Abstract

Osteoarthritis is a common complex disease imposing a large public-health burden. Here, we performed a genome-wide association study for osteoarthritis, using data across 16.5 million variants from the UK Biobank resource. After performing replication and meta-analysis in up to 30,727 cases and 297,191 controls, we identified nine new osteoarthritis loci, in all of which the most likely causal variant was noncoding. For three loci, we detected association with biologically relevant radiographic endophenotypes, and in five signals we identified genes that were differentially expressed in degraded compared with intact articular cartilage from patients with osteoarthritis. We established causal effects on osteoarthritis for higher body mass index but not for triglyceride levels or genetic predisposition to type 2 diabetes.

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Acknowledgements

This research was conducted by using the UK Biobank Resource under application no. 9979. This work was funded by the Wellcome Trust (WT098051). We are grateful to R. Brooks, J. Choudhary and T. Roumeliotis for their contribution to the functional genomics data collection. The Human Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Organization for Health Research and Development (ZonMw); the Netherlands Genomics Initiative (NGI)/Netherlands Organisation of Scientific Research (NWO); the Netherlands Consortium for Healthy Aging (NCHA), Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam.

Author information

Author notes

  1. These authors contributed equally: Eleni Zengini, Konstantinos Hatzikotoulas and Ioanna Tachmazidou.

Affiliations

  1. Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK

    • Eleni Zengini
    •  & Jeremy M. Wilkinson
  2. 5th Psychiatric Department, Dromokaiteio Psychiatric Hospital, Athens, Greece

    • Eleni Zengini
  3. Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK

    • Konstantinos Hatzikotoulas
    • , Ioanna Tachmazidou
    • , Julia Steinberg
    • , Lorraine Southam
    • , Sophie Hackinger
    • , Arthur Gilly
    • , Daniel Suveges
    • , Britt Killian
    •  & Eleftheria Zeggini
  4. GSK, R&D Target Sciences, Medicines Research Centre, Stevenage, UK

    • Ioanna Tachmazidou
  5. Cancer Research Division, Cancer Council NSW, Sydney, New South Wales, Australia

    • Julia Steinberg
  6. Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil

    • Fernando P. Hartwig
  7. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    • Fernando P. Hartwig
    •  & George Davey Smith
  8. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    • Lorraine Southam
  9. Departments of Internal Medicine and Epidemiology, Erasmus MC, Rotterdam, the Netherlands

    • Cindy G. Boer
    • , Andre G. Uitterlinden
    •  & Joyce B. J. van Meurs
  10. deCODE genetics/Amgen, Reykjavik, Iceland

    • Unnur Styrkarsdottir
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  11. Department of Orthopaedic Surgery, Akureyri Hospital, Akureyri, Iceland

    • Thorvaldur Ingvarsson
  12. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    • Thorvaldur Ingvarsson
    • , Helgi Jonsson
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  13. Institution of Health Science, University of Akureyri, Akureyri, Iceland

    • Thorvaldur Ingvarsson
  14. Department of Medicine, Landspitali, National University Hospital of Iceland, Reykjavik, Iceland

    • Helgi Jonsson
  15. 2nd Department of Orthopaedic Surgery, Konstantopouleio General Hospital, National and Kapodistrian University of Athens, Athens, Greece

    • George C. Babis
  16. Division of Trauma & Orthopaedic Surgery, Department of Surgery, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK

    • Andrew McCaskie
  17. Population Health Sciences, University of Bristol, Bristol, UK

    • George Davey Smith
  18. National Institute for Health Research, Bristol Biomedical Research Centre, University Hospitals Bristol, NHS Foundation Trust and University of Bristol, Bristol, UK

    • George Davey Smith

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Contributions

Association analyses: E. Zengini, K.H., I.T., L.S., J.S., S.H. and A.G. Mendelian randomization: F.P.H. and G.D.S. Functional genomics sample collection: A.McC., J.M.W. and E. Zeggini. Functional genomics analyses: J.S. and L.S. Endophenotype analyses: C.G.B., A.G.U. and J.B.J.v.M. Replication analyses: U.S., T.I., H.J., U.T. and K.S. Bioinformatics: A.G., D.S. and B.K. Student supervision: K.H., G.C.B., G.D.S., J.M.W. and E. Zeggini. Manuscript writing: E. Zengini, K.H., I.T., J.S., F.P.H., L.S., C.G.B., U.S., D.S., J.B.J.v.M., G.D.S., J.M.W. and E. Zeggini.

Competing interests

U.T., U.S. and K.S. are employees of deCODE genetics/Amgen.

Corresponding author

Correspondence to Eleftheria Zeggini.

Integrated supplementary information

  1. Study-design flowchart. OA: Osteoarthritis; UKBB: UK Biobank; n: sample size; SNP: single nucleotide polymorphism; QC: quality control; 1000G: 1000 Genomes Project; AF: allele frequency; PC: principal component.

  2. Supplementary Figure 2 Manhattan plots

    a) Self-reported OA; b) Hospital diagnosed OA; c) Hospital diagnosed hip OA; d) Hospital diagnosed knee OA; e) Hospital diagnosed hip and/or knee OA; the horizontal line denotes the genome-wide significance threshold. OA: osteoarthritis.

  3. Supplementary Figure 3 QQ plots

    a) Self-reported OA; b) Hospital diagnosed OA; c) Hospital diagnosed hip OA; d) Hospital diagnosed knee OA; e) Hospital diagnosed hip and/or knee OA. OA: Osteoarthritis; N: number of variants analysed following quality control; MAF: minor allele frequency; Lambda: genomic inflation factor.

  4. Supplementary Figure 4 Genome-wide-association summary statistics of the self-reported and sensitivity analysis for self-reported OA

    a) qq plot for self-reported OA; b) Manhattan plot for self-reported OA; c) qq plot for the sensitivity analysis of self-reported OA; d) Manhattan plot for the sensitivity analysis of self-reported OA. OA: Osteoarthritis; N: number of variants analysed following quality control; MAF: minor allele frequency; Lambda: genomic inflation factor; the horizontal line denotes the genome-wide significance threshold.

  5. Supplementary Figure 5 Correlation across annotations and corresponding correlations across PPAs in an exemplary fine-mapped region (indexed by rs2820436)

    a: Histogram of all annotations (CADD, CADD-Phred, Eigen, Eigen-Phred, EigenPC, EigenPC-Phred, Combined CADD-Phred and Eigen-Phred) and scatter plots between pairs of annotations. b: Pearson correlation coefficient of the posterior probability of association (PPA) across variants in the fine-mapped region for the different annotations used.

  6. Supplementary Figure 6 Gene-set representation onto protein–protein-interaction networks

    Large coloured circles represent the proteins of each gene set, while grey small circles represent proteins found in the InWeb database (high-confidence interaction set) that have a direct (proteins that directly bind each other) or indirect (any other proteins connected to the direct network) connectivity. a) Anatomical structure morphogenesis b) Ion channel transport c) Activation of MAPK activity d) Histidine metabolism e) Recruitment of mitotic centrosome proteins and complexes.

  7. Supplementary Figure 7 LD-regression Venn diagram

    Using LD regression, we identify 23 phenotypes with significant genetic correlation with self-reported OA, 31 with hospital diagnosed OA, 26 with hospital diagnosed hip and/or knee OA, 23 with hospital diagnosed knee OA, and 4 with hospital diagnosed hip OA, with large overlap between the identified phenotypes (35 phenotypes in total; significance at 5% FDR). OA: osteoarthritis.

  8. Supplementary Figure 8 ORs and risk-allele frequencies for established and novel OA-associated variants

    The sibling relative risk ratio (λs) for each variant is shown by the different size of each circle. The darker color of the circles shows the percentage of variance explained on the liability scale (h2L) (calculated assuming 10% prevalence of OA). Totals of λs and h2L are shown at the top. OA: osteoarthritis.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Note and Supplementary Tables 1, 4–6, 8–10, 19–21 and 28–30

  2. Life Sciences Reporting Summary

  3. Supplementary Table 27: Results of one-sample MR analyses in UK Biobank

    Regression coefficients are shown for continuous exposure phenotypes, and odds ratio are shown for binary exposure phenotypes. MR: Mendelian randomization; OA: osteoarthritis; OR: odds ratio; SE: standard error; CI: confidence interval; N: sample size

  4. Supplementary Table 2

    ICD10 code exclusion criteria for the definition of controls

  5. Supplementary Table 3

    ICD10 code inclusion criteria for the definition of cases

  6. Supplementary Table 7: Association summary statistics and power calculations for established OA loci present in UK Biobank

    RA, risk allele; RAF, risk allele frequency; OR, odds ratio; CI, confidence interval; OA, osteoarthritis; EA, effect allele; EAF, effect allele frequency; N/A, not available a: variant studied separately in Asian and European populations b: RA is defined as the allele with OR >1 in the respective study c: Allele on reverse strand d: Power is calculated on the basis of the EAF and OR estimates from the UK Biobank OA datasets and assuming 4 controls per case e: Power is calculated on the basis of the reported OR estimates from each discovery study (referenced in the last column) and effect allele frequencies from the UK Biobank OA datasets respectively. For rs143383 we used the OR from the European population. f: Power is calculated on the basis of the replication OR estimates from each discovery study and effect allele frequencies from the UK Biobank OA datasets, respectively. For rs11842874 we used the OR from the "arcOGEN replication set1" replication dataset. For rs12901071 we used the relevant OR from the deCODE replication dataset. For rs143383 we used the relevant OR from the European population and particularly the OR from the "Ten studies from large-scale meta-analysis" replication cohort. For rs7639618 we used the OR from the Japanese replication dataset. g: The number of cases required for 80% power is calculated on the basis of EAF and OR estimates from the UK Biobank OA datasets and assuming 4 controls per case

  7. Supplementary Table 11: Association summary statistics for the 173 prioritised SNPs in the discovery, replication and overall meta-analysis for self-reported OA

    OA: Osteoarthritis; EA: Effect allele; EAF: Effect allele frequency; OR: Odds ratio; PV: p-value; chr: chromosome

  8. Supplementary Table 12: Association summary statistics for the 173 prioritised SNPs in the discovery, replication and overall meta-analysis for hospital diagnosed OA

    OA: osteoarthritis; EA: Effect allele; EAF: Effect allele frequency; OR: Odds ratio; PV: p-value; chr: chromosome

  9. Supplementary Table 13: Association summary statistics for the 173 prioritised SNPs in the discovery, replication and overall meta-analysis for hospital diagnosed knee and/or hip OA

    OA: osteoarthritis; EA: Effect allele; EAF: Effect allele frequency; OR: Odds ratio; PV: p-value; chr: chromosome

  10. Supplementary Table 14: Association summary statistics for the 173 prioritised SNPs in the discovery, replication and overall meta-analysis for hospital diagnosed hip OA

    OA: osteoarthritis; EA: Effect allele; EAF: Effect allele frequency; OR: Odds ratio; PV: p-value; chr: chromosome

  11. Supplementary Table 15: Association summary statistics for the 173 prioritised SNPs in the discovery, replication and overall meta-analysis for hospital diagnosed knee OA

    OA: osteoarthritis; EA: Effect allele; EAF: Effect allele frequency; OR: Odds ratio; PV: p-value; chr: chromosome

  12. Supplementary Table 16: eQTL analysis in GTEx

    Significant eQTL associations (at 5% FDR) for the 9 osteoarthritis signals as provided by the GTEx portal

  13. Supplementary Table 17: Detailed fine-mapping results for the OA-associated loci

    For each variant (columns A-D), we report its association summary statistics in UK Biobank (G-K), its predicted consequence (L) and the nearest gene (M), as well as the region centred around the variant used for the fine mapping (N-O). For each region, we report the Bayes’ factor (F) and the posterior probability that there be at least 1 causal variant in the region (E), followed by the variants (P) in the 95% credible intervals (defined to contain the minimum set of variants that jointly have at least 95% probability of including the causal variant). Summary statistics (S-W), Bayes’ factors (Q) and the posterior probabilities of association (PPA, column R) of each variant in the credible intervals are also reported. For regions in which the sum of probabilities of causality of all variants in the fine-mapped region is less than 0.95, we report the lower confidence interval achieved. EA: effect allele; NEA: non-effect allele; EAF: affect allele frequency; SE: standard error; N: sample size; BF: Bayes factor; PPA: posterior probability of association; OA: osteoarthritis

  14. Supplementary Table 18: Fine-mapping results for the OA-associated loci

    For each index variant (columns A-D), we report its predicted consequence (E) and the nearest protein coding gene (F). For each region constructed around the index variant, we report the Bayes’ factor (H) and the posterior probability that there be at least 1 causal variant in the region (G). Column I reports the number of variants in the resulting 95% credible intervals (defined to contain the minimum set of variants that jointly have at least 95% probability of including the causal variant). For each region, the number of variants in the credible interval with posterior probability >0.1, and their combined probability are listed in column J and K, respectively. Properties (L-Q, T-X), summary statistics (Y-AA), Bayes’ factors (R) and the posterior probabilities of association (S) of the top variants in the credible intervals are also reported. Only variants with probability of causality >0.1 are shown, or the top variant if its probability of causality is

  15. Supplementary Table 22: Description of studies used to identify genetic instruments for MR analyses

    MR: Mendelian randomization. aNot available in MR-Base for this record, so this value was extracted from another GWAS of the same phenotype (PMID 22504420) available in MR-Base

  16. Supplementary Table 23: Summary association results used in the two-sample MR analyses

    MR: Mendelian randomization; EA: effect allele; NEA: non-effect allele; EAF: effect allele frequency; SE: standard error; OA: osteoarthritis

  17. Supplementary Table 24: Summary statistics of each set of genetic instruments used in the two-sample MR analyses

    MR: Mendelian randomization; OA: osteoarthritis

  18. Supplementary Table 25: Causal OR estimates of OA for each exposure phenotype from two-sample MR analyses

    For exposure phenotypes with at least 10 genetic instruments, all methods were applied. For exposure phenotypes with less than 10 genetic instruments, only the inverse-variance weighting method was applied to calculate a pooled causal effect estimate, and only the heterogeneity test was used to assess horizontal pleiotropy. For exposure phenotypes with only one genetic instrument, the ratio method was used to calculate the causal effect estimate. bStandard error of the ln(OR). ϕ: tunning parameter of the mode-based estimate. The smaller the value of ϕ, the more stringent the analysis is. OR: odds ratio; OA: osteoarthritis; MR: Mendelian randomization; SE: standard error; CI: confidence interval

  19. Supplementary Table 26: Power (%) to detect different OR of OA in MR analyses using the inverse-variance weighting method

    OR: odds ratio; OA: osteoarthritis; MR: Mendelian randomization