Prioritization of nasal polyp-associated genes by integrating GWAS and eQTL summary data

Abstract

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Background: Nasal polyps (NP) are benign inflammatory growths of nasal and paranasal sinus mucosa that can substantially impair patients’ quality of life by various symptoms such as nasal obstruction, insomnia, and anosmia. NP often relapse even after surgical treatment, and the curative therapy would be challenging without understanding the underlying mechanisms. Genome wide association studies (GWASs) on NP have been conducted; however, few genes that are causally associated with NP have been identified.Methods: We aimed to prioritize NP associated genes for functional follow-up studies using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on NP and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 5,554 NP cases and 258,553 controls with 34 genome-wide significant loci from the FinnGen consortium (data freeze 8) and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium.Results: The SMR analysis identified several genes including TNFRSF18, CTSK, and IRF1 that were associated with NP due to not linkage but pleiotropy or causality. The COLOC analysis strongly suggested that these genes and the trait of NP were affected by shared causal variants, and thus were colocalized. An enrichment analysis by Metascape suggested that these genes might be involved in the biological process of cellular response to cytokine stimulus.Conclusion: We could prioritize several NP associated genes including TNFRSF18, CTSK, and IRF1 for follow-up functional studies in future to elucidate the underlying disease mechanisms.

Keywords

• nasal polyps
• genome-wide association studies
• expression quantitative trait locus
• gene prioritization
• summary data-based Mendelian randomization
• Bayesian colocalization