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Biomedical subjects

Xiao Chang

Publications and source records attributed to Xiao Chang.

2 recordsLinked to original sources
PubMed2026

Shared genetic architecture and therapeutic targets across paediatric immune-mediated diseases.

OBJECTIVES: Paediatric-onset immune-mediated inflammatory diseases (IMIDs), including juvenile idiopathic arthritis and related rheumatic diseases, remain genetically undercharacterised. We aimed to define shared and category-specific genetic architecture across paediatric IMIDs, compare signals with adult IMIDs, and identify therapeutic opportunities. METHODS: We analysed 24 paediatric IMIDs classified as autoimmune, polygenic-autoinflammatory, mixed-pattern, or allergic. Genome-wide association analyses included 18,086 cases and 131,019 controls of European ancestry. We estimated single nucleotide polymorphism (SNP)-based heritability, genetic correlations, and polygenic overlap; performed subset-based meta-analysis; and conducted functional annotation, gene prioritisation, pathway and protein network analyses, adult-IMID comparison, and drug-target prioritisation. RESULTS: SNP-based heritability ranged from 28.9% for allergic IMIDs to 61.9% for autoimmune IMIDs. Genetic correlation and polygenic modelling supported partial sharing across categories with category-specific components. Meta-analysis identified 39 genome-wide significant loci outside the Major Histocompatibility Complex (MHC) region, including 15 previously unreported loci; 19 loci were shared between categories. Gene-prioritisation and protein interaction analyses identified a core MHC-centred antigen-presentation network, with category-enriched modules involving complement, innate/barrier pathways, epithelial biology, and type 2 immunity. Enriched pathways included nuclear factor κB signalling, T helper 17 related pathways, Janus kinase-signal transducer and activator of transcription signalling, programmed cell death protein 1/programmed death‑ligand 1, cytotoxic T‑lymphocyte associated protein 4 regulation, and osteoclast differentiation, several of which are relevant to rheumatic diseases. Paediatric IMIDs shared broad polygenic architecture with adult IMIDs, whereas top-ranked genes converged strongly with adult rheumatic diseases. Priority Index analysis identified 178 high-scoring genes, including 43 approved or investigational IMID drug targets. CONCLUSIONS: Paediatric-onset IMIDs share core pathways with adult forms but exhibit distinct genetic architecture shaped by age-specific immune and neurodevelopmental biology. These findings provide a genomic framework for paediatric precision medicine, guiding classification, risk prediction, and therapeutic development.

Humans↗
PubMed2026

Deep learning and statistical methods identify novel asthma risk variants in Europeans.

BACKGROUND: Asthma is a common heritable respiratory disorder with a complex genetic basis. Although large-scale genome-wide association studies have identified many risk loci, the full spectrum of its polygenic architecture remains to be defined. OBJECTIVE: We refined the genetic landscape of asthma in individuals of European ancestry and improve polygenic risk prediction through statistical and deep learning-based methods. METHODS: We conducted the largest genome-wide association study meta-analysis of asthma in individuals of European ancestry, combining data from the Global Biobank Meta-analysis Initiative (121,940 cases, 1,254,131 controls) and the Million Veteran Program (36,823 cases, 398,278 controls). To enhance discovery, we applied pleiotropy-informed multitrait analysis and conditional false discovery rate approaches, each incorporating eosinophil counts as a secondary trait. In parallel, we used a Transformer-based deep learning framework to further prioritize variants and improve polygenic risk prediction. RESULTS: The meta-analysis identified 69 independent genome-wide significant loci (P&#x2009;<&#x2009;5 &#xd7; 10-8) not previously reported in asthma. Multitrait analysis of genome-wide association studies, conditional false discovery rate, and deep learning approaches uncovered additional candidate loci. Functional annotation and expression quantitative trait locus mapping implicated novel genes in immune regulation, airway remodeling, and metabolic processes. Polygenic risk score models derived from deep learning-prioritized variants outperformed those based on conventional genome-wide association study and standard statistical approaches. CONCLUSIONS: Our study yields a comprehensive map of asthma-associated loci in European ancestry populations, improves genetic risk prediction, and informs future mechanistic studies.

Humans↗