Publications by authors named "Lu-Chen Weng"

55 Publications

Deep learning enables genetic analysis of the human thoracic aorta.

Nat Genet 2021 Nov 26. Epub 2021 Nov 26.

Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32-1.54, P = 3.3 × 10). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images.
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http://dx.doi.org/10.1038/s41588-021-00962-4DOI Listing
November 2021

Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation.

Circ Genom Precis Med 2021 10 31;14(5):e003355. Epub 2021 Aug 31.

Cardiovascular Research Center (S.K., L.-C.W., P.T.E., S.A.L.), Massachusetts General Hospital, Boston.

Background: Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years.

Methods: We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration.

Results: Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720-0.824) and Predict-AF (0.749-0.831) had largely comparable discrimination, both favorable to continuous age (0.675-0.801). Calibration was similar using CHARGE-AF (slope range, 0.67-0.87) and Predict-AF (0.65-0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024-0.057) but more favorable among individuals aged <65 years (0.062-0.11). Using Predict-AF among 99 530 individuals aged ≥65 years across each sample, 70 849 had AF risk <5%, of whom 69 067 (97.5%) did not develop AF, whereas 28 681 had AF risk ≥5%, of whom 2264 (7.9%) developed AF. Of 11 379 individuals aged <65 years with AF risk ≥5%, 435 (3.8%) developed AF before age 65 years, with roughly half (46.9%) meeting anticoagulation criteria.

Conclusions: AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.
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http://dx.doi.org/10.1161/CIRCGEN.121.003355DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8530935PMC
October 2021

Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis.

Circ Genom Precis Med 2021 08 28;14(4):e003300. Epub 2021 Jul 28.

Regeneron Genetics Center, Tarrytown, NY. Departments of Medicine, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (S.R.).

Background: Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood.

Methods: Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval).

Results: We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (, , and ), a controversial monogenic SCD gene (), and novel genes ( and ) involved in cardiac conduction. Loss-of-function and pathogenic variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (=8.4×10). Similar variants in and (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (=4×10), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals.

Conclusions: Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.
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http://dx.doi.org/10.1161/CIRCGEN.120.003300DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8373440PMC
August 2021

Accelerometer-derived physical activity and risk of atrial fibrillation.

Eur Heart J 2021 07;42(25):2472-2483

Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, MA, USA.

Aims: Physical activity may be an important modifiable risk factor for atrial fibrillation (AF), but associations have been variable and generally based on self-reported activity.

Methods And Results: We analysed 93 669 participants of the UK Biobank prospective cohort study without prevalent AF who wore a wrist-based accelerometer for 1 week. We categorized whether measured activity met the standard recommendations of the European Society of Cardiology, American Heart Association, and World Health Organization [moderate-to-vigorous physical activity (MVPA) ≥150 min/week]. We tested associations between guideline-adherent activity and incident AF (primary) and stroke (secondary) using Cox proportional hazards models adjusted for age, sex, and each component of the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score. We also assessed correlation between accelerometer-derived and self-reported activity. The mean age was 62 ± 8 years and 57% were women. Over a median of 5.2 years, 2338 incident AF events occurred. In multivariable adjusted models, guideline-adherent activity was associated with lower risks of AF [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.75-0.89; incidence 3.5/1000 person-years, 95% CI 3.3-3.8 vs. 6.5/1000 person-years, 95% CI 6.1-6.8] and stroke (HR 0.76, 95% CI 0.64-0.90; incidence 1.0/1000 person-years, 95% CI 0.9-1.1 vs. 1.8/1000 person-years, 95% CI 1.6-2.0). Correlation between accelerometer-derived and self-reported MVPA was weak (Spearman r = 0.16, 95% CI 0.16-0.17). Self-reported activity was not associated with incident AF or stroke.

Conclusions: Greater accelerometer-derived physical activity is associated with lower risks of AF and stroke. Future preventive efforts to reduce AF risk may be most effective when targeting adherence to objective activity thresholds.
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http://dx.doi.org/10.1093/eurheartj/ehab250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291334PMC
July 2021

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Nature 2021 02 10;590(7845):290-299. Epub 2021 Feb 10.

The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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http://dx.doi.org/10.1038/s41586-021-03205-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875770PMC
February 2021

Clinical Application of a Novel Genetic Risk Score for Ischemic Stroke in Patients With Cardiometabolic Disease.

Circulation 2021 Feb 13;143(5):470-478. Epub 2020 Nov 13.

TIMI Study Group, Boston, MA (N.A.M., F.K.K., F.N., G.M.M., R.P.G., B.M.S., M.L.O'D., E.M.A., E.B., M.S.S., C.T.R.).

Background: Genome-wide association studies have identified single-nucleotide polymorphisms that are associated with an increased risk of stroke. We sought to determine whether a genetic risk score (GRS) could identify subjects at higher risk for ischemic stroke after accounting for traditional clinical risk factors in 5 trials across the spectrum of cardiometabolic disease.

Methods: Subjects who had consented for genetic testing and who were of European ancestry from the ENGAGE AF-TIMI 48 (Effective Anticoagulation with Factor Xa Next Generation in Atrial Fibrillation), SOLID-TIMI 52 (Stabilization of Plaques Using Darapladib), SAVOR-TIMI 53 (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus), PEGASUS-TIMI 54 (Prevention of Cardiovascular Events in Patients With Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin), and FOURIER (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Patients With Elevated Risk) trials were included in this analysis. A set of 32 single-nucleotide polymorphisms associated with ischemic stroke was used to calculate a GRS in each patient and identify tertiles of genetic risk. A Cox model was used to calculate hazard ratios for ischemic stroke across genetic risk groups, adjusted for clinical risk factors.

Results: In 51 288 subjects across the 5 trials, a total of 960 subjects had an ischemic stroke over a median follow-up period of 2.5 years. After adjusting for clinical risk factors, a higher GRS was strongly and independently associated with increased risk for ischemic stroke ( trend=0.009). In comparison with individuals in the lowest third of the GRS, individuals in the middle and top tertiles of the GRS had adjusted hazard ratios of 1.15 (95% CI, 0.98-1.36) and 1.24 (95% CI 1.05-1.45) for ischemic stroke, respectively. Stratification into subgroups revealed that the performance of the GRS appeared stronger in the primary prevention cohort with an adjusted hazard ratio for the top versus lowest tertile of 1.27 (95% CI, 1.04-1.53), in comparison with an adjusted hazard ratio of 1.06 (95% CI, 0.81-1.41) in subjects with previous stroke. In an exploratory analysis of patients with atrial fibrillation and CHADS-VASc score of 2, high genetic risk conferred a 4-fold higher risk of stroke and an absolute risk equivalent to those with CHADS-VASc score of 3.

Conclusions: Across a broad spectrum of subjects with cardiometabolic disease, a 32-single-nucleotide polymorphism GRS was a strong, independent predictor of ischemic stroke. In patients with atrial fibrillation but lower CHADS-VASc scores, the GRS identified patients with risk comparable to those with higher CHADS-VASc scores.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.120.051927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856243PMC
February 2021

Associations Between Alcohol Intake and Genetic Predisposition With Atrial Fibrillation Risk in a National Biobank.

Circ Genom Precis Med 2020 12 6;13(6):e003111. Epub 2020 Nov 6.

Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge (J.L.H., L.-C.W., S.H.C., S.J.J., V.N.M., S.K., P.T.E., S.A.L.).

Background: Excess alcohol intake and inherited predisposition may increase risk of atrial fibrillation (AF). We assessed the association between alcohol intake, polygenic predisposition to AF, and incident AF in the UK Biobank, a prospective cohort study.

Methods: In 376 776 UK Biobank participants enrolled between 2006 and 2010, we tested alcohol consumption (stratified by the Centers of Disease Control and Prevention acceptable range of ≤98 g/wk for women or ≤196 g/wk for men; and as a continuous variable) and an AF polygenic risk score for association with incident AF.

Results: Among participants (47.5% male, mean age 56.9 years), 6293 developed AF during a median of 6.9 years of follow-up. Alcohol consumption was associated with AF (hazard ratio, 1.10 [95% CI, 1.05-1.16] for intake above an acceptable range; hazard ratio, 1.04 per 100 g/wk [95% CI, 1.02-1.06]). An AF polygenic risk score was associated with AF (hazard ratio, 1.38 per SD [95% CI, 1.35-1.41]). In models including both alcohol and the AF polygenic risk score, each remained associated with AF. The 5-year cumulative risk of AF for individuals with alcohol intake above an acceptable range and in the highest decile of polygenic risk was 2.33% (95% CI, 2.07-2.59), compared with 0.69% (95% CI, 0.58-0.80) for those with alcohol intake within an acceptable range and in the lowest decile of polygenic risk.

Conclusions: Alcohol consumption is associated with increased risk of AF across a range of polygenic predisposition to AF and adds to inherited and clinical predisposition to increase AF susceptibility. Preventive efforts focused on minimizing alcohol intake may be broadly applicable.
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http://dx.doi.org/10.1161/CIRCGEN.120.003111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738370PMC
December 2020

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

Limitations of Contemporary Guidelines for Managing Patients at High Genetic Risk of Coronary Artery Disease.

J Am Coll Cardiol 2020 06;75(22):2769-2780

Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address:

Background: Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear.

Objectives: This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention.

Methods: A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy.

Results: Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p < 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p < 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor.

Conclusions: Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.
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http://dx.doi.org/10.1016/j.jacc.2020.04.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346975PMC
June 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

Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

Stroke 2020 05 7;51(5):1396-1403. Epub 2020 Apr 7.

Cardiac Arrhythmia Service, Division of Cardiology, (P.T.E., S.A.L.), Massachusetts General Hospital, Boston.

Background and Purpose- Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods- We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHADS-VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record-Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism by calculating C statistics and sensitivity/specificity cutoffs for cardioembolic stroke. Results- Of 1431 individuals with ischemic stroke (age, 65±15; 40% women), 323 (22.6%) had cardioembolism. AF risk was significantly associated with cardioembolism (CHADS-VASc: odds ratio [OR] per SD, 1.69 [95% CI, 1.49-1.93]; Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score: OR, 2.22 [95% CI, 1.90-2.60]; electronic Health Record-Based AF: OR, 2.55 [95% CI, 2.16-3.04]). Discrimination was greater for Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score (C index, 0.695 [95% CI, 0.663-0.726]) and Electronic Health Record-Based AF score (0.713 [95% CI, 0.681-0.744]) versus CHADS-VASc (C index, 0.651 [95% CI, 0.619-0.683]). Examination of AF scores across a range of thresholds indicated that AF risk may facilitate identification of individuals at low likelihood of cardioembolism (eg, negative likelihood ratios for Electronic Health Record-Based AF score ranged 0.31-0.10 at sensitivity thresholds 0.90-0.99). Conclusions- AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.
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http://dx.doi.org/10.1161/STROKEAHA.120.028837DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188588PMC
May 2020

Novel Risk Modeling Approach of Atrial Fibrillation With Restricted Mean Survival Times: Application in the Framingham Heart Study Community-Based Cohort.

Circ Cardiovasc Qual Outcomes 2020 04 31;13(4):e005918. Epub 2020 Mar 31.

National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, MA (L.S., S.R.P., H.L., E.J.B., L.T.).

Background: Risk prediction models for atrial fibrillation (AF) do not give information about when AF might develop. Restricted mean survival time (RMST) quantifies risk into the time domain. Our objective was to use RMST to re-express individualized AF risk predictions.

Methods And Results: We included AF-free participants from the Framingham Heart Study community-based cohorts. We predicted new-onset AF over 10-year follow-up according to baseline covariates: age, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, antihypertensive treatment, diabetes mellitus, prevalent heart failure, and prevalent myocardial infarction. First, we fitted a Cox regression model and estimated the 10-year predicted risk of AF. Second, we fitted an RMST model and estimated the predicted mean time free of AF and alive over a time horizon of 10 years. We included 7586 AF-free participants contributing to 11 088 examinations (mean age 61±11 years, 44% were men). During 10-year follow-up, 822 participants developed AF. The Cox and RMST models were in agreement regarding the direction, strength, and statistical significance of associations for all covariates. Low (<5%), intermediate (5%-15%), and high (>15%) 10-year predicted risk of AF corresponded to predicted mean time alive and free of AF of 9.9, 9.6, and 8.8 years, respectively. A 60-year-old woman with a body mass index of 25 kg/m, no use of hypertension treatment and no history of heart failure had a predicted mean time alive and free of AF of 9.9 years, whereas a 70-year-old man with a body mass index of 30 kg/m, use of hypertension treatment, and with prevalent heart failure had a predicted mean time alive and free of AF of 7.9 years.

Conclusions: The RMST can be used to develop risk prediction models to express results in a time scale. RMST may offer a complementary risk communication tool for AF in clinical practice.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.119.005918DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7176529PMC
April 2020

Initial Precipitants and Recurrence of Atrial Fibrillation.

Circ Arrhythm Electrophysiol 2020 03 12;13(3):e007716. Epub 2020 Feb 12.

Division of Cardiology (S.K., S.A.L.), Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston.

Background: Atrial fibrillation (AF) may occur after an acute precipitant and subsequently resolve. Management guidelines for AF in these settings are unclear as the risk of recurrent AF and related morbidity is poorly understood. We examined the relations between acute precipitants of AF and long-term recurrence of AF in a clinical setting.

Methods: From a multi-institutional longitudinal electronic medical record database, we identified patients with newly diagnosed AF between 2000 and 2014. We developed algorithms to identify acute AF precipitants (surgery, sepsis, pneumonia, pneumothorax, respiratory failure, myocardial infarction, thyrotoxicosis, alcohol, pericarditis, pulmonary embolism, and myocarditis). We assessed risks of AF recurrence in individuals with and without a precipitant and the relations between AF recurrence and heart failure, stroke, and mortality.

Results: Among 10 723 patients with newly diagnosed AF (67.9±9.9 years, 41% women), 19% had an acute AF precipitant, the most common of which were cardiac surgery (22%), pneumonia (20%), and noncardiothoracic surgery (15%). The cumulative incidence of AF recurrence at 5 years was 41% among individuals with a precipitant compared with 52% in those without a precipitant (adjusted hazard ratio [HR], 0.75 [95% CI, 0.69-0.81]; <0.001). The lowest risk of recurrence among those with precipitants occurred with postoperative AF (5-year incidence 32% in cardiac surgery and 39% in noncardiothoracic surgery). Regardless of the presence of an initial precipitant, recurrent AF was associated with increased adjusted risks of heart failure (hazard ratio, 2.74 [95% CI, 2.39-3.15]; <0.001), stroke (hazard ratio, 1.57 [95% CI, 1.30-1.90]; <0.001), and mortality (hazard ratio, 2.96 [95% CI, 2.70-3.24]; <0.001).

Conclusions: AF after an acute precipitant frequently recurs, although the risk of recurrence is lower than among individuals without an acute precipitant. Recurrence is associated with substantial long-term morbidity and mortality. Future studies should address surveillance and management after newly diagnosed AF in the setting of an acute precipitant.
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http://dx.doi.org/10.1161/CIRCEP.119.007716DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141776PMC
March 2020

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

PLoS Genet 2019 12 23;15(12):e1008500. Epub 2019 Dec 23.

Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.
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http://dx.doi.org/10.1371/journal.pgen.1008500DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953885PMC
December 2019

Development and Validation of a Prediction Model for Atrial Fibrillation Using Electronic Health Records.

JACC Clin Electrophysiol 2019 11 2;5(11):1331-1341. Epub 2019 Oct 2.

Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts; Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts. Electronic address:

Objectives: This study sought to determine whether the risk of atrial fibrillation AF can be estimated accurately by using routinely ascertained features in the electronic health record (EHR) and whether AF risk is associated with stroke.

Background: Early diagnosis of AF and treatment with anticoagulation may prevent strokes.

Methods: Using a multi-institutional EHR, this study identified 412,085 individuals 45 to 95 years of age without prevalent AF between 2000 and 2014. A prediction model was derived and validated for 5-year AF risk by using split-sample validation and model performance was compared with other methods of AF risk assessment.

Results: Within 5 years, 14,334 individuals developed AF. In the derivation sample (7,216 AF events of 206,042 total), the optimal risk model included sex, age, race, smoking, height, weight, diastolic blood pressure, hypertension, hyperlipidemia, heart failure, coronary heart disease, valvular disease, prior stroke, peripheral arterial disease, chronic kidney disease, hypothyroidism, and quadratic terms for height, weight, and age. In the validation sample (7,118 AF events of 206,043 total) the AF risk model demonstrated good discrimination (C-statistic: 0.777; 95% confidence interval [CI:] 0.771 to 0.783) and calibration (0.99; 95% CI: 0.96 to 1.01). Model discrimination and calibration were superior to CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) (C-statistic: 0.753; 95% CI: 0.747 to 0.759; calibration slope: 0.72; 95% CI: 0.71 to 0.74), CHEST (Coronary artery disease / chronic obstructive pulmonary disease; Hypertension; Elderly [age ≥75 years]; Systolic heart failure; Thyroid disease [hyperthyroidism]) (C-statistic: 0.754; 95% CI: 0.747 to 0.762; calibration slope: 0.44; 95% CI: 0.43 to 0.45), and CHADS-VASc (Congestive heart failure, Hypertension, Age ≥75 years, Diabetes mellitus, Prior stroke, transient ischemic attack [TIA], or thromboembolism, Vascular disease, Age 65-74 years, Sex category [female]) scores (C-statistic: 0.702; 95% CI: 0.693 to 0.710; calibration slope: 0.37; 95% CI: 0.36 to 0.38). AF risk discriminated incident stroke (n = 4,814; C-statistic: 0.684; 95% CI: 0.677 to 0.692) and stroke within 90 days of incident AF (n = 327; C-statistic: 0.789; 95% CI: 0.764 to 0.814).

Conclusions: A model developed from a real-world EHR database predicted AF accurately and stratified stroke risk. Incorporating AF prediction into EHRs may enable risk-guided screening for AF.
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http://dx.doi.org/10.1016/j.jacep.2019.07.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884135PMC
November 2019

Monogenic and Polygenic Contributions to Atrial Fibrillation Risk: Results From a National Biobank.

Circ Res 2020 01 6;126(2):200-209. Epub 2019 Nov 6.

From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.).

Rationale: Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in and early-onset AF.

Objective: We sought to determine the contribution of rare and common genetic variation to AF risk in the general population.

Methods: The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk.

Results: The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, (odds ratio, 2.71, =2.50×10). The association with AF was more significant (odds ratio, 6.15, =3.26×10) when restricting to LOF variants located in exons highly expressed in cardiac tissue (). Overall, 0.44% of individuals carried variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while variants only accounted for 0.2%.

Conclusions: Both monogenic and polygenic factors contribute to AF risk in the general population. While rare variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.
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http://dx.doi.org/10.1161/CIRCRESAHA.119.315686DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007701PMC
January 2020

Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery.

Circulation 2018 Nov 11. Epub 2018 Nov 11.

Cardiovascular Research Center, Massachusetts General Hospital, United States.

Background: Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery.

Methods: We defined all-cause HF among 488010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4158) and echocardiographic data (n=30201).

Results: We identified 7382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci ( P<1×10), the majority linked to upstream HF risk factors, ie, coronary artery disease ( CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation ( PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy ( BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-offunction variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10).

Conclusions: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independently of clinical HF risk factors and that are associated with subclinical left ventricular dysfunction.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.118.035774DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6511334PMC
November 2018

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes.

Neurol Genet 2018 Dec 3;4(6):e293. Epub 2018 Dec 3.

Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD.

Objective: We sought to assess whether genetic risk factors for atrial fibrillation (AF) can explain cardioembolic stroke risk.

Methods: We evaluated genetic correlations between a previous genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.

Results: We observed a strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson r = 0.77 and 0.76, respectively, across SNPs with < 4.4 × 10 in the previous AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio [OR] per SD = 1.40, = 1.45 × 10), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per SD = 1.07, = 0.004), but no other primary stroke subtypes (all > 0.1).

Conclusions: Genetic risk of AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.
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http://dx.doi.org/10.1212/NXG.0000000000000293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283455PMC
December 2018

Association Between Titin Loss-of-Function Variants and Early-Onset Atrial Fibrillation.

JAMA 2018 12;320(22):2354-2364

Department of Molecular and Functional Genomics, Geisinger, Danville, Pennsylvania.

Importance: Atrial fibrillation (AF) is the most common arrhythmia affecting 1% of the population. Young individuals with AF have a strong genetic association with the disease, but the mechanisms remain incompletely understood.

Objective: To perform large-scale whole-genome sequencing to identify genetic variants related to AF.

Design, Setting, And Participants: The National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine Program includes longitudinal and cohort studies that underwent high-depth whole-genome sequencing between 2014 and 2017 in 18 526 individuals from the United States, Mexico, Puerto Rico, Costa Rica, Barbados, and Samoa. This case-control study included 2781 patients with early-onset AF from 9 studies and identified 4959 controls of European ancestry from the remaining participants. Results were replicated in the UK Biobank (346 546 participants) and the MyCode Study (42 782 participants).

Exposures: Loss-of-function (LOF) variants in genes at AF loci and common genetic variation across the whole genome.

Main Outcomes And Measures: Early-onset AF (defined as AF onset in persons <66 years of age). Due to multiple testing, the significance threshold for the rare variant analysis was P = 4.55 × 10-3.

Results: Among 2781 participants with early-onset AF (the case group), 72.1% were men, and the mean (SD) age of AF onset was 48.7 (10.2) years. Participants underwent whole-genome sequencing at a mean depth of 37.8 fold and mean genome coverage of 99.1%. At least 1 LOF variant in TTN, the gene encoding the sarcomeric protein titin, was present in 2.1% of case participants compared with 1.1% in control participants (odds ratio [OR], 1.76 [95% CI, 1.04-2.97]). The proportion of individuals with early-onset AF who carried a LOF variant in TTN increased with an earlier age of AF onset (P value for trend, 4.92 × 10-4), and 6.5% of individuals with AF onset prior to age 30 carried a TTN LOF variant (OR, 5.94 [95% CI, 2.64-13.35]; P = 1.65 × 10-5). The association between TTN LOF variants and AF was replicated in an independent study of 1582 patients with early-onset AF (cases) and 41 200 control participants (OR, 2.16 [95% CI, 1.19-3.92]; P = .01).

Conclusions And Relevance: In a case-control study, there was a statistically significant association between an LOF variant in the TTN gene and early-onset AF, with the variant present in a small percentage of participants with early-onset AF (the case group). Further research is necessary to understand whether this is a causal relationship.
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http://dx.doi.org/10.1001/jama.2018.18179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6436530PMC
December 2018

Electronic physician notifications to improve guideline-based anticoagulation in atrial fibrillation: a randomized controlled trial.

J Gen Intern Med 2018 12 3;33(12):2070-2077. Epub 2018 Aug 3.

Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA.

Background: Oral anticoagulants reduce the risk of stroke in patients with atrial fibrillation. However, many patients with atrial fibrillation at elevated stroke risk are not treated with oral anticoagulants.

Objective: To test whether electronic notifications sent to primary care physicians increase the proportion of ambulatory patients prescribed oral anticoagulants.

Design: Randomized controlled trial conducted from February to May 2017 within 18 practices in an academic primary care network.

Participants: Primary care physicians (n = 175) and their patients with atrial fibrillation, at elevated stroke risk, and not prescribed oral anticoagulants.

Intervention: Patients of each physician were randomized to the notification or usual care arm. Physicians received baseline email notifications and up to three reminders with patient information, educational material and primary care guidelines for anticoagulation management, and surveys in the notification arm.

Main Measures: The primary outcome was the proportion of patients prescribed oral anticoagulants at 3 months in the notification (n = 972) vs. usual care (n = 1364) arms, compared using logistic regression with clustering by physician. Secondary measures included survey-based physician assessment of reasons why patients were not prescribed oral anticoagulants and how primary care physicians might be influenced by the notification.

Key Results: Over 3 months, a small proportion of patients were newly prescribed oral anticoagulants with no significant difference in the notification (3.9%, 95% CI 2.8-5.3%) and usual care (3.2%, 95% CI 2.4-4.2%) arms (p = 0.37). The most common, non-exclusive reasons why patients were not on oral anticoagulants included atrial fibrillation was transient (30%) or paroxysmal (12%), patient/family declined (22%), high bleeding risk (20%), fall risk (19%), and frailty (10%). For 95% of patients, physicians stated they would not change their management after reviewing the alert.

Conclusions: Electronic physician notification did not increase anticoagulation in patients with atrial fibrillation at elevated stroke risk. Primary care physicians did not prescribe anticoagulants because they perceived the bleeding risk was too high or stroke risk was too low.

Trial Registration: ClinicalTrials.gov identifier NCT02950285.
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http://dx.doi.org/10.1007/s11606-018-4612-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258628PMC
December 2018

Frequency of Cardiac Rhythm Abnormalities in a Half Million Adults.

Circ Arrhythm Electrophysiol 2018 07;11(7):e006273

Cardiovascular Research Center (S.H.C., L.-C.W., E.Y.W., P.T.E., S.A.L.)

Background: The frequency of cardiac rhythm abnormalities and their risk factors in community-dwelling adults are not well characterized.

Methods: We determined the frequency of rhythm abnormalities in the UK Biobank, a national prospective cohort. We tested associations between risk factors and incident rhythm abnormalities using multivariable proportional hazards regression.

Results: Of 502 627 adults (median age, 58 years [interquartile range, 13]; 54.4% women), 2.35% had a baseline rhythm abnormality. The prevalence increased with age with 4.84% of individuals aged 65 to 73 years affected. During 3 368 332 person-years of follow-up, 15 906 new rhythm abnormalities were detected (4.72 per 1000 person-years; 95% confidence interval [CI]: 4.65-4.80). Atrial fibrillation (3.11 per 1000 person-years; 95% CI: 3.05-3.17), bradyarrhythmias (0.89 per 1000 person-years; 95% CI: 0.86-0.92), and conduction system diseases (1.06 per 1000 person-years; 95% CI: 1.02-1.09) were more common than supraventricular (0.51 per 1000 person-years; 95% CI: 0.48-0.53) and ventricular arrhythmias (0.57 per 1000 person-years; 95% CI: 0.55-0.60). Older age (hazard ratio [HR]: 2.35 per 10-year increase; 95% CI: 2.29-2.41; <0.01), male sex (HR: 1.83; 95% CI: 1.76-1.89; <0.01), hypertension (HR: 1.49; 95% CI: 1.44-1.54; <0.01), chronic kidney disease (HR: 1.95; 95% CI: 1.67-2.27; <0.01), and heart failure (HR: 1.99; 95% CI: 1.76-2.26; <0.01) were associated with new rhythm abnormalities.

Conclusions: The frequency of rhythm abnormalities in middle-aged to older community-dwelling adults is substantial. Atrial fibrillation, bradyarrhythmias, and conduction system diseases account for most rhythm conditions.
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http://dx.doi.org/10.1161/CIRCEP.118.006273DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051725PMC
July 2018

Pleiotropic effects of n-6 and n-3 fatty acid-related genetic variants on circulating hemostatic variables.

Thromb Res 2018 08 1;168:53-59. Epub 2018 Jun 1.

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, 1300 South Second Street, WBOB 300, Minneapolis, MN 55454, USA. Electronic address:

Introduction: Data from epidemiological studies and clinical trials suggest an influence of dietary and circulating polyunsaturated fatty acids (PUFAs) on the hemostasis profile. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) related to plasma PUFAs levels. We aimed to investigate whether the SNPs related to plasma PUFAs levels were also associated with plasma levels of hemostatic variables.

Materials And Methods: We tested the associations between 9 PUFA-related SNPs and 6 hemostatic variables in 9035 European Americans (EAs) and 2702 African Americans (AAs) in the Atherosclerosis Risk in Communities (ARIC) Study. We then conducted a replication study by looking-up our novel observed associations in three published GWAS for hemostatic factors in different EA populations.

Results: We observed a novel linoleic acid-related locus at the JMJD1C region associated with factor VII activity (FVIIc): rs10740118 and rs1935, Beta (p) = -1.31 (1 × 10) and 1.37 (5 × 10) in EAs, respectively, and - 1.24 (5 × 10) and 1.28 (3 × 10) in meta-analysis of EAs and AAs of ARIC. This novel association was replicated in two of three independent EA populations (p = 0.01 and 0.03 in meta-analyses). We confirmed previously reported associations at the docosapentaenoic acid-related GCKR locus with protein C and FVIIc and at JMJD1C with fibrinogen. Adjustment for plasma PUFAs did not abolish the associations between these loci and hemostatic variables.

Conclusions: Our study identified a novel association for FVIIc at JMJD1C, a histone demethylase that plays a role in DNA repair and possibly transcription regulation and RNA processing.
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http://dx.doi.org/10.1016/j.thromres.2018.05.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6089352PMC
August 2018

Predictors of oral anticoagulant non-prescription in patients with atrial fibrillation and elevated stroke risk.

Am Heart J 2018 06 10;200:24-31. Epub 2018 Mar 10.

Veterans Affairs Eastern Colorado Health Care System, University of Colorado School of Medicine, Denver, Colorado.

Background: Many patients with atrial fibrillation (AF) and elevated stroke risk are not prescribed oral anticoagulation (OAC) despite evidence of benefit. Identification of factors associated with OAC non-prescription could lead to improvements in care.

Methods And Results: Using NCDR PINNACLE, a United States-based ambulatory cardiology registry, we examined factors associated with OAC non-prescription in patients with non-valvular AF at elevated stroke risk (CHADS-VASc ≥2) between January 5, 2008 and December 31, 2014. Among 674,841 patients, 57% were treated with OAC (67% of whom were treated with warfarin). OAC prescription varied widely (28%-75%) across preselected strata of age, stroke risk (CHADS-VASc), and bleeding risk (HAS-BLED), generally indicating that older patients at high stroke and low bleeding risk are commonly treated with OAC. Other factors associated with OAC non-prescription included reversible AF etiology; female sex; liver, renal, or vascular disease; and physician versus non-physician provider. Antiplatelet use was common (57%) and associated with the greatest risk of OAC non-prescription (odds ratio [OR] 4.44, 95% confidence interval [CI] 4.39-4.49).

Conclusions: In this registry of AF patients, older patients at elevated stroke and low bleeding risk were commonly treated with OAC. However, a variety of factors were associated with OAC non-prescription. Specifically, antiplatelet use was prevalent and associated with the highest likelihood of OAC non-prescription. Future studies are warranted to understand provider and patient rationale that may underlie observed associations with OAC non-prescription.
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http://dx.doi.org/10.1016/j.ahj.2018.03.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005198PMC
June 2018

Multi-ethnic genome-wide association study for atrial fibrillation.

Nat Genet 2018 06 11;50(9):1225-1233. Epub 2018 Jun 11.

Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and has a complex heritability. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.
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http://dx.doi.org/10.1038/s41588-018-0133-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136836PMC
June 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

Lifetime risk of atrial fibrillation according to optimal, borderline, or elevated levels of risk factors: cohort study based on longitudinal data from the Framingham Heart Study.

BMJ 2018 04 26;361:k1453. Epub 2018 Apr 26.

Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA

Objective: To examine the association between risk factor burdens-categorized as optimal, borderline, or elevated-and the lifetime risk of atrial fibrillation.

Design: Community based cohort study.

Setting: Longitudinal data from the Framingham Heart Study.

Participants: Individuals free of atrial fibrillation at index ages 55, 65, and 75 years were assessed. Smoking, alcohol consumption, body mass index, blood pressure, diabetes, and history of heart failure or myocardial infarction were assessed as being optimal (that is, all risk factors were optimal), borderline (presence of borderline risk factors and absence of any elevated risk factor), or elevated (presence of at least one elevated risk factor) at index age.

Main Outcome Measure: Lifetime risk of atrial fibrillation at index age up to 95 years, accounting for the competing risk of death.

Results: At index age 55 years, the study sample comprised 5338 participants (2531 (47.4%) men). In this group, 247 (4.6%) had an optimal risk profile, 1415 (26.5%) had a borderline risk profile, and 3676 (68.9%) an elevated risk profile. The prevalence of elevated risk factors increased gradually when the index ages rose. For index age of 55 years, the lifetime risk of atrial fibrillation was 37.0% (95% confidence interval 34.3% to 39.6%). The lifetime risk of atrial fibrillation was 23.4% (12.8% to 34.5%) with an optimal risk profile, 33.4% (27.9% to 38.9%) with a borderline risk profile, and 38.4% (35.5% to 41.4%) with an elevated risk profile. Overall, participants with at least one elevated risk factor were associated with at least 37.8% lifetime risk of atrial fibrillation. The gradient in lifetime risk across risk factor burden was similar at index ages 65 and 75 years.

Conclusions: Regardless of index ages at 55, 65, or 75 years, an optimal risk factor profile was associated with a lifetime risk of atrial fibrillation of about one in five; this risk rose to more than one in three a third in individuals with at least one elevated risk factor.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5917175PMC
http://dx.doi.org/10.1136/bmj.k1453DOI Listing
April 2018

Evaluation of the relationship between plasma lipids and abdominal aortic aneurysm: A Mendelian randomization study.

PLoS One 2018 12;13(4):e0195719. Epub 2018 Apr 12.

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.

Studies have reported that higher circulating levels of total cholesterol (TC), low-density lipoprotein (LDL) cholesterol and lower of high-density lipoprotein (HDL) cholesterol may be associated with increased risk of abdominal aortic aneurysm (AAA). Whether dyslipidemia causes AAA is still unclear and is potentially testable using a Mendelian randomization (MR) approach. We investigated the associations between blood lipids and AAA using two-sample MR analysis with SNP-lipids association estimates from a published genome-wide association study of blood lipids (n = 188,577) and SNP-AAA association estimates from European Americans (EAs) of the Atherosclerosis Risk in Communities (ARIC) study (n = 8,793). We used inverse variance weighted (IVW) MR as the primary method and MR-Egger regression and weighted median MR estimation as sensitivity analyses. Over a median of 22.7 years of follow-up, 338 of 8,793 ARIC participants experienced incident clinical AAA. Using the IVW method, we observed positive associations of plasma LDL cholesterol and TC with the risk of AAA (odds ratio (OR) = 1.55, P = 0.02 for LDL cholesterol and OR = 1.61, P = 0.01 for TC per 1 standard deviation of lipid increment). Using the MR-Egger regression and weighted median methods, we were able to validate the association of AAA risk with TC, although the associations were less consistent for LDL cholesterol due to wider confidence intervals. Triglycerides and HDL cholesterol were not associated with AAA in any of the MR methods. Assuming instrumental variable assumptions are satisfied, our finding suggests that higher plasma TC and LDL cholesterol are causally associated with the increased risk of AAA in EAs.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195719PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896990PMC
July 2018

Heritability of Atrial Fibrillation.

Circ Cardiovasc Genet 2017 Dec;10(6)

From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA.

Background: Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified.

Methods And Results: We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated using a variance components method with variants having a minor allele frequency ≥1%. We evaluated in age, sex, and genomic strata of interest. The for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions.

Conclusions: Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF.
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http://dx.doi.org/10.1161/CIRCGENETICS.117.001838DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966046PMC
December 2017

Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation.

Circulation 2018 03 12;137(10):1027-1038. Epub 2017 Nov 12.

Cardiovascular Research Center (L.-C.W., O.L.H., P.T.E., S.A.L.)

Background: The long-term probability of developing atrial fibrillation (AF) considering genetic predisposition and clinical risk factor burden is unknown.

Methods: We estimated the lifetime risk of AF in individuals from the community-based Framingham Heart Study. Polygenic risk for AF was derived using a score of ≈1000 AF-associated single-nucleotide polymorphisms. Clinical risk factor burden was calculated for each individual using a validated risk score for incident AF comprised of height, weight, systolic and diastolic blood pressure, current smoking status, antihypertensive medication use, diabetes mellitus, history of myocardial infarction, and history of heart failure. We estimated the lifetime risk of AF within tertiles of polygenic and clinical risk.

Results: Among 4606 participants without AF at 55 years of age, 580 developed incident AF (median follow-up, 9.4 years; 25th-75th percentile, 4.4-14.3 years). The lifetime risk of AF >55 years of age was 37.1% and was substantially influenced by both polygenic and clinical risk factor burden. Among individuals free of AF at 55 years of age, those in low-polygenic and clinical risk tertiles had a lifetime risk of AF of 22.3% (95% confidence interval, 15.4-9.1), whereas those in high-risk tertiles had a risk of 48.2% (95% confidence interval, 41.3-55.1). A lower clinical risk factor burden was associated with later AF onset after adjusting for genetic predisposition (<0.001).

Conclusions: In our community-based cohort, the lifetime risk of AF was 37%. Estimation of polygenic AF risk is feasible and together with clinical risk factor burden explains a substantial gradient in long-term AF risk.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.117.031431DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840011PMC
March 2018

Diminished Expression Is Associated With Increased Risk of Atrial Fibrillation and Shortening of the Cardiac Action Potential.

Circ Cardiovasc Genet 2017 Oct;10(5)

From the Cardiovascular Research Center (N.R.T., E.V.D., R.R.C., J.Y., W.J.H., H.S.J., V.A.P., L.-C.W., R.W.M., J.L.-M., S.A.L., D.J.M., P.T.E.) and Department of Surgery (G.V.), Massachusetts General Hospital, Boston; National Heart, Lung and Blood Institute's and Boston University's Framingham Heart, MA (H.L., E.J.B., K.L.L.); Computational Biomedicine Section (H.L.), Cardiology Section (E.J.B.), and Preventive Medicine Section (E.J.B.), Department of Medicine, Boston University School of Medicine, MA; Department of Medicine I, University Hospital Munich, Campus Grosshadern, Ludwig-Maximilians-University, Germany (M.F.S.); Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge (M.I., L.M.); Department of Epidemiology (E.J.B.) and Department of Biostatistics (K.L.L.), Boston University School of Public Health, MA; and Program in Medical and Populations Genetics, Broad Institute, Cambridge, MA (S.A.L., D.J.M., P.T.E.).

Background: Atrial fibrillation (AF) affects over 33 million individuals worldwide. Genome-wide association studies have identified at least 30 AF loci, but the mechanisms through which individual variants lead to altered disease risk have remained unclear for the majority of these loci. At the 1q24 locus, we hypothesized that the transcription factor could be a strong candidate gene as it is expressed in the pulmonary veins, a source of AF in many individuals. We sought to identify the molecular mechanism, whereby variation at 1q24 may lead to AF susceptibility.

Methods And Results: We sequenced a ≈158 kb region encompassing in 962 individuals with and without AF. We identified a broad region of association with AF at the 1q24 locus. Using in silico prediction and functional validation, we identified an enhancer that interacts with the promoter of in cells of cardiac lineage. Within this enhancer, we identified a single-nucleotide polymorphism, rs577676, which alters enhancer activity in a mouse atrial cell line and in embryonic zebrafish and differentially regulates expression in human left atria. We found that suppression of in human embryonic stem cell-derived cardiomyocytes and embryonic zebrafish resulted in shortening of the atrial action potential duration, a hallmark of AF.

Conclusions: We have identified a functional genetic variant that alters expression, ultimately resulting in electrophysiological alterations in atrial myocytes that may promote AF.
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http://dx.doi.org/10.1161/CIRCGENETICS.117.001902DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679717PMC
October 2017
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