Allelic expression imbalance in articular cartilage and subchondral bone refined genome-wide association signals in osteoarthritis. (30th August 2022)

• Record Type: Journal Article
• Authors: Coutinho de Almeida, Rodrigo; Tuerlings, Margo; Ramos, Yolande; Den Hollander, Wouter; Suchiman, Eka; Lakenberg, Nico; Nelissen, Rob G H H; Mei, Hailiang; Meulenbelt, Ingrid
• Is Part Of: Rheumatology. Volume 62:Number 4(2023)
• Page Start: 1669
• Page End: 1676
• Publication Date: 2022-08-30
• Subjects: allelic expression imbalance -- long non-coding RNA -- GWAS -- osteoarthritis -- cartilage -- subchondral bone; Rheumatism -- Periodicals; Rheumatology -- Periodicals; 616.723005
• DOI: 10.1093/rheumatology/keac498
• Languages: English
• ISSNs: 1462-0324

Abstract

Abstract: Objectives: To present an unbiased approach to identify positional transcript single nucleotide polymorphisms (SNPs) of osteoarthritis (OA) risk loci by allelic expression imbalance (AEI) analyses using RNA sequencing of articular cartilage and subchondral bone from OA patients. Methods: RNA sequencing from 65 articular cartilage and 24 subchondral bone from OA patients was used for AEI analysis. AEI was determined for all genes present in the 100 regions reported by the genome-wide association studies (GWAS) catalog that were also expressed in cartilage or bone. The count fraction of the alternative allele (φ) was calculated for each heterozygous individual with the risk SNP or with the SNP in linkage disequilibrium (LD) with it (r 2 > 0.6). Furthermore, a meta-analysis was performed to generate a meta-φ (null hypothesis median φ = 0.49) and P -value for each SNP. Results: We identified 30 transcript SNPs (28 in cartilage and two in subchondral bone) subject to AEI in 29 genes. Notably, 10 transcript SNPs were located in genes not previously reported in the GWAS catalog, including two long intergenic non-coding RNAs (lincRNAs), MALAT1 (meta-φ = 0.54, FDR = 1.7×10 −4 ) and ILF3-DT (meta-φ = 0.6, FDR = 1.75×10 −5 ). Moreover, 12 drugs were interacting with seven genes displaying AEI, of which seven drugs have been already approved. Conclusions: By prioritizing proxy transcript SNPs that mark AEI in cartilage and/or subchondral bone at loci harbouring GWAS signals, we present an unbiased approach to identify the most likely functional OA risk-SNP and gene. We identified 10 new potential OA risk genes ready for further translation towards underlying biological mechanisms.