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 kno

SUBMITTER: Dang H 

PROVIDER: S-EPMC7703903 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature