Mechanistic insights into Down syndrome comorbidities via convergent RNA-seq and TWAS signals.

Down syndrome (DS) is caused by trisomy of chromosome 21 and is associated with diverse clinical manifestations, yet the molecular pathways linking chromosome-21 dosage effects to DS comorbidities remain poorly defined. Here we address this gap by applying a network-based, integrative framework that combines whole-blood transcriptomic data with gene-trait associations to uncover mechanistic insights into DS-associated conditions. First, we performed matrix factorization using PLIER on Human Trisome Project (HTP) RNA-Seq profiles from 304 trisomy-21 (T21) and 95 euploid (D21) individuals, deriving 156 biologically interpretable gene modules. We then identified 92 modules whose activity differed significantly between T21 and D21 and annotated these with prior-knowledge and KEGG pathways. To connect modules to clinical traits, we integrated PrediXcan-derived TWAS results from the UK Biobank, revealing 25 T21-specific modules with significant gene-trait associations (FDR < 0.1), including modules linked to cardiovascular, hematological, immune, metabolic, and neurological phenotypes relevant to DS. Using HTP clinical records as a replication cohort, 13 of these modules reliably predicted comorbidity status (AUC > 0.65, mAPS > 0.65). Most notably module 37, an interferon-stimulated gene network, whose elevated expression robustly distinguished DS individuals with pulmonary hypertension (AUC = 0.76, mAPS = 0.73). Overall, our study demonstrates that integrating blood-derived gene modules with population-scale genetic data uncovers coherent molecular signatures underlying DS comorbidities, identifies candidate biomarkers and therapeutic targets (e.g., , , ), and highlights the power of combining transcriptomic and genetic evidence to elucidate complex disease mechanisms.