Potassium channel gene mutations rarely cause atrial fibrillation

BACKGROUND: Mutations in several potassium channel subunits have been associated with rare forms of atrial fibrillation. In order to explore the role of potassium channels in inherited typical forms of the arrhythmia, we have screened a cohort of patients from a referral clinic for mutations in the channel subunit genes implicated in the arrhythmia. We sought to determine if mutations in KCNJ2 and KCNE1-5 are a common cause of atrial fibrillation. METHODS: Serial patients with lone atrial fibrillation or atrial fibrillation with hypertension were enrolled between June 1, 2001 and January 6, 2005. Each patient underwent a standardized interview and physical examination. An electrocardiogram, echocardiogram and blood sample for genetic analysis were also obtained. Patients with a family history of AF were screened for mutations in KCNJ2 and KCNE1-5 using automated sequencing. RESULTS: 96 patients with familial atrial fibrillation were enrolled. Eighty-three patients had lone atrial fibrillation and 13 had atrial fibrillation and hypertension. Patients had a mean age of 56 years at enrollment and 46 years at onset of atrial fibrillation. Eighty-one percent of patients had paroxysmal atrial fibrillation at enrollment. Unlike patients with an activating mutation in KCNQ1, the patients had a normal QT(c )interval with a mean of 412 ± 42 ms. Echocardiography revealed a normal mean ejection fraction of 62.0 ± 7.2 % and mean left atrial dimension of 39.9 ± 7.0 mm. A number of common polymorphisms in KCNJ2 and KCNE1-5 were identified, but no mutations were detected. CONCLUSION: Mutations in KCNJ2 and KCNE1-5 rarely cause typical atrial fibrillation in a referral clinic population

Association between Variants on Chromosome 4q25, 16q22 and 1q21 and Atrial Fibrillation in the Polish Population

Genome-wide studies have shown that polymorphisms on chromosome 4q25, 16q22 and 1q21 correlate with atrial fibrillation (AF). However, the distribution of these polymorphisms differs significantly among populations.To test the polymorphisms on chromosome 4q25, 16q22 and 1q21 in a group of patients (pts) that underwent catheter ablation of AF.Four hundred and ten patients with AF that underwent pulmonary vein isolation were included in the study. Control group (n = 550) was taken from healthy population, matched for age, sex and presence of hypertension. All participants were genotyped for the presence of the rs2200733, rs10033464, rs17570669, rs3853445, rs6838973 (4q25), rs7193343 (16q22) and rs13376333 (1q21) polymorphisms.All the polymorphisms tested (except rs17570669) correlated significantly with AF in univariate analysis (p values between 0.039 for rs7193343 and 2.7e-27 for rs2200733), with the odds ratio (OR) 0.572 and 0.617 for rs3853445 and rs6838973, respectively (protective role) and OR 1.268 to 3.52 for the other polymorphisms. All 4q25 SNPs tested but rs3853445 were independently linked with AF in multivariate logistic regression analysis. In haplotype analysis six out of nine 4q25 haplotypes were significantly linked with AF. The T allele of rs2200733 favoured increased number of episodes of AF per month (p = 0.045) and larger pulmonary vein diameter (recessive model, p = 0.032).Patients qualified for catheter ablation of AF have a significantly higher frequency of 4q25, 16q22 and 1q21 variants than the control group. The T allele of rs2200733 favours larger pulmonary veins and increased number of episodes of AF

Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.

Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification

Atrial Fibrillation Genetic Risk and Ischemic Stroke Mechanisms

$\textit{Background and Purpose:}$ 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. $\textit{Methods:}$ 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. $\textit{Results:}$ 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. $\textit{Conclusions:}$ 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.Dr. Lubitz is supported by NIH grants K23HL114724 and a Doris Duke Charitable Foundation Clinical Scientist Development Award 2014105. Dr. Traylor is supported by a British Heart Foundation programme grant (RG/16/4/32218). Dr. Ellinor and Benjamin are supported by 1RO1HL092577, R01HL128914. Dr. Ellinor is supported by grants from the National Institutes of Health K24HL105780 and an Established Investigator Award from the American Heart Association (13EIA14220013) and by the Fondation Leducq (14CVD01). Dr. Dichgans and Dr. Malik were supported by grants from the Deutsche Forschungsgemeinschaft (CRC 1123 [B3] and Munich Cluster for Systems Neurology [SyNergy]), the German Federal Ministry of Education and Research (BMBF, e:Med programme e:AtheroSysMed), the FP7/2007-2103 European Union project CVgenes@target (grant agreement No Health-F2-2013-601456), the European Union Horizon2020 projects SVDs@target (grant agreement No 66688) and CoSTREAM (grant agreement No 667375), the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain), the Vascular Dementia Research Foundation, and the Jackstaedt Foundation

How to deal with the early GWAS data when imputing and combining different arrays is necessary

Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case–control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (RT2) for the post-imputation QCs in publications

Blood-based cardiometabolic phenotypes in atrial fibrillation and their associated risk: EAST-AFNET 4 biomolecule study.

BACKGROUND: Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. Their role in defining subphenotypes of AF is not known. METHODS AND RESULTS: This prespecified analysis of the EAST-AFNET4 biomolecule study assigned patients to clusters using polytomous variable latent class analysis (poLCA) based on baseline concentrations of thirteen precisely-quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow up 2.9 years).Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest-risk cluster was dominated by elevated BMP10, IGFBP7, NT-proBNP, ANGPT2 and GDF15. Patients in the lowest-risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of hsCRP, IL-6, and D-dimer. Patients in the highest-risk cluster had a 5-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (pinteraction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients. CONCLUSIONS: Biomolecule concentrations identify cardiometabolic subphenotypes in patients with atrial fibrillation at high and low cardiovascular risk