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Common variants at PVT1, ATG13–AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency

Nature Genetics volume 48, pages 1425–1429 (2016)Cite this article

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Abstract

Selective immunoglobulin A deficiency (IgAD) is the most common primary immunodeficiency in Europeans. Our genome-wide association study (GWAS) meta-analysis of 1,635 patients with IgAD and 4,852 controls identified four new significant (P < 5 × 10−8) loci and association with a rare IFIH1 variant (p.Ile923Val). Peak new variants (PVT1, P = 4.3 × 10−11; ATG13–AMBRA1, P = 6.7 × 10−10; AHI1, P = 8.4 × 10−10; CLEC16A, P = 1.4 × 10−9) overlapped with autoimmune markers (3/4) and correlated with 21 putative regulatory variants, including expression quantitative trait loci (eQTLs) for AHI1 and DEXI and DNase hypersensitivity sites in FOXP3+ regulatory T cells. Pathway analysis of the meta-analysis results showed striking association with the KEGG pathway for IgA production (pathway P < 0.0001), with 22 of the 30 annotated pathway genes containing at least one variant with P ≤ 0.05 in the IgAD meta-analysis. These data suggest that a complex network of genetic effects, including genes known to influence the biology of IgA production, contributes to IgAD.

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Figure 1: Genome-wide significant loci in the IgAD meta-analysis.
Figure 2: KEGG pathway for IgA production associated with IgAD.

References

  1. Pan-Hammarström, Q. & Hammarström, L. Antibody deficiency diseases. Eur. J. Immunol. 38, 327–333 (2008).

    PubMed  Google Scholar 

  2. Suzuki, K. et al. Aberrant expansion of segmented filamentous bacteria in IgA-deficient gut. Proc. Natl. Acad. Sci. USA 101, 1981–1986 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Cong, Y., Feng, T., Fujihashi, K., Schoeb, T.R. & Elson, C.O. A dominant, coordinated T regulatory cell–IgA response to the intestinal microbiota. Proc. Natl. Acad. Sci. USA 106, 19256–19261 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Borte, S. et al. Interleukin-21 restores immunoglobulin production ex vivo in patients with common variable immunodeficiency and selective IgA deficiency. Blood 114, 4089–4098 (2009).

    CAS  PubMed  Google Scholar 

  5. Cao, A.T. et al. Interleukin (IL)-21 promotes intestinal IgA response to microbiota. Mucosal Immunol. 8, 1072–1082 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ferreira, R.C. et al. High-density SNP mapping of the HLA region identifies multiple independent susceptibility loci associated with selective IgA deficiency. PLoS Genet. 8, e1002476 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Oen, K., Petty, R.E. & Schroeder, M.L. Immunoglobulin A deficiency: genetic studies. Tissue Antigens 19, 174–182 (1982).

    CAS  PubMed  Google Scholar 

  8. Ludvigsson, J.F., Neovius, M. & Hammarström, L. Association between IgA deficiency & other autoimmune conditions: a population-based matched cohort study. J. Clin. Immunol. 34, 444–451 (2014).

    CAS  PubMed  Google Scholar 

  9. Ferreira, R.C. et al. Association of IFIH1 and other autoimmunity risk alleles with selective IgA deficiency. Nat. Genet. 42, 777–780 (2010).

    CAS  PubMed  Google Scholar 

  10. Kiryluk, K. et al. Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat. Genet. 46, 1187–1196 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J.A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Cunninghame Graham, D.S. et al. Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with systemic lupus erythematosus. PLoS Genet. 7, e1002341 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Li, Y. et al. Carriers of rare missense variants in IFIH1 are protected from psoriasis. J. Invest. Dermatol. 130, 2768–2772 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Gregersen, P.K. et al. Risk for myasthenia gravis maps to a (151) Pro→Ala change in TNIP1 and to human leukocyte antigen–B*08. Ann. Neurol. 72, 927–935 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Shigemoto, T. et al. Identification of loss of function mutations in human genes encoding RIG-I and MDA5: implications for resistance to type I diabetes. J. Biol. Chem. 284, 13348–13354 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Van Eyck, L. et al. Brief report: IFIH1 mutation causes systemic lupus erythematosus with selective IgA deficiency. Arthritis Rheumatol. 67, 1592–1597 (2015).

    CAS  PubMed  Google Scholar 

  17. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  PubMed  Google Scholar 

  18. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  20. Tseng, Y.Y. et al. PVT1 dependence in cancer with MYC copy-number increase. Nature 512, 82–86 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Ringrose, A. et al. Evidence for an oncogenic role of AHI-1 in Sezary syndrome, a leukemic variant of human cutaneous T-cell lymphomas. Leukemia 20, 1593–1601 (2006).

    CAS  PubMed  Google Scholar 

  22. Zhou, L.L. et al. AHI-1 interacts with BCR-ABL and modulates BCR-ABL transforming activity and imatinib response of CML stem/progenitor cells. J. Exp. Med. 205, 2657–2671 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Nazio, F. et al. mTOR inhibits autophagy by controlling ULK1 ubiquitylation, self-association and function through AMBRA1 and TRAF6. Nat. Cell Biol. 15, 406–416 (2013).

    CAS  PubMed  Google Scholar 

  24. Pierdominici, M. et al. Role of autophagy in immunity and autoimmunity, with a special focus on systemic lupus erythematosus. FASEB J. 26, 1400–1412 (2012).

    CAS  PubMed  Google Scholar 

  25. Clarke, A.J. et al. Autophagy is activated in systemic lupus erythematosus and required for plasmablast development. Ann. Rheum. Dis. 74, 912–920 (2015).

    PubMed  Google Scholar 

  26. Pengo, N. et al. Plasma cells require autophagy for sustainable immunoglobulin production. Nat. Immunol. 14, 298–305 (2013).

    CAS  PubMed  Google Scholar 

  27. Davison, L.J. et al. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum. Mol. Genet. 21, 322–333 (2012).

    CAS  PubMed  Google Scholar 

  28. Todd, J.A. et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat. Genet. 39, 857–864 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Kawamoto, S. et al. Foxp3+ T cells regulate immunoglobulin a selection and facilitate diversification of bacterial species responsible for immune homeostasis. Immunity 41, 152–165 (2014).

    CAS  PubMed  Google Scholar 

  30. Chung, D., Yang, C., Li, C., Gelernter, J. & Zhao, H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 10, e1004787 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. Li, J. et al. Association of CLEC16A with human common variable immunodeficiency disorder and role in murine B cells. Nat. Commun. 6, 6804 (2015).

    CAS  PubMed  Google Scholar 

  32. Schuster, C. et al. The autoimmunity-associated gene CLEC16A modulates thymic epithelial cell autophagy and alters T cell selection. Immunity 42, 942–952 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Yaspan, B.L. et al. Genetic analysis of biological pathway data through genomic randomization. Hum. Genet. 129, 563–571 (2011).

    PubMed  PubMed Central  Google Scholar 

  34. Lee, P.H., O'Dushlaine, C., Thomas, B. & Purcell, S.M. INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28, 1797–1799 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Schmidl, C. et al. The enhancer and promoter landscape of human regulatory and conventional T-cell subpopulations. Blood 123, e68–e78 (2014).

    CAS  PubMed  Google Scholar 

  37. Fontenot, J.D. et al. Regulatory T cell lineage specification by the forkhead transcription factor Foxp3. Immunity 22, 329–341 (2005).

    CAS  PubMed  Google Scholar 

  38. Bennett, C.L. et al. The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3. Nat. Genet. 27, 20–21 (2001).

    CAS  PubMed  Google Scholar 

  39. Notarangelo, L.D. et al. Primary immunodeficiencies: 2009 update. J. Allergy Clin. Immunol. 124, 1161–1178 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Magnusson, P.K. et al. The Swedish Twin Registry: establishment of a biobank and other recent developments. Twin Res. Hum. Genet. 16, 317–329 (2013).

    PubMed  Google Scholar 

  41. Salvi, E. et al. Genomewide association study using a high-density single nucleotide polymorphism array and case–control design identifies a novel essential hypertension susceptibility locus in the promoter region of endothelial NO synthase. Hypertension 59, 248–255 (2012).

    CAS  PubMed  Google Scholar 

  42. Mitchell, M.K., Gregersen, P.K., Johnson, S., Parsons, R. & Vlahov, D. The New York Cancer Project: rationale, organization, design, and baseline characteristics. J. Urban Health 81, 301–310 (2004).

    PubMed  PubMed Central  Google Scholar 

  43. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  PubMed  Google Scholar 

  44. Price, A.L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135, author reply 135–139 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Kosoy, R. et al. Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America. Hum. Mutat. 30, 69–78 (2009).

    PubMed  PubMed Central  Google Scholar 

  46. Tian, C. et al. Analysis and application of European genetic substructure using 300 K SNP information. PLoS Genet. 4, e4 (2008).

    PubMed  PubMed Central  Google Scholar 

  47. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Delaneau, O., Zagury, J.F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    CAS  PubMed  Google Scholar 

  49. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).

    CAS  PubMed  Google Scholar 

  51. Pruim, R.J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  53. Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Dixon, A.L. et al. A genome-wide association study of global gene expression. Nat. Genet. 39, 1202–1207 (2007).

    CAS  PubMed  Google Scholar 

  55. Duan, S. et al. Genetic architecture of transcript-level variation in humans. Am. J. Hum. Genet. 82, 1101–1113 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. He, X. et al. Sherlock: detecting gene–disease associations by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92, 667–680 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Barrett, J.C. et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703–707 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the individuals who participated in this study as case and control study subjects. We thank B. Yaspan, J. Kim, J. Sitrin and T. Hung for insightful discussion, J. Tom and O. Mayba for helpful feedback on a manuscript draft, and A. Bruce for sharing her graphical expertise. Genentech funded the current GWAS. A. Lee and P. Gregersen performed sample genotyping at the Laboratory of Genomics and Human Genetics at the Feinstein Institute for Medical Research. These studies were supported by the US National Institutes of Health (U19AI067152 and AR043274), the Swedish Research Council, the European Research Council (242551-ImmunoSwitch), EURO-PADnet grant 201549, CETOCOEN PLUS and the Fondazione C. Golgi, Brescia, Italy. Financial support was also provided through the regional agreement on medical training and clinical research (ALF) between the Stockholm County Council and Karolinska Institutet. Population allele and genotype frequencies were obtained from a data source funded by the Nordic Center of Excellence in Disease Genetics based on regional samples from Finland and Sweden. The Helsinki 4 study was supported by the Yale Center for Human Genetics and Genomics and the Yale Program on Neurogenetics and by US National Institutes of Health grants R01NS057756 and U24NS051869.

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Authors and Affiliations

  1. Department of Human Genetics, Genentech, Inc., South San Francisco, California, USA

    Paola G Bronson, Diana Chang, Ward Ortmann, Robert R Graham & Timothy W Behrens

  2. Department of Bioinformatics and Computational Biology, Genentech, Inc., South San Francisco, California, USA

    Tushar Bhangale

  3. Department of Biochemistry, School of Medicine, University of California, Davis, Davis, California, USA

    Michael F Seldin

  4. Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, Cambridge, UK

    Ricardo C Ferreira

  5. Department of Immunology, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, IdISSC, Madrid, Spain

    Elena Urcelay

  6. Department of Immunology, Hospital San Pedro de Alcántara, Cáceres, Spain

    Luis Fernández Pereira

  7. Instituto de Parasitología y Biomedicina López-Neyra, CSIC, Granada, Spain

    Javier Martin

  8. Department of Clinical and Experimental Sciences, Pediatrics Clinic, University of Brescia, Spedali Civili di Brescia, Brescia, Italy

    Alessandro Plebani & Vassilios Lougaris

  9. Department of Clinical and Experimental Sciences, Institute for Molecular Medicine, A. Nocivelli, University of Brescia, Spedali Civili di Brescia, Brescia, Italy

    Alessandro Plebani & Vassilios Lougaris

  10. Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden

    Vanda Friman

  11. Molecular Genetics Laboratory, Centre for Cardiovascular Surgery and Transplantation, Brno, Czech Republic

    Tomáš Freiberger

  12. Central European Institute of Technology, Masaryk University, Brno, Czech Republic

    Tomáš Freiberger

  13. Department of Clinical Immunology and Allergy, Faculty of Medicine, Masaryk University, St. Anne's University Hospital, Brno, Czech Republic

    Jiri Litzman & Vojtech Thon

  14. Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic

    Vojtech Thon

  15. Division of Clinical Immunology and Transfusion Medicine, Karolinska Institutet, Stockholm, Sweden

    Qiang Pan-Hammarström & Lennart Hammarström

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  1. Paola G Bronson
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Contributions

P.G.B. and D.C. carried out the analyses for this study. P.G.B., L.H., T.W.B., R.R.G. and T.B. conceived and directed this study. L.H., Q.P.-H., A.P., V.L., T.F., J.L., E.U., L.F.P., V.F. and V.T. performed subject diagnosis, coordinated the enrollment of subjects and provided access to genotyping data sets. M.F.S. and J.M. provided access to genotypes for healthy controls. M.F.S. provided guidance for addressing population structure due to ancestry. M.F.S., Q.P.-H., R.C.F., T.B., R.R.G. and W.O. contributed to data access and analysis. P.G.B., T.W.B., L.H. and R.R.G. wrote the manuscript with collaboration from coauthors. All authors discussed the results and commented on the manuscript.

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Correspondence to Paola G Bronson or Lennart Hammarström.

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Integrated supplementary information

Supplementary Figure 1 Association plot for the significant (P < 5 × 10−8) IgAD GWAS locus at HLA-DQ.

rs116041786 hg19 has been renamed as rs9272226 in newer builds. The regional association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value and is labeled with the SNP name; the color of the other dots represents the level of linkage disequilibrium (r2) with the peak SNP. The lines at the top of the plot represent the location and number of directly genotyped SNPs in each cohort.

Supplementary Figure 2 Association plot for the significant (P < 5 × 10−8) IgAD GWAS locus at IFIH1.

The regional association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value and is labeled with the SNP name; the color of the other dots represents the level of linkage disequilibrium (r2) with the peak SNP. The lines at the top of the plot represent the location and number of directly genotyped SNPs in each cohort.

Supplementary Figure 3 Association plot for the significant (P < 5 × 10−8) IgAD GWAS locus at PVT1.

The regional association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value and is labeled with the SNP name; the color of the other dots represents the level of linkage disequilibrium (r2) with the peak SNP. The lines at the top of the plot represent the location and number of directly genotyped SNPs in each cohort.

Supplementary Figure 4 Association plot for the significant (P < 5 × 10−8) IgAD GWAS locus at ATG13–AMBRA1.

The regional association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value and is labeled with the SNP name; the color of the other dots represents the level of linkage disequilibrium (r2) with the peak SNP. The lines at the top of the plot represent the location and number of directly genotyped SNPs in each cohort.

Supplementary Figure 5 Association plots for the significant (P < 5 × 10−8) IgAD GWAS locus at AHI1.

The regional association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value and is labeled with the SNP name; the color of the other dots represents the level of linkage disequilibrium (r2) with the peak SNP. The lines at the top of the plot represent the location and number of directly genotyped SNPs in each cohort.

Supplementary Figure 6 Association plot for the significant (P < 5 × 10−8) IgAD GWAS locus at CLEC16A.

The regional association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value and is labeled with the SNP name; the color of the other dots represents the level of linkage disequilibrium (r2) with the peak SNP. The lines at the top of the plot represent the location and number of directly genotyped SNPs in each cohort.

Supplementary Figure 7 Association plot for additional IgAD GWAS loci with P < 1 × 10−5.

The Manhattan association plot displays P values from a genome-wide meta-analysis of ~9.5 million variants in 1,635 patients with IgAD and 4,852 controls. Each dot represents a P value for a genotyped or imputed SNP, where the highest dot has the smallest P value. Loci with P values that did not reach genome-wide significance but had P < 1 × 10−5 are labeled.

Supplementary Figure 8 GTEx box plot of AHI1 gene expression for the cis-eQTL rs2179781 in esophagus mucosa (n = 241).

The box plot displays gene expression levels for the novel IgAD locus AHI1 stratified by genotype at the rs2179781 cis-eQTL in esophagus mucosa, based on GTEx analysis release v6 (http://gtexportal.org/). The reference allele is A and was associated with reduced risk for IgAD (P = 8.8 × 10−10) and reduced AHI1 expression (P = 3 × 10−12). This eQTL had the strongest log Bayes factor (LBF) score (6.83) in the Bayesian statistical framework Sherlock. “Homo Ref” refers to the homozygous reference allele genotype, AA; “Het” refers to the heterozygous genotype, AC; and “Homo Alt” refers to the homozygous alternate allele genotype, CC.

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Bronson, P., Chang, D., Bhangale, T. et al. Common variants at PVT1, ATG13–AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency. Nat Genet 48, 1425–1429 (2016). https://doi.org/10.1038/ng.3675

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