Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

To understand the genetic underpinnings of atrial fibrillation (AF) in the Japanese population, we performed a large-scale genome-wide association study comprising 9,826 cases of AF among 150,272 individuals and identified five new susceptibility loci, including East Asian-specific rare variants. A trans-ancestry meta-analysis of >1 million individuals, including 77,690 cases, identified 35 novel loci. Leveraging gene expression and epigenomic datasets to prioritize putative causal genes and their transcription factors revealed the involvement of IL6R gene and transcription factor ERG besides the known ones. Further, we constructed a polygenic risk score (PRS) for AF, using the trans-ancestry meta-analysis. PRS was associated with an increased risk of long-term cardiovascular and stroke mortality, and segregated individuals with cardioembolic stroke in undiagnosed AF patients. Our results provide novel biological and clinical insights into AF genetics and suggest their potential for clinical applications.

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Faculty Opinions recommendation of Exome sequencing followed by large-scale genotyping fails to identify single rare variants of large effect in idiopathic generalized epilepsy.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717952610.793458111 ◽

2012 ◽

Author(s):

Melanie Bahlo

Keyword(s):

Exome Sequencing ◽

Large Scale ◽

Rare Variants ◽

Idiopathic Generalized Epilepsy ◽

Generalized Epilepsy

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Inflammatory Biomarkers in Atrial Fibrillation

Current Medicinal Chemistry ◽

10.2174/0929867324666170727103357 ◽

2019 ◽

Vol 26 (5) ◽

pp. 837-854 ◽

Cited By ~ 7

Author(s):

Effimia Zacharia ◽

Nikolaos Papageorgiou ◽

Adam Ioannou ◽

Gerasimos Siasos ◽

Spyridon Papaioannou ◽

...

Keyword(s):

Atrial Fibrillation ◽

Plasma Levels ◽

Large Scale ◽

Inflammatory Biomarkers ◽

Inflammatory Pathways ◽

Inflammatory State ◽

Anti Inflammatory ◽

Underlying Mechanisms

During the last few years, a significant number of studies have attempted to clarify the underlying mechanisms that lead to the presentation of atrial fibrillation (AF). Inflammation is a key component of the pathophysiological processes that lead to the development of AF; the amplification of inflammatory pathways triggers AF, and, in tandem, AF increases the inflammatory state. Indeed, the plasma levels of several inflammatory biomarkers are elevated in patients with AF. In addition, the levels of specific inflammatory biomarkers may provide information regarding to the AF duration. Several small studies have assessed the role of anti-inflammatory treatment in atrial fibrillation but the results have been contradictory. Large-scale studies are needed to evaluate the role of inflammation in AF and whether anti-inflammatory medications should be routinely administered to patients with AF.

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Rare variants in KDR, encoding VEGF Receptor 2, are associated with tetralogy of Fallot

Genetics in Medicine ◽

10.1038/s41436-021-01212-y ◽

2021 ◽

Author(s):

Doris Škorić-Milosavljević ◽

Najim Lahrouchi ◽

Fernanda M. Bosada ◽

Gregor Dombrowsky ◽

Simon G. Williams ◽

...

Keyword(s):

Exome Sequencing ◽

Tetralogy Of Fallot ◽

Large Scale ◽

Rare Variants ◽

Growth Factor Receptor ◽

Vegf Receptor ◽

Loss Of Function ◽

Endothelial Growth Factor

Abstract Purpose Rare genetic variants in KDR, encoding the vascular endothelial growth factor receptor 2 (VEGFR2), have been reported in patients with tetralogy of Fallot (TOF). However, their role in disease causality and pathogenesis remains unclear. Methods We conducted exome sequencing in a familial case of TOF and large-scale genetic studies, including burden testing, in >1,500 patients with TOF. We studied gene-targeted mice and conducted cell-based assays to explore the role of KDR genetic variation in the etiology of TOF. Results Exome sequencing in a family with two siblings affected by TOF revealed biallelic missense variants in KDR. Studies in knock-in mice and in HEK 293T cells identified embryonic lethality for one variant when occurring in the homozygous state, and a significantly reduced VEGFR2 phosphorylation for both variants. Rare variant burden analysis conducted in a set of 1,569 patients of European descent with TOF identified a 46-fold enrichment of protein-truncating variants (PTVs) in TOF cases compared to controls (P = 7 × 10-11). Conclusion Rare KDR variants, in particular PTVs, strongly associate with TOF, likely in the setting of different inheritance patterns. Supported by genetic and in vivo and in vitro functional analysis, we propose loss-of-function of VEGFR2 as one of the mechanisms involved in the pathogenesis of TOF.

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Feasibility and Evaluation of a Large-Scale External Validation Approach for Patient-Level Prediction in an International Data Network: Validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

10.21203/rs.2.11750/v2 ◽

2020 ◽

Author(s):

Jenna Marie Reps ◽

Ross Williams ◽

Seng Chan You ◽

Thomas Falconer ◽

Evan Minty ◽

...

Keyword(s):

Atrial Fibrillation ◽

Large Scale ◽

Data Science ◽

Prediction Models ◽

External Validation ◽

Scale Up ◽

R Package ◽

Prognostic Models ◽

Healthcare Data ◽

Patient Level

Abstract Objective: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Materials & Methods: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.Discussion: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. Conclusion : In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

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Pregnancy-related complications and incidence of atrial fibrillation: a systematic review and meta-analysis

EP Europace ◽

10.1093/europace/euab116.145 ◽

2021 ◽

Vol 23 (Supplement_3) ◽

Author(s):

T Al Bahhawi ◽

A Aqeeli ◽

S L Harrison ◽

D A Lane ◽

I Buchan ◽

...

Keyword(s):

Atrial Fibrillation ◽

Systematic Review ◽

Cohort Studies ◽

Observational Studies ◽

Large Scale ◽

Data Extraction ◽

Meta Analysis ◽

Significant Heterogeneity ◽

Random Effects Models ◽

Increased Risk

Abstract Funding Acknowledgements Type of funding sources: None. Background Pregnancy-related complications have been previously associated with incident cardiovascular disease. However, data are scarce on the association between pregnancy-related complications and incident atrial fibrillation (AF). This systematic review examines associations between pregnancy-related complications and incident AF. Methods A systematic search of the literature utilising MEDLINE and EMBASE (Ovid) was conducted from 1990 to 6 April 2020. Observational studies examining the association between pregnancy-related complications including hypertensive disorders of pregnancy (HDP), gestational diabetes, placental abruption, preterm birth, low birth weight, small-for-gestational-age and stillbirth, and incidence of AF were included. Screening and data extraction were conducted independently by two reviewers. Inverse-variance random-effects models were used to pool hazard ratios. Results: Six observational studies met the inclusion criteria one case-control study and five retrospective cohort studies, with four studies eligible for meta-analysis. Sample sizes ranged from 1,839-1,303,365. Mean/median follow-up for the cohort studies ranged from 7-36 years. Most studies reported an increased risk of incident AF associated with pregnancy-related complications. The pooled summary statistic from four studies reflected a greater risk of incident AF for HDP (hazard ratio (HR) 1.47, 95% confidence intervals (CI) 1.18-1.84; I2 = 84%) and from three studies for pre-eclampsia (HR 1.71, 95% CI 1.41-2.06; I2 = 64%; Figure). Conclusions The results of this review suggest that pregnancy-related complications particularly pre-eclampsia appear to be associated with higher risk of incident AF. The small number of included studies and the significant heterogeneity in the pooled results suggest further large-scale prospective studies are required to confirm the association between pregnancy-related complications and AF. Abstract Figure.

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Voltage mapping as a method of left atrial fibrosis evaluation

Patologiya krovoobrashcheniya i kardiokhirurgiya ◽

10.21688/1681-3472-2016-2-111-116 ◽

2016 ◽

Vol 20 (2) ◽

pp. 111 ◽

Cited By ~ 1

Author(s):

O. V. Sapelnikov ◽

Yu. A. Shuvalova ◽

D. Yu. Cherkashin ◽

A. A. Krupnov ◽

A. S. Partigulova ◽

...

Keyword(s):

Atrial Fibrillation ◽

Left Atrial ◽

Large Scale ◽

Atrial Fibrosis ◽

Instrumental Data ◽

Voltage Mapping ◽

Special Software ◽

Mechanisms Of Development ◽

Left Atrial Fibrosis ◽

And Control

Aim: This pilot study is designed to better understand the mechanisms of development and control of atrial fibrillation. Methods: The correlation between fibrosis index (FI), which was calculated intraoperatively with special software, and clinical and instrumental data was analyzed. Also evaluated were FI values as compared to AF catheter ablation outcomes. Results: Voltage mapping may be considered as a possible alternative to MRI examination and in some cases it is more informative. Conclusion: It was found out that the preliminary results received are a good start for planning a large-scale study in this area related to assessment of the predicative and practical value of the fibrosis index.

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Feasibility and Evaluation of a Large-Scale External Validation Approach for Patient-Level Prediction in an International Data Network: Validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

10.21203/rs.2.11750/v3 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jenna Marie Reps ◽

Ross D Williams ◽

Seng Chan You ◽

Thomas Falconer ◽

Evan Minty ◽

...

Keyword(s):

Atrial Fibrillation ◽

Large Scale ◽

Data Science ◽

Prediction Models ◽

External Validation ◽

Scale Up ◽

R Package ◽

Prognostic Models ◽

Healthcare Data ◽

Patient Level

Abstract Background: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Methods: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.Conclusion : This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

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Abstract 367: Extreme High-Density Lipoprotein Cholesterol Genetics: An Assortment of Large and Small Polygenic Effects

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.37.suppl_1.367 ◽

2017 ◽

Vol 37 (suppl_1) ◽

Author(s):

Jacqueline S Dron ◽

Jian Wang ◽

Cécile Low-Kam ◽

Sumeet A Khetarpal ◽

John F Robinson ◽

...

Keyword(s):

Large Scale ◽

Genetic Basis ◽

Rare Variants ◽

Association Studies ◽

Density Lipoprotein ◽

Copy Number Variations ◽

Genome Wide Association Studies ◽

Common Variants ◽

Targeted Next Generation Sequencing ◽

Common Genetic Variants

Rationale: Although HDL-C levels are known to have a complex genetic basis, most studies have focused solely on identifying rare variants with large phenotypic effects to explain extreme HDL-C phenotypes. Objective: Here we concurrently evaluate the contribution of both rare and common genetic variants, as well as large-scale copy number variations (CNVs), towards extreme HDL-C concentrations. Methods: In clinically ascertained patients with low ( N =136) and high ( N =119) HDL-C profiles, we applied our targeted next-generation sequencing panel (LipidSeq TM ) to sequence genes involved in HDL metabolism, which were subsequently screened for rare variants and CNVs. We also developed a novel polygenic trait score (PTS) to assess patients’ genetic accumulations of common variants that have been shown by genome-wide association studies to associate primarily with HDL-C levels. Two additional cohorts of patients with extremely low and high HDL-C (total N =1,746 and N =1,139, respectively) were used for PTS validation. Results: In the discovery cohort, 32.4% of low HDL-C patients carried rare variants or CNVs in primary ( ABCA1 , APOA1 , LCAT ) and secondary ( LPL , LMF1 , GPD1 , APOE ) HDL-C–altering genes. Additionally, 13.4% of high HDL-C patients carried rare variants or CNVs in primary ( SCARB1 , CETP , LIPC , LIPG ) and secondary ( APOC3 , ANGPTL4 ) HDL-C–altering genes. For polygenic effects, patients with abnormal HDL-C profiles but without rare variants or CNVs were ~2-fold more likely to have an extreme PTS compared to normolipidemic individuals, indicating an increased frequency of common HDL-C–associated variants in these patients. Similar results in the two validation cohorts demonstrate that this novel PTS successfully quantifies common variant accumulation, further characterizing the polygenic basis for extreme HDL-C phenotypes. Conclusions: Patients with extreme HDL-C levels have various combinations of rare variants, common variants, or CNVs driving their phenotypes. Fully characterizing the genetic basis of HDL-C levels must extend to encompass multiple types of genetic determinants—not just rare variants—to further our understanding of this complex, controversial quantitative trait.

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