Epigenomic partitioning of an polygenic risk score for asthma reveals distinct genetically driven disease pathways

Background: Asthma is common chronic inflammatory disease of the airways with a heterogenous clinical presentation. Individual differences in asthma susceptibility remain poorly understood, although genetics is thought to play a major role. Aim: To build a polygenic risk score (PRS) for asthma and determine whether predictive genetic variants can be epigenomically linked to specific pathophysiological mechanisms. Methods: PRSs were constructed using data from genome-wide association studies and performance was validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14,926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and exacerbations. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from 14 primary cell types, including lung epithelial cells and T lymphocytes was used for epigenomic PRS partitioning. Results: All PRSs successfully predicted risk to develop asthma and related outcomes, with the strongest predictive power (2.42 odds ratios per PRS standard deviation, area under the curve of 0.736) achieved for childhood-onset asthma. PRSs allowed for stratification of the Rotterdam Study cohort into groups at low or high risk to develop asthma. PRS partitioning using genome-wide epigenomic profiles identified 5 clusters of variants within gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. Conclusions: PRSs can predict whether individuals in a Dutch cohort developed asthma and asthma-related phenotypes, which is most effective for childhood-onset asthma. Importantly, we show that PRS partitioning based on epigenomics data dissects a genetic risk score into blocks of regulatory variants with differential predictive power, which likely represent distinct genetically driven disease pathways. These findings have potential implications for personalized risk mitigation and treatment strategies. Overall design: Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) data for histone 3 lysine 4 dimethylation (H3K4me2) modifications for the following cell types: naive CD8+ T cells (CD3+CD8+CD45RA+), memory CD8+ T cells (CD3+CD8+CD45RO+), naive B cells (CD3-CD19+CD27-IgD+), memory B cells (CD3-CD19+CD27+IgD-) resting eosinophils (CD33-CD66b+SIGLEC8+CD16-) resting neutrophils (CD33-CD66b+SIGLEC8-CD16+), lung epithelium (Bronchial brush in air-liquid interface culture), resting mast cells (Folkerts et al. Allergy, 2020).