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精准医学时代的基因组学与多组学

Genomics and multiomics in the age of precision medicine.

作者信息

Mani Srinivasan, Lalani Seema R, Pammi Mohan

机构信息

Department of Pediatrics, University at Buffalo, Buffalo, NY, USA.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.

出版信息

Pediatr Res. 2025 Apr 4. doi: 10.1038/s41390-025-04021-0.

Abstract

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

摘要

精准医学是一种变革性的医疗保健模式,它利用对一个人的基因组、环境、生活方式及其相互作用的了解来提供定制化的医疗保健服务。精准医学有潜力改善人群的健康状况和生产力,增强患者对医疗保健的信任和满意度,并在个体和人群层面产生健康成本效益。通过更快且具有成本效益的基因组学数据,新一代测序技术为我们提供了动力,促使我们去了解人群中基因、饮食和生活方式之间复杂相互作用的细微差别,这些相互作用具有异质性。包括转录组学、蛋白质组学、表观基因组学、代谢组学和微生物组学在内的多组学技术的出现,增强了将基因组学数据最大化应用于改善健康结果所需的知识。整合多组学,即将多个“组学”数据相互叠加,包括它们之间的相互联系和相互作用,比单独研究其中任何一个能更好地帮助我们理解人类健康和疾病。如今,随着生物信息学、数据科学和人工智能的显著进步,整合这些多组学数据成为可能。我们的综述从广泛的视角阐述了以基因组学为先、叠加其他组学数据的方法的实用性和可行性,为在儿科医疗保健和研究中采用整合多组学方法提供了一个实用模型。影响:精准医学实现了从传统的、被动的疾病控制方法到主动的疾病预防和健康维护的范式转变。生物信息学、数据科学和人工智能的显著进步使整合多组学变得可行,并比单独研究其中任何一个能更好地帮助我们理解人类健康和疾病。基因型优先方法或反向表型分析有潜力通过识别新的基因型 - 表型关联、通过拓宽遗传变异的表型谱来增强疾病的亚分类以及理解遗传变异的功能机制,克服表型优先方法的局限性。

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