Publications by authors named "Albert Vernon Smith"

73 Publications

A Noncoding Variant Near PPP1R3B Promotes Liver Glycogen Storage and MetS, but Protects Against Myocardial Infarction.

J Clin Endocrinol Metab 2021 Jan;106(2):372-387

Brigham and Women's Hospital, Havard University, Boston, MA, USA.

Context: Glycogen storage diseases are rare. Increased glycogen in the liver results in increased attenuation.

Objective: Investigate the association and function of a noncoding region associated with liver attenuation but not histologic nonalcoholic fatty liver disease.

Design: Genetics of Obesity-associated Liver Disease Consortium.

Setting: Population-based.

Main Outcome: Computed tomography measured liver attenuation.

Results: Carriers of rs4841132-A (frequency 2%-19%) do not show increased hepatic steatosis; they have increased liver attenuation indicative of increased glycogen deposition. rs4841132 falls in a noncoding RNA LOC157273 ~190 kb upstream of PPP1R3B. We demonstrate that rs4841132-A increases PPP1R3B through a cis genetic effect. Using CRISPR/Cas9 we engineered a 105-bp deletion including rs4841132-A in human hepatocarcinoma cells that increases PPP1R3B, decreases LOC157273, and increases glycogen perfectly mirroring the human disease. Overexpression of PPP1R3B or knockdown of LOC157273 increased glycogen but did not result in decreased LOC157273 or increased PPP1R3B, respectively, suggesting that the effects may not all occur via affecting RNA levels. Based on electronic health record (EHR) data, rs4841132-A associates with all components of the metabolic syndrome (MetS). However, rs4841132-A associated with decreased low-density lipoprotein (LDL) cholesterol and risk for myocardial infarction (MI). A metabolic signature for rs4841132-A includes increased glycine, lactate, triglycerides, and decreased acetoacetate and beta-hydroxybutyrate.

Conclusions: These results show that rs4841132-A promotes a hepatic glycogen storage disease by increasing PPP1R3B and decreasing LOC157273. rs4841132-A promotes glycogen accumulation and development of MetS but lowers LDL cholesterol and risk for MI. These results suggest that elevated hepatic glycogen is one cause of MetS that does not invariably promote MI.
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http://dx.doi.org/10.1210/clinem/dgaa855DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823249PMC
January 2021

Genetic loci associated with prevalent and incident myocardial infarction and coronary heart disease in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

PLoS One 2020 13;15(11):e0230035. Epub 2020 Nov 13.

The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, United States of America.

Background: Genome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome.

Methods And Results: Using samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10-7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event.

Conclusion: This study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230035PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665790PMC
December 2020

Associations of autozygosity with a broad range of human phenotypes.

Nat Commun 2019 10 31;10(1):4957. Epub 2019 Oct 31.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht University, Utrecht, 3584 CX, The Netherlands.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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http://dx.doi.org/10.1038/s41467-019-12283-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823371PMC
October 2019

A genome-wide association study identifies genetic loci associated with specific lobar brain volumes.

Commun Biol 2019 2;2:285. Epub 2019 Aug 2.

17Department of Biomedical Data Sciences, Statistical Genetics, Leiden University Medical Center, Leiden, 2333ZA the Netherlands.

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications ( and ), or close to genes causing Mendelian brain-related diseases ( and ). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.
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http://dx.doi.org/10.1038/s42003-019-0537-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677735PMC
April 2020

A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure.

Hum Mol Genet 2019 08;28(15):2615-2633

Icelandic Heart Association, Kopavogur, Iceland.

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.
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http://dx.doi.org/10.1093/hmg/ddz070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644157PMC
August 2019

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

Neurology 2019 Jan 16. Epub 2019 Jan 16.

Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.

Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.

Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, = 1.77 × 10; and LINC00539/ZDHHC20, = 5.82 × 10. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits ( value for BI, = 9.38 × 10; = 5.23 × 10 for hypertension), smoking ( = 4.4 × 10; = 1.2 × 10), diabetes ( = 1.7 × 10; = 2.8 × 10), previous cardiovascular disease ( = 1.0 × 10; = 2.3 × 10), stroke ( = 3.9 × 10; = 3.2 × 10), and MRI-defined white matter hyperintensity burden ( = 1.43 × 10; = 3.16 × 10), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI ( ≤ 0.0022), without indication of directional pleiotropy.

Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.
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http://dx.doi.org/10.1212/WNL.0000000000006851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369905PMC
January 2019

Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume.

Nat Commun 2018 09 26;9(1):3945. Epub 2018 Sep 26.

Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald 17475, Germany.

The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρ = -0.59, p-value = 3.14 × 10), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology.
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http://dx.doi.org/10.1038/s41467-018-06234-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158214PMC
September 2018

Genetic overlap between vascular pathologies and Alzheimer's dementia and potential causal mechanisms.

Alzheimers Dement 2019 01 19;15(1):65-75. Epub 2018 Sep 19.

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Gerontology Research Unit, Massachusetts General Hospital, Boston, MA, USA. Electronic address:

Introduction: We sought to examine the genetic overlap between vascular pathologies and Alzheimer's disease (AD) dementia, and the potential mediating role of vascular pathologies between AD-related genetic variants and late-life cognition.

Methods: For 2907 stroke-free older individuals, we examined the association of polygenic risk scores for AD dementia (ADPRSs) with vascular pathologies and with cognition. Mediation analyses addressed whether association between ADPRSs and cognition was mediated by a vascular pathology.

Results: ADPRSs were associated with lobar cerebral microbleeds, white matter lesion load, and coronary artery calcification, mostly explained by single nucleotide polymorphisms in the 19q13 region. The effect of ADPRSs on cognition was partially but significantly mediated by cerebral microbleeds, white matter lesions, and coronary artery calcification.

Discussion: Our findings provide evidence for genetic overlap, mostly due to apolipoprotein E (APOE) gene, between vascular pathologies and AD dementia. The association between AD polygenic risk and late-life cognition is mediated in part via effects on vascular pathologies.
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http://dx.doi.org/10.1016/j.jalz.2018.08.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435328PMC
January 2019

Multiethnic meta-analysis identifies ancestry-specific and cross-ancestry loci for pulmonary function.

Nat Commun 2018 07 30;9(1):2976. Epub 2018 Jul 30.

University of California Los Angeles, Los Angeles, CA, 90095, USA.

Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (N = 60,552), African (N = 8429), Asian (N = 9959), and Hispanic/Latino (N = 11,775) ethnicities. We identify over 50 additional loci at genome-wide significance in ancestry-specific or multiethnic meta-analyses. Using recent fine-mapping methods incorporating functional annotation, gene expression, and differences in linkage disequilibrium between ethnicities, we further shed light on potential causal variants and genes at known and newly identified loci. Several of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12. Our study highlights the utility of multiethnic and integrative genomics approaches to extend existing knowledge of the genetics of lung function and clinical relevance of implicated loci.
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http://dx.doi.org/10.1038/s41467-018-05369-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065313PMC
July 2018

ExomeChip-Wide Analysis of 95 626 Individuals Identifies 10 Novel Loci Associated With QT and JT Intervals.

Circ Genom Precis Med 2018 01;11(1):e001758

From the Predoctoral Training Program in Human Genetics (N.A.B.) and McKusick-Nathans Institute of Genetic Medicine (N.A.B., D.E.A.), Johns Hopkins School of Medicine, Baltimore, MD; Cardiovascular Health Research Unit, Department of Medicine (J.A.B., J.C.B., T.A., N.S.), Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), and Cardiovascular Health Research Unit, Department of Epidemiology (S.R.H.), University of Washington, Seattle; Icelandic Heart Association, Kopavogur (A.V.S., V.G.); Faculty of Medicine, University of Iceland, Reykavik (A.V.S., V.G.); Clinical Pharmacology Department, William Harvey Research Institute, Barts and London School of Medicine and Dentistry (H.R.W., P.B.M.) and NIHR Barts Cardiovascular Biomedical Research Unit (H.R.W., P.B.M.), Queen Mary University of London, United Kingdom; Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L., Z.X.); School for Cardiovascular Diseases, Maastricht Center for Systems Biology and Department of Biochemistry, Maastricht University, The Netherlands (A.I.); Genetic Epidemiology Unit, Department of Epidemiology (A.I., C.M.v.D.) and Department of Medical Informatics (J.A.K.), Erasmus University Medical Center, Rotterdam, The Netherlands; Biostatistics Department, Boston University School of Public Health, MA (C.-T.L.); Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (J.M., C.H.), Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine (A.C.), and Usher Institute for Population Health Sciences and Informatics (I.R.), University of Edinburgh, United Kingdom; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA (F.R., P.T.E., S.A.L., J.R.); Center for Human Genetic Research (F.R., J.R.), Cardiovascular Research Center (P.L.H., P.T.E., S.A.L.), and Center for Human Genetic Research and Cardiovascular Research Center (C.H.N.-C.), Harvard Medical School, Massachusetts General Hospital, Boston; Department of Cardiovascular Sciences (L.M.H., C.P.N., N.J.S.) and Genetic Epidemiology Group, Department of Health Sciences (M.D.T.), University of Leicester, United Kingdom; NIHR Leicester Cardiovascular Biomedical Research Unit (L.M.H., C.P.N.) and NIHR Leicester Respiratory Biomedical Research Unit (M.D.T.), Glenfield Hospital, United Kingdom; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences (N.G., J.B.-J., T.H., O.P.), Department of Clinical Medicine, Faculty of Health and Medical Sciences (A.L.), and Laboratory of Experimental Cardiology (J.K.K.), University of Copenhagen, Denmark; Department of Data Science, School of Population Health (H.M.) and Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Institute of Genetic Epidemiology (M.M.-N.), Institute of Epidemiology II (A.P., M.W., S.P.), Research Unit of Molecular Epidemiology (M.W.), and Institute of Human Genetics (T.M.), Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg; Department of Medicine I, University Hospital Munich, Ludwig-Maximilians University, Germany (M.M.-N., M.F.S., S.K.); DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance (M.M.-N., M.F.S., A.P., T.M., S.K.); MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Scotland (J.E.H.); Department of Cardiology (N.V., R.A.d.B., P.v.d.M., P.v.d.H.) and Department of Internal Medicine (M.E.), University Medical Center Groningen, University of Groningen, The Netherlands; Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance (X.G., J.Y., Y.-D.I.C.); Department of Clinical Epidemiology (R.L.-G., R.d.M.) and University of Split School of Medicine (I.K., O.P.), University of Split, Croatia; Departments of Cardiology (S.T., J.W.J., A.C.M.), Gerontology and Geriatrics (S.T.), and Public Health and Primary Care (D.O.M.-K.), Leiden University Medical Center, The Netherlands; Departments of Medical Informatics (M.v.d.B.), Epidemiology (B.H.C.S.), and Epidemiology (M.E.), Erasmus MC - University Medical Center Rotterdam, The Netherlands; Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University, Greifswald, Germany (S.W., U.V., G.H.); DZHK (German Centre for Cardiovascular Research), partner site Greifswald (S.W., U.V., H.V., S.B.F., M.D.); Cardiogenetics Lab, Genetics and Molecular Cell Sciences Research Centre, Cardiovascular and Cell Sciences Institute, St George's, University of London, United Kingdom (B.P.P., Y.J.); Division Heart and Lungs, Department of Cardiology, (J.v.S., F.W.A.) and Julius Center for Health Sciences and Primary Care (M.L.B.), University Medical Center Utrecht, The Netherlands; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (J.H., C.K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (L.-P.L., N.M., T.L.); Department of Clinical Physiology, Tampere University Hospital, University of Tampere School of Medicine, Finland (M.K.); Division of Nephrology and Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City (M.L.); Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.); Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC (E.Z.S.); Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD (T.B.H., L.J.L.); Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom (S.P., A.D.); Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, United Kingdom (A.D.M.); Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen (A.L.); Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark (A.L.); German Center for Diabetes Research, Neuherberg (A.P.); Institute of Human Genetics, Technische Universität München, Germany (T.M.); Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands (J.W.J.); Interuniversity Cardiology Institute of Netherlands, Utrecht (J.W.J.); Inspectorate of Health Care, Utrecht, The Netherlands (B.H.C.S.); Human Genomics Facility (F.R.) and Human Genotyping Facility (A.U.), Erasmus MC - University Medical Center Rotterdam, The Netherlands; Institute for Community Medicine (H.V.) and Department of Internal Medicine B (S.B.F., M.D.), University Medicine Greifswald, Germany; Department of Twin Research and Genetic Epidemiology, King's College London, United Kingdom (M.M., T.D.S.); Stanford School of Medicine, CA (M.P.); Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.T.R.); Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge (C.H.N.-C.); NIHR Leicester Biomedical Research Unit in Cardiovascular Disease, United Kingdom (N.J.S.); Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht (F.W.A.); and Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, United Kingdom (F.W.A.).

Background: QT interval, measured through a standard ECG, captures the time it takes for the cardiac ventricles to depolarize and repolarize. JT interval is the component of the QT interval that reflects ventricular repolarization alone. Prolonged QT interval has been linked to higher risk of sudden cardiac arrest.

Methods And Results: We performed an ExomeChip-wide analysis for both QT and JT intervals, including 209 449 variants, both common and rare, in 17 341 genes from the Illumina Infinium HumanExome BeadChip. We identified 10 loci that modulate QT and JT interval duration that have not been previously reported in the literature using single-variant statistical models in a meta-analysis of 95 626 individuals from 23 cohorts (comprised 83 884 European ancestry individuals, 9610 blacks, 1382 Hispanics, and 750 Asians). This brings the total number of ventricular repolarization associated loci to 45. In addition, our approach of using coding variants has highlighted the role of 17 specific genes for involvement in ventricular repolarization, 7 of which are in novel loci.

Conclusions: Our analyses show a role for myocyte internal structure and interconnections in modulating QT interval duration, adding to previous known roles of potassium, sodium, and calcium ion regulation, as well as autonomic control. We anticipate that these discoveries will open new paths to the goal of making novel remedies for the prevention of lethal ventricular arrhythmias and sudden cardiac arrest.
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http://dx.doi.org/10.1161/CIRCGEN.117.001758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992491PMC
January 2018

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

Nat Genet 2018 04 9;50(4):559-571. Epub 2018 Apr 9.

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.
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http://dx.doi.org/10.1038/s41588-018-0084-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898373PMC
April 2018

A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure.

Am J Hum Genet 2018 03 15;102(3):375-400. Epub 2018 Feb 15.

Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA.

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
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http://dx.doi.org/10.1016/j.ajhg.2018.01.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5985266PMC
March 2018

New Blood Pressure-Associated Loci Identified in Meta-Analyses of 475 000 Individuals.

Circ Cardiovasc Genet 2017 Oct;10(5)

Background: Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association.

Methods And Results: Here, we augment the sample with 140 886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, ≈475 000), and the other in the subset of individuals of European descent (≈423 000). Twenty-one SNVs were genome-wide significant (<5×10) for BP, of which 4 are new BP loci: rs9678851 (missense, ), rs7437940 (), rs13303 (missense, ), and rs1055144 (). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, ) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at ) was genome-wide significant.

Conclusions: We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.
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http://dx.doi.org/10.1161/CIRCGENETICS.117.001778DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5776077PMC
October 2017

Genetic Interactions with Age, Sex, Body Mass Index, and Hypertension in Relation to Atrial Fibrillation: The AFGen Consortium.

Sci Rep 2017 09 12;7(1):11303. Epub 2017 Sep 12.

Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.

It is unclear whether genetic markers interact with risk factors to influence atrial fibrillation (AF) risk. We performed genome-wide interaction analyses between genetic variants and age, sex, hypertension, and body mass index in the AFGen Consortium. Study-specific results were combined using meta-analysis (88,383 individuals of European descent, including 7,292 with AF). Variants with nominal interaction associations in the discovery analysis were tested for association in four independent studies (131,441 individuals, including 5,722 with AF). In the discovery analysis, the AF risk associated with the minor rs6817105 allele (at the PITX2 locus) was greater among subjects ≤ 65 years of age than among those > 65 years (interaction p-value = 4.0 × 10). The interaction p-value exceeded genome-wide significance in combined discovery and replication analyses (interaction p-value = 1.7 × 10). We observed one genome-wide significant interaction with body mass index and several suggestive interactions with age, sex, and body mass index in the discovery analysis. However, none was replicated in the independent sample. Our findings suggest that the pathogenesis of AF may differ according to age in individuals of European descent, but we did not observe evidence of statistically significant genetic interactions with sex, body mass index, or hypertension on AF risk.
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http://dx.doi.org/10.1038/s41598-017-09396-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5595875PMC
September 2017

Loss of Cardioprotective Effects at the Locus as a Result of Gene-Smoking Interactions.

Circulation 2017 Jun 1;135(24):2336-2353. Epub 2017 May 1.

From Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia (D.S., W.Z.); Center for Non-Communicable Diseases, Karachi, Pakistan (D.S., A.R., P.M.F., PROMIS); Department of Public Health and Primary Care, University of Cambridge, United Kingdom (R.Y., W.K.H., EPIC-CVD); Department of Cardiovascular Sciences, University of Leicester, United Kingdom (C.P.N., N.J.S.); Cardiology Division, Department of Medicine, Vanderbilt University, Nashville, TN (J.F.F., K.O.); Division of Cardiovascular Medicine, Radcliffe Department of Medicine & Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom (A.G., M.F.); The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY (R.D.); Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY (R.D.); Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Canada (A.F.R.S., R.M.); Institute for Genetic Medicine and Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles (J.H., H.A.); Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom (W.Z., J.C.C., J.K.); Department of Cardiology, Ealing Hospital NHS Trust, Middlesex, United Kingdom (W.Z., J.C.C.); Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden (R.J.S.); Helsinki University Central Hospital HUCH Heart and Lung Center, Helsinki, Uusimaa, Finland (J.S.); Cardiology Division, Department of Medicine and the Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY (R.C.B., M.P.R.); William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.K., E.M., P.D.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.S., A.D.); Department of Dietetics-Nutrition, Harokopio University, Athens, Greece (E.M., G.D.); National Institute for Health and Welfare, Helsinki, Finland (K.K., A.J., V.S., K.K., M.P.); MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, United Kingdom (J.H.Z., R.S.); INSERM, UMRS1138, Centre de Recherche des Cordeliers, Paris, France (D.G., N.W.); Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC (S.H.S.); Icelandic Heart Association, Kopavogur, Iceland (A.V.S., V.G.); Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, United Kingdom (N.v.Z., C.N.A.P.); Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC (A.J.C., D.W.B.); Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, Germany (C.W., J.E.); DZHK (German Research Center for Cardiovascular Research) partner site Hamburg-Lübeck-Kiel, Germany (C.W., J.E.); Deutsches Herzzentrum München, Technische Universität München, Germany (T.K., L.Z., H.S.); Klinikum rechts der Isar, München, Germany (T.K.); DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Germany (L.Z., H.S.); Department of Genetics, Washington University School of Medicine, St. Louis, MO (M.A.P., M.F.F.); Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston (A.G.); Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA (A.G.); Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Sweden (L.L.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (N.L.P.); Department of Biostatistics Boston University School of Public Health Framingham Heart Study, MA (C.C.W.); Faculty of Medicine, University of Iceland, Reykjavik (A.V.S., V.G.); University of Helsinki, Institute for Molecular Medicine, Finland (FIMM) (A.J., M.P.); Department of Medicine, Mannheim Medical Faculty, Heidelberg University, Germany (M.E.K.); Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, United Kingdom (A.S.H.); Synlab Academy, Synlab Services GmbH, Mannheim, Germany and Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Austria (W.M.); National Heart, Lung, and Blood Institute and the Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.O'D.); Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Sweden (E.I.); Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, CA (E.I.); Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden (U.D.F.); Lebanese American University, School of Medicine, Beirut (P.Z.); Department of Health Sciences, University of Leicester, United Kingdom (J.R.T.); Imperial College Healthcare NHS Trust, London, United Kingdom (J.C.C., J.K.); Cardiovascular Science, National Heart and Lung Institute, Imperial College London, United Kingdom (J.K.); Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia (P.D.); deCODE Genetics, Sturlugata 8, IS-101 Reykjavik, Iceland (G.T., K.S.); University of Iceland, School of Medicine, Reykjavik, Iceland (G.T., K.S.); Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge (S.K.); Cardiovascular Research Center, Massachusetts General Hospital, Boston (S.K.); Center for Human Genetic Research, Massachusetts General Hospital, Boston (S.K.); Department of Medicine, Harvard Medical School, Boston, MA (S.K.); Department of Genetics, University of Pennsylvania, Philadelphia (D.J.R.); and Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.T.N., D.J.R.).

Background: Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk.

Methods: We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a value of <1.0×10 (Bonferroni correction for 50 tests).

Results: We identified novel gene-smoking interaction for a variant upstream of the gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (=1.3×10) in comparison with 5% in ever-smokers (=2.5×10), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction value=8.7×10). The protective T allele at rs7178051 was also associated with reduced expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular expression may contribute to the loss of CHD protection in smokers.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.116.022069DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5612779PMC
June 2017

Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

PLoS Genet 2017 Apr 27;13(4):e1006528. Epub 2017 Apr 27.

Estonian Genome Center, University of Tartu, Tartu, Estonia.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.
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http://dx.doi.org/10.1371/journal.pgen.1006528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407576PMC
April 2017

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Nat Commun 2017 04 26;8:14977. Epub 2017 Apr 26.

Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.
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http://dx.doi.org/10.1038/ncomms14977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414044PMC
April 2017

Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function.

J Clin Invest 2017 May 10;127(5):1798-1812. Epub 2017 Apr 10.

Background: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function.

Methods: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function.

Results: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue.

Conclusion: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies.

Funding: For detailed information per study, see Acknowledgments.
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http://dx.doi.org/10.1172/JCI84840DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409098PMC
May 2017

Rare and low-frequency coding variants alter human adult height.

Nature 2017 02 1;542(7640):186-190. Epub 2017 Feb 1.

Netherlands Comprehensive Cancer Organisation, Utrecht, 3501 DB, The Netherlands.

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and 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.
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http://dx.doi.org/10.1038/nature21039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302847PMC
February 2017

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.

Int J Epidemiol 2017 06;46(3):894-904

Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK.

Background: Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV 1 (forced expiratory volume in 1 second) or FEV 1 /FVC (FEV 1 /forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The aim of the present study was to assess, using a genetic risk score approach, whether the effect of these 26 loci on pulmonary function is influenced by smoking.

Methods: We evaluated the interaction between smoking exposure, considered as either ever vs never or pack-years, and a 26-single nucleotide polymorphisms (SNPs) genetic risk score in relation to FEV 1 or FEV 1 /FVC in 50 047 participants of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and SpiroMeta consortia.

Results: We identified an interaction ( βint  = -0.036, 95% confidence interval, -0.040 to -0.032, P  = 0.00057) between an unweighted 26 SNP genetic risk score and smoking status (ever/never) on the FEV 1 /FVC ratio. In interpreting this interaction, we showed that the genetic risk of falling below the FEV /FVC threshold used to diagnose chronic obstructive pulmonary disease is higher among ever smokers than among never smokers. A replication analysis in two independent datasets, although not statistically significant, showed a similar trend in the interaction effect.

Conclusions: This study highlights the benefit of using genetic risk scores for identifying interactions missed when studying individual SNPs and shows, for the first time, that persons with the highest genetic risk for low FEV 1 /FVC may be more susceptible to the deleterious effects of smoking.
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http://dx.doi.org/10.1093/ije/dyw318DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837518PMC
June 2017

KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference.

Proc Natl Acad Sci U S A 2016 12 28;113(50):14372-14377. Epub 2016 Nov 28.

Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, United Kingdom.

Excessive alcohol consumption is a major public health problem worldwide. Although drinking habits are known to be inherited, few genes have been identified that are robustly linked to alcohol drinking. We conducted a genome-wide association metaanalysis and replication study among >105,000 individuals of European ancestry and identified β-Klotho (KLB) as a locus associated with alcohol consumption (rs11940694; P = 9.2 × 10). β-Klotho is an obligate coreceptor for the hormone FGF21, which is secreted from the liver and implicated in macronutrient preference in humans. We show that brain-specific β-Klotho KO mice have an increased alcohol preference and that FGF21 inhibits alcohol drinking by acting on the brain. These data suggest that a liver-brain endocrine axis may play an important role in the regulation of alcohol drinking behavior and provide a unique pharmacologic target for reducing alcohol consumption.
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http://dx.doi.org/10.1073/pnas.1611243113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167198PMC
December 2016

Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.

Am J Hum Genet 2016 Jul 23;99(1):22-39. Epub 2016 Jun 23.

Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA.

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.
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http://dx.doi.org/10.1016/j.ajhg.2016.05.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005433PMC
July 2016

Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals.

Am J Hum Genet 2016 Jul 23;99(1):40-55. Epub 2016 Jun 23.

Department of Cardiology, Heart Center, Tampere University Hospital, Tampere 33521, Finland; University of Tampere, School of Medicine, Tampere 33514, Finland.

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.
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http://dx.doi.org/10.1016/j.ajhg.2016.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005441PMC
July 2016

Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits.

Am J Hum Genet 2016 Jul 23;99(1):8-21. Epub 2016 Jun 23.

Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA.

Red blood cell (RBC) traits are important heritable clinical biomarkers and modifiers of disease severity. To identify coding genetic variants associated with these traits, we conducted meta-analyses of seven RBC phenotypes in 130,273 multi-ethnic individuals from studies genotyped on an exome array. After conditional analyses and replication in 27,480 independent individuals, we identified 16 new RBC variants. We found low-frequency missense variants in MAP1A (rs55707100, minor allele frequency [MAF] = 3.3%, p = 2 × 10(-10) for hemoglobin [HGB]) and HNF4A (rs1800961, MAF = 2.4%, p < 3 × 10(-8) for hematocrit [HCT] and HGB). In African Americans, we identified a nonsense variant in CD36 associated with higher RBC distribution width (rs3211938, MAF = 8.7%, p = 7 × 10(-11)) and showed that it is associated with lower CD36 expression and strong allelic imbalance in ex vivo differentiated human erythroblasts. We also identified a rare missense variant in ALAS2 (rs201062903, MAF = 0.2%) associated with lower mean corpuscular volume and mean corpuscular hemoglobin (p < 8 × 10(-9)). Mendelian mutations in ALAS2 are a cause of sideroblastic anemia and erythropoietic protoporphyria. Gene-based testing highlighted three rare missense variants in PKLR, a gene mutated in Mendelian non-spherocytic hemolytic anemia, associated with HGB and HCT (SKAT p < 8 × 10(-7)). These rare, low-frequency, and common RBC variants showed pleiotropy, being also associated with platelet, white blood cell, and lipid traits. Our association results and functional annotation suggest the involvement of new genes in human erythropoiesis. We also confirm that rare and low-frequency variants play a role in the architecture of complex human traits, although their phenotypic effect is generally smaller than originally anticipated.
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http://dx.doi.org/10.1016/j.ajhg.2016.05.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005438PMC
July 2016

Genome-Wide Association Study for Incident Myocardial Infarction and Coronary Heart Disease in Prospective Cohort Studies: The CHARGE Consortium.

PLoS One 2016 7;11(3):e0144997. Epub 2016 Mar 7.

Departments of Cardiology and Epidemiology, Toulouse University Hospital, Toulouse, France.

Background: Data are limited on genome-wide association studies (GWAS) for incident coronary heart disease (CHD). Moreover, it is not known whether genetic variants identified to date also associate with risk of CHD in a prospective setting.

Methods: We performed a two-stage GWAS analysis of incident myocardial infarction (MI) and CHD in a total of 64,297 individuals (including 3898 MI cases, 5465 CHD cases). SNPs that passed an arbitrary threshold of 5×10-6 in Stage I were taken to Stage II for further discovery. Furthermore, in an analysis of prognosis, we studied whether known SNPs from former GWAS were associated with total mortality in individuals who experienced MI during follow-up.

Results: In Stage I 15 loci passed the threshold of 5×10-6; 8 loci for MI and 8 loci for CHD, for which one locus overlapped and none were reported in previous GWAS meta-analyses. We took 60 SNPs representing these 15 loci to Stage II of discovery. Four SNPs near QKI showed nominally significant association with MI (p-value<8.8×10-3) and three exceeded the genome-wide significance threshold when Stage I and Stage II results were combined (top SNP rs6941513: p = 6.2×10-9). Despite excellent power, the 9p21 locus SNP (rs1333049) was only modestly associated with MI (HR = 1.09, p-value = 0.02) and marginally with CHD (HR = 1.06, p-value = 0.08). Among an inception cohort of those who experienced MI during follow-up, the risk allele of rs1333049 was associated with a decreased risk of subsequent mortality (HR = 0.90, p-value = 3.2×10-3).

Conclusions: QKI represents a novel locus that may serve as a predictor of incident CHD in prospective studies. The association of the 9p21 locus both with increased risk of first myocardial infarction and longer survival after MI highlights the importance of study design in investigating genetic determinants of complex disorders.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144997PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4780701PMC
July 2016

Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies.

PLoS Genet 2016 Feb 24;12(2):e1005874. Epub 2016 Feb 24.

UMR INSERM U1122, IGE-PCV "Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire", Faculté de Pharmacie, Université de Lorraine, Nancy, France.

Vascular endothelial growth factor (VEGF) is an angiogenic and neurotrophic factor, secreted by endothelial cells, known to impact various physiological and disease processes from cancer to cardiovascular disease and to be pharmacologically modifiable. We sought to identify novel loci associated with circulating VEGF levels through a genome-wide association meta-analysis combining data from European-ancestry individuals and using a dense variant map from 1000 genomes imputation panel. Six discovery cohorts including 13,312 samples were analyzed, followed by in-silico and de-novo replication studies including an additional 2,800 individuals. A total of 10 genome-wide significant variants were identified at 7 loci. Four were novel loci (5q14.3, 10q21.3, 16q24.2 and 18q22.3) and the leading variants at these loci were rs114694170 (MEF2C, P = 6.79 x 10(-13)), rs74506613 (JMJD1C, P = 1.17 x 10(-19)), rs4782371 (ZFPM1, P = 1.59 x 10(-9)) and rs2639990 (ZADH2, P = 1.72 x 10(-8)), respectively. We also identified two new independent variants (rs34528081, VEGFA, P = 1.52 x 10(-18); rs7043199, VLDLR-AS1, P = 5.12 x 10(-14)) at the 3 previously identified loci and strengthened the evidence for the four previously identified SNPs (rs6921438, LOC100132354, P = 7.39 x 10(-1467); rs1740073, C6orf223, P = 2.34 x 10(-17); rs6993770, ZFPM2, P = 2.44 x 10(-60); rs2375981, KCNV2, P = 1.48 x 10(-100)). These variants collectively explained up to 52% of the VEGF phenotypic variance. We explored biological links between genes in the associated loci using Ingenuity Pathway Analysis that emphasized their roles in embryonic development and function. Gene set enrichment analysis identified the ERK5 pathway as enriched in genes containing VEGF associated variants. eQTL analysis showed, in three of the identified regions, variants acting as both cis and trans eQTLs for multiple genes. Most of these genes, as well as some of those in the associated loci, were involved in platelet biogenesis and functionality, suggesting the importance of this process in regulation of VEGF levels. This work also provided new insights into the involvement of genes implicated in various angiogenesis related pathologies in determining circulating VEGF levels. The understanding of the molecular mechanisms by which the identified genes affect circulating VEGF levels could be important in the development of novel VEGF-related therapies for such diseases.
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http://dx.doi.org/10.1371/journal.pgen.1005874DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766012PMC
February 2016

Novel Genetic Loci Associated With Retinal Microvascular Diameter.

Circ Cardiovasc Genet 2016 Feb 13;9(1):45-54. Epub 2015 Nov 13.

Background: There is increasing evidence that retinal microvascular diameters are associated with cardiovascular and cerebrovascular conditions. The shared genetic effects of these associations are currently unknown. The aim of this study was to increase our understanding of the genetic factors that mediate retinal vessel size.

Methods And Results: This study extends previous genome-wide association study results using 24 000+ multiethnic participants from 7 discovery cohorts and 5000+ subjects of European ancestry from 2 replication cohorts. Using the Illumina HumanExome BeadChip, we investigate the association of single-nucleotide polymorphisms and variants collectively across genes with summary measures of retinal vessel diameters, referred to as the central retinal venule equivalent and the central retinal arteriole equivalent. We report 4 new loci associated with central retinal venule equivalent, one of which is also associated with central retinal arteriole equivalent. The 4 single-nucleotide polymorphisms are rs7926971 in TEAD1 (P=3.1×10(-) (11); minor allele frequency=0.43), rs201259422 in TSPAN10 (P=4.4×10(-9); minor allele frequency=0.27), rs5442 in GNB3 (P=7.0×10(-10); minor allele frequency=0.05), and rs1800407 in OCA2 (P=3.4×10(-8); minor allele frequency=0.05). The latter single-nucleotide polymorphism, rs1800407, was also associated with central retinal arteriole equivalent (P=6.5×10(-12)). Results from the gene-based burden tests were null. In phenotype look-ups, single-nucleotide polymorphism rs201255422 was associated with both systolic (P=0.001) and diastolic blood pressures (P=8.3×10(-04)).

Conclusions: Our study expands the understanding of genetic factors influencing the size of the retinal microvasculature. These findings may also provide insight into the relationship between retinal and systemic microvascular disease.
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http://dx.doi.org/10.1161/CIRCGENETICS.115.001142DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758888PMC
February 2016

Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE Consortium.

PLoS One 2015 30;10(10):e0140496. Epub 2015 Oct 30.

Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America.

Background: Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals.

Methods: Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases).

Results: Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10-8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140496PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627813PMC
June 2016