A Categorical ANCOVA Approach to Severity Endophenotype-Specific Genome-Wide Association Studies in Childhood Asthma

Objective: Asthma is a complex and heterogeneous syndrome, making it hard to predict disease progression and suitable treatments. One strategy for reducing this uncertainty is to define genetic subtypes, or endophenotypes, that capture shared biological mechanisms. Most genome-wide studies, however, compare one subgroup against all others within a single cohort and rarely replicate their findings. We aimed to determine whether simultaneously modeling all asthma endophenotypes improves the discovery and replication of genetic associations compared with the standard one-versus-rest approach. Methods: We analyzed common single-nucleotide polymorphisms (SNPs) in the Childhood Asthma Management Program (CAMP) using an analysis of covariance (ANCOVA) across all severity-related endophenotypes, adjusting for age, sex, and ancestry principal components. SNPs showing genome-wide significance were tested for replication in the Genetics of Asthma in Costa Rican Children Study (GACRS). For comparison, we performed traditional one-versus-rest logistic regression analyses within each cohort, using identical covariates and endophenotype labels. Results: The ANCOVA identified 244 genome-wide significant SNPs in CAMP, of which six unique loci replicated in GACRS. In contrast, logistic regression recovered only four significant contrasts from those six loci in CAMP and replicated just one in GACRS. Conclusions: Our findings highlight genetic variants that are associated with asthma severity endophenotypes and demonstrate that modeling all clinical subtypes simultaneously can reveal biologically meaningful signals that are missed by standard pairwise design.