Multimodal gene prioritization reveals nonlinear regulatory architecture in childhood-onset asthma

Asthma is a heritable complex disease that disproportionately burdens minority and admixed populations in the US. However, the causal genes and regulatory mechanisms governing inherited risk remain largely unresolved. We performed a European-ancestry meta-analysis of 141,894 cases and 1,361,846 controls drawn from the Trans-national Asthma Genetic Consortium (TAGC) and Global Biobank Meta-analysis Initiative (GBMI), yielding an estimated h ² _SNP of 0.056 (SE = 0.0038) and 275 independently associated loci. To enhance mechanistic inference beyond variant-level associations, we developed a multimodal framework to predict asthma risk integrating GWAS summary statistics, bulk tissue expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and single-cell gene eQTL data from the OneK1K Project. We performed transcriptome-wide association studies (TWAS) and subsequently applied probabilistic fine-mapping with FOCUS to prioritize putative causal genes expressed in bulk tissues and higher resolution immune cell populations. Fine-mapping asthma-associated genes implicated barrier-immune and metabolic-endocrine tissues alongside adaptive T-cell subsets as the primary mediators of asthma genetic risk, resolving canonical CD4+ Th2 effector genes including IL1RL1 , TSLP , STAT6 , and GATA3 . Using these prioritized genes, we constructed a polygenic transcriptome risk score (PTRS) using random forest to integrate gene-level effects across critical tissues and cell types. Evaluated in two ancestrally distinct pediatric asthma cohorts, the Childhood Asthma Management Program (CAMP) and the Genetics of Asthma in Costa Rica Study (GACRS), our PTRS demonstrated improved transferability over the standard variant-level and gene-level baseline models. While modest common variant heritability limits the discriminative power of our models, we estimated a theoretical maximum achievable area under the receiver operating characteristic (AUROC) curve of 0.64. Our integrative nonlinear model of PRS-CSx and cross-modal (bulk tissue and single cell) FOCUS PTRS resulted in the best cross-cohort performance (CAMP AUC = 0.632, sd = 0.04, 3.55 case/control odds ratio in top vs. bottom quartiles), representing an increase of +0.118 AUC over PRS-CSx, +0.067 AUC over tissue-specific TWAS pruning and thresholding, and +0.041 AUC over cell-type-specific FOCUS PTRS. Our results demonstrate that modeling nonlinear interactions between variant- and gene-level effects across both bulk tissue and single cell eQTL data improves our ability to determine high-risk individuals and to explain the likely mechanisms driving genetic susceptibility of childhood-onset asthma.

Multimodal putative causal gene prioritization integrates GWAS summary statistics, bulk tissue, and single-cell expression data to resolve effector genes underlying childhood-onset asthma.Gene-level fine-mapping of underlying asthma GWAS loci reveals CD4+ Th2 effector genes, which are known drivers of eosinophilic airway inflammation, across multiple tissue and cell types including CD4+ cytotoxic T cells, naïve CD4+ T cells, and natural killer cells.We constructed and evaluated variant-level multi-ancestry asthma PRS and gene-level PTRS for genes in fine-mapped credible sets.We identified tissues and cell types that consistently helped discriminate asthma cases from controls, namely esophagus mucosa and CD4+ naïve T cells.We found that nonlinear modeling of both variant-level and gene-level effects across top tissues and cell types, tested across various integration strategies, substantially outperformed linear models in the distinction of asthma cases from controls.The model that most effectively distinguished cases from controls was a cross-modal model of PRS-CSx combined with the esophagus mucosa and CD4+ naïve T cell PTRS models, resulting in an AUC = 0.632 ± 0.040 and case/control enrichment odds ratio of 3.55 in top/bottom quartiles.Overall modest discrimination between cases and controls with genetic predictors supports asthma as a complex disease with a substantial non-genetic component.