Open AccessTranscriptome wide association studies: general framework and methods
Author(s) -
Xie Yuhan,
Shan Nayang,
Zhao Hongyu,
Hou Lin
Publication year - 2021
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.15302/j-qb-020-0228
Subject(s) - genome wide association study , expression quantitative trait loci , genetic association , computational biology , trait , computer science , quantitative trait locus , false positive paradox , association (psychology) , colocalization , biology , data science , machine learning , genetics , gene , psychology , neuroscience , single nucleotide polymorphism , genotype , psychotherapist , programming language
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 difficulties 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 briefly 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.