Introduction: Asthma is a complex, polygenic, heterogenous
inflammatory disease. Recently, we generated a list of 128 independent
single nucleotide polymorphisms (SNPs) associated with asthma in
genome-wide association studies. However, it is unknown if asthma SNPs
are associated with specific asthma-associated traits such as high
eosinophil counts, atopy, and airway obstruction, revealing molecular
endotypes of this disease. Here, we aim to identify the association
between asthma SNPs and asthma-associated traits and assess e-QTLs to
reveal their downstream functional effects and find drug targets.
Methods: Association analyses between 128 asthma SNPs and
associated traits (blood eosinophil numbers, atopy, airway obstruction,
airway hyperresponsiveness) were conducted using regression modelling in
population-based studies (Lifelines N=32,817 / Vlagtwedde-Vlaardingen
N=1,554) and an asthma cohort (Dutch Asthma GWAS N=917). Functional
enrichment and pathway analysis were performed with genes linked to the
significant SNPs by e-QTL analysis. Genes were investigated to generate
novel drug targets. Results: We identified 69 asthma SNPs that
were associated with at least one trait, with 20 SNPs being associated
with multiple traits. The SNP annotated to SMAD3 was the most
pleiotropic. In total, 42 SNPs were associated with eosinophil counts,
18 SNPs with airway obstruction, and 21 SNPs with atopy. We identified
genetically driven pathways regulating eosinophilia, atopy and airway
obstruction. The largest network of eosinophilia contained two genes (
IL4R, TSLP) targeted by drugs currently available for
eosinophilic asthma. Several novel targets were identified.
Conclusion: Many asthma SNPs are associated with blood
eosinophil counts and genetically driven molecular pathways of
asthma-associated traits were identified.