Biobank-driven genomic discovery yields new insight into atrial fibrillation biology.
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ABSTRACT:
SUBMITTER: Nielsen JB
PROVIDER: S-EPMC6530775 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Publications
Biobank-driven genomic discovery yields new insight into atrial fibrillation biology.
Nielsen Jonas B JB Thorolfsdottir Rosa B RB Fritsche Lars G LG Zhou Wei W Skov Morten W MW Graham Sarah E SE Herron Todd J TJ McCarthy Shane S Schmidt Ellen M EM Sveinbjornsson Gardar G Surakka Ida I Mathis Michael R MR Yamazaki Masatoshi M Crawford Ryan D RD Gabrielsen Maiken E ME Skogholt Anne Heidi AH Holmen Oddgeir L OL Lin Maoxuan M Wolford Brooke N BN Dey Rounak R Dalen Håvard H Sulem Patrick P Chung Jonathan H JH Backman Joshua D JD Arnar David O DO Thorsteinsdottir Unnur U Baras Aris A O'Dushlaine Colm C Holst Anders G AG Wen Xiaoquan X Hornsby Whitney W Dewey Frederick E FE Boehnke Michael M Kheterpal Sachin S Mukherjee Bhramar B Lee Seunggeun S Kang Hyun M HM Holm Hilma H Kitzman Jacob J Shavit Jordan A JA Jalife José J Brummett Chad M CM Teslovich Tanya M TM Carey David J DJ Gudbjartsson Daniel F DF Stefansson Kari K Abecasis Gonçalo R GR Hveem Kristian K Willer Cristen J CJ
Nature genetics 20180730 9
To identify genetic variation underlying atrial fibrillation, the most common cardiac arrhythmia, we performed a genome-wide association study of >1,000,000 people, including 60,620 atrial fibrillation cases and 970,216 controls. We identified 142 independent risk variants at 111 loci and prioritized 151 functional candidate genes likely to be involved in atrial fibrillation. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious ...[more]
Similar Datasets
Project description:AimsThe underlying mechanisms of atrial fibrillation (AF) are largely unknown. Inflammation may underlie atrial remodelling. Autoimmune diseases, related to increased systemic inflammation, may therefore be associated with new-onset AF.Methods and resultsParticipants from the population-based UK Biobank were screened for rheumatic fever, gastrointestinal autoimmune diseases, autoimmune diseases targeting the musculoskeletal system and connective tissues, and neurological autoimmune diseases. Between 2006 and 2022, participants were followed for incident AF. Cox proportional hazards regression analyses were performed to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) to quantify associations. 494 072 participants free from AF were included (median age 58.0 years, 54.8% women). After a median of 12.8 years, 27 194 (5.5%) participants were diagnosed with new-onset AF. Rheumatic fever without heart involvement (HR, 95% CI: 1.47, 1.26-1.72), Crohn's disease (1.23, 1.05-1.45), ulcerative colitis (1.17, 1.06-1.31), rheumatoid arthritis (1.39, 1.28-1.51), polyarteritis nodosa (1.82, 1.04-3.09), systemic lupus erythematosus (1.82, 1.41-2.35), and systemic sclerosis (2.32, 1.57-3.44) were associated with a larger AF risk. In sex-stratified analyses, rheumatic fever without heart involvement, multiple sclerosis, Crohn's disease, seropositive rheumatoid arthritis, psoriatic and enteropathic arthropathies, systemic sclerosis and ankylosing spondylitis were associated with larger AF risk in women, whereas only men showed a larger AF risk associated with ulcerative colitis.ConclusionsVarious autoimmune diseases are associated with new-onset AF, more distinct in women. Our findings elaborate on the pathophysiological differences in autoimmunity and AF risk between men and women.
| S-EPMC10062304 | biostudies-literature
Project description:AimsUndetected atrial fibrillation (AF) is a major health concern. Blood biomarkers associated with AF could simplify patient selection for screening and further inform ongoing research towards stratified prevention and treatment of AF.Methods and resultsForty common cardiovascular biomarkers were quantified in 638 consecutive patients referred to hospital [mean ± standard deviation age 70 ± 12 years, 398 (62%) male, 294 (46%) with AF] with known AF or ≥2 CHA2DS2-VASc risk factors. Paroxysmal or silent AF was ruled out by 7-day ECG monitoring. Logistic regression with forward selection and machine learning algorithms were used to determine clinical risk factors, imaging parameters, and biomarkers associated with AF. Atrial fibrillation was significantly associated with age [bootstrapped odds ratio (OR) per year = 1.060, 95% confidence interval (1.04-1.10); P = 0.001], male sex [OR = 2.022 (1.28-3.56); P = 0.008], body mass index [BMI, OR per unit = 1.060 (1.02-1.12); P = 0.003], elevated brain natriuretic peptide [BNP, OR per fold change = 1.293 (1.11-1.63); P = 0.002], elevated fibroblast growth factor-23 [FGF-23, OR = 1.667 (1.36-2.34); P = 0.001], and reduced TNF-related apoptosis-induced ligand-receptor 2 [TRAIL-R2, OR = 0.242 (0.14-0.32); P = 0.001], but not other biomarkers. Biomarkers improved the prediction of AF compared with clinical risk factors alone (net reclassification improvement = 0.178; P < 0.001). Both logistic regression and machine learning predicted AF well during validation [area under the receiver-operator curve = 0.684 (0.62-0.75) and 0.697 (0.63-0.76), respectively].ConclusionThree simple clinical risk factors (age, sex, and BMI) and two biomarkers (elevated BNP and elevated FGF-23) identify patients with AF. Further research is warranted to elucidate FGF-23 dependent mechanisms of AF.
| S-EPMC6475521 | biostudies-literature
Project description:CONTEXT:Although the heritability of atrial fibrillation (AF) is established, the contribution of familial AF to predicting new-onset AF remains unknown. OBJECTIVE:To determine whether familial occurrence of AF is associated with new-onset AF beyond established risk factors. DESIGN, SETTING, AND PARTICIPANTS:The Framingham Heart Study, a prospective community-based cohort study started in 1948. Original and Offspring Cohort participants were aged at least 30 years, were free of AF at the baseline examination, and had at least 1 parent or sibling enrolled in the study. The 4421 participants in this analysis (mean age, 54 [SD, 13] years; 54% women) were followed up through December 31, 2007. MAIN OUTCOME MEASURES:Incremental predictive value of incorporating different features of familial AF (any familial AF, premature familial AF [onset ?65 years old], number of affected relatives, and youngest age of onset in a relative) into a risk model for new-onset AF. RESULTS:Across 11,971 examinations during the period 1968-2007, 440 participants developed AF. Familial AF occurred among 1185 participants (26.8%) and premature familial AF occurred among 351 participants (7.9%). Atrial fibrillation occurred more frequently among participants with familial AF than without familial AF (unadjusted absolute event rates of 5.8% and 3.1%, respectively). The association was not attenuated by adjustment for AF risk factors (multivariable-adjusted hazard ratio, 1.40; 95% confidence interval [CI], 1.13-1.74) or reported AF-related genetic variants. Among the different features of familial AF examined, premature familial AF was associated with improved discrimination beyond traditional risk factors to the greatest extent (traditional risk factors, C statistic, 0.842 [95% CI, 0.826-0.858]; premature familial AF, C statistic, 0.846 [95% CI, 0.831-0.862]; P = .004). Modest changes in integrated discrimination improvement were observed with premature familial AF (2.1%). Net reclassification improvement (assessed using 8-year risk thresholds of <5%, 5%-10%, and >10%) did not change significantly with premature familial AF (index statistic, 0.011; 95% CI, -0.021 to 0.042; P = .51), although categoryless net reclassification was improved (index statistic, 0.127; 95% CI, 0.064-0.189; P = .009). CONCLUSIONS:In this cohort, familial AF was associated with an increased risk of AF that was not attenuated by adjustment for AF risk factors including genetic variants. Assessment of premature familial AF was associated with a very slight increase in predictive accuracy compared with traditional risk factors.
| S-EPMC3073054 | biostudies-literature
Project description:BACKGROUND:Genome-wide association studies have identified 23 loci for atrial fibrillation (AF), but the mechanisms responsible for these associations, as well as the causal genes and genetic variants, remain undefined. METHODS:To identify the effect of common genetic variants on gene expression that might explain the mechanisms linking genome-wide association loci with AF risk, we performed RNA sequencing of left atrial appendages from a biracial cohort of 265 subjects. RESULTS:Combining gene expression data with genome-wide single nucleotide polymorphism data, we found that approximately two-thirds of the expressed genes were regulated in cis by common genetic variants at a false discovery rate of <0.05, defined as cis-expression quantitative trait loci. Twelve of 23 reported AF genome-wide association loci displayed genome-wide significant cis-expression quantitative trait loci, at PRRX1 (chromosome 1q24), SNRNP27 (1q24), CEP68 (2p14), FKBP7 (2q31), KCNN2 (5q22), FAM13B (5q31), CAV1 (7q31), ASAH1 (8p22), MYOZ1 (10q22), C11ORF45 (11q24), TBX5 (12q24), and SYNE2 (14q23), suggesting that altered expression of these genes plays a role in AF susceptibility. Allelic expression imbalance was used as an independent method to characterize the cis-control of gene expression. One thousand two hundred forty-eight of 5153 queried genes had cis-single nucleotide polymorphisms that significantly regulated allelic expression at a false discovery rate of <0.05. CONCLUSIONS:We provide a genome-wide catalog of the genetic control of gene expression in human left atrial appendage. These data can be used to confirm the relevance of genome-wide association loci and to direct future functional studies to identify the genes and genetic variants responsible for complex diseases such as AF.
| S-EPMC5858469 | biostudies-literature
Project description:Background and objectivesPrior studies have shown that stroke patients treated with percutaneous left atrial appendage occlusion (LAAO) for non-valvular atrial fibrillation (NVAF) experience better outcomes than similar patients treated with warfarin. We investigated the impact of percutaneous left atrial appendage closure on post-stroke neurological outcomes in NVAF patients, compared with non-vitamin K antagonist oral anticoagulant (NOAC) therapy.MethodsMedical records for 1,427 patients in multiple registries and for 1,792 consecutive patients at 6 Korean hospitals were reviewed with respect to LAAO or NOAC treatment. Stroke severity in patients who experienced ischemic stroke or transient ischemic attack after either treatment was assessed with modified Rankin Scale (mRS) scoring at hospital discharge and at 3 and 12 months post-stroke.ResultsmRS scores were significantly lower in LAAO patients at 3 (p<0.01) and 12 months (p<0.01) post-stroke, despite no significant differences in scores before the ischemic cerebrovascular event (p=0.22). The occurrences of disabling ischemic stroke in the LAAO and NOAC groups were 36.7% and 44.2% at discharge (p=0.47), 23.3% and 44.2% at 3 months post-stroke (p=0.04), and 13.3% and 43.0% at 12 months post-stroke (p=0.01), respectively. Recovery rates for disabling ischemic stroke at discharge to 12 months post-stroke were significantly higher for LAAO patients (50.0%) than for NOAC patients (5.6%) (p<0.01).ConclusionsPercutaneous LAAO was associated with more favorable neurological outcomes after ischemic cerebrovascular event than NOAC treatment.
| S-EPMC8263298 | biostudies-literature
Project description:Asteraceae, or the sunflower family, is the largest family of flowering plants and is usually considered difficult to work with, not only due to its size, but also because of the abundant cases of polyploidy and ancient whole-genome duplications. Traditional molecular systematics studies were often impaired by the low levels of variation found in chloroplast markers and the high paralogy of traditional nuclear markers like ITS. Next-generation sequencing and novel phylogenomics methods, such as target capture and Hyb-Seq, have provided new ways of studying the phylogeny of the family with great success. While the resolution of the backbone of the family is in progress with some results already published, smaller studies focusing on internal clades of the phylogeny are important to increase sampling and allow morphological, biogeography, and diversification analyses, as well as serving as basis to test the current infrafamilial classification. Vernonieae is one of the largest tribes in the family, accounting for approximately 1,500 species. From the 1970s to the 1990s, the tribe went through several reappraisals, mainly due to the splitting of the mega genus Vernonia into several smaller segregates. Only three phylogenetic studies focusing on the Vernonieae have been published to date, both using a few molecular markers, overall presenting low resolution and support in deepest nodes, and presenting conflicting topologies when compared. In this study, we present the first attempt at studying the phylogeny of Vernonieae using phylogenomics. Even though our sampling includes only around 4% of the diversity of the tribe, we achieved complete resolution of the phylogeny with high support recovering approximately 700 nuclear markers obtained through target capture. We also analyzed the effect of missing data using two different matrices with different number of markers and the difference between concatenated and gene tree analysis.
| S-EPMC6843069 | biostudies-literature
Project description:The aim of this study is to perform transcriptome analysis on mouse left atrium tissue after long-term ibrutinib treatment or cardiac CSK knockout, in order to compared the enriched gene clusters.
2020-04-01 | GSE139427 | GEO
Project description:RationaleGenome-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.ObjectiveWe sought to determine the contribution of rare and common genetic variation to AF risk in the general population.MethodsThe 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.ResultsThe 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%.ConclusionsBoth 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.
| S-EPMC7007701 | biostudies-literature
Project description:ObjectiveAtrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients.MethodsTo develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA2DS2-VASc for prediction of ischemic stroke among AF patients, was used for comparison to the best performing ML model.FindingsThe best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA2DS2-VASc was statistically significant based on DeLong's test (p-value = 2.20E-06). In addition, the SHAP analysis showed that several peripheral blood biomarkers (e.g. creatinine, glycated haemoglobin, monocytes) were associated with ischemic stroke, which are not considered by CHA2DS2-VASc.ImplicationsThe best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients.
| S-EPMC10987914 | biostudies-literature
Project description:Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency-linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated [Formula: see text]. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average [Formula: see text] value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.
| S-EPMC9351350 | biostudies-literature