Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating...

Area of Science:

• Genomics
• Systems Biology
• Pharmacogenomics

Background:

• Transcriptome-wide association studies (TWAS) link genetic variation to gene expression and complex traits.
• Incorporating functional genomic data can enhance the accuracy and power of TWAS.

Purpose of the Study:

• To improve transcriptome prediction and identify gene-trait associations by integrating epigenome data into TWAS.
• To discover novel drug repurposing opportunities and understand gene pleiotropy in complex trait susceptibility.

Main Methods:

• Joint analysis of 14 large-scale transcriptome datasets and 58 human and mouse traits.
• Integration of epigenome data to estimate the functional impact of genetic variation on gene expression.
• Drug repurposing analysis and investigation of agonistic pleiotropy for genetically correlated traits.

Main Results:

• A significant improvement in transcriptome prediction accuracy and power for detecting expression-trait associations.
• Identification of 13,724 significant expression-trait associations converging on biological processes and phenotypes.
• Discovery of compounds for drug repurposing and elucidation of shared/distinct pathways for genetically correlated traits.

Conclusions:

• Integrating epigenome data enhances TWAS, providing deeper insights into complex trait pathogenesis.
• This comprehensive analysis reveals the specificity and convergence of gene expression in disease susceptibility.
• Findings support novel therapeutic strategies through drug repurposing and understanding gene-trait relationships.