IGREX for quantifying the impact of genetically regulated expression on phenotypes

Area of Science:

• Genetics
• Bioinformatics
• Systems Biology

Background:

• Transcriptome-wide association studies (TWAS) link gene expression to traits using GWAS and eQTL data.
• TWAS accuracy depends on matching eQTL data conditions to trait-relevant cell/tissue types.
• Identifying the specific tissue or cellular context for TWAS is challenging due to diverse eQTL data availability.

Purpose of the Study:

• To develop and validate IGREX, a novel method for precisely quantifying the proportion of phenotypic variation attributable to genetically regulated expression (GREX).
• To assess the tissue-specific impact of GREX on various phenotypes using a comprehensive eQTL reference panel.
• To explore new avenues for TWAS by incorporating trans-eQTLs and alternative splicing events.

Main Methods:

• Developed the IGREX method and tool for quantifying GREX-attributed phenotypic variation.
• Utilized a reference eQTL panel from 48 GTEx tissue types.
• Input included individual-level or summary-level GWAS data.
• Incorporated trans-eQTLs and analyzed genetically regulated alternative splicing events.

Main Results:

• Demonstrated IGREX's capability to precisely quantify GREX's contribution to phenotypic variation.
• Evaluated tissue-specific IGREX impacts across a broad range of phenotypes.
• Observed significant GREX effects, particularly for immune-related protein biomarkers.
• Identified potential for incorporating trans-eQTLs and alternative splicing in future TWAS.

Conclusions:

• IGREX offers a precise approach to quantifying GREX's role in phenotypic variation, enhancing TWAS.
• The method highlights the importance of tissue-specific eQTL data in understanding trait associations.
• Future TWAS analyses can benefit from integrating trans-eQTLs and alternative splicing for broader insights.