Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis

Zengini, Eleni; Hatzikotoulas, Konstantinos; Tachmazidou, Ioanna; Steinberg, Julia; Hartwig, Fernando Pires; Southam, Lorraine; Hackinger, Sophie; Boer, Cindy G.; Styrkársdóttir, Unnur; Gilly, Arthur; Süveges, Dániel; Killian, Britt; Ingvarsson, Þorvaldur; Jónsson, Helgi; Babis, George C.; McCaskie, Andrew W.; Uitterlinden, André G.; Meurs, Joyce B. J. van; Þorsteinsdóttir, Unnur; Stefánsson, Kāri; Smith, George Davey; Wilkinson, J. Mark; Zeggini, Eleftheria

doi:10.1038/s41588-018-0079-y

Cited by 253 publications

(276 citation statements)

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“…We investigated 18 novel OA risk loci that had been reported as being associated with the disease at a significance level close to or surpassing the genome‐wide threshold of P < 5 × 10 −8 (Table ). If the OA‐associated SNP was directly genotyped on an Illumina HumanOmniExpress array, we utilized those SNP data.…”

Section: Methodsmentioning

confidence: 99%

“…We had both methylation and genotype data available for a total of 87 patients who had undergone knee or hip joint arthroplasty (57 knee OA patients, 14 hip OA patients, and 16 patients who had undergone hip replacement due to a femoral neck fracture (Supplementary Table 1 OA loci investigation and mQTL analysis. We investigated 18 novel OA risk loci that had been reported as being associated with the disease at a significance level close to or surpassing the genome-wide threshold of P < 5 × 10 −8 (13)(14)(15)(16)(17)(18)(19)(20) ( Table 1). If the OA-associated SNP was directly genotyped on an Illumina HumanOmniExpress array, we utilized those SNP data.…”

Section: Methodsmentioning

confidence: 99%

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Prioritization of PLEC and GRINA as Osteoarthritis Risk Genes Through the Identification and Characterization of Novel Methylation Quantitative Trait Loci

Rice

Tselepi

Sorial

et al. 2019

Arthritis & Rheumatology

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Objective To identify methylation quantitative trait loci (mQTLs) correlating with osteoarthritis (OA) risk alleles and to undertake mechanistic characterization as a means of target gene prioritization. Methods We used genome‐wide genotyping and cartilage DNA methylation array data in a discovery screen of novel OA risk loci. This was followed by methylation, gene expression analysis, and genotyping studies in additional cartilage samples, accompanied by in silico analyses. Results We identified 4 novel OA mQTLs. The most significant mQTL contained 9 CpG sites where methylation correlated with OA risk genotype, with 5 of the CpG sites having P values <1 × 10−10. The 9 CpG sites reside in an interval of only 7.7 kb within the PLEC gene and form 2 distinct clusters. We were able to prioritize PLEC and the adjacent gene GRINA as independent targets of the OA risk. We identified PLEC and GRINA expression QTLs operating in cartilage, as well as methylation‐expression QTLs operating on the 2 genes. GRINA and PLEC also demonstrated differential expression between OA hip and non‐OA hip cartilage. Conclusion PLEC encodes plectin, a cytoskeletal protein that maintains tissue integrity by regulating intracellular signaling in response to mechanical stimuli. GRINA encodes the ionotropic glutamate receptor TMBIM3 (transmembrane BAX inhibitor 1 motif–containing protein family member 3), which regulates cell survival. Based on our results, we hypothesize that in a joint predisposed to OA, expression of these genes alters in order to combat aberrant biomechanics, and that this is epigenetically regulated. However, carriage of the OA risk–conferring allele at this locus hinders this response and contributes to disease development.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prioritization of PLEC and GRINA as Osteoarthritis Risk Genes Through the Identification and Characterization of Novel Methylation Quantitative Trait Loci

Rice

Tselepi

Sorial

et al. 2019

Arthritis & Rheumatology

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“…(15) LD score regression was also used to examine genetic correlations between the HSMs and hip osteoarthritis, (16) anthropometric traits (height (17) and waist circumference (18) ), and femoral neck bone mineral density (FN BMD). (19) SNP associations with other traits GWAS-identified SNPs were examined in relation to OA using summary statistics available from the arcOGEN hip OA GWAS (16) (https://www.arcogen.org.uk/), a recent UK Biobank hip OA GWAS, (20) and a hip fracture GWAS (as yet unpublished; see Supplemental Methods). In addition, a look-up was performed on GWASs of anthropometric and bone-related traits collected in the MRBase database.…”

Section: Snp Heritability Analysismentioning

confidence: 99%

Identification of Novel Loci Associated With Hip Shape: A Meta-Analysis of Genomewide Association Studies

Baird

Evans

Kamanu

et al. 2018

Journal of Bone and Mineral Research

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We aimed to report the first genomewide association study (GWAS) meta‐analysis of dual‐energy X‐ray absorptiometry (DXA)‐derived hip shape, which is thought to be related to the risk of both hip osteoarthritis and hip fracture. Ten hip shape modes (HSMs) were derived by statistical shape modeling using SHAPE software, from hip DXA scans in the Avon Longitudinal Study of Parents and Children (ALSPAC; adult females), TwinsUK (mixed sex), Framingham Osteoporosis Study (FOS; mixed), Osteoporotic Fractures in Men study (MrOS), and Study of Osteoporotic Fractures (SOF; females) (total N = 15,934). Associations were adjusted for age, sex, and ancestry. Five genomewide significant ( p < 5 × 10 −9 , adjusted for 10 independent outcomes) single‐nucleotide polymorphisms (SNPs) were associated with HSM1, and three SNPs with HSM2. One SNP, in high linkage disequilibrium with rs2158915 associated with HSM1, was associated with HSM5 at genomewide significance. In a look‐up of previous GWASs, three of the identified SNPs were associated with hip osteoarthritis, one with hip fracture, and five with height. Seven SNPs were within 200 kb of genes involved in endochondral bone formation, namely SOX9 , PTHrP , RUNX1 , NKX3‐2 , FGFR4 , DICER1 , and HHIP . The SNP adjacent to DICER1 also showed osteoblast cis‐regulatory activity of GSC , in which mutations have previously been reported to cause hip dysplasia. For three of the lead SNPs, SNPs in high LD ( r 2 > 0.5) were identified, which intersected with open chromatin sites as detected by ATAC‐seq performed on embryonic mouse proximal femora. In conclusion, we identified eight SNPs independently associated with hip shape, most of which were associated with height and/or mapped close to endochondral bone formation genes, consistent with a contribution of processes involved in limb growth to hip shape and pathological sequelae. These findings raise the possibility that genetic studies of hip shape might help in understanding potential pathways involved in hip osteoarthritis and hip fracture. © 2018 The Authors. Journal of Bone and Mineral Research Published by Wiley Periodicals, Inc.

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“…Genetic factors contribute to 50-80% of the variance in bone mineral density (BMD), the major determinant of osteoporosis susceptibility, and account for >50% of the variance in susceptibility to OA [23][24][25] . Nevertheless, recent large-scale genome-wide association studies (GWAS) have defined only a proportion of the heritability in BMD and OA susceptibility 26,27 , underscoring the need for alternative approaches to identify genes contributing to these diseases. We hypothesized that mutations in genes with enriched expression in osteocytes would cause rare monogenic skeletal dysplasias and mineral disorders, and contribute to the risk of common polygenic skeletal diseases including osteoporosis and osteoarthritis.To address this hypothesis, we developed a method to define a transcriptomic map of the osteocyte network and used it to investigate relationships between genes with enriched expression in osteocytes and human skeletal disease.…”

mentioning

confidence: 99%

Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease

Youlten

Kemp

Logan

et al. 2020

Preprint

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Osteocytes are master regulators of the skeleton. We map the transcriptome of osteocytes at different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cell-network. We define an osteocyte transcriptome signature, 1239 genes that distinguishes osteocytes from other cells. 77% have no known role in the skeleton.We show they are enriched for genes controlling neuronal network formation, suggesting this program is important in the osteocyte network. We evaluated 19 skeletal parameters in 733 mouse lines with functional-gene-deletions and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human homologues that cause monogenic skeletal dysplasias (P=6x10 -17 ), and associated with polygenic diseases, osteoporosis (P=1.8×10 -13 ), and osteoarthritis (P=2.6×10 -6 ). This reveals the molecular landscape that regulates osteocyte network formation and function, and establishes the importance of osteocytes in human skeletal disease. IntroductionThe skeleton is a highly dynamic structure that changes in shape and composition throughout life. Osteocytes are the most abundant cell type in bone and have emerged as master regulators of the skeleton. These enigmatic cells are connected via ramifying dendritic processes that form a complex multicellular network distributed throughout mineralized bone 1,2 .The scale and complexity of the osteocyte network is comparable to neurons in the brain, with 42 billion osteocytes present in the human skeleton forming 23 trillion connections 2,3 . This network enables osteocytes to detect and respond to mechanical strain, hormones and local growth factors and cytokines 1 . The network responds by regulating the formation and activity of osteoclasts and osteoblasts, instructing these cells to repair damaged bone, controlling bone mass and composition, and ensuring the optimal distribution of bone tissue in response to mechanical stress. Osteocytes also remove and replace bone surrounding the osteocyte network by the process of perilacunar remodeling, liberating calcium and phosphate in response to endocrine demands 4 . These features allow the osteocyte network to maintain both the structural integrity of the skeleton and mineral homeostasis. Osteocytes also have regulatory functions beyond the skeleton, including in skeletal muscle, adipose tissue, the central nervous system and in the control of phosphate homeostasis and energy expenditure, indicating the network acts as an important and endocrine organ 5-8 .Although osteocytes are pivotal in controlling the skeleton, the molecular programs that regulate their formation and function are poorly defined. Osteocytes are entombed in bone making them challenging to isolate and study. As a result osteocytes have been omitted from large-scale efforts to map tissue-specific transcriptomes 9-12 and studies of their transcriptome are limited [13][14][15][16] . Consequently, the influenc...

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Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis

Cited by 253 publications

References 58 publications

Prioritization of PLEC and GRINA as Osteoarthritis Risk Genes Through the Identification and Characterization of Novel Methylation Quantitative Trait Loci

Prioritization of PLEC and GRINA as Osteoarthritis Risk Genes Through the Identification and Characterization of Novel Methylation Quantitative Trait Loci

Identification of Novel Loci Associated With Hip Shape: A Meta-Analysis of Genomewide Association Studies

Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease

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