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Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

机译:利用多OMIC方法整合功能网络中影响冠状动脉疾病的基因

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Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our “second genome”—the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine.
机译:冠状动脉疾病(CAD)和心肌梗塞(MI)仍然是世界范围内导致死亡的主要原因,因此迫切需要更好地了解疾病病因和更有效的治疗策略。遗传易感性以及环境和生活方式被认为是造成疾病风险的原因。这些多种因素之间可能发生非线性复杂的相互作用,涉及多种分子水平,多种细胞类型,组织和器官的同时病理变化。最近的技术进步已使可用的组学数据从基因组,表观基因组,转录组,蛋白质组,代谢组到微生物组成倍地扩展。跨多个组学领域的多层信息集成,即所谓的多组学方法,目前有望成为精密医学的道路。确实,迫切需要对基因型与表型之间的关系进行更有意义的解释,并开发针对个别患者的成功疗法。在这篇综述中,我们将总结综合多组学在阐明CAD / MI病因方面的最新发现和应用。特别关注过去十年中在全基因组关联研究(GWAS)中依次确定的既定疾病易感性基因座。此外,除了常染色体基因组,我们还将考虑“第二基因组”(线粒体基因组)中的遗传变异。最后,我们将总结该领域当前的挑战,并指出为成功,有效地将这些方法应用于精密医学而需要的未来研究方向。

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