Meta-analysis identifies loci affecting levels of the potential osteoarthritis biomarkers sCOMP and uCTX-II with genome wide significance

Ramos, Yolande F M; Metrustry, Sarah; Arden, N K; Bay‐Jensen, Anne‐Christine; Beekman, Marian; Ajm., de Craen; Cupples, L. Adrienne; Esko, Tõnu; Εvangelou, Εvangelos; Felson, David T.; Hart, Deborah; Jpa., Ioannidis; Karsdal, Morten A.; Kloppenburg, Margreet; Lafeber, Floris P. J. G.; Metspalu, Andres; Panoutsopoulou, Kalliope; Slagboom, P. Eline; Spector, Tim D.; Ewe., van Spil; Uitterlinden, André G.; Zhu, Yuanjun; Valdes, Ana M.; Meurs, Joyce B. J. van; Meulenbelt, Ingrid

doi:10.1136/jmedgenet-2014-102478

Cited by 19 publications

(14 citation statements)

References 39 publications

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“…SNPs with <95% call rate, Hardy‐Weinberg equilibrium <10 −4 , MAF <0.01, or located on the sex chromosomes were removed prior to imputation against the 1000 GenomesV3 March 2012 reference panel . We removed SNPs for which the imputation quality of 0.4 was not met .…”

Section: Methodsmentioning

confidence: 99%

Annotating Transcriptional Effects of Genetic Variants in Disease‐Relevant Tissue: Transcriptome‐Wide Allelic Imbalance in Osteoarthritic Cartilage

Hollander

Pulyakhina

Boer

et al. 2019

Arthritis & Rheumatology

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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 .

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Section: Methodsmentioning

confidence: 99%

Annotating Transcriptional Effects of Genetic Variants in Disease‐Relevant Tissue: Transcriptome‐Wide Allelic Imbalance in Osteoarthritic Cartilage

Hollander

Pulyakhina

Boer

et al. 2019

Arthritis & Rheumatology

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“…SNPs with a call rate of <95%, Hardy‐Weinberg equilibrium of <10 −4 , minor allele frequency of <0.01, or located on the sex chromosomes were removed prior to imputation together with Leiden Longevity Study data against the 1000 Genomes version 3 March 2012 reference panel (http://csg.sph.umich.edu/abecasis/MACH/download/1000G.2012-03-14.html) . Next, SNPs that were homozygous in all the overlapping samples with the methylation (n = 23) and expression (n = 24) data sets were removed prior to analyses, as were SNPs that did not meet an imputation quality of 0.4 .…”

Section: Methodsmentioning

confidence: 99%

Transcriptional Associations of Osteoarthritis‐Mediated Loss of Epigenetic Control in Articular Cartilage

Hollander

Ramos

Bömer

et al. 2015

Arthritis & Rheumatology

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Objective. To identify osteoarthritis (OA) progressionmodulating pathways in articular cartilage and their respective regulatory epigenetic and genetic determinants in end-stage disease.Methods. Transcriptional activity of CpG was assessed using gene expression data and DNA methylation data for preserved and lesional articular cartilage samples. Disease-responsive transcriptionally active CpG were identified by means of differential methylation between preserved and lesional cartilage. Transcriptionally relevant genetic determinants were addressed by means of single-nucleotide polymorphisms (SNPs) proximal to the OA-responsive transcriptionally active CpG. Statistical analyses were corrected for age, sex, joint, and technical covariates. A random effect was included to correct for possible correlations between paired samples.Results. Of 9,838 transcribed genes in articular cartilage, 2,324 correlated with the methylation status of 3,748 transcriptionally active CpG; both negative (n 5 1,741) and positive (n 5 2,007) correlations were observed. Hypomethylation and hypermethylation (false discovery rate of <0.05, |Db| > 0.05) were observed for 62 and 25 transcriptionally active CpG, respectively, covering 70 unique genes. Enrichment for developmental and extracellular matrix maintenance pathways indicated possible reactivation of endochondral ossification. Finally, we observed 31 and 26 genes for which methylation and expression, respectively, were additionally affected by genetic variation.Conclusion. We identified tissue-specific genes involved in OA disease progression, reflected by genetic and pathologic epigenetic regulation of transcription, primarily at genes involved in development. Therefore, transcriptionally active SNPs near these genes may serve as putative susceptibility alleles. Our results constitute an important step toward understanding the reported widespread epigenetic changes occurring in OA articular cartilage and toward subsequent development of treatments targeting disease-driving pathways.Osteoarthritis (OA) is the most prevalent arthritis among the elderly (1) and is currently recognized as a disease of the whole joint (2). A well-described hallmark of OA is articular cartilage degradation (3). The single cell type present in articular cartilage is the articular chondrocyte, which is a highly specialized, maturation-arrested, nonproliferating cell. To ensure articular cartilage integrity throughout life, the articular chondrocyte needs to adapt its behavior in response to external signals, such as mechanical stress, aging, or microtraumas (4). To facilitate

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“…51 COMP (Gene ID: 1311) is found about 122kb from cg05295841, and its level of expression, which is influenced by several polymorphisms, has been associated with OA. 52 Approximately 25kb away from cg17890983, SMAD9 (Gene ID: 4093) is involved in TGF-β and BMP signaling, which themselves are involved in regulating bone and cartilage development. 53,54 Lastly, ALOX12 (Gene ID: 239) is found about 7kb away from cg02329670, and although polymorphisms in this gene are not associated with bone density, they are associated with fat accumulation 55 which is associated with OA.…”

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confidence: 99%

Assessment of DNA methylation patterns in the bone and cartilage of a nonhuman primate model of osteoarthritis

Housman

Havill

Quillen

et al. 2017

Preprint

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Objective: Osteoarthritis (OA) impacts humans and several other animals. Thus, the mechanisms underlying this disorder, such as specific skeletal tissue DNA methylation patterns, may be evolutionary conserved. However, associations between methylation and OA have not been readily studied in nonhuman animals. Baboons serve as important models of disease and develop OA at rates similar to those in humans. Therefore, this study investigated the associations between methylation and OA in baboons to advance the evolutionary understanding of OA.Design: Trabecular bone and cartilage was collected from the medial condyles of adult female baboon femora, five with and five without knee OA. The Infinium HumanMethylation450BeadChip (450K array) was used to identify DNA methylation patterns in these tissues.Results: Approximately 44% of the 450K array probes reliably align to the baboon genome, contain a CpG site of interest, and maintain a wide distribution throughout the genome. Of the two filtering methods tested, both identified significantly differentially methylated positions (DMPs) between healthy and OA individuals in cartilage tissues, and some of these patterns overlap with those previously identified in humans. Conversely, no DMPs were found between tissue types or between disease states in bone tissues. Conclusions:Overall, the 450K array can be used to measure genome-wide DNA methylation in baboon tissues and identify significant associations with complex traits. The results of this study indicate that some DNA methylation patterns associated with OA are evolutionarily conserved, while others are not. This warrants further investigation in a larger and more phylogenetically diverse sample set.

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Meta-analysis identifies loci affecting levels of the potential osteoarthritis biomarkers sCOMP and uCTX-II with genome wide significance

Cited by 19 publications

References 39 publications

Annotating Transcriptional Effects of Genetic Variants in Disease‐Relevant Tissue: Transcriptome‐Wide Allelic Imbalance in Osteoarthritic Cartilage

Annotating Transcriptional Effects of Genetic Variants in Disease‐Relevant Tissue: Transcriptome‐Wide Allelic Imbalance in Osteoarthritic Cartilage

Transcriptional Associations of Osteoarthritis‐Mediated Loss of Epigenetic Control in Articular Cartilage

Assessment of DNA methylation patterns in the bone and cartilage of a nonhuman primate model of osteoarthritis

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