Transcriptome analysis in osteoarthritis primary tissues identifies high-confidence effector genes.

Authors

Georgia Katsoula, Ana Luiza Arruda, Mauro Tutino, Ene Reimann, Norbert Bittner, Peter Kreitmaier, Karan M Shah, Diane Swift, Lorraine Southam, Siim Suutre, Galadriel Lucía Velázquez Silva, Kaspar Tootsi, Aare Märtson, Reedik Mägi, J Mark Wilkinson, Eleftheria Zeggini

Published in

Nature communications. Jun 29, 2026. Epub Jun 29, 2026.

Abstract

Osteoarthritis, a whole-joint degenerative disorder, is a major public health burden that affects nearly 600 million individuals worldwide, with no disease-modifying treatment. Molecular profiling of relevant tissues is crucial for understanding the biology of disease development. Here, we generate a comprehensive map of cis- and trans- transcriptional regulation in disease-relevant primary tissues from knee osteoarthritis patients: macroscopically intact (low-grade, n = 261) and degenerated (high-grade, n = 212) cartilage, synovium (n = 277), and fat pad (n = 92). We identify 10,166 unique expression quantitative trait loci (eQTL)-associated genes, 61.1% of which have not been reported in previous osteoarthritis eQTL studies, and uncover cartilage grade-specific genetic regulation. Using the largest osteoarthritis genome-wide association study to date, we find colocalization evidence with 136 genes and prioritize 45 high-confidence effector genes. At colocalizing loci, osteoarthritis risk alleles are associated with increased expression of genes involved in chondrogenic and hypertrophic signaling and decreased expression of genes encoding regulatory and modulatory components of these pathways. By integrating the eQTL maps with functional data, we delineate regulatory architectures for osteoarthritis risk variants, including promoter-enhancer loops and transcription factor binding effects. Finally, we provide directional evidence highlighting drugs targeting LGALS3 and SMAD7 as repurposing opportunities for osteoarthritis treatment.

PMID:
42373672
Bibliographic data and abstract were imported from PubMed on 30 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.