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

The identification of pathogenically-relevant genes and tissues for complex traits can be a difficult task. We developed an approach named genome-wide imputed differential expression enrichment (GIDEE), to prioritise trait-relevant tissues by combining genome-wide association study (GWAS) summary statistic data with tissue-specific expression quantitative trait loci (eQTL) data from 49 GTEx tissues. Our GIDEE approach analyses robustly imputed gene expression and tests for enrichment of differentially expressed genes in each tissue. Two tests (mean squared z-score and empirical Brown's method) utilise the full distribution of differential expression p-values across all genes, while two binomial tests assess the proportion of genes with tissue-wide significant differential expression. GIDEE was applied to nine training datasets with known trait-relevant tissues and ranked 49 GTEx tissues using the individual and combined enrichment tests. The best-performing enrichment test produced an average rank of 1.55 out of 49 for the known trait-relevant tissue across the nine training datasets-ranking the correct tissue first five times, second three times, and third once. Subsequent application of the GIDEE approach to 20 test datasets-whose pathogenic tissues or cell types are uncertain or unknown-provided important prioritisation of tissues relevant to the trait's regulatory architecture. GIDEE prioritisation may thus help identify both pathogenic tissues and suitable proxy tissue/cell models (e.g., using enriched tissues/cells that are more easily accessible). The application of our GIDEE approach to GWAS datasets will facilitate follow-up in silico and in vitro research to determine the functional consequence(s) of their risk loci.

Automated summary

We developed a method named GIDEE to prioritize trait-relevant tissues by combining GWAS summary statistic data with tissue-specific eQTL data. The best-performing GIDEE method ranked the known trait-relevant tissue first multiple times across nine training datasets. Application of the GIDEE approach to test datasets provided important prioritization of tissues relevant to the trait's regulatory architecture.