Transcriptome wide association studies: general framework and methods

IF 0.6 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Quantitative Biology Pub Date : 2021-01-01 DOI:10.15302/J-QB-020-0228
Yu-Xiao Xie, N. Shan, Hongyu Zhao, Lin Hou
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引用次数: 2

Abstract

Background : Genome-wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and dif fi culties in biological interpretation. With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome-wide association studies (TWAS) provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. Results : In this review, we will introduce the general framework of TWAS, the relevant resources, and the computational tools. Extensions of the original TWAS methods will also be discussed. Furthermore, we will brie fl y introduce methods that are closely related to TWAS, including MR-based methods and colocalization approaches. Connection and difference between these approaches will be discussed. Conclusion : Finally, we will summarize strengths, limitations, and potential directions for TWAS. Author summary: Transcriptome-wide association studies (TWAS) provide an important framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. In this review, we systematically review the general framework and methods of transcriptome-wide association studies, and discuss their strengths, limitations, and potential future directions.
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全转录组关联研究:一般框架和方法
背景:在过去的十年中,全基因组关联研究(GWAS)已经成功地鉴定了数以万计的与复杂人类性状相关的遗传变异,然而,它们仍然受到有限的统计能力和生物学解释困难的阻碍。随着表达数量性状位点(eQTL)研究的进展,转录组全关联研究(transcriptome-wide association studies, TWAS)通过整合GWAS和eQTL研究的信息,提供了一个检测基因-性状相关性的框架。结果:在这篇综述中,我们将介绍TWAS的总体框架、相关资源和计算工具。还将讨论原始TWAS方法的扩展。此外,我们将简要介绍与TWAS密切相关的方法,包括基于mr的方法和共定位方法。将讨论这些方法之间的联系和区别。结论:最后总结了TWAS的优势、局限性和潜在的发展方向。作者总结:转录组全关联研究(Transcriptome-wide association studies, TWAS)通过整合来自GWAS和eQTL研究的信息,为检测基因-性状关联提供了一个重要的框架。在这篇综述中,我们系统地回顾了转录组关联研究的一般框架和方法,并讨论了它们的优势、局限性和潜在的未来方向。
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来源期刊
Quantitative Biology
Quantitative Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
5.00
自引率
3.20%
发文量
264
期刊介绍: Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and gain a deeper understanding of the life sciences. It aims to provide a platform for not only the analysis but also the integration and construction of biological systems. It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers in order to promote rapid communication and exchange between scientists in the East and the West. The content of Quantitative Biology will mainly focus on the two broad and related areas: ·bioinformatics and computational biology, which focuses on dealing with information technologies and computational methodologies that can efficiently and accurately manipulate –omics data and transform molecular information into biological knowledge. ·systems and synthetic biology, which focuses on complex interactions in biological systems and the emergent functional properties, and on the design and construction of new biological functions and systems. Its goal is to reflect the significant advances made in quantitatively investigating and modeling both natural and engineered life systems at the molecular and higher levels. The journal particularly encourages original papers that link novel theory with cutting-edge experiments, especially in the newly emerging and multi-disciplinary areas of research. The journal also welcomes high-quality reviews and perspective articles.
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