Evidence map›Paper›PMID 41060636›Full record

ArticleJAMA cardiology2025

Contributions of Common, Rare, and Somatic Genetic Variants to Incidence of Atrial Fibrillation.

Rufan Zhang, Min Seo Kim, Wanqing Yin, Shuangqiao Liao, Xinyu Zhu, Xiong Yang, Kang Yu, Yang Sui, Carolina Roselli, Shaan Khurshid and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAMA cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Rufan ZhangChina National Center for Bioinformation, Beijing, China.
Min Seo KimMedical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Wanqing YinChina National Center for Bioinformation, Beijing, China.
Shuangqiao LiaoChina National Center for Bioinformation, Beijing, China.
Xinyu ZhuChina National Center for Bioinformation, Beijing, China.
Xiong YangChina National Center for Bioinformation, Beijing, China.
Kang YuChina National Center for Bioinformation, Beijing, China.
Yang SuiMedical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Carolina RoselliCardiovascular Research Center, Massachusetts General Hospital, Boston.
Shaan KhurshidMedical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Qiuli ChenChina National Center for Bioinformation, Beijing, China.
Yunga AChina National Center for Bioinformation, Beijing, China.
Hongqiang ZhaoChina National Center for Bioinformation, Beijing, China.
Tingfeng XuChina National Center for Bioinformation, Beijing, China.
Xiufeng HuangZhejiang Provincial Clinical Research Center for Pediatric Precision Medicine, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Jun PuDepartment of Cardiology, Renji Hospital, School of Medicine, State Key Laboratory for Systems Medicine for Cancer, Shanghai Cancer Institute, Shanghai Jiao Tong University, Shanghai, China.
Zhaoqi LiuChina National Center for Bioinformation, Beijing, China.
Pradeep NatarajanMedical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Guangyao ZhaiBeijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Patrick T EllinorMedical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Minxian WangChina National Center for Bioinformation, Beijing, China.
Akl C FahedMedical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

Funding

Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimationK23HL169839 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Shaan Khurshid · 2023 to 2026
$860k
NHLBI NIH HHS K23 HL169839
6 · The paper itself

Abstract

Importance: Atrial fibrillation (AF) has a complex genetic architecture involving common, rare, and somatic variants. The association between these components requires further investigation. Objective: To examine the individual and combined contributions of polygenic, monogenic, and somatic genetic variants to AF incidence, and develop an integrated genomic model (IGM-AF) for improved risk prediction. Design, Setting, and Participants: This cohort study used whole-genome sequence data from participants of the UK Biobank, with follow-up for AF events through hospital records, death registries, and self-report. The UK Biobank recruited participants aged 40 to 69 years in the UK between 2006 and 2010. Study data were analyzed from August 2022 to November 2024. Exposures: IGM-AF comprising an AF polygenic risk score (PRS), a composite rare variant gene set (AFgeneset), and somatic variants associated with clonal hematopoiesis of indeterminate potential (CHIP). Clinical AF risk was estimated using the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score. Main Outcomes and Measures: The primary outcome was hazard ratios (HRs) for 5-year incident AF attributable to PRS, AFgeneset, CHIP, and their interactions. The predictive performance of IGM-AF and its components was quantified using HRs, C statistics, and reclassification indices. Results: A total of 416 085 individuals (mean [SD] age, 56.6 [8.0] years; 224 642 female [54.0%]) with 30 797 AF cases were included. The PRS (HR per 1 SD, 1.65; 95% CI, 1.63-1.67; P < 1 × 10-8), AFgeneset (HR, 1.63; 95% CI, 1.52-1.75; P = 1.46 × 10-42), and CHIP (HR, 1.26; 95% CI, 1.15-1.38; P = 1.41 × 10-6) were associated with incident AF. The 5-year cumulative incidence of AF was at least 2-fold among individuals having all 3 genetic drivers (common, rare, and somatic drivers) compared with those with only 1 driver. Integration of IGM-AF with a clinical risk model (CHARGE-AF) showed higher predictive performance (C statistic, 0.80; 95% CI, 0.80-0.80) compared with IGM-AF and CHARGE-AF alone. The classification of the at-risk population for AF was improved when IGM-AF was added to CHARGE-AF (net reclassification index, 0.08; 95% CI, 0.07-0.09). Conclusions and Relevance: Results of this cohort study demonstrated the complementary value of common, rare, and somatic variants in shaping genomic AF risk. Leveraging comprehensive genetic information may enhance screening and preventive interventions for AF.

Indexed as

Atrial FibrillationGenetic VariationAdultAgedCohort StudiesFemaleGenetic Predisposition to DiseaseHumansIncidenceMaleMiddle AgedRisk AssessmentRisk FactorsUnited Kingdom

Identifiers

PMID41060636
PMCPMC12509083

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.