Monogenic and Polygenic Contributions to QTc Prolongation in the Population.

BACKGROUND: Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population. METHODS: We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed. RESULTS: Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS). CONCLUSIONS: QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Clinical and Genetic Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Atrial fibrillation (AF) is a common cause of stroke but may not be detectable at the time of stroke. We hypothesized that an AF polygenic risk score (PRS) can discriminate between cardioembolic stroke and noncardioembolic strokes. METHODS: We evaluated AF and stroke risk in 26 145 individuals of European descent from the Stroke Genetics Network case-control study. AF genetic risk was estimated using 3 recently developed PRS methods (LDpred-funct-inf, sBayesR, and PRS-CS) and 2 previously validated PRSs. We performed logistic regression of each AF PRS on AF status and separately cardioembolic stroke, adjusting for clinical risk score (CRS), imputation group, and principal components. We calculated model discrimination of AF and cardioembolic stroke using the concordance statistic (c-statistic) and compared c-statistics using 2000-iteration bootstrapping. We also assessed reclassification of cardioembolic stroke with the addition of PRS to either CRS or a modified CHA2DS2-VASc score alone. RESULTS: Each AF PRS was significantly associated with AF and with cardioembolic stroke after adjustment for CRS. Addition of each AF PRS significantly improved discrimination as compared with CRS alone (P<0.01). When combined with the CRS, both PRS-CS and LDpred scores discriminated both AF and cardioembolic stroke (c-statistic 0.84 for AF; 0.74 for cardioembolic stroke) better than 3 other PRS scores (P<0.01). Using PRS-CS PRS and CRS in combination resulted in more appropriate reclassification of stroke events as compared with CRS alone (event reclassification [net reclassification indices]+=14% [95% CI, 10%-18%]; nonevent reclassification [net reclassification indices]-=17% [95% CI, 15%-0.19%]) or the modified CHA2DS2-VASc score (net reclassification indices+=11% [95% CI, 7%-15%]; net reclassification indices-=14% [95% CI, 12%-16%]) alone. CONCLUSIONS: Addition of polygenic risk of AF to clinical risk factors modestly improves the discrimination of cardioembolic from noncardioembolic strokes, as well as reclassification of stroke subtype. Polygenic risk of AF may be a useful biomarker for identifying strokes caused by AF.

Genome-wide association study reveals novel genetic loci: a new polygenic risk score for mitral valve prolapse.

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-ß signalling molecules and spectrin ß. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention.

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

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 (P 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 CHA2DS2-VASc score of 2, high genetic risk conferred a 4-fold higher risk of stroke and an absolute risk equivalent to those with CHA2DS2-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 CHA2DS2-VASc scores, the GRS identified patients with risk comparable to those with higher CHA2DS2-VASc scores.

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

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 TTN 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, TTN (odds ratio, 2.71, P=2.50×10-8). The association with AF was more significant (odds ratio, 6.15, P=3.26×10-14) when restricting to LOF variants located in exons highly expressed in cardiac tissue (TTNLOF). Overall, 0.44% of individuals carried TTNLOF 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 TTNLOF variants only accounted for 0.2%. CONCLUSIONS: Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTNLOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.

Accelerometer-derived physical activity and risk of atrial fibrillation.

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.

Development of a Prediction Model for Ascending Aortic Diameter Among Asymptomatic Individuals.

Importance: Ascending thoracic aortic disease is an important cause of sudden death in the US, yet most aortic aneurysms are identified incidentally. Objective: To develop and validate a clinical score to estimate ascending aortic diameter. Design, Setting, and Participants: Using an ongoing magnetic resonance imaging substudy of the UK Biobank cohort study, which had enrolled participants from 2006 through 2010, score derivation was performed in 30 018 participants and internal validation in an additional 6681. External validation was performed in 1367 participants from the Framingham Heart Study (FHS) offspring cohort who had undergone computed tomography from 2002 through 2005, and in 50 768 individuals who had undergone transthoracic echocardiography in the Community Care Cohort Project, a retrospective hospital-based cohort of longitudinal primary care patients in the Mass General Brigham (MGB) network between 2001-2018. Exposures: Demographic and clinical variables (11 covariates that would not independently prompt thoracic imaging). Main Outcomes and Measures: Ascending aortic diameter was modeled with hierarchical group least absolute shrinkage and selection operator (LASSO) regression. Correlation between estimated and measured diameter and performance for identifying diameter 4.0 cm or greater were assessed. Results: The 30 018-participant training cohort (52% women), were a median age of 65.1 years (IQR, 58.6-70.6 years). The mean (SD) ascending aortic diameter was 3.04 (0.31) cm for women and 3.32 (0.34) cm for men. A score to estimate ascending aortic diameter explained 28.2% of the variance in aortic diameter in the UK Biobank validation cohort (95% CI, 26.4%-30.0%), 30.8% in the FHS cohort (95% CI, 26.8%-34.9%), and 32.6% in the MGB cohort (95% CI, 31.9%-33.2%). For detecting individuals with an ascending aortic diameter of 4 cm or greater, the score had an area under the receiver operator characteristic curve of 0.770 (95% CI, 0.737-0.803) in the UK Biobank, 0.813 (95% CI, 0.772-0.854) in the FHS, and 0.766 (95% CI, 0.757-0.774) in the MGB cohorts, although the model significantly overestimated or underestimated aortic diameter in external validation. Using a fixed-score threshold of 3.537, 9.7 people in UK Biobank, 1.8 in the FHS, and 4.6 in the MGB cohorts would need imaging to confirm 1 individual with an ascending aortic diameter of 4 cm or greater. The sensitivity at that threshold was 8.9% in the UK Biobank, 11.3% in the FHS, and 18.8% in the MGB cohorts, with specificities of 98.1%, 99.2%, and 96.2%, respectively. Conclusions and Relevance: A prediction model based on common clinically available data was derived and validated to predict ascending aortic diameter. Further research is needed to optimize the prediction model and to determine whether its use is associated with improved outcomes.

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

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 488 010 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=30 201). 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<1×10-6), 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-of-function variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10-5). 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.

Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

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 CHA2DS2-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 (CHA2DS2-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 CHA2DS2-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.

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

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 (P<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.

A Functional Variant Associated with Atrial Fibrillation Regulates PITX2c Expression through TFAP2a.

The most significantly associated genetic locus for atrial fibrillation (AF) is in chromosomal region 4q25, where four independent association signals have been identified. Although model-system studies suggest that altered PITX2c expression might underlie the association, the link between specific variants and the direction of effect on gene expression remains unknown for all four signals. In the present study, we analyzed the AF-associated region most proximal to PITX2 at 4q25. First, we identified candidate regulatory variants that might confer AF risk through a combination of mammalian conservation, DNase hypersensitivity, and histone modification from ENCODE and the Roadmap Epigenomics Project, as well as through in vivo analysis of enhancer activity in embryonic zebrafish. Within candidate regions, we then identified a single associated SNP, rs2595104, which displayed dramatically reduced enhancer activity with the AF risk allele. CRISPR-Cas9-mediated deletion of the rs2595104 region and editing of the rs2595104 risk allele in human stem-cell-derived cardiomyocytes resulted in diminished PITX2c expression in comparison to that of the non-risk allele. This differential activity was mediated by activating enhancer binding protein 2 alpha (TFAP2a), which bound robustly to the non-risk allele at rs2595104, but not to the risk allele, in cardiomyocytes. In sum, we found that the AF-associated SNP rs2595104 altered PITX2c expression via interaction with TFAP2a. Such a pathway could ultimately contribute to AF susceptibility at the PITX2 locus associated with AF.

Whole Exome Sequencing in Atrial Fibrillation.

Atrial fibrillation (AF) is a morbid and heritable arrhythmia. Over 35 genes have been reported to underlie AF, most of which were described in small candidate gene association studies. Replication remains lacking for most, and therefore the contribution of coding variation to AF susceptibility remains poorly understood. We examined whole exome sequencing data in a large community-based sample of 1,734 individuals with and 9,423 without AF from the Framingham Heart Study, Cardiovascular Health Study, Atherosclerosis Risk in Communities Study, and NHLBI-GO Exome Sequencing Project and meta-analyzed the results. We also examined whether genetic variation was enriched in suspected AF genes (N = 37) in AF cases versus controls. The mean age ranged from 59 to 73 years; 8,656 (78%) were of European ancestry. None of the 99,404 common variants evaluated was significantly associated after adjusting for multiple testing. Among the most significantly associated variants was a common (allele frequency = 86%) missense variant in SYNPO2L (rs3812629, p.Pro707Leu, [odds ratio 1.27, 95% confidence interval 1.13-1.43, P = 6.6x10-5]) which lies at a known AF susceptibility locus and is in linkage disequilibrium with a top marker from prior analyses at the locus. We did not observe significant associations between rare variants and AF in gene-based tests. Individuals with AF did not display any statistically significant enrichment for common or rare coding variation in previously implicated AF genes. In conclusion, we did not observe associations between coding genetic variants and AF, suggesting that large-effect coding variation is not the predominant mechanism underlying AF. A coding variant in SYNPO2L requires further evaluation to determine whether it is causally related to AF. Efforts to identify biologically meaningful coding variation underlying AF may require large sample sizes or populations enriched for large genetic effects.

Genetic Risk Prediction of Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. METHODS: To 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 <1×10-3 to <1×10-8 in a prior independent genetic association study. RESULTS: Incident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13-1.46; P=1.5×10-4) to 1.67 (25 variants; 95% confidence interval, 1.47-1.90; P=9.3×10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39-4.58; P=2.7×10-3). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20-4.40; P=0.01). CONCLUSIONS: Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms.

Meta-analysis of 65,734 individuals identifies TSPAN15 and SLC44A2 as two susceptibility loci for venous thromboembolism.

Venous thromboembolism (VTE), the third leading cause of cardiovascular mortality, is a complex thrombotic disorder with environmental and genetic determinants. Although several genetic variants have been found associated with VTE, they explain a minor proportion of VTE risk in cases. We undertook a meta-analysis of genome-wide association studies (GWASs) to identify additional VTE susceptibility genes. Twelve GWASs totaling 7,507 VTE case subjects and 52,632 control subjects formed our discovery stage where 6,751,884 SNPs were tested for association with VTE. Nine loci reached the genome-wide significance level of 5 × 10(-8) including six already known to associate with VTE (ABO, F2, F5, F11, FGG, and PROCR) and three unsuspected loci. SNPs mapping to these latter were selected for replication in three independent case-control studies totaling 3,009 VTE-affected individuals and 2,586 control subjects. This strategy led to the identification and replication of two VTE-associated loci, TSPAN15 and SLC44A2, with lead risk alleles associated with odds ratio for disease of 1.31 (p = 1.67 × 10(-16)) and 1.21 (p = 2.75 × 10(-15)), respectively. The lead SNP at the TSPAN15 locus is the intronic rs78707713 and the lead SLC44A2 SNP is the non-synonymous rs2288904 previously shown to associate with transfusion-related acute lung injury. We further showed that these two variants did not associate with known hemostatic plasma markers. TSPAN15 and SLC44A2 do not belong to conventional pathways for thrombosis and have not been associated to other cardiovascular diseases nor related quantitative biomarkers. Our findings uncovered unexpected actors of VTE etiology and pave the way for novel mechanistic concepts of VTE pathophysiology.

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

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 (CHA2DS2-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 (CHA2DS2-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.

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

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.

Identification of Genetic Variants Linking Protein C and Lipoprotein Metabolism: The ARIC Study (Atherosclerosis Risk in Communities).

OBJECTIVE: Previous studies have identified common genetic variants in 4 chromosomal regions that together account for 14% to 15% of the variance in circulating levels of protein C. To further characterize the genetic architecture of protein C, we obtained denser coverage at some loci, extended investigation of protein C to low-frequency and rare variants, and searched for new associations in genes known to influence protein C. APPROACH AND RESULTS: Genetic associations with protein C antigen level were evaluated in ≤10 778 European and 3190 black participants aged 45 to 64 years. Analyses included >26 million autosomal variants available after imputation to the 1000 Genomes reference panel along with additional low-frequency and rare variants directly genotyped using the Illumina ITMAT-Broad-CARe chip and Illumina HumanExome BeadChip. Genome-wide significant associations (P<5×10-8) were found for common variants in the GCKR, PROC, BAZ1B, and PROCR-EDEM2 regions in whites and PROC and PROCR-EDEM2 regions in blacks, confirming earlier findings. In a novel finding, the low-density lipoprotein cholesterol-lowering allele of rs12740374, located in the CELSR2-PSRC1-SORT1 region, was associated with lower protein C level in both whites and blacks, reaching genome-wide significance in a meta-analysis combining results from both groups (P=1.4×10-9). To further investigate a possible link between lipid metabolism and protein C level, we conducted Mendelian randomization analyses using 185 lipid-related genetic variants as instrumental variables. The results indicated that triglycerides, and possibly low-density lipoprotein cholesterol, influence protein C levels. CONCLUSIONS: Discovery of variants influencing circulating protein C levels in the CELSR2-PSRC1-SORT1 region may indicate a novel genetic link between lipoprotein metabolism and hemostasis.

Genetic Variants Associated with Circulating Parathyroid Hormone.

Parathyroid hormone (PTH) is a primary calcium regulatory hormone. Elevated serum PTH concentrations in primary and secondary hyperparathyroidism have been associated with bone disease, hypertension, and in some studies, cardiovascular mortality. Genetic causes of variation in circulating PTH concentrations are incompletely understood. We performed a genome-wide association study of serum PTH concentrations among 29,155 participants of European ancestry from 13 cohort studies (n=22,653 and n=6502 in discovery and replication analyses, respectively). We evaluated the association of single nucleotide polymorphisms (SNPs) with natural log-transformed PTH concentration adjusted for age, sex, season, study site, and principal components of ancestry. We discovered associations of SNPs from five independent regions with serum PTH concentration, including the strongest association with rs6127099 upstream of CYP24A1 (P=4.2 × 10-53), a gene that encodes the primary catabolic enzyme for 1,25-dihydroxyvitamin D and 25-dihydroxyvitamin D. Each additional copy of the minor allele at this SNP associated with 7% higher serum PTH concentration. The other SNPs associated with serum PTH concentration included rs4074995 within RGS14 (P=6.6 × 10-17), rs219779 adjacent to CLDN14 (P=3.5 × 10-16), rs4443100 near RTDR1 (P=8.7 × 10-9), and rs73186030 near CASR (P=4.8 × 10-8). Of these five SNPs, rs6127099, rs4074995, and rs219779 replicated. Thus, common genetic variants located near genes involved in vitamin D metabolism and calcium and renal phosphate transport associated with differences in circulating PTH concentrations. Future studies could identify the causal variants at these loci, and the clinical and functional relevance of these variants should be pursued.

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

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.

Atrial Fibrillation Genetic Risk and Ischemic Stroke Mechanisms.

BACKGROUND AND PURPOSE: Atrial fibrillation (AF) is a leading cause of cardioembolic stroke, but the relationship between AF and noncardioembolic stroke subtypes are unclear. Because AF may be unrecognized, and because AF has a substantial genetic basis, we assessed for predisposition to AF across ischemic stroke subtypes. METHODS: We examined associations between AF genetic risk and Trial of Org 10172 in Acute Stroke Treatment stroke subtypes in 2374 ambulatory individuals with ischemic stroke and 5175 without from the Wellcome Trust Case-Control Consortium 2 using logistic regression. We calculated AF genetic risk scores using single-nucleotide polymorphisms associated with AF in a previous independent analysis across a range of preselected significance thresholds. RESULTS: There were 460 (19.4%) individuals with cardioembolic stroke, 498 (21.0%) with large vessel, 474 (20.0%) with small vessel, and 814 (32.3%) individuals with strokes of undetermined cause. Most AF genetic risk scores were associated with stroke, with the strongest association (P=6×10-4) attributed to scores of 944 single-nucleotide polymorphisms (each associated with AF at P<1×10-3 in a previous analysis). Associations between AF genetic risk and stroke were enriched in the cardioembolic stroke subset (strongest P=1.2×10-9, 944 single-nucleotide polymorphism score). In contrast, AF genetic risk was not significantly associated with noncardioembolic stroke subtypes. CONCLUSIONS: Comprehensive AF genetic risk scores were specific for cardioembolic stroke. Incomplete workups and subtype misclassification may have limited the power to detect associations with strokes of undetermined pathogenesis. Future studies are warranted to determine whether AF genetic risk is a useful biomarker to enhance clinical discrimination of stroke pathogeneses.