Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Funding Information: T.R.G. and J.Z. receive research funding from GlaxoSmithKline. T.R.G. receives research funding from Biogen. U.S., K.S., L. Stefánsdóttir, G.B., S.H.L., U.T., and G. T. are employed by deCODE genetics/Amgen Inc. A.M.V. is a consultant for Zoe Global Ltd. All other authors report no competing interests. All Regeneron Genetics Center banner authors are current employees and/or stockholders of Regeneron Pharmaceuticals. Funding Information: We thank Nigel W. Rayner and Ahmed Elhakeem for their input. This research was conducted using the UK Biobank Resource under application numbers 9979 and 23359. G.D.S. and T.R.G. work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol MC_UU_00011/1&4. A.M.V. is funded by the NIHR Nottingham BRC. J.Z. is funded by a Vice-Chancellor Fellowship from the University of Bristol. This research was also funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/4). Study design and project coordination, E. Zeggini; Writing Group, U.S. J.B.J.v.M. J.M.W. M.T.M.L. K.S.E.C. L.S. C.G.B. K.H. Y.Z. R.C.d.A. L. Stef?nsd?ttir, E. Zeggini, A.P.M. G.T. P.C.S. J.Z. and T.R.G.; Core Analyses, C.G.B. K. Hatzikotoulas, L. Southam, L. Stef?nsd?ttir, Y.Z. R.C.d.A. T.T.W. J.Z. A.H. M.T.-L. and J.M.W.; Individual Study Design and Principal Investigators, E. Zeggini, U.S. J.B.J.v.M. M.T.M.L. I.M. J.M.W. T.E. K. Hveem, S.I. K.S.E.C. A.T. A.M.V. K.S. P.E.S. P.C. G.D.S. J.H.T. T.R.G. S.A.L. G.C.B. A.G.U. U.T. P.K. J.H.K. arcOGEN Consortium, HUNT All-In Pain, ARGO Consortium, and Regeneron Genetics Center; Analyses, Genotyping, and Phenotyping in Individual Studies, C.G.B. K.H. L. Southam, J.M.W. L. Stef?nsd?ttir, Y.Z. R.C.d.A. T.T.W. J.Z. A.H. M.T.-L. A.H.S. C.T. E. Zengini, A.B. G.T. G.B. H.J. T.I. R.M. H.T. M.K. M.T. R.R.G.H.H.N. M.M. J.P.Y.C. D.S. J.-A.Z. A.L. M.B.J. L.F.T. B.W. M.E.G. J.S. M.S. G.A. A.G. S.H.L. arcOGEN Consortium, HUNT All-In Pain, ARGO Consortium, and Regeneron Genetics Center. All authors contributed to the final version of the manuscript. T.R.G. and J.Z. receive research funding from GlaxoSmithKline. T.R.G. receives research funding from Biogen. U.S. K.S. L. Stef?nsd?ttir, G.B. S.H.L. U.T. and G. T. are employed by deCODE genetics/Amgen Inc. A.M.V. is a consultant for Zoe Global Ltd. All other authors report no competing interests. All Regeneron Genetics Center banner authors are current employees and/or stockholders of Regeneron Pharmaceuticals. Funding Information: We thank Nigel W. Rayner and Ahmed Elhakeem for their input. This research was conducted using the UK Biobank Resource under application numbers 9979 and 23359. G.D.S. and T.R.G. work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol MC_UU_00011/1&4. A.M.V. is funded by the NIHR Nottingham BRC . J.Z. is funded by a Vice-Chancellor Fellowship from the University of Bristol . This research was also funded by the UK Medical Research Council Integrative Epidemiology Unit ( MC_UU_00011/4 ). Publisher Copyright: © 2021 The AuthorsOsteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.Peer reviewe

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation

Towards improved decision support in the assessment and management of pain for people with dementia in hospital: a systematic meta-review and observational study

BackgroundPain and dementia are common in older people, and impaired cognitive abilities make it difficult for them to communicate their pain. Pain, if poorly managed, impairs health and well-being. Accurate pain assessment in this vulnerable group is challenging for hospital staff, but essential for appropriate management. Robust methods for identifying, assessing and managing pain are needed.Aims and objectivesTwo studies were undertaken to inform the development of a decision support tool to aid hospital staff in the recognition, assessment and management of pain. The first was a meta-review of systematic reviews of observational pain assessment instruments with three objectives: (1) to identify the tools available to assess pain in adults with dementia; (2) to identify in which settings they were used and with what patient populations; and (3) to assess their reliability, validity and clinical utility. The second was a multisite observational study in hospitals with four objectives: (1) to identify information currently used by clinicians when detecting and managing pain in patients with dementia; (2) to explore existing processes for detecting and managing pain in these patients; (3) to identify the role (actual/potential) of carers in this process; and (4) to explore the organisational context in which health professionals operate. Findings also informed development of health economics data collection forms to evaluate the implementation of a new decision support intervention in hospitals.MethodsFor the meta-review of systematic reviews, 12 databases were searched. Reviews of observational pain assessment instruments that provided psychometric data were included. Papers were quality assessed and data combined using narrative synthesis. The observational study used an ethnographic approach in 11 wards in four UK hospitals. This included non-participant observation of 31 patients, audits of patient records, semistructured interviews with 52 staff and four carers, informal conversations with staff and carers and analysis of ward documents and policies. Thematic analysis of the data was undertaken by the project team.ResultsData from eight systematic reviews including 28 tools were included in the meta-review. Most tools showed moderate to good reliability, but information about validity, feasibility and clinical utility was scarce. The observational study showed complex ward cultures and routines, with variations in time spent with patients, communication patterns and management practices. Carer involvement was rare. No pain decision support tools were observed in practice. Information about pain was elicited in different ways, at different times, by different health-care staff and recorded in separate documents. Individual staff made sense of patients’ pain by creating their own ‘overall picture’ from available information.LimitationsGrey literature and non-English-language papers were excluded from the meta-review. Sample sizes in the observational study were smaller than planned owing to poor documentation of patients’ dementia diagnoses, gatekeeping by staff and difficulties in gaining consent/assent. Many patients had no or geographically distant carers, or a spouse who was too unwell and/or reluctant to participate.ConclusionsNo single observational pain scale was clearly superior to any other. The traditional linear concept of pain being assessed, treated and reassessed by single individuals did not ‘fit’ with clinical reality. A new approach enabling effective communication among patients, carers and staff, centralised recording of pain-related information, and an extended range of pain management interventions is proposed [Pain And Dementia Decision Support (PADDS)]. This was not tested with users, but a follow-on study aims to codesign PADDS with carers and clinicians, then introduce education on staff/patient/carer communications and use of PADDS within a structured implementation plan. PADDS will need to be tested in differing ward contexts

Erratum : Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations (Cell (2021) 184(18) (4784–4818.e17), (S0092867421009417), (10.1016/j.cell.2021.07.038))

Publisher Copyright: © 2021 The Author(s)(Cell 184, 4784–4818.e1–e16, September 2, 2021) In this article, we carried out a multi-cohort GWAS meta-analysis for 11 osteoarthritis phenotypes. Since publication, we have become aware of the following typographical errors that were introduced during preparation of the manuscript and resulted from multiple authors editing a single shared document. In Table 1, there were two typographical errors: for spine osteoarthritis, the correct number of cases and controls is 28,372/305,578 (originally written as 28,3721/3057,578). In Table 2, there were 11 typographical errors: HipOA rs781661531 EAF is 0.9997 (not 7 × 10−4), HandOA rs10062749 p is 2.04 × 10−9 (not 2.04 × 10−09), TJR rs116934101 OR is 1.06 (not 1.106), TJR rs10824456 OR is 0.95 (not 10.95), KneeOA rs1426371 OR is 0.95 (not 10.95), KneeHipOA rs551471509 EAF is 0.9996 (not 6 × 10−4), KneeOA rs11705555 p is 2.99 × 10−9 (not 3.00 × 10−9), KneeHipOA rs2856821 OR is 1.05 (not 1.11), female-specific AllOA rs10453201 95%CI is 1.03–1.06 (not 1.02–1.06), the nearest gene to female-specific THR rs116112221 is FANCL (not FALCL1), and the nearest gene to female-specific THR rs10282983 is C8orf34 (not C3ORF34). In Table S2, there were two typographical errors: cell 42E is 0.3 (not 0.72) and cell 42F is 1.06 (not 1.08). In the discussion, fibrillin 2 was inadvertently labelled as FNB2 in two places, instead of FBN2. Finally, we inadvertently used an incorrect version of Figure 1, in which the information on the list of SNVs in (C) was incomplete and 12 typographical errors were introduced during the submission process. Specifically, in Figure 1A, the number of cases and controls of (1) Knee and/or Hip OA is 89,741 cases/ 400,604 controls (not 90,865 cases/ 402,824 controls), (2) Hand OA is 20,901 cases/ 282,881 controls (not 21,186 cases/ 285,101 controls), (3) Spine OA is 28,372 / 305,578 (not 28,731/307,798), (4) Hip OA is 36,445 cases/ 316,943 controls (not 36,520 cases/ 317,590 controls), and (5) Knee OA is 62,497 cases/ 333,557 controls (not 63,498 cases/ 335,777 controls). In Figure 1C, we have updated the SNVs listed in the panels, and the following sequences are now included: rs13107325, rs17615906, and rs3884606 in the Hip Osteoarthritis (and/or THR) section; rs11164653 in the Knee and/or Hip Osteoarthritis (and/or TJR) section; rs10062749, rs11071366, rs1530586, rs216175, rs58973023, and rs74676797 in the Knee Osteoarthritis (and/or TKR) section; rs11164653, rs1530586, rs17615906, and rs9908159 in the All OA section; and rs10062749 in the Hand and Knee Osteoarthritis or TKR section. Also, rs3771501 has been removed from the Hand and Knee Osteoarthritis or TKR section. These errors have now been corrected in the online version of the paper. The authors apologize for any inconvenience they may have caused the readers. [Figure presented] [Figure presented

Genome-wide association study identifies RNF123 locus as associated with chronic widespread musculoskeletal pain

The dataset (CWP_GWAS_EU_ANCESTRY_UKB.txt) contains summary statistics for discovery GWAS of chronic widespread pain based on northern Europeans from UK Biobank comprising 6,914 cases of chronic widespread musculoskeletal pain and 242,929 controls. The sensitivity GWAS (CWP_sensitivity_GWAS_EU_ANCESTRY_UKB.txt) dataset contains summary statistics derived from 6,914 cases of chronic widespread musculoskeletal pain and 223,606 controls. Methodological details available here, https://doi.org/10.1101/2020.11.30.2024100

Genome-wide association study identifies RNF123 locus as associated with chronic widespread musculoskeletal pain

The dataset (CWP_GWAS_EU_ANCESTRY_UKB.txt) contains summary statistics for discovery GWAS of chronic widespread pain based on northern Europeans from UK Biobank comprising 6,914 cases of chronic widespread musculoskeletal pain and 242,929 controls. The sensitivity GWAS (CWP_sensitivity_GWAS_EU_ANCESTRY_UKB.txt) dataset contains summary statistics derived from 6,914 cases of chronic widespread musculoskeletal pain and 223,606 controls. Methodological details available here, https://doi.org/10.1101/2020.11.30.2024100

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.</p

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation. © 2021 The Author