Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases.
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases.
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DOI：
10.1016/bs.adgen.2015.11.004
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发表时间：
2016
影响因子：
--
通讯作者：
Hu YJ
中科院分区：
文献类型：
--
作者：
Sun YV;Hu YJ
复杂和动态的分子网络与人类疾病有关。高通量技术使组学研究能够询问数千至数百万具有相似生化特性的标记物（例如RNA转录物的转录组学）。然而，单层的“组学”只能提供对疾病生物学机制的有限见解。在GWAS的情况下，虽然已经确定了复杂疾病和性状的数千个SNP，但相关基因座的功能意义和机制在很大程度上是未知的。此外，基因组变异本身并不能解释整个生命周期中疾病风险的变化。DNA、RNA、蛋白质和代谢物通常具有互补作用，共同执行某种生物功能。生命过程中组学层之间的这种互补效应和协同相互作用只能通过多个分子层的综合研究来捕捉。在单组学发现研究成功的基础上，人群研究开始采用多组学方法来更好地了解分子功能和疾病病因。多组学方法整合了从不同组学水平获得的数据，以了解它们之间的相互关系以及对疾病过程的综合影响。在这里，我们总结了人口研究中可用的主要组学方法，并回顾了询问多个组学层的综合方法和方法，这些方法和方法增强了人类疾病的基因发现和功能分析。我们试图为不同类型的多组学数据和研究设计提供分析建议，以指导新兴的多组学研究，并建议改进现有的分析方法。
Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (e.g. transcriptomics for RNA transcripts). However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease. In the case of GWAS, although thousands of SNPs have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life-course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.
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影响因子：
4.4
作者：
Bibikova, Marina;Barnes, Bret;Shen, Richard
通讯作者：
Shen, Richard
影响因子：
3.7
作者：
Bocklandt S;Lin W;Sehl ME;Sánchez FJ;Sinsheimer JS;Horvath S;Vilain E
通讯作者：
Vilain E
影响因子：
3.3
作者：
Al Awam, Khaled;Haussleiter, Ida Sibylle;Thome, Johannes
通讯作者：
Thome, Johannes
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM