Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
Curr Atheroscler Rep. 2023 Feb;25(2):55-65. doi: 10.1007/s11883-022-01078-8. Epub 2023 Jan 3.
'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.
Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
“组学研究提供了对生物实体（如基因组、表观基因组、转录组、蛋白质组、代谢组或微生物组）的全面描述。本文综述了这些类型的‘组学’的独特性质及其作为心血管疾病因果中介的作用。此外，还讨论了整合多种类型的‘组学’数据以提高预测能力、改善因果推断和阐明生物学机制的应用和挑战。
随着多组学方法的应用越来越广泛，它们提供了正交证据，克服了单一类型的‘组学’数据的局限性。具有多种类型的‘组学’数据的研究改善了疾病状态的诊断和预测，并深入了解了潜在的病理生理机制，超越了任何单一类型的‘组学’数据。例如，基因组中的疾病相关基因座可以与其他‘组学’数据相结合，优先考虑因果基因，并了解非编码变异的功能。或者，孟德尔随机化等技术可以利用遗传学为与疾病相关的分子提供因果作用的证据，并阐明它们在疾病发病机制中的作用。随着技术的进步，‘组学’研究的成本将继续下降，数据集将越来越容易为研究人员获取。‘组学’数据固有的无偏性质非常适合探索性分析，以发现疾病的因果中介，而多组学是一个新兴的学科，利用每种类型的‘组学’数据的优势提供的见解大于其各部分的总和。
