TWAS Analysis

Introduction of TWAS Analysis

Fig 1. Schematic of the TWAS approach. (Gusev A. Ko A, et al. 2016)

Fig 1. Schematic of the TWAS approach. (Gusev A. Ko A, et al. 2016)

TWAS Analysis Process

The TWAS analysis process can be divided into two main steps:

  • First of all, use the genetic variation information near the gene to construct a transcription level prediction model, such as using the elastic net regression model in machine learning in PrediXcan.
  • Secondly, use the model to predict the gene expression level of the research object and make an association analysis with the phenotype.
Fig 2. Analysis Process of TWAS Analysis.

Fig 2. Analysis Process of TWAS Analysis.

Analysis Content

Compared with genome-wide association study, the TWAS research strategy has the following advantages:

  • Compared with SNP, gene-based analysis has lower multiple comparison pressure.
  • The analysis results are presented in the form of specific genes instead of SNPs. The biological significance of genes is more direct, which is convenient for subsequent functional research and result transformation.
  • Compared with transcriptome monoomics studies, transcriptome studies based on the genetic variation of germline genomes will not have the problem of reversed causality, and are less affected by confounding factors.
  • The GTEx database has provided extremely rich genome and transcriptome data. Researchers can use a variety of human tissue and cell data as a reference panel to build models. The transition from GWAS to TWAS can be achieved without additional sample testing.
  • Increasingly mature artificial intelligence analysis methods are used in TWAS research, and the prediction results are becoming more and more accurate.

Application Filed

  • Research on susceptibility genes for tumors and complex diseases.
  • Analysis of special traits of animals and plants.
  • Disease warning, genetic counseling, early diagnosis, risk assessment and drug selection.

CD ComputaBio provides TWAS analysis based on PrediXcan according to customers’ requirement. In the TWAS analysis process, in addition to providing analysis methods using PrediXcan, we can also provide different analysis methods according to your analysis needs, such as FUSION, top eQTL, TIGAR, CTIMP and MR-JTI and other cutting-edge analysis methods. In addition, we have a professional analysis team to provide the most reasonable forecasting model and analysis strategy based on your data. You only need to provide raw data or third-party data, and CD ComputaBio will provide you with a complete analysis report. Regarding TWAS analysis, if you have any questions, please feel free to contact us, we look forward to working with you.


  • Gusev A. Ko A, et al. Integrative approaches for large-scale transcriptome-wide association studies [J]. Nature Genetics. 201doi:10.1038/ng.3506. (2016)


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