Abstract:Background
Transcriptome-wide association studies (TWAS) improve to detect functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from reference panels in relevant tissues. Herein, we developed machine learning-based prediction models using a novel Korean TWAS model for early progression to stricturing or penetrating phenotypes in Korean patients with Crohn’s disease (CD).
Methods
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