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

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

Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects.

Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course

Using multivariable Mendelian randomization to estimate the causal effect of bone mineral density on osteoarthritis risk, independently of body mass index

Objectives: Observational analyses suggest that high bone mineral density (BMD) is a risk factor for osteoarthritis (OA); it is unclear whether this represents a causal effect or shared aetiology and whether these relationships are body mass index (BMI)-independent. We performed bidirectional Mendelian randomization (MR) to uncover the causal pathways between BMD, BMI and OA. Methods: One-sample (1S)MR estimates were generated by two-stage least-squares regression. Unweighted allele scores instrumented each exposure. Two-sample (2S)MR estimates were generated using inverse-variance weighted random-effects meta-analysis. Multivariable MR (MVMR), including BMD and BMI instruments in the same model, determined the BMI-independent causal pathway from BMD to OA. Latent causal variable (LCV) analysis, using weight-adjusted femoral neck (FN)-BMD and hip/knee OA summary statistics, determined whether genetic correlation explained the causal effect of BMD on OA. Results: 1SMR provided strong evidence for a causal effect of BMD estimated from heel ultrasound (eBMD) on hip and knee OA {odds ratio [OR]hip = 1.28 [95% confidence interval (CI) = 1.05, 1.57], p = 0.02, ORknee = 1.40 [95% CI = 1.20, 1.63], p = 3 × 10-5, OR per standard deviation [SD] increase}. 2SMR effect sizes were consistent in direction. Results suggested that the causal pathways between eBMD and OA were bidirectional (βhip = 1.10 [95% CI = 0.36, 1.84], p = 0.003, βknee = 4.16 [95% CI = 2.74, 5.57], p = 8 × 10-9, β = SD increase per doubling in risk). MVMR identified a BMI-independent causal pathway between eBMD and hip/knee OA. LCV suggested that genetic correlation (i.e. shared genetic aetiology) did not fully explain the causal effects of BMD on hip/knee OA. Conclusions: These results provide evidence for a BMI-independent causal effect of eBMD on OA. Despite evidence of bidirectional effects, the effect of BMD on OA did not appear to be fully explained by shared genetic aetiology, suggesting a direct action of bone on joint deterioration

Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers

Recent studies have shown an association between cigarettes per day (CPD) and a nonsynonymous single-nucleotide polymorphism in CHRNA5, rs16969968.To determine whether the association between rs16969968 and smoking is modified by age at onset of regular smoking.Primary data.Available genetic studies containing measures of CPD and the genotype of rs16969968 or its proxy.Uniform statistical analysis scripts were run locally. Starting with 94,050 ever-smokers from 43 studies, we extracted the heavy smokers (CPD >20) and light smokers (CPD ≤10) with age-at-onset information, reducing the sample size to 33,348. Each study was stratified into early-onset smokers (age at onset ≤16 years) and late-onset smokers (age at onset >16 years), and a logistic regression of heavy vs light smoking with the rs16969968 genotype was computed for each stratum. Meta-analysis was performed within each age-at-onset stratum.Individuals with 1 risk allele at rs16969968 who were early-onset smokers were significantly more likely to be heavy smokers in adulthood (odds ratio [OR] = 1.45; 95% CI, 1.36-1.55; n = 13,843) than were carriers of the risk allele who were late-onset smokers (OR = 1.27; 95% CI, 1.21-1.33, n = 19,505) (P = .01).These results highlight an increased genetic vulnerability to smoking in early-onset smokers