Conditional transcriptome-wide association study for fine-mapping candidate causal genes.

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genetics

Background:

• Transcriptome-wide association studies (TWASs) integrate genome-wide association studies (GWASs) with expression data to identify genes linked to complex traits.
• Existing TWAS methods face challenges in precisely fine-mapping causal genes within associated regions.

Purpose of the Study:

• To develop and validate GIFT (gene-based integrative fine-mapping through conditional TWAS), a novel method for fine-mapping putatively causal genes.
• To improve the accuracy and resolution of gene identification in complex trait association studies.

Main Methods:

• GIFT performs conditional TWAS analysis, explicitly controlling for the genetically predicted expression (GReX) of all other genes in a local region.
• The method employs a frequentist approach, models expression correlation and linkage disequilibrium across multiple genes, and uses a likelihood framework to handle expression prediction uncertainty.
• GIFT generates calibrated P-values for robust fine-mapping.

Main Results:

• Application of GIFT to six UK Biobank traits demonstrated significant improvement in fine-mapping resolution, narrowing the set size of putatively causal genes by 32.16-91.32% compared to existing methods.
• GIFT identified key genes implicating vessel regulation in blood pressure and lipid metabolism in regulating lipid levels.

Conclusions:

• GIFT is an effective and statistically rigorous method for fine-mapping causal genes in TWAS.
• The identified genes provide novel insights into the biological mechanisms underlying complex traits like blood pressure and lipid levels.