Prof. Dr. Regina Betz
Institute of Human Genetics
regina.betz@uni-bonn.de View member: Prof. Dr. Regina Betz
The Journal of allergy and clinical immunology
BACKGROUND: Transcriptome-wide association studies (TWAS) identify genetically regulated expression (GReX) components and can pinpoint causal genes in GWAS, but are often limited by using a single cellular context.
OBJECTIVE: We hypothesized that modeling GReX across multiple conditions could enhance power to identify causal genes for complex inflammatory diseases.
METHODS: We conducted TWAS on 400 transcriptomes under eight pro-inflammatory cytokine stimulations in keratinocytes, modeling GReX for 18,599 genes against GWAS from seven inflammatory skin diseases: atopic dermatitis, psoriasis, acne, alopecia areata, systemic sclerosis, SLE, and vitiligo.
RESULTS: Our TWAS identified 274 loci from the seven diseases that harbor a single significant TWAS gene association. We nominated causal genes and their associated pro-inflammatory cytokine stimuli, including ERAP2 for psoriasis with IL-17A+TNF stimulation; WNT10A for acne with IFNγ stimulation; RAET1L, MAP3K11, and ITGAM for alopecia areata, acne, and SLE, respectively, with TNF stimulation. Notably, our TWAS-identified genes showed overwhelming evidence of colocalization with GWAS signals (p = 1.03×10), and our method successfully captured over 85% of all genes with colocalizing eQTLs. Using single cell resolution spatial profiling, we further demonstrated the modulation of TWAS signals in keratinocytes by close proximity to TNF/IL-17 expressing cells in psoriatic skin.
CONCLUSION: Modeling gene expression across relevant cellular states substantially improves the power and resolution of TWAS.
CLINICAL IMPLICATION: Our findings indicate that genetic signals for complex skin conditions shape inflammatory responses in the epithelium and provide a roadmap of how susceptibility loci modulate shared and unique cytokine responses in keratinocytes for different inflammatory skin diseases.
Copyright © 2026. Published by Elsevier Inc.
PMID: 42203178
Institute of Human Genetics
regina.betz@uni-bonn.de View member: Prof. Dr. Regina Betz