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Genetic Pleiotropy and Causal Pathways Linking Glycemic Traits to Asthma: An Integrated Proteogenomic Investigation
Lin Chen1, Juntao Lin1, Yan Zhao1
1National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Children's Hospital of Chongqing Medical University, Chongqing 400014, China.
None:
Background: While diabetes is a recognized risk factor for asthma, the shared genetic components between diabetes/glycemic traits and asthma remain unclear. This study investigates the genetic associations, causal relationships, and underlying mechanisms linking these conditions. Methods: We assessed global genetic correlations using linkage disequilibrium score regression (LDSC), high-definition likelihood analysis (HDL), and genetic covariance analysis (GNOVA). Trait pairs with significant correlations subsequently underwent genetic overlap analysis (Genetic analysis integrating Pleiotropy and functional Annotation, GPA) and local genetic correlation analysis (Local Genetic Variant Association Analysis, LAVA). Cross-phenotype association (CPASSOC) and multitrait analysis of GWAS (MTAG) identified potential pleiotropic loci, followed by colocalization and functional annotation. Proteome-wide association study (PWAS) revealed proteins and pathways shared between diabetes/glycemic traits and asthma. Generalized summary-data-based Mendelian randomization (GSMR) was used to evaluate causal effects between diabetes/glycemic traits and asthma. Results: Significant genetic correlations were observed between body mass index (BMI) and asthma (rg = 0.280-0.397; FDR < 0.05), type 2 diabetes mellitus (T2DM) and asthma (rg = 0.240-0.289; FDR < 0.05) across all three methods. GPA revealed significant genome-wide genetic overlap, highest for BMI and asthma (pleiotropy association ratio [PAR] = 0.377) and T2DM-asthma (PAR = 0.353). LAVA identified 111 significant local correlation regions, predominantly between T2DM and asthma (70 regions). Colocalization analysis identified 24 shared pleiotropic loci, predominantly on chromosome 8. Local genetic correlation analysis revealed extensive correlations between T2DM and asthma. PWAS identified 46 shared proteins, with IL6R, MAPK3, and CSF2 being key hubs. Protein-protein interaction analysis highlighted enrichment in JAK-STAT signaling, Th1/Th2 differentiation, and IL-17 pathways. GSMR demonstrated causal effects of BMI (OR = 1.47, 95% CI: 1.42-1.53, FDR < 0.05) and T2DM (OR = 1.06, 95% CI: 1.04-1.08, FDR < 0.05) on increased asthma risk, with no evidence of reverse causality. Conclusions: Obesity (BMI) and T2DM exert causal effects on asthma risk via shared genetic loci and inflammatory pathways, particularly involving IL6R, MAPK3, CSF2, and JAK-STAT signaling. Targeting these colocalized proteins may offer potential therapeutic strategies.
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