Publications by authors named "Aaron Isaacs"

131 Publications

Leukocyte gene expression in post-thrombotic syndrome.

Thromb Res 2021 06 8;202:40-42. Epub 2021 Mar 8.

Thrombosis Expertise Center, Heart+Vascular Center, Maastricht University Medical Center (MUMC), Maastricht, the Netherlands; Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.

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http://dx.doi.org/10.1016/j.thromres.2021.03.007DOI Listing
June 2021

Dynamic risk assessment to improve quality of care in patients with atrial fibrillation: the 7th AFNET/EHRA Consensus Conference.

Europace 2021 Mar;23(3):329-344

Atrial Fibrillation NETwork (AFNET), Münster, Germany.

Aims: The risk of developing atrial fibrillation (AF) and its complications continues to increase, despite good progress in preventing AF-related strokes.

Methods And Results: This article summarizes the outcomes of the 7th Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA) held in Lisbon in March 2019. Sixty-five international AF specialists met to present new data and find consensus on pressing issues in AF prevention, management and future research to improve care for patients with AF and prevent AF-related complications. This article is the main outcome of an interactive, iterative discussion between breakout specialist groups and the meeting plenary. AF patients have dynamic risk profiles requiring repeated assessment and risk-based therapy stratification to optimize quality of care. Interrogation of deeply phenotyped datasets with outcomes will lead to a better understanding of the cardiac and systemic effects of AF, interacting with comorbidities and predisposing factors, enabling stratified therapy. New proposals include an algorithm for the acute management of patients with AF and heart failure, a call for a refined, data-driven assessment of stroke risk, suggestions for anticoagulation use in special populations, and a call for rhythm control therapy selection based on risk of AF recurrence.

Conclusion: The remaining morbidity and mortality in patients with AF needs better characterization. Likely drivers of the remaining AF-related problems are AF burden, potentially treatable by rhythm control therapy, and concomitant conditions, potentially treatable by treating these conditions. Identifying the drivers of AF-related complications holds promise for stratified therapy.
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http://dx.doi.org/10.1093/europace/euaa279DOI Listing
March 2021

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Nat Commun 2021 01 5;12(1):24. Epub 2021 Jan 5.

Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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http://dx.doi.org/10.1038/s41467-020-19366-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785747PMC
January 2021

Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation.

Circ Genom Precis Med 2020 10 21;13(5):387-395. Epub 2020 Aug 21.

DZHK (German Center for Cardiovascular Research), partner site Greifswald, Germany (A.T., U.V., M.D., S.B.F.).

Background: The P-wave duration (PWD) is an electrocardiographic measurement that represents cardiac conduction in the atria. Shortened or prolonged PWD is associated with atrial fibrillation (AF). We used exome-chip data to examine the associations between common and rare variants with PWD.

Methods: Fifteen studies comprising 64 440 individuals (56 943 European, 5681 African, 1186 Hispanic, 630 Asian) and ≈230 000 variants were used to examine associations with maximum PWD across the 12-lead ECG. Meta-analyses summarized association results for common variants; gene-based burden and sequence kernel association tests examined low-frequency variant-PWD associations. Additionally, we examined the associations between PWD loci and AF using previous AF genome-wide association studies.

Results: We identified 21 common and low-frequency genetic loci (14 novel) associated with maximum PWD, including several AF loci (, , , , , , , ). The top variants at known sarcomere genes () were associated with longer PWD and increased AF risk. However, top variants at other loci (eg, and ) were associated with longer PWD but lower AF risk.

Conclusions: Our results highlight multiple novel genetic loci associated with PWD, and underscore the shared mechanisms of atrial conduction and AF. Prolonged PWD may be an endophenotype for several different genetic mechanisms of AF.
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http://dx.doi.org/10.1161/CIRCGEN.119.002874DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578098PMC
October 2020

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.

Nat Commun 2020 05 21;11(1):2542. Epub 2020 May 21.

Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.
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http://dx.doi.org/10.1038/s41467-020-15706-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242331PMC
May 2020

Non-alcoholic fatty liver disease and cardiovascular disease: assessing the evidence for causality.

Diabetologia 2020 02 11;63(2):253-260. Epub 2019 Nov 11.

Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.

Non-alcoholic fatty liver disease (NAFLD) is highly prevalent among individuals with type 2 diabetes. Although epidemiological studies have shown that NAFLD is associated with cardiovascular disease (CVD), it remains unknown whether NAFLD is an active contributor or an innocent bystander. Plasma lipids, low-grade inflammation, impaired fibrinolysis and hepatokines are potential mediators of the relationship between NAFLD and CVD. The Mendelian randomisation approach can help to make causal inferences. Studies that used common variants in PNPLA3, TM6SF2 and GCKR as instruments to investigate the relationship between NAFLD and coronary artery disease (CAD) have reported contrasting results. Variants in PNPLA3 and TM6SF2 were found to protect against CAD, whereas variants in GCKR were positively associated with CAD. Since all three genes have been associated with non-alcoholic steatohepatitis, the second stage of NAFLD, the question of whether low-grade inflammation is an important mediator of the relationship between NAFLD and CAD arises. In contrast, the differential effects of these genes on plasma lipids (i.e. lipid-lowering for PNPLA3 and TM6SF2, and lipid-raising for GCKR) strongly suggest that plasma lipids account for their differential effects on CAD risk. This concept has recently been confirmed in an extended set of 12 NAFLD susceptibility genes. From these studies it appears that plasma lipids are an important mediator between NAFLD and CVD risk. These findings have important clinical implications, particularly for the design of anti-NAFLD drugs that also affect lipid metabolism.
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http://dx.doi.org/10.1007/s00125-019-05024-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6946734PMC
February 2020

Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium.

Sci Rep 2019 08 12;9(1):11623. Epub 2019 Aug 12.

Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10), methionine (p-value = 9.2 × 10), tyrosine (p-value = 2.1 × 10), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.
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http://dx.doi.org/10.1038/s41598-019-47282-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6690953PMC
August 2019

Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals.

Sci Rep 2019 07 1;9(1):9439. Epub 2019 Jul 1.

Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.

Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.
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http://dx.doi.org/10.1038/s41598-019-45823-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602949PMC
July 2019

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis.

Nat Commun 2019 06 13;10(1):2581. Epub 2019 Jun 13.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.
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http://dx.doi.org/10.1038/s41467-019-10487-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565679PMC
June 2019

Relationship Between Nonalcoholic Fatty Liver Disease Susceptibility Genes and Coronary Artery Disease.

Hepatol Commun 2019 Apr 11;3(4):587-596. Epub 2019 Feb 11.

Cardiovascular Research Institute Maastricht, Maastricht University Maastricht the Netherlands.

Coronary artery disease (CAD) is the principal cause of death in patients with nonalcoholic fatty liver disease (NAFLD). The aim of the present study was to investigate whether NAFLD is causally involved in the pathogenesis of CAD. For this, previously reported NAFLD susceptibility genes were clustered and tested for an association with CAD in the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis plus the Coronary Artery Disease Genetics (CARDIoGRAMplusC4D) Consortium data set. The role of plasma lipids as a potential mediator was explored by using data from the Global Lipids Genetics Consortium. Statistical analyses revealed that the combination of 12 NAFLD genes was not associated with CAD in 60,801 CAD cases and 123,504 controls (odds ratio [OR] per NAFLD risk allele 1.0; 95% confidence interval [CI], 0.99-1.00). In a subsequent sensitivity analysis, a positive relationship was observed after exclusion of gene variants that are implicated in NAFLD through impaired very low-density lipoprotein secretion (i.e., microsomal triglyceride transfer protein [] patatin-like phospholipase domain containing 3 [] phosphatidylethanolamine N-methyltransferase [] and transmembrane 6 superfamily member 2 []) (OR, 1.01; 95% CI, 1.00-1.02). Clustering of the excluded genes showed a significant negative relationship with CAD (OR, 0.97; 95% CI, 0.96-0.99). A substantial proportion of the observed heterogeneity between the individual NAFLD genes in relation to CAD could be explained by plasma lipids, as reflected by a strong relationship between plasma lipids and CAD risk conferred by the NAFLD susceptibility genes ( = 0.76; = 0.004 for low-density lipoprotein cholesterol). NAFLD susceptibility genes do not cause CAD . The relationship between these genes and CAD appears to depend to a large extent on plasma lipids. These observations strongly suggest taking plasma lipids into account when designing a new drug to target NAFLD.
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http://dx.doi.org/10.1002/hep4.1319DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6442707PMC
April 2019

Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits.

Eur J Hum Genet 2019 06 24;27(6):952-962. Epub 2019 Jan 24.

Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital-The Capital Region, Copenhagen, Denmark.

Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which six were successfully replicated. At these seven loci, we performed conditional analyses and annotated significant SNPs (in exons and regulatory regions), demonstrating involvement of cardiac-related pathways and regulation of nearby genes.
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http://dx.doi.org/10.1038/s41431-018-0295-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777533PMC
June 2019

GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.

Nat Commun 2018 12 3;9(1):5141. Epub 2018 Dec 3.

Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA.

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.
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http://dx.doi.org/10.1038/s41467-018-07340-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277418PMC
December 2018

PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.

Nat Commun 2018 07 25;9(1):2904. Epub 2018 Jul 25.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.
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http://dx.doi.org/10.1038/s41467-018-04766-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6060178PMC
July 2018

Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6.

Genome Biol 2018 07 17;19(1):87. Epub 2018 Jul 17.

Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.

Background: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.

Results: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.

Conclusions: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.
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http://dx.doi.org/10.1186/s13059-018-1457-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048820PMC
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

Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval.

Circ Genom Precis Med 2018 05;11(5):e002037

Section of Computational Biomedicine (H.L.) and Section of Cardiovascular Medicine (E.J.B.), Department of Medicine, Boston University School of Medicine, MA. National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, MA (H.L., E.J.B.). Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, University of Utrecht, The Netherlands (J.v.S., F.W.A.). Icelandic Heart Association, Kopavogur (A.V.S., V.G.). Faculty of Medicine, University of Iceland, Reykjavik (A.V.S., V.G.). Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine (N.A.B.) and McKusick-Nathans Institute of Genetic Medicine (D.E.A.), Johns Hopkins University School of Medicine, Baltimore, MD. William Harvey Research Institute (H.R.W., P.B.M.) and NIHR Barts Cardiovascular Research Unit (H.R.W., P.B.M.), Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom. Cardiovascular Health Research Unit, Department of Medicine (J.A.B., J.C.B., C.M.S.), Department of Biostatistics (K.M.R.), Cardiovascular Health Research Unit, Division of Cardiology, Departments of Medicine and Epidemiology (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. Center for Human Genetic Research (F. Radmanesh, J.R.) and Cardiovascular Research Center (P.L.H., L.-C.W., H.S.J., W.H., A.H., N.R.T., P.T.E., S.A.L.), Massachusetts General Hospital, Boston. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA (L.-C.W., P.T.E., S.A.L.). Department of Cardiovascular Sciences, University of Leicester, United Kingdom (L.H., C.P.N., N.J.S.). NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, United Kingdom (L.H., C.P.N., N.J.S.). The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences (N.G., J.B.-J., O. Pedersen, T.H.), Laboratory of Experimental Cardiology (J.K.K.), and Department of Clinical Medicine, Faculty of Health and Medical Sciences (A.L.), University of Copenhagen, Denmark. Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University Munich, Germany (M.M.-N., M.F.S., S.K.). Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany (K.S.). DZHK (German Cardiovascular Research Centre), Partner Site: Munich Heart Alliance, Germany (M.M.-N., M.F.S., A.P., T.M., S.K.). Institute of Genetic Epidemiology (M.M.-N., K.S.), Institute of Epidemiology II (A.P., M.W.), Research Unit of Molecular Epidemiology (M.W.), and Institute of Human Genetics (T.M.), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (T.B., J.M., C.H.) and Usher Institute of Population Health Sciences and Informatics (I.R.), University of Edinburgh, United Kingdom. University of Groningen, University Medical Center Groningen, Department of Cardiology, The Netherlands (N.V., R.A.d.B., P.v.d.M., P.v.d.H.). Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (H.J.L., Y.-D.I.C., J.Y., X.G., K.D.T., J.I.R.). Department of Clinical Epidemiology (R.L.-G., D.O.M.-K.) and Department of Cardiology (S.T., J.W.J.), Leiden University Medical Center, The Netherlands. Department of Medical Informatics (M.E.v.d.B.), Human Genomics Facility (F. Rivadeneira), Human Genotyping Facility (A.U.), and Department of Epidemiology (M.E., B.H. Stricker), 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., G.H., U.V.). DZHK (German Cardiovascular Research Centre), Partner Site Greifswald, Germany (S.W., H.V., S.B.F., U.V., M.D.). Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (J.H., C.K.). Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences (L.-P.L., T.L.) and Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Life Sciences (M.K.), University of Tampere, Finland. Department of Data Science (H.M.) and Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD (T.B.H., L.J.L.). 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.). Medical Research Institute (J.M.C.) and Division of Population Health Sciences (B.H. Smith), Ninewells Hospital and Medical School, University of Dundee, United Kingdom. Department of Medical Informatics (J.A.K.) and Genetic Epidemiology Unit, Department of Epidemiology (C.M.v.D.), Erasmus MC, Rotterdam, The Netherlands. TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, Hangzhou, China (Z.X., C.W.). Division of Cardiology, Department of Medicine, UPMC Heart and Vascular Institute, University of Pittsburgh, PA (J.W.M.). German Center for Diabetes Research, Neuherberg, Germany (A.P.). Institute of Human Genetics, Technische Universität München, Germany (T.M.). Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen (A.L.). Department of Clinical Experimental Research, Rigshospitalet, Denmark (A.L.). British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Scotland (S.P.). 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.). Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands (M.L.B.). Division of Cardiovascular Medicine, Department of Medicine, Stanford University 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.). Kaiser Permanente Washington Health Research Institute, Kaiser Foundation Health Plan of Washington, Seattle (B.M.P., S.R.H.). Faculty of Medicine, University of Split, Croatia (O. Polasek). Cardiogenetics Lab, Genetics and Molecular Cell Sciences Research Centre, Cardiovascular and Cell Sciences Institute, St George's, University of London, Cranmer Terrace, United Kingdom (B.P.P., Y.J.). Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands (F.W.A.). Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom (F.W.A.). Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, United Kingdom; CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio) and Department of Biochemistry, Maastricht University, The Netherlands (A.I.).

Background: Electrical conduction from the cardiac sinoatrial node to the ventricles is critical for normal heart function. Genome-wide association studies have identified more than a dozen common genetic loci that are associated with PR interval. However, it is unclear whether rare and low-frequency variants also contribute to PR interval heritability.

Methods: We performed large-scale meta-analyses of the PR interval that included 83 367 participants of European ancestry and 9436 of African ancestry. We examined both common and rare variants associated with the PR interval.

Results: We identified 31 genetic loci that were significantly associated with PR interval after Bonferroni correction (<1.2×10), including 11 novel loci that have not been reported previously. Many of these loci are involved in heart morphogenesis. In gene-based analysis, we found that multiple rare variants at (=5.9×10) and (=1.1×10) were associated with PR interval. locus also was implicated in the common variant analysis, whereas was a novel locus.

Conclusions: We identified common variants at 11 novel loci and rare variants within 2 gene regions that were significantly associated with PR interval. Our findings provide novel insights to the current understanding of atrioventricular conduction, which is critical for cardiac activity and an important determinant of health.
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http://dx.doi.org/10.1161/CIRCGEN.117.002037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951629PMC
May 2018

A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy.

BMC Med Genomics 2018 03 5;11(1):22. Epub 2018 Mar 5.

Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.

Background: Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them.

Methods: The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data.

Results: We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied.

Conclusions: We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.
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http://dx.doi.org/10.1186/s12920-018-0339-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838853PMC
March 2018

Fifteen Genetic Loci Associated With the Electrocardiographic P Wave.

Circ Cardiovasc Genet 2017 Aug;10(4)

Background: The P wave on an ECG is a measure of atrial electric function, and its characteristics may serve as predictors for atrial arrhythmias. Increased mean P-wave duration and P-wave terminal force traditionally have been used as markers for left atrial enlargement, and both have been associated with increased risk of atrial fibrillation. Here, we explore the genetic basis of P-wave morphology through meta-analysis of genome-wide association study results for P-wave duration and P-wave terminal force from 12 cohort studies.

Methods And Results: We included 44 456 individuals, of which 6778 (16%) were of African ancestry. Genotyping, imputation, and genome-wide association study were performed at each study site. Summary-level results were meta-analyzed centrally using inverse-variance weighting. In meta-analyses of P-wave duration, we identified 6 significant (<5×10) novel loci and replicated a prior association with S We identified 3 loci at , , and that were jointly associated with the PR interval, PR segment, and P-wave duration. We identified 6 novel loci in meta-analysis of P-wave terminal force. Four of the identified genetic loci were significantly associated with gene expression in 329 left atrial samples. Finally, we observed that some of the loci associated with the P wave were linked to overall atrial conduction, whereas others identified distinct phases of atrial conduction.

Conclusions: We have identified 6 novel genetic loci associated with P-wave duration and 6 novel loci associated with P-wave terminal force. Future studies of these loci may aid in identifying new targets for drugs that may modify atrial conduction or treat atrial arrhythmias.
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http://dx.doi.org/10.1161/CIRCGENETICS.116.001667DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5567993PMC
August 2017

Heritability in a SCN5A-mutation founder population with increased female susceptibility to non-nocturnal ventricular tachyarrhythmia and sudden cardiac death.

Heart Rhythm 2017 12 3;14(12):1873-1881. Epub 2017 Aug 3.

Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, The Netherlands. Electronic address:

Background: Heritable cardiac-sodium channel dysfunction is associated with various arrhythmia syndromes, some predisposing to ventricular fibrillation. Phenotypic diversity among carriers of identical-by-descent mutations is often remarkable, suggesting influences of genetic modifiers.

Objective: The purpose of this study was to identify a unique SCN5A-mutation founder population with mixed clinical phenotypes and sudden cardiac death, and to investigate the heritability of electromechanical traits besides the SCN5A-mutation effect.

Methods: The 16-generation founder population segregating SCN5A c.4850_4852delTCT, p.(Phe1617del), was comprehensively phenotyped. Variance component analysis was used to evaluate the mutation's effects and assess heritability.

Results: In 45 p.(Phe1617del) positives, the mutation associated strongly with QTc prolongation (472 ± 60 ms vs 423 ± 35 ms in 26 mutation negatives; P <.001; odds ratio for long-QT syndrome 22.4; 95% confidence interval 4.5-224.2; P <.001) and electromechanical window (EMW) negativity (-29 ± 47 ms vs 34 ± 26 ms; P <.001). Overlapping phenotypes including conduction delay and Brugada syndrome were noted in 19. Polymorphic ventricular tachyarrhythmias occurred mostly in the daytime, after arousal-evoked heart-rate acceleration and repolarization prolongation. Cox proportional hazards regression analysis revealed female gender as an independent risk factor for cardiac events (hazard ratio 5.1; 95% confidence interval 1.6-16.3; P = .006). p.(Phe1617del) was an important determinant of QTc, QTc, and EMW, explaining 18%, 28%, and 37%, respectively, of the trait's variance. Significant heritability was observed for PQ interval (P = .003) after accounting for the p.(Phe1617del) effect.

Conclusion: This SCN5A-p.(Phe1617del) founder population with phenotypic divergence and overlap reveals long-QT syndrome-related and arousal-evoked ventricular tachyarrhythmias with a female preponderance. Variance component analysis indicates additional genetic variance for PQ interval hidden in the genome, besides a dominant p.(Phe1617del) effect on QTc and EMW.
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http://dx.doi.org/10.1016/j.hrthm.2017.07.036DOI Listing
December 2017

Discovery of novel heart rate-associated loci using the Exome Chip.

Hum Mol Genet 2017 06;26(12):2346-2363

Genetic Epidemiology Unit, Dept. of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, NL.

Resting heart rate is a heritable trait, and an increase in heart rate is associated with increased mortality risk. Genome-wide association study analyses have found loci associated with resting heart rate, at the time of our study these loci explained 0.9% of the variation. This study aims to discover new genetic loci associated with heart rate from Exome Chip meta-analyses.Heart rate was measured from either elecrtrocardiograms or pulse recordings. We meta-analysed heart rate association results from 104 452 European-ancestry individuals from 30 cohorts, genotyped using the Exome Chip. Twenty-four variants were selected for follow-up in an independent dataset (UK Biobank, N = 134 251). Conditional and gene-based testing was undertaken, and variants were investigated with bioinformatics methods.We discovered five novel heart rate loci, and one new independent low-frequency non-synonymous variant in an established heart rate locus (KIAA1755). Lead variants in four of the novel loci are non-synonymous variants in the genes C10orf71, DALDR3, TESK2 and SEC31B. The variant at SEC31B is significantly associated with SEC31B expression in heart and tibial nerve tissue. Further candidate genes were detected from long-range regulatory chromatin interactions in heart tissue (SCD, SLF2 and MAPK8). We observed significant enrichment in DNase I hypersensitive sites in fetal heart and lung. Moreover, enrichment was seen for the first time in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples by including our novel variants.Our findings advance the knowledge of the genetic architecture of heart rate, and indicate new candidate genes for follow-up functional studies.
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http://dx.doi.org/10.1093/hmg/ddx113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458336PMC
June 2017

PNPLA3, TM6SF2, and MBOAT7 Genotypes and Coronary Artery Disease.

Gastroenterology 2017 03 1;152(4):912-913. Epub 2017 Feb 1.

Department of Internal Medicine, Division of Endocrinology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, the Netherlands.

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http://dx.doi.org/10.1053/j.gastro.2016.12.020DOI Listing
March 2017

A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk.

Sci Rep 2017 01 24;7:41034. Epub 2017 Jan 24.

Department of Epidemiology, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands.

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC.
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http://dx.doi.org/10.1038/srep41034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5259777PMC
January 2017

Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity.

Nature 2017 01 21;541(7635):81-86. Epub 2016 Dec 21.

German Center for Diabetes Research (DZD), München-Neuherberg, Germany.

Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10, range P = 9.2 × 10 to 6.0 × 10; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10, range P = 5.5 × 10 to 6.1 × 10, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
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http://dx.doi.org/10.1038/nature20784DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570525PMC
January 2017

Disease variants alter transcription factor levels and methylation of their binding sites.

Nat Genet 2017 01 5;49(1):131-138. Epub 2016 Dec 5.

Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands.

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.
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http://dx.doi.org/10.1038/ng.3721DOI Listing
January 2017

Identification of context-dependent expression quantitative trait loci in whole blood.

Nat Genet 2017 01 5;49(1):139-145. Epub 2016 Dec 5.

Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands.

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.
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http://dx.doi.org/10.1038/ng.3737DOI Listing
January 2017

A Combined Linkage and Exome Sequencing Analysis for Electrocardiogram Parameters in the Erasmus Rucphen Family Study.

Front Genet 2016 8;7:190. Epub 2016 Nov 8.

Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Center for Medical Systems BiologyLeiden, Netherlands.

Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the gene associated with QT (rs74608430; = 2.8 × 10, minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait's genetic variability in ERF ( = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca levels ( = 3.3 × 10) and AMPK stimulated fatty acid oxidation in muscle ( = 4.1 × 10). Look-ups in bioinformatics resources showed that expression of is associated with and expression. This finding was not replicated in the Rotterdam study. Combining the bioinformatics information with the association and linkage analyses, emerges as a strong candidate gene for QT interval.
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http://dx.doi.org/10.3389/fgene.2016.00190DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5099142PMC
November 2016

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape.

Nat Commun 2016 11 23;7:13357. Epub 2016 Nov 23.

Department of Kinesiology, Laval University, Québec, Québec, Canada G1V 0A6.

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
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http://dx.doi.org/10.1038/ncomms13357DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114527PMC
November 2016

Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis.

Circ Cardiovasc Genet 2016 Dec 21;9(6):511-520. Epub 2016 Nov 21.

Background: The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease.

Methods And Results: We studied a total of 25 109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52 869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247 870 DNA sequence variants (231 539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3×10). The APOE ε2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1×10) and 1.4% reduced carotid intima-media thickness (P=4×10) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the ε2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of ε2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1×10).

Conclusions: Exome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE ε2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.
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http://dx.doi.org/10.1161/CIRCGENETICS.116.001572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5418659PMC
December 2016