Genome-wide imputed differential expression enrichment analysis identifies trait-relevant tissues

Area of Science:

• Genetics
• Bioinformatics
• Systems Biology

Background:

• Identifying disease-relevant genes and tissues for complex traits is challenging.
• Genome-wide association studies (GWAS) identify genetic loci associated with traits, but pinpointing causal tissues remains difficult.
• Tissue-specific expression quantitative trait loci (eQTL) data provides insights into gene regulation across different tissues.

Purpose of the Study:

• To develop and validate a novel computational approach, genome-wide imputed differential expression enrichment (GIDEE), for prioritizing trait-relevant tissues.
• To integrate genome-wide association study (GWAS) summary statistics with GTEx tissue-specific eQTL data.
• To enhance the identification of pathogenic tissues and facilitate functional follow-up studies for complex traits.

Main Methods:

• Developed the GIDEE approach, which analyzes imputed gene expression and tests for differential expression enrichment in 49 GTEx tissues.
• Utilized four statistical tests: mean squared z-score, empirical Brown's method, and two binomial tests.
• Applied GIDEE to nine training datasets with known trait-relevant tissues and 20 test datasets with unknown relevant tissues.

Main Results:

• GIDEE successfully ranked known trait-relevant tissues with an average rank of 1.55 out of 49 in training datasets.
• The best-performing enrichment test ranked the correct tissue first five times, second three times, and third once.
• Application to test datasets provided crucial prioritization of tissues relevant to trait regulatory architecture.

Conclusions:

• GIDEE is a robust method for prioritizing trait-relevant tissues using GWAS and eQTL data.
• The approach can identify pathogenic tissues and suggest accessible proxy tissues for research.
• GIDEE facilitates downstream in silico and in vitro investigations into the functional consequences of genetic risk loci.