Abstract：
BACKGROUND: Atrial fibrillation (AF), the most common atrial arrhythmia, presents with varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. In this study we aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on a substantial cohort of Taiwanese individuals, to evaluate the predictive efficacy of the model.
METHODS: There were 75,121 subjects, that included 5694 AF patients and 69,427 normal control subjects with GWAS data, and we merged polygenic risk scores from AF-associated SNPs with phenome-wide association study-derived risk factors. Advanced statistical and machine learning techniques were used to develop and evaluate AF predictive models for discrimination and calibration.
RESULTS: The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using polygenic risk score-continuous shrinkage analysis for AF exhibited an area under the curve of 0.600 (P < 0.001), which improved to 0.855 (P < 0.001) after adjusting for age and sex. Phenome-wide association study analysis showed the top 10 diseases associated with these genes were circulatory system diseases.
CONCLUSIONS: Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.
METHODS: There were 75,121 subjects, that included 5694 AF patients and 69,427 normal control subjects with GWAS data, and we merged polygenic risk scores from AF-associated SNPs with phenome-wide association study-derived risk factors. Advanced statistical and machine learning techniques were used to develop and evaluate AF predictive models for discrimination and calibration.
RESULTS: The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using polygenic risk score-continuous shrinkage analysis for AF exhibited an area under the curve of 0.600 (P < 0.001), which improved to 0.855 (P < 0.001) after adjusting for age and sex. Phenome-wide association study analysis showed the top 10 diseases associated with these genes were circulatory system diseases.
CONCLUSIONS: Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.
摘要：
背景：心房颤动（AF），最常见的房性心律失常,呈现不同的临床表现。尽管鉴定了与AF相关的遗传基因座,特别是在特定人群中，亚洲种族的研究仍然有限。本研究旨在利用全基因组关联研究(GWAS)对大量台湾人进行的房颤相关单核苷酸多态性(SNPs),建立房颤预测模型。评估模型的预测功效。
方法：涉及75,121名受试者，包括5,694例房颤患者和69,427例具有GWAS数据的正常对照，本研究将房颤相关SNPs的多基因风险评分(PRS)与全表型关联研究(PheWAS)衍生的风险因素合并起来.采用先进的统计和机器学习技术来开发和评估用于辨别和校准的AF预测模型。
结果：该研究确定了与房颤相关的前30个显著SNP，主要在10号和16号染色体上，涉及像NEURL1，SH3PXD2A，INA,NT5C2、STN1和ZFHX3。值得注意的是,INA,NT5C2和STN1与AF新连接。使用PRS-CS分析对AF的GWAS预测能力显示曲线下面积（AUC）为0.600（P<0.001），调整年龄和性别后提高到0.855(P<0.001)。PheWAS分析显示，与这些基因相关的前10位疾病是循环系统疾病。
结论：整合遗传和表型数据可提高房颤预测模型的准确性和临床相关性。研究结果表明，有希望完善房颤风险评估，实现个性化干预，减少房颤相关的发病率和死亡率负担。
方法：涉及75,121名受试者，包括5,694例房颤患者和69,427例具有GWAS数据的正常对照，本研究将房颤相关SNPs的多基因风险评分(PRS)与全表型关联研究(PheWAS)衍生的风险因素合并起来.采用先进的统计和机器学习技术来开发和评估用于辨别和校准的AF预测模型。
结果：该研究确定了与房颤相关的前30个显著SNP，主要在10号和16号染色体上，涉及像NEURL1，SH3PXD2A，INA,NT5C2、STN1和ZFHX3。值得注意的是,INA,NT5C2和STN1与AF新连接。使用PRS-CS分析对AF的GWAS预测能力显示曲线下面积（AUC）为0.600（P<0.001），调整年龄和性别后提高到0.855(P<0.001)。PheWAS分析显示，与这些基因相关的前10位疾病是循环系统疾病。
结论：整合遗传和表型数据可提高房颤预测模型的准确性和临床相关性。研究结果表明，有希望完善房颤风险评估，实现个性化干预，减少房颤相关的发病率和死亡率负担。
