2024年6月11日出版的《自然-遗传学》杂志发表了美国西奈山伊坎医学院Ron Do组的最新研究成果。他们利用外显子组序列分析和机器学习相结合的方法，揭示了与冠状动脉疾病（CAD）标记相关的罕见编码变异。

研究人员测试了英国生物库、"我们所有人 "研究计划和BioMe生物库中罕见和超罕见编码变异与CAD计算机模拟评分之间的关联。研究在17个基因中发现了相关性，其中至少14个基因表现出与先前遗传学、生物学和/或临床发现CAD存在中等程度的关联。

研究还在321个聚集的CAD基因中发现了很多超稀疏编码变异，这表明还有更多未知的超稀疏变异关联有待发现。这些结果拓展了人们对CAD遗传病因学的理解，并证明了数字标记能实现与复杂疾病的遗传关联研究。

据悉，冠状动脉疾病是由多种致病因素和致病过程共同导致的疾病。利用机器学习和电子健康记录临床数据建立的冠状动脉疾病计算机模拟评分，可以捕捉到这一疾病的进展过程、严重程度和诊断延误，并能加强冠状动脉疾病的基因发现工作。

附：英文原文

Title: Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease

Author: Petrazzini, Ben Omega, Forrest, Iain S., Rocheleau, Ghislain, Vy, Ha My T., Mrquez-Luna, Carla, Duffy, ine, Chen, Robert, Park, Joshua K., Gibson, Kyle, Goonewardena, Sascha N., Malick, Waqas A., Rosenson, Robert S., Jordan, Daniel M., Do, Ron

Issue&Volume: 2024-06-11

Abstract: Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disease progression, severity and underdiagnosis on this spectrum and could enhance genetic discovery efforts for CAD. Here we tested associations of rare and ultrarare coding variants with the in silico score for CAD in the UK Biobank, All of Us Research Program and BioMe Biobank. We identified associations in 17 genes; of these, 14 show at least moderate levels of prior genetic, biological and/or clinical support for CAD. We also observed an excess of ultrarare coding variants in 321 aggregated CAD genes, suggesting more ultrarare variant associations await discovery. These results expand our understanding of the genetic etiology of CAD and illustrate how digital markers can enhance genetic association investigations for complex diseases. A machine learning-based, continuous in silico coronary artery disease (CAD) score built using electronic health record data is applied to rare variant association analysis of CAD, implicating novel candidate genes and biological mechanisms.

DOI: 10.1038/s41588-024-01791-x

Source: https://www.nature.com/articles/s41588-024-01791-x