CD Genomics as one of the providers of transcriptome-wide association study analysis (TWAS analysis), we use bioinformatics to help you quickly study the causal relationship between gene expression and complex traits. Our unique data analysis skills and appropriate software tools such as FUNSION, PrediXcan, TIGAR, CTIMP and MR-JTI can meet customers' personalized laboratory data analysis needs, and provide you with easy-to-interpret data analysis reports.
Introduction of TWAS
Transcriptome-wide association study (TWAS) is an analysis method widely used in genetic epidemiological studies to find genes associated with complex phenotypes (such as type 2 diabetes, tumors). Compared with Genome-wide association study (GWAS), TWAS uses transcriptional regulation as a mediator between genetic variation and phenotype, and converts the association between a single genetic variation and phenotype into genes / transcripts and phenotypic association.
The basic bioinformatics analysis idea of TWAS is to first perform genotyping and transcriptome sequencing in a small sample population to obtain genotype data and gene expression data. Using genotype data and gene expression data as a training set, and fit a model of the relationship between gene expression and genotype, so as to obtain the estimated value of the effect of genotype on gene expression. Then use the model to estimate the gene expression of a large sample of people with genotyping results. Finally, the correlation analysis between the phenotype and predicted gene expression of the large sample population is carried out.
Fig 1. TWAS analysis results by different methods for WTCCC traits. (Yuan Z , Zhu H , et al. 2020)
Application of TWAS
Research on susceptibility sites of complex diseases.
Analysis of special traits of animals and plants.
Disease warning, genetic counseling, early diagnosis, risk assessment and drug selection.
Advantages of TWAS Analysis
In the process of studying the causal relationship between gene expression and complex traits, 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.
CD Genomics TWAS Analysis Pipeline
Fig 2. Pipeline of TWAS analysis.
The TWAS analysis process can be divided into two main steps:
Firstly, use the genetic variation information near the gene to construct a transcription level prediction model.
Secondly, use the model to predict the gene expression level of the research object and make an association analysis with the phenotype.
How It Works
CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.
Bioinformatics-Analysis, a division of CD Genomics, provides Transcriptome-wide Association Study.analysis according to customer’s requirements. As one of the very few TWAS analysis service providers, we provide you with professional biological information analysis services based on cutting-edge scientific research. If you are interested in our services, please contact us for more detailed information.
References
- Yuan Z , Zhu H , et al. Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies[J]. Nature Communications, 2020, 11(1).
- Gusev A. Ko A, et al. Integrative approaches for large-scale transcriptome-wide association studies [J]. Nature Genetics. 201doi:10.1038/ng.3506. (2016).
