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Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P

BackgroundThe most prominent risk factor for atrial fibrillation (AF) is chronological age; however, underlying mechanisms are unexplained. Algorithms using epigenetic modifications to the human genome effectively predict chronological age. Chronological and epigenetic predicted ages may diverge in a phenomenon referred to as epigenetic age acceleration (EAA), which may reflect accelerated biological aging. We sought to evaluate for associations between epigenetic age measures and incident AF.MethodsMeasures for 4 epigenetic clocks (Horvath, Hannum, DNA methylation [DNAm] PhenoAge, and DNAm GrimAge) and an epigenetic predictor of PAI-1 (plasminogen activator inhibitor-1) levels (ie, DNAm PAI-1) were determined for study participants from 3 population-based cohort studies. Cox models evaluated for associations with incident AF and results were combined via random-effects meta-analyses. Two-sample summary-level Mendelian randomization analyses evaluated for associations between genetic instruments of the EAA measures and AF.ResultsAmong 5600 participants (mean age, 65.5 years; female, 60.1%; Black, 50.7%), there were 905 incident AF cases during a mean follow-up of 12.9 years. Unadjusted analyses revealed all 4 epigenetic clocks and the DNAm PAI-1 predictor were associated with statistically significant higher hazards of incident AF, though the magnitudes of their point estimates were smaller relative to the associations observed for chronological age. The pooled EAA estimates for each epigenetic measure, with the exception of Horvath EAA, were associated with incident AF in models adjusted for chronological age, race, sex, and smoking variables. After multivariable adjustment for additional known AF risk factors that could also potentially function as mediators, pooled EAA measures for 2 clocks remained statistically significant. Five-year increases in EAA measures for DNAm GrimAge and DNAm PhenoAge were associated with 19% (adjusted hazard ratio [HR], 1.19 [95% CI, 1.09-1.31]; P

BackgroundAlterations 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.MethodsUsing 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).ResultsWe found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals.ConclusionsOur 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.

BackgroundAdherence to nicotine patches relates to cessation. This is the first study to examine the validity of self-reported nicotine patch adherence relative to saliva cotinine.MethodsWe used data from 198 clinical trial participants who received 11 weeks of nicotine patches, self-reported patch use, had saliva cotinine 1-week after the start of treatment assessed, and were not smoking when saliva was collected (CO < 6). Self-reported patch adherence was defined as: 3-day (before saliva collection), 7-day (before saliva collection), 3-week use (7 days before, and 14 days after, saliva collection), and 11-week use (7 days before, and 10 weeks after, saliva collection). Analyses, including receiver operating characteristic curves, considered differences in nicotine metabolism. Sensitivity, specificity and positive (PPV) and negative predictive value (NPV) assessed optimal cotinine cut-point for adherence.ResultsSelf-reported 7-day (r = 0.13) and 3-week (r = 0.13) patch use marginally correlated with week 1 cotinine (p's = 0.08) but not 3-day or 11-week. Significant area under the curve (AUC) values of 0.67 (95 %CI: 0.55-0.79) and 0.72 (95 %CI: 0.57-0.88) were found using 7-day self-report for the overall sample and for slow metabolizers (p's

Abstract Background All-cause mortality following atrial fibrillation (AF) has decreased over time. Data regarding temporal trends in causes of death among individuals with AF are scarce. The aim of our study was to analyze temporal trends in cause-specific mortality and predictors for cardiovascular (CVD) and non-CVD deaths among participants with incident AF in the Framingham Heart Study. Methods We categorized all newly diagnosed AF cases according to age at AF diagnosis (

BackgroundAtrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke.MethodsTo determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from

It is unclear whether genetic markers interact with risk factors to influence atrial fibrillation (AF) risk. We performed genome-wide interaction analyses between genetic variants and age, sex, hypertension, and body mass index in the AFGen Consortium. Study-specific results were combined using meta-analysis (88,383 individuals of European descent, including 7,292 with AF). Variants with nominal interaction associations in the discovery analysis were tested for association in four independent studies (131,441 individuals, including 5,722 with AF). In the discovery analysis, the AF risk associated with the minor rs6817105 allele (at the PITX2 locus) was greater among subjects ≤ 65 years of age than among those > 65 years (interaction p-value = 4.0 × 10-5). The interaction p-value exceeded genome-wide significance in combined discovery and replication analyses (interaction p-value = 1.7 × 10-8). We observed one genome-wide significant interaction with body mass index and several suggestive interactions with age, sex, and body mass index in the discovery analysis. However, none was replicated in the independent sample. Our findings suggest that the pathogenesis of AF may differ according to age in individuals of European descent, but we did not observe evidence of statistically significant genetic interactions with sex, body mass index, or hypertension on AF risk.

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74g

Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility. We performed a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65,237 AF-free referents collected from 15 studies for discovery. We examined putative interactions between genome-wide SNPs and 17 known AF-related SNPs. The top interactions were then tested for association in an independent cohort for replication, which included more than 2,363 AF cases and 114,746 AF-free referents. One interaction, between rs7164883 at the HCN4 locus and rs4980345 at the SLC28A1 locus, was found to be significantly associated with AF in the discovery cohorts (interaction OR = 1.44, 95% CI: 1.27-1.65, P = 4.3 × 10-8). Eight additional gene-gene interactions were also marginally significant (P

BackgroundGenome-wide association studies (GWAS) have identified common genetic variants that predispose to atrial fibrillation (AF). It is unclear whether rare and low-frequency variants in genes implicated by such GWAS confer additional risk of AF.ObjectiveTo study the association of genetic variants with AF at GWAS top loci.MethodsIn the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study, we selected and sequenced 77 target gene regions from GWAS loci of complex diseases or traits, including 4 genes hypothesized to be related to AF (PRRX1, CAV1, CAV2, and ZFHX3). Sequencing was performed in participants with (n = 948) and without (n = 3330) AF from the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Massachusetts General Hospital.ResultsOne common variant (rs11265611; P = 1.70 × 10(-6)) intronic to IL6R (interleukin-6 receptor gene) was significantly associated with AF after Bonferroni correction (odds ratio 0.70; 95% confidence interval 0.58-0.85). The variant was not genotyped or imputed by prior GWAS, but it is in linkage disequilibrium (r(2) = .69) with the single-nucleotide polymorphism, with the strongest association with AF so far at this locus (rs4845625). In the rare variant joint analysis, damaging variants within the PRRX1 region showed significant association with AF after Bonferroni correction (P = .01).ConclusionsWe identified 1 common single-nucleotide polymorphism and 1 gene region that were significantly associated with AF. Future sequencing efforts with larger sample sizes and more comprehensive genome coverage are anticipated to identify additional AF-related variants.

Background Previous studies assessing the association between body mass index (BMI) and atrial fibrillation (AF) did not account for time‐varying covariates, which may be affected by previous BMI. We illustrate how the g‐formula can account for time‐varying confounding. Methods and Results We included 4392 participants from the Framingham Heart Study who were AF free at ages 45 to 55 years, and followed them for up to 20 years. We estimated hazard ratios (HRs) comparing time‐varying nonobese versus obese with Cox models. We used the g‐formula to compare nonobese versus obese and 10% annual decrease in BMI (until normal weight is reached) versus natural course. We estimated HRs and differences in restricted mean survival times, the mean difference in time alive and AF free. We adjusted for sex, age, and time‐varying risk factors. Cox models indicated that nonobese participants had a decreased rate of AF versus obese participants (HR, 0.83; 95% CI, 0.72–0.97). G‐formula analyses comparing everyone had they been nonobese versus obese yielded stronger associations (HR, 0.73; 95% CI, 0.58–0.91). The restricted mean survival time was 19.22 years had everyone been nonobese and 19.03 years had everyone been obese (difference, 2.25 months; 95% CI, −0.66 to 5.16). When assessing a 10% annual decrease in BMI, the association was weaker (HR 0.96; 95% CI, 0.86–1.08). Conclusions Decreased BMI was associated with a lower rate of AF after accounting for time‐varying covariates that depend on previous exposure using the g‐formula, which Cox models cannot accommodate. Absolute measures like the restricted mean survival time difference offer context to relative measures of association.

BackgroundAdvancing age is a prominent risk factor for atrial fibrillation (AF). Shorter telomere length is a biomarker of biological aging, but the link between shorter telomere length and increased risk of AF remains unclear. We examined the association between shorter leukocyte telomere length (LTL) and incident AF. Methods and ResultsWe included AF‐free participants from the observational Framingham Heart Study Offspring cohort from 1995 to 1998, who had LTL measurements. We examined the association between baseline LTL and incident AF with multivariable Cox models adjusted for age, sex, current smoking, height, weight, systolic and diastolic blood pressure, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction, and history of heart failure. The study sample comprised 1143 AF‐free participants (52.8% women), with mean age of 60±8 years. The mean LTL at baseline was 6.95±0.57 kb. During 15.1±4.2 years mean follow‐up, 184 participants (64 women) developed AF. Chronological age was associated with increased risk of AF (hazard ratio per 10‐year increase, 2.16; 95% confidence interval, 1.71–2.72). There was no significant association between LTL and incident AF (hazard ratio per 1 SD decrease LTL, 1.01; 95% confidence interval, 0.86–1.19). Our study was observational in nature; hence, we could not exclude residual confounding and we were unable to establish causal pathways. ConclusionsIn our moderate‐sized community‐based cohort, we did not find evidence for a significant association between LTL and risk of incident AF.

BackgroundObesity is an important risk factor for nonalcoholic fatty liver disease and atrial fibrillation (AF). Less is known about the relations between nonalcoholic fatty liver disease and AF. We sought to evaluate the association between fatty liver and prevalent and incident AF in the community. Methods and ResultsWe examined Framingham Heart Study participants who underwent a study‐directed computed tomography scan, had hepatic steatosis (HS) evaluated, and did not report heavy alcohol use between 2002 and 2005. We evaluated cross‐sectional associations between liver fat and prevalent AF with logistic regression models. We assessed the relations between liver fat and incident AF during 12‐year follow‐up with Cox proportional hazards models. Of 2122 participants (53% women; mean age, 59.0±9.6 years), 20% had HS. AF prevalence (n=62) among individuals with HS was 4% compared to 3% among those without HS. There was no significant association between HS (measured as continuous or dichotomous variables) and prevalent AF in age‐ and sex‐adjusted or multivariable‐adjusted models. Incidence of AF (n=153) among participants with and without HS was 8.7 cases and 7.8 cases per 1000 person‐years, respectively. In age‐ and sex‐adjusted and multivariable‐adjusted models, there were no significant associations between continuous or dichotomous measures of HS and incident AF. ConclusionsIn our community‐based, longitudinal cohort study, liver fat by computed tomography scan was not significantly associated with increased prevalence or incidence of AF over 12 years of follow‐up.

Background--Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genomewide 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, h2g) 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 h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g 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. [ABSTRACT FROM AUTHOR]

Background--Polygenic risk scores (PGS) enable rapid estimation of genome-wide susceptibility for traits, which may be useful in clinical settings, such as prediction of QT interval. In this study, we sought to validate PGS for QT interval in 2 real-world cohorts of European ancestry (EA) and African ancestry (AA). Methods and Results--Two thousand nine hundred and fifteen participants of EA and 366 of AA in the MGH CAMP study (Cardiology and Metabolic Patient) were genotyped on a genome-wide array and imputed to the 1000 Genomes reference panel. An additional 820 EA and 57 AA participants in the Partners Biobank were genotyped and used for validation. PGS were created for each individual using effect estimates from association tests with QT interval obtained from prior genome-wide association studies, with variants selected based from multiple significance thresholds in the original study. In regression models, clinical variables explained ≈9% to 10% of total variation in resting QTc in EA individuals and ≈12% to 18% in AA individuals. The PGS significantly increased variation explained at most significance thresholds (P<0.001), with a trend toward increased variation explained at more stringent P value cut points in the CAMP EA cohort (P<0.05). In AA individuals, PGS provided no improvement in variation explained at any significance threshold. Conclusions--For individuals of European descent, PGS provided a significant increase in variation in QT interval explained compared with a model with only nongenetic factors at nearly every significance level. There was no apparent benefit gained by relaxing the significance threshold from conventional genome-wide significance (P<5x10-8). [ABSTRACT FROM AUTHOR]

The associations of long-term patterns of risk factors and the risk of incident atrial fibrillation (AF) are incompletely characterized. Among 4351 Framingham Study participants (mean age 50±11 years at baseline examination, 57% women) from the original and offspring cohorts, we defined longitudinal patterns, referred to as trajectories, of AF risk factors and a composite AF risk score using ≈16 years of data. We used Cox proportional hazards models to examine the association of trajectories to 15-year risk of AF. During follow-up, 719 participants developed AF. Five distinct trajectory groups were identified for systolic blood pressure (BP): groups 1 and 2 (normotensive throughout), group 3 (prehypertensive), group 4 (hypertensive initially with decreasing BP), and group 5 (hypertensive and increasing BP). In multivariable-adjusted analyses, compared with group 1, groups 4 (hazard ratio 2.05; 95% confidence interval 1.24-3.37) and 5 (hazard ratio 1.95; 95% confidence interval 1.08-3.49) were associated with incident AF. Three trajectory groups were identified for antihypertensive treatment. Compared with the group with no treatment throughout, the other 2 groups were associated with increased risk of incident AF. Distinct trajectories for diastolic BP, smoking, diabetes mellitus, and the composite risk score were not associated with increased 15-year risk of AF. Longitudinal trajectories may distinguish how exposures related to AF contribute toward prospective AF risk. Distinct trajectory groups with persistently elevated systolic BP and longer antihypertensive treatment are associated with increased risk of incident AF. [ABSTRACT FROM AUTHOR]

Background: Few epidemiologic cohort studies have evaluated atrial flutter (flutter) as an arrhythmia distinct from atrial fibrillation (AF).Objective: The purpose of this study was to examine the clinical correlates of flutter and its associated outcomes to distinguish them from those associated with AF in the Framingham Heart Study.Methods: We reviewed and adjudicated electrocardiograms (ECGs) previously classified as flutter or AF/flutter and another 100 ECGs randomly selected from AF cases. We examined the clinical correlates of flutter by matching up to 5 AF and 5 referents to each flutter case using a nested case referent design. We determined the 10-year outcomes associated with flutter with Cox models.Results: During mean follow-up of 33.0 ± 12.2 years, 112 participants (mean age 72 ± 10 years, 30% women) developed flutter. In multivariable analyses, smoking (odds ratio [OR] 2.84, 95% confidence interval [CI] 1.54-5.23), increased PR interval (OR 1.28 per SD, 95% CI 1.03-1.60), myocardial infarction (OR 2.25, 95% CI 1.05-4.80) and heart failure (OR 5.22, 95% CI 1.26-21.64) were associated with incident flutter. In age- and sex-adjusted models, flutter (vs referents) was associated with 10-year increased risk of AF (hazard ratio [HR] 5.01, 95% CI 3.14-7.99), myocardial infarction (HR 3.05, 95% CI 1.42-6.59), heart failure (HR 4.14, 95% CI 1.90-8.99), stroke (HR 2.17, 95% CI 1.13-4.17), and mortality (HR 2.00, 95% CI 1.44-2.79).Conclusion: We identified the clinical correlates associated with flutter and observed that flutter was associated with multiple adverse outcomes. [ABSTRACT FROM AUTHOR]

Background: Advancing age is a prominent risk factor for atrial fibrillation (AF). Shorter telomere length is a biomarker of biological aging, but the link between shorter telomere length and increased risk of AF remains unclear. We examined the association between shorter leukocyte telomere length (LTL) and incident AF. Methods and Results: We included AF‐free participants from the observational Framingham Heart Study Offspring cohort from 1995 to 1998, who had LTL measurements. We examined the association between baseline LTL and incident AF with multivariable Cox models adjusted for age, sex, current smoking, height, weight, systolic and diastolic blood pressure, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction, and history of heart failure. The study sample comprised 1143 AF‐free participants (52.8% women), with mean age of 60±8 years. The mean LTL at baseline was 6.95±0.57 kb. During 15.1±4.2 years mean follow‐up, 184 participants (64 women) developed AF. Chronological age was associated with increased risk of AF (hazard ratio per 10‐year increase, 2.16; 95% confidence interval, 1.71–2.72). There was no significant association between LTL and incident AF (hazard ratio per 1 SD decrease LTL, 1.01; 95% confidence interval, 0.86–1.19). Our study was observational in nature; hence, we could not exclude residual confounding and we were unable to establish causal pathways. Conclusions: In our moderate‐sized community‐based cohort, we did not find evidence for a significant association between LTL and risk of incident AF.

Background: Thyroid cancer affects over ½ million people in the U.S. and the incidence of thyroid cancer has increased worldwide at a rate higher than any other cancer, while survival has remained largely unchanged. The aim of this research was to develop, calibrate and verify a mathematical disease model to simulate the natural history of papillary thyroid cancer, which will serve as a platform to assess the effectiveness of clinical and cancer control interventions. Methods: Herein, we modeled the natural pre-clinical course of both benign and malignant thyroid nodules with biologically relevant health states from normal to detected nodule. Using established calibration techniques, optimal parameter sets for tumor growth characteristics, development rate, and detection rate were used to fit Surveillance Epidemiology and End Results (SEER) incidence data and other calibration targets. Results: Model outputs compared to calibration targets demonstrating sufficient calibration fit and model validation are presented including primary targets of SEER incidence data and size distribution at detection of malignancy. Additionally, we show the predicted underlying benign and malignant prevalence of nodules in the population, the probability of detection based on size of nodule, and estimates of growth over time in both benign and malignant nodules. Conclusions: This comprehensive model provides a dynamic platform employable for future comparative effectiveness research. Future model analyses will test and assess various clinical management strategies to improve patient outcomes related to thyroid cancer and optimize resource utilization for patients with thyroid nodules.

The genetic background of atrial fibrillation (AF) in whites and African Americans is largely unknown. Genes in cardiovascular pathways have not been systematically investigated.We examined a panel of approximately 50 000 common single-nucleotide polymorphisms (SNPs) in 2095 cardiovascular candidate genes and AF in 3 cohorts with participants of European (n=18 524; 2260 cases) or African American descent (n=3662; 263 cases) in the National Heart, Lung, and Blood Institute's Candidate Gene Association Resource. Results in whites were followed up in the German Competence Network for AF (n=906, 468 cases). The top result was assessed in relation to incident ischemic stroke in the Cohorts for Heart and Aging Research in Genomic Epidemiology Stroke Consortium (n=19 602 whites, 1544 incident strokes). SNP rs4845625 in the IL6R gene was associated with AF (relative risk [RR] C allele, 0.90; 95% confidence interval [CI], 0.85-0.95; P=0.0005) in whites but did not reach statistical significance in African Americans (RR, 0.86; 95% CI, 0.72-1.03; P=0.09). The results were comparable in the German AF Network replication, (RR, 0.71; 95% CI, 0.57-0.89; P=0.003). No association between rs4845625 and stroke was observed in whites. The known chromosome 4 locus near PITX2 in whites also was associated with AF in African Americans (rs4611994; hazard ratio, 1.40; 95% CI, 1.16-1.69; P=0.0005).In a community-based cohort meta-analysis, we identified genetic association in IL6R with AF in whites. Additionally, we demonstrated that the chromosome 4 locus known from recent genome-wide association studies in whites is associated with AF in African Americans. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) involves substantial electrophysiological, structural and contractile remodeling. We hypothesize that characterizing gene expression might uncover important pathways related to AF. Methods and Results: We performed genome-wide whole blood transcriptomic profiling (Affymetrix Human Exon 1.0 ST Array) of 2446 participants (mean age 66±9 years, 55% women) from the Offspring cohort of Framingham Heart Study. The study included 177 participants with prevalent AF, 143 with incident AF during up to 7 years follow up, and 2126 participants with no AF. We identified seven genes statistically significantly up-regulated with prevalent AF. The most significant gene, PBX1 (P = 2.8×10−7), plays an important role in cardiovascular development. We integrated differential gene expression with gene-gene interaction information to identify several signaling pathways possibly involved in AF-related transcriptional regulation. We did not detect any statistically significant transcriptomic associations with incident AF. Conclusion: We examined associations of gene expression with AF in a large community-based cohort. Our study revealed several genes and signaling pathways that are potentially involved in AF-related transcriptional regulation.