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Background <p>Atrial fibrillation (AF) is the most common type of arrhythmia and is linked to an increased risk of death, stroke, and other complications. Although studies have explored the pathophysiology of AF, its genetic mechanisms remain poorly understood, limiting the development of effective treatments.</p> Methods <p>We conducted a large-scale meta-analysis of genome-wide association study (GWAS) data from 1,347,178 participants in the FinnGen and Nielsen studies to identify novel genetic variants of AF. The novel genes were validated by expression quantitative trait loci (eQTLs) using a transcriptome-wide association study. The cross-phenotype genetic correlation analyses were conducted for our new GWAS. Afterward, the GWAS results were integrated with protein quantitative trait loci data from the UK Biobank, and Mendelian randomization with postselection inference and colocalization analyses were used to infer causal relationships between proteins and AF. We also conducted downstream analysis for the identified genes, including querying the Mouse Genome Informatics database, gene enrichment analysis, gene expression analysis, and druggability assessment.</p> Results <p>Our meta-analysis revealed 244 genome-wide significant AF risk variants, 77 of which were novel. In total, 372 genes were identified by a transcriptome-wide association study. Additionally, 155 proteins were significantly associated with AF, with 28 showing strong colocalization with AF genetic signals. Gene expression and enrichment analyses revealed that the identified genes were predominantly expressed in the heart and blood vessels, with related biological pathways such as cardiac development, myocardial hypertrophy and fibrosis. Single-nucleus RNA sequencing results revealed that these genes are expressed mainly in cardiomyocytes and macrophages. The quantitative real-time polymerase chain reaction results revealed significant changes in the expression of most genes in the AF group compared with the control group. Through druggability analysis, we identified potential AF drug targets, some of which are linked to approved or clinical-stage drugs.</p> Conclusion <p>This study provides new insights into the genetic and proteomic basis of AF. We hope our findings will inspire further research and preclinical work to advance drug development programs for AF.</p>

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Genomic data-driven framework for drug target discovery in atrial fibrillation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月15日

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  • Yuhang Tao,
  • Qi Liu,
  • Yuxing Wang,
  • Ruhong Jiang,
  • Kai Zhang

摘要

Background

Atrial fibrillation (AF) is the most common type of arrhythmia and is linked to an increased risk of death, stroke, and other complications. Although studies have explored the pathophysiology of AF, its genetic mechanisms remain poorly understood, limiting the development of effective treatments.

Methods

We conducted a large-scale meta-analysis of genome-wide association study (GWAS) data from 1,347,178 participants in the FinnGen and Nielsen studies to identify novel genetic variants of AF. The novel genes were validated by expression quantitative trait loci (eQTLs) using a transcriptome-wide association study. The cross-phenotype genetic correlation analyses were conducted for our new GWAS. Afterward, the GWAS results were integrated with protein quantitative trait loci data from the UK Biobank, and Mendelian randomization with postselection inference and colocalization analyses were used to infer causal relationships between proteins and AF. We also conducted downstream analysis for the identified genes, including querying the Mouse Genome Informatics database, gene enrichment analysis, gene expression analysis, and druggability assessment.

Results

Our meta-analysis revealed 244 genome-wide significant AF risk variants, 77 of which were novel. In total, 372 genes were identified by a transcriptome-wide association study. Additionally, 155 proteins were significantly associated with AF, with 28 showing strong colocalization with AF genetic signals. Gene expression and enrichment analyses revealed that the identified genes were predominantly expressed in the heart and blood vessels, with related biological pathways such as cardiac development, myocardial hypertrophy and fibrosis. Single-nucleus RNA sequencing results revealed that these genes are expressed mainly in cardiomyocytes and macrophages. The quantitative real-time polymerase chain reaction results revealed significant changes in the expression of most genes in the AF group compared with the control group. Through druggability analysis, we identified potential AF drug targets, some of which are linked to approved or clinical-stage drugs.

Conclusion

This study provides new insights into the genetic and proteomic basis of AF. We hope our findings will inspire further research and preclinical work to advance drug development programs for AF.