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

Area of Science:

• Genetics
• Pulmonology
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) have identified genetic loci associated with cystic fibrosis (CF) lung disease, but these explain only a fraction of the heritability.
• A significant portion of genetic variation influencing complex traits like CF lung disease may be mediated by gene expression.
• Integrating GWAS with gene expression data is crucial for identifying causal variants and understanding disease mechanisms.

Purpose of the Study:

• To identify novel genetic modifiers of cystic fibrosis (CF) lung disease severity.
• To leverage gene expression data from CF cohorts and reference datasets to impute transcriptional regulation.
• To prioritize candidate genes for therapeutic development in CF.

Main Methods:

• Utilized expression data from cystic fibrosis (CF) cohorts and Genotype-Tissue Expression (GTEx) datasets.
• Generated predictive models to impute transcriptional regulation from genetic variance.
• Tested imputed gene expression for association with CF lung disease severity.
• Combined results from alternative approaches to identify consensus candidate modifier genes.

Main Results:

• Identified 379 candidate modifier genes for CF lung disease.
• Found 52 candidate modifiers with consensus across approaches, 28 near known GWAS loci.
• Several identified genes are involved in CF pathophysiology (immunity, inflammation, CFTR function).
• HLA Class II genes, CEP72, EXOC3, and TPPP showed consistent association with CF lung disease severity across tissues.

Conclusions:

• This study provides a prioritized list of candidate genes that modify CF lung disease severity.
• The findings aid in understanding the genetic architecture of CF lung disease and identifying potential therapeutic targets.
• Imputing gene expression from genetic variance offers a powerful approach to discover novel disease modifiers.