Publications by authors named "Jeffrey Haessler"

46 Publications

Epigenome-wide association study of mitochondrial genome copy number.

Hum Mol Genet 2021 Aug 20. Epub 2021 Aug 20.

Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.

We conducted cohort- and race-specific epigenome-wide association analyses of mtDNA copy number (mtDNA CN) measured in whole blood from participants of African and European origins in five cohorts (n = 6182, mean age 57-67 years, 65% women). In the meta-analysis of all the participants, we discovered 21 mtDNA CN-associated CpG sites (p < 1 x 10-7), with a 0.7 to 3.0 standard deviation increase (3 CpGs) or decrease (18 CpGs) in mtDNA CN corresponding to a 1% increase in DNA methylation. Several significant CpGs have been reported to be associated with at least two risk factors (e.g. chronological age or smoking) for cardiovascular disease (CVD). Five genes (PRDM16, NR1H3, XRCC3, POLK, and PDSS2), which harbor nine significant CpGs, are known to be involved in mitochondrial biosynthesis and functions. For example, NR1H3 encodes a transcription factor that is differentially expressed during an adipose tissue transition. The methylation level of cg09548275 in NR1H3 was negatively associated with mtDNA CN (effect size = -1.71, p = 4 x 10-8) and positively associated with the NR1H3 expression level (effect size = 0.43, p = 0.0003), which indicates that the methylation level in NR1H3 may underlie the relationship between mtDNA CN, the NR1H3 transcription factor, and energy expenditure. In summary, the study results suggest that mtDNA CN variation in whole blood is associated with DNA methylation levels in genes that are involved in a wide range of mitochondrial activities. These findings will help reveal molecular mechanisms between mtDNA CN and CVD.
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http://dx.doi.org/10.1093/hmg/ddab240DOI Listing
August 2021

Analyses of biomarker traits in diverse UK biobank participants identify associations missed by European-centric analysis strategies.

J Hum Genet 2021 Aug 11. Epub 2021 Aug 11.

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

Despite the dramatic underrepresentation of non-European populations in human genetics studies, researchers continue to exclude participants of non-European ancestry, as well as variants rare in European populations, even when these data are available. This practice perpetuates existing research disparities and can lead to important and large effect size associations being missed. Here, we conducted genome-wide association studies (GWAS) of 31 serum and urine biomarker quantitative traits in African (n = 9354), East Asian (n = 2559), and South Asian (n = 9823) ancestry UK Biobank (UKBB) participants. We adjusted for all known GWAS catalog variants for each trait, as well as novel signals identified in a recent European ancestry-focused analysis of UKBB participants. We identify 7 novel signals in African ancestry and 2 novel signals in South Asian ancestry participants (p < 1.61E-10). Many of these signals are highly plausible, including a cis pQTL for the gene encoding gamma-glutamyl transferase and PIEZO1 and G6PD variants with impacts on HbA1c through likely erythrocytic mechanisms. This work illustrates the importance of using the genetic data we already have in diverse populations, with novel discoveries possible in even modest sample sizes.
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http://dx.doi.org/10.1038/s10038-021-00968-0DOI Listing
August 2021

BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.

HGG Adv 2021 Jul 12;2(3). Epub 2021 Jun 12.

Framingham Heart Study, Framingham, MA, USA.

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.
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http://dx.doi.org/10.1016/j.xhgg.2021.100040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321319PMC
July 2021

Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

BMC Genomics 2021 Jun 9;22(1):432. Epub 2021 Jun 9.

Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Background: Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented.

Results: We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations.

Conclusions: Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.
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http://dx.doi.org/10.1186/s12864-021-07745-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191001PMC
June 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

Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study.

PLoS Genet 2020 03 30;16(3):e1008684. Epub 2020 Mar 30.

Department of Medicine, University of Washington Medical Center, Seattle, Washington, United States of America.

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.
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http://dx.doi.org/10.1371/journal.pgen.1008684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145272PMC
March 2020

Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism.

Blood 2019 11;134(19):1645-1657

Boston VA Healthcare System, Boston, MA.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality. To advance our understanding of the biology contributing to VTE, we conducted a genome-wide association study (GWAS) of VTE and a transcriptome-wide association study (TWAS) based on imputed gene expression from whole blood and liver. We meta-analyzed GWAS data from 18 studies for 30 234 VTE cases and 172 122 controls and assessed the association between 12 923 718 genetic variants and VTE. We generated variant prediction scores of gene expression from whole blood and liver tissue and assessed them for association with VTE. Mendelian randomization analyses were conducted for traits genetically associated with novel VTE loci. We identified 34 independent genetic signals for VTE risk from GWAS meta-analysis, of which 14 are newly reported associations. This included 11 newly associated genetic loci (C1orf198, PLEK, OSMR-AS1, NUGGC/SCARA5, GRK5, MPHOSPH9, ARID4A, PLCG2, SMG6, EIF5A, and STX10) of which 6 replicated, and 3 new independent signals in 3 known genes. Further, TWAS identified 5 additional genetic loci with imputed gene expression levels differing between cases and controls in whole blood (SH2B3, SPSB1, RP11-747H7.3, RP4-737E23.2) and in liver (ERAP1). At some GWAS loci, we found suggestive evidence that the VTE association signal for novel and previously known regions colocalized with expression quantitative trait locus signals. Mendelian randomization analyses suggested that blood traits may contribute to the underlying risk of VTE. To conclude, we identified 16 novel susceptibility loci for VTE; for some loci, the association signals are likely mediated through gene expression of nearby genes.
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http://dx.doi.org/10.1182/blood.2019000435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871304PMC
November 2019

Genetic analyses of diverse populations improves discovery for complex traits.

Nature 2019 06 19;570(7762):514-518. Epub 2019 Jun 19.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.
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http://dx.doi.org/10.1038/s41586-019-1310-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785182PMC
June 2019

Leveraging linkage evidence to identify low-frequency and rare variants on 16p13 associated with blood pressure using TOPMed whole genome sequencing data.

Hum Genet 2019 Feb 22;138(2):199-210. Epub 2019 Jan 22.

Division of General Medicine, Columbia University Medical Center, New York, NY, 10032, USA.

In this study, we investigated low-frequency and rare variants associated with blood pressure (BP) by focusing on a linkage region on chromosome 16p13. We used whole genome sequencing (WGS) data obtained through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program on 395 Cleveland Family Study (CFS) European Americans (CFS-EA). By analyzing functional coding variants and non-coding rare variants with CADD score > 10 residing within the chromosomal region in families with linkage evidence, we observed 25 genes with nominal statistical evidence (burden or SKAT p < 0.05). One of the genes is RBFOX1, an evolutionarily conserved RNA-binding protein that regulates tissue-specific alternative splicing that we previously reported to be associated with BP using exome array data in CFS. After follow-up analysis of the 25 genes in ten independent TOPMed studies with individuals of European, African, and East Asian ancestry, and Hispanics (N = 29,988), we identified variants in SLX4 (p = 2.19 × 10) to be significantly associated with BP traits when accounting for multiple testing. We also replicated the associations previously reported for RBFOX1 (p = 0.007). Follow-up analysis with GTEx eQTL data shows SLX4 variants are associated with gene expression in coronary artery, multiple brain tissues, and right atrial appendage of the heart. Our study demonstrates that linkage analysis of family data can provide an efficient approach for detecting rare variants associated with complex traits in WGS data.
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http://dx.doi.org/10.1007/s00439-019-01975-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404531PMC
February 2019

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Nat Genet 2019 02 14;51(2):237-244. Epub 2019 Jan 14.

Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Italy.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders. They are heritable and etiologically related behaviors that have been resistant to gene discovery efforts. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.
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http://dx.doi.org/10.1038/s41588-018-0307-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358542PMC
February 2019

Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies.

Nat Commun 2019 01 3;10(1):29. Epub 2019 Jan 3.

Department of Pathology, Erasmus Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands.

Chronic kidney disease (CKD) affects ~10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assemble genome-wide association studies of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals of diverse ancestry. We identify 127 distinct association signals with homogeneous effects on eGFR across ancestries and enrichment in genomic annotations including kidney-specific histone modifications. Fine-mapping reveals 40 high-confidence variants driving eGFR associations and highlights putative causal genes with cell-type specific expression in glomerulus, and in proximal and distal nephron. Mendelian randomisation supports causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure and hypertension. These results define novel molecular mechanisms and putative causal genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.
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http://dx.doi.org/10.1038/s41467-018-07867-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318312PMC
January 2019

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

Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Hum Mol Genet 2018 08;27(16):2940-2953

Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.

C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.
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http://dx.doi.org/10.1093/hmg/ddy211DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6077792PMC
August 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

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.

Nat Genet 2018 04 12;50(4):524-537. Epub 2018 Mar 12.

Institute of Cardiovascular Research, Royal Holloway University of London, London, UK, and Ashford and St Peters Hospital, Surrey, UK.

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.
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http://dx.doi.org/10.1038/s41588-018-0058-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968830PMC
April 2018

Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture using Genomics and Epidemiology (PAGE) consortium.

Diabetologia 2017 12 13;60(12):2384-2398. Epub 2017 Sep 13.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.

Aims/hypothesis: Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies.

Methods: A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported SNP association(s) and denser coverage of SNP markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant SNP associations were further examined using bioinformatic functional annotation.

Results: Previously reported SNP associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513.

Conclusions/interpretation: These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries.

Data Availability: The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.
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http://dx.doi.org/10.1007/s00125-017-4405-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918310PMC
December 2017

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci.

Hum Mol Genet 2016 12;25(24):5500-5512

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies.
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http://dx.doi.org/10.1093/hmg/ddw358DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5721937PMC
December 2016

Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation.

Nat Genet 2017 Jun 17;49(6):946-952. Epub 2017 Apr 17.

Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany.

Atrial fibrillation affects more than 33 million people worldwide and increases the risk of stroke, heart failure, and death. Fourteen genetic loci have been associated with atrial fibrillation in European and Asian ancestry groups. To further define the genetic basis of atrial fibrillation, we performed large-scale, trans-ancestry meta-analyses of common and rare variant association studies. The genome-wide association studies (GWAS) included 17,931 individuals with atrial fibrillation and 115,142 referents; the exome-wide association studies (ExWAS) and rare variant association studies (RVAS) involved 22,346 cases and 132,086 referents. We identified 12 new genetic loci that exceeded genome-wide significance, implicating genes involved in cardiac electrical and structural remodeling. Our results nearly double the number of known genetic loci for atrial fibrillation, provide insights into the molecular basis of atrial fibrillation, and may facilitate the identification of new potential targets for drug discovery.
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http://dx.doi.org/10.1038/ng.3843DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585859PMC
June 2017

Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci.

Hum Genet 2017 06 8;136(6):771-800. Epub 2017 Apr 8.

Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.
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http://dx.doi.org/10.1007/s00439-017-1787-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485655PMC
June 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

Rare variants in fox-1 homolog A (RBFOX1) are associated with lower blood pressure.

PLoS Genet 2017 03 27;13(3):e1006678. Epub 2017 Mar 27.

Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.

Many large genome-wide association studies (GWAS) have identified common blood pressure (BP) variants. However, most of the identified BP variants do not overlap with the linkage evidence observed from family studies. We thus hypothesize that multiple rare variants contribute to the observed linkage evidence. We performed linkage analysis using 517 individuals in 130 European families from the Cleveland Family Study (CFS) who have been genotyped on the Illumina OmniExpress Exome array. The largest linkage peak was observed on chromosome 16p13 (MLOD = 2.81) for systolic blood pressure (SBP). Follow-up conditional linkage and association analyses in the linkage region identified multiple rare, coding variants in RBFOX1 associated with reduced SBP. In a 17-member CFS family, carriers of the missense variant rs149974858 are normotensive despite being obese (average BMI = 60 kg/m2). Gene-based association test of rare variants using SKAT-O showed significant association with SBP (p-value = 0.00403) and DBP (p-value = 0.0258) in the CFS participants and the association was replicated in large independent replication studies (N = 57,234, p-value = 0.013 for SBP, 0.0023 for PP). RBFOX1 is expressed in brain tissues, the atrial appendage and left ventricle in the heart, and in skeletal muscle tissues, organs/tissues which are potentially related to blood pressure. Our study showed that associations of rare variants could be efficiently detected using family information.
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http://dx.doi.org/10.1371/journal.pgen.1006678DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5386302PMC
March 2017

Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array.

PLoS One 2016 14;11(12):e0167758. Epub 2016 Dec 14.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0167758PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156387PMC
July 2017

Trans-ethnic Fine Mapping Highlights Kidney-Function Genes Linked to Salt Sensitivity.

Am J Hum Genet 2016 Sep;99(3):636-646

Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751 85, Sweden.

We analyzed genome-wide association studies (GWASs), including data from 71,638 individuals from four ancestries, for estimated glomerular filtration rate (eGFR), a measure of kidney function used to define chronic kidney disease (CKD). We identified 20 loci attaining genome-wide-significant evidence of association (p < 5 × 10(-8)) with kidney function and highlighted that allelic effects on eGFR at lead SNPs are homogeneous across ancestries. We leveraged differences in the pattern of linkage disequilibrium between diverse populations to fine-map the 20 loci through construction of "credible sets" of variants driving eGFR association signals. Credible variants at the 20 eGFR loci were enriched for DNase I hypersensitivity sites (DHSs) in human kidney cells. DHS credible variants were expression quantitative trait loci for NFATC1 and RGS14 (at the SLC34A1 locus) in multiple tissues. Loss-of-function mutations in ancestral orthologs of both genes in Drosophila melanogaster were associated with altered sensitivity to salt stress. Renal mRNA expression of Nfatc1 and Rgs14 in a salt-sensitive mouse model was also reduced after exposure to a high-salt diet or induced CKD. Our study (1) demonstrates the utility of trans-ethnic fine mapping through integration of GWASs involving diverse populations with genomic annotation from relevant tissues to define molecular mechanisms by which association signals exert their effect and (2) suggests that salt sensitivity might be an important marker for biological processes that affect kidney function and CKD in humans.
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http://dx.doi.org/10.1016/j.ajhg.2016.07.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011075PMC
September 2016

Four Susceptibility Loci for Gallstone Disease Identified in a Meta-analysis of Genome-Wide Association Studies.

Gastroenterology 2016 Aug 16;151(2):351-363.e28. Epub 2016 Apr 16.

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minnesota.

Background & Aims: A genome-wide association study (GWAS) of 280 cases identified the hepatic cholesterol transporter ABCG8 as a locus associated with risk for gallstone disease, but findings have not been reported from any other GWAS of this phenotype. We performed a large-scale, meta-analysis of GWASs of individuals of European ancestry with available prior genotype data, to identify additional genetic risk factors for gallstone disease.

Methods: We obtained per-allele odds ratio (OR) and standard error estimates using age- and sex-adjusted logistic regression models within each of the 10 discovery studies (8720 cases and 55,152 controls). We performed an inverse variance weighted, fixed-effects meta-analysis of study-specific estimates to identify single-nucleotide polymorphisms that were associated independently with gallstone disease. Associations were replicated in 6489 cases and 62,797 controls.

Results: We observed independent associations for 2 single-nucleotide polymorphisms at the ABCG8 locus: rs11887534 (OR, 1.69; 95% confidence interval [CI], 1.54-1.86; P = 2.44 × 10(-60)) and rs4245791 (OR, 1.27; P = 1.90 × 10(-34)). We also identified and/or replicated associations for rs9843304 in TM4SF4 (OR, 1.12; 95% CI, 1.08-1.16; P = 6.09 × 10(-11)), rs2547231 in SULT2A1 (encodes a sulfoconjugation enzyme that acts on hydroxysteroids and cholesterol-derived sterol bile acids) (OR, 1.17; 95% CI, 1.12-1.21; P = 2.24 × 10(-10)), rs1260326 in glucokinase regulatory protein (OR, 1.12; 95% CI, 1.07-1.17; P = 2.55 × 10(-10)), and rs6471717 near CYP7A1 (encodes an enzyme that catalyzes conversion of cholesterol to primary bile acids) (OR, 1.11; 95% CI, 1.08-1.15; P = 8.84 × 10(-9)). Among individuals of African American and Hispanic American ancestry, rs11887534 and rs4245791 were associated positively with gallstone disease risk, whereas the association for the rs1260326 variant was inverse.

Conclusions: In this large-scale GWAS of gallstone disease, we identified 4 loci in genes that have putative functions in cholesterol metabolism and transport, and sulfonylation of bile acids or hydroxysteroids.
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http://dx.doi.org/10.1053/j.gastro.2016.04.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959966PMC
August 2016
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