AR techniques (p=0.62). The processing time for AR-based approach was considerably shorter (from 5.44 to 5.05 s) when lower sampling frequencies and model order values were used. Higher levels of DS presented higher rates of DF agreement (sampling frequency of 37.5Hz). Conclusion: We have demonstrated the feasibility of using AR spectral estimation methods for producing 3D DF maps and characterised their differences to the maps produced using the FFT technique, offering an alternative approach for 3D DF compu- tation in human persAF studies

Large-Scale Analysis of Determinants, Stability, and Heritability of High-Density Lipoprotein Cholesterol Efflux Capacity.

OBJECTIVE: Cholesterol efflux capacity (CEC) has emerged as a biomarker of coronary artery disease risk beyond plasma high-density lipoprotein (HDL) cholesterol (HDL-C) level. However, the determinants of CEC are incompletely characterized. We undertook a large-scale family-based population study to identify clinical, biochemical, and HDL particle parameter determinants of CEC, characterize reasons for the discordancy with HDL-C, quantify its heritability, and assess its stability over 10 to 12 years. APPROACHES AND RESULTS: CEC was quantified in 1988 individuals from the GRAPHIC (Genetic Regulation of Arterial Pressure of Humans in the Community) cohort, comprising individuals from 2 generations from 520 white nuclear families. Serum lipid and lipoprotein levels were determined by ultracentrifugation or nuclear magnetic resonance and HDL particle size and number quantified by nuclear magnetic resonance. Ninety unrelated individuals had repeat CEC measurements in samples collected after 10 to 12 years. CEC was positively correlated with HDL-C (R=0.62; P<0.0001). Among clinical and biochemical parameters, age, systolic blood pressure, alcohol consumption, serum albumin, triglycerides, phospholipids, and lipoprotein(a) were independently associated with CEC. Among HDL particle parameters, HDL particle number, particle size, and apolipoprotein A-II level were independently associated with CEC. Serum triglyceride level partially explained discordancy between CEC and HDL-C. CEC measurements in samples collected 10 to 12 years apart were strongly correlated (r=0.73; P<0.0001). Heritability of CEC was 0.31 (P=3.89×10(-14)) without adjustment for HDL-C and 0.13 (P=1.44×10(-3)) with adjustment. CONCLUSIONS: CEC is a stable trait over time, is influenced by specific clinical, serum, and HDL particle parameters factors beyond HDL-C, can be maintained in persons with a low plasma HDL-C by elevated serum triglyceride level, and is modestly independently heritable