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Annotating Transcriptional Effects of Genetic Variants in Disease‐Relevant Tissue: Transcriptome‐Wide Allelic Imbalance in Osteoarthritic Cartilage
Author(s) -
Hollander Wouter,
Pulyakhina Irina,
Boer Cindy,
Bomer Nils,
Breggen Ruud,
Arindrarto Wibowo,
Couthino de Almeida Rodrigo,
Lakenberg Nico,
Sentner Thom,
Laros Jeroen F. J.,
‘t Hoen Peter A. C.,
Slagboom Eline P. E.,
Nelissen Rob G. H. H.,
Meurs Joyce,
Ramos Yolande F. M.,
Meulenbelt Ingrid
Publication year - 2019
Publication title -
arthritis and rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.106
H-Index - 314
eISSN - 2326-5205
pISSN - 2326-5191
DOI - 10.1002/art.40748
Subject(s) - single nucleotide polymorphism , transcriptome , biology , allele , genetics , cartilage , snp , candidate gene , gene , osteoarthritis , false discovery rate , bioinformatics , gene expression , genotype , medicine , pathology , anatomy , alternative medicine
Objective Multiple single‐nucleotide polymorphisms ( SNP s) conferring susceptibility to osteoarthritis ( OA ) mark imbalanced expression of positional genes in articular cartilage, reflected by unequally expressed alleles among heterozygotes (allelic imbalance [ AI ]). We undertook this study to explore the articular cartilage transcriptome from OA patients for AI events to identify putative disease‐driving genetic variation. Methods AI was assessed in 42 preserved and 5 lesioned OA cartilage samples (from the Research Arthritis and Articular Cartilage study) for which RNA sequencing data were available. The count fraction of the alternative alleles among the alternative and reference alleles together ( φ ) was determined for heterozygous individuals. A meta‐analysis was performed to generate a meta‐ φ and P value for each SNP with a false discovery rate ( FDR ) correction for multiple comparisons. To further validate AI events, we explored them as a function of multiple additional OA features. Results We observed a total of 2,070 SNP s that consistently marked AI of 1,031 unique genes in articular cartilage. Of these genes, 49 were found to be significantly differentially expressed (fold change <0.5 or >2, FDR <0.05) between preserved and paired lesioned cartilage, and 18 had previously been reported to confer susceptibility to OA and/or related phenotypes. Moreover, we identified notable highly significant AI SNP s in the CRLF 1 , WWP 2 , and RPS 3 genes that were related to multiple OA features. Conclusion We present a framework and resulting data set for researchers in the OA research field to probe for disease‐relevant genetic variation that affects gene expression in pivotal disease‐affected tissue. This likely includes putative novel compelling OA risk genes such as CRLF 1 , WWP 2 , and RPS 3 .