Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation

Abstract

Genome wide association studies (GWAS) have identified several genomic loci with candidate modifiers of cystic fibrosis (CF) lung disease, but only a small proportion of the expected genetic contribution is accounted for at these loci. We leveraged expression data from CF cohorts, and Genotype-Tissue Expression (GTEx) reference data sets from multiple human tissues to generate predictive models, which were used to impute transcriptional regulation from genetic variance in our GWAS population. The imputed gene expression was tested for association with CF lung disease severity. By comparing and combining results from alternative approaches, we identified 379 candidate modifier genes. We delved into 52 modifier candidates that showed consensus between approaches, and 28 of them were near known GWAS loci. A number of these genes are implicated in the pathophysiology of CF lung disease (e.g., immunity, infection, inflammation, HLA pathways, glycosylation, and mucociliary clearance) and the CFTR protein biology (e.g., cytoskeleton, microtubule, mitochondrial function, lipid metabolism, endoplasmic reticulum/Golgi, and ubiquitination). Gene set enrichment results are consistent with current knowledge of CF lung disease pathogenesis. HLA Class II genes on chr6, and CEP72, EXOC3, and TPPP near the GWAS peak on chr5 are most consistently associated with CF lung disease severity across the tissues tested. The results help to prioritize genes in the GWAS regions, predict direction of gene expression regulation, and identify new candidate modifiers throughout the genome for potential therapeutic development.