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A large-scale screen for coding variants predisposing to psoriasis

Abstract

To explore the contribution of functional coding variants to psoriasis, we analyzed nonsynonymous single-nucleotide variants (SNVs) across the genome by exome sequencing in 781 psoriasis cases and 676 controls and through follow-up validation in 1,326 candidate genes by targeted sequencing in 9,946 psoriasis cases and 9,906 controls from the Chinese population. We discovered two independent missense SNVs in IL23R and GJB2 of low frequency and five common missense SNVs in LCE3D, ERAP1, CARD14 and ZNF816A associated with psoriasis at genome-wide significance. Rare missense SNVs in FUT2 and TARBP1 were also observed with suggestive evidence of association. Single-variant and gene-based association analyses of nonsynonymous SNVs did not identify newly associated genes for psoriasis in the regions subjected to targeted resequencing. This suggests that coding variants in the 1,326 targeted genes contribute only a limited fraction of the overall genetic risk for psoriasis.

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Figure 1: Haplotype analyses in FUT2, IL23R, GJB2 and TARBP1.

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References

  1. Zhang, X. Genome-wide association study of skin complex diseases. J. Dermatol. Sci. 66, 89–97 (2012).

    Article  CAS  Google Scholar 

  2. Tsoi, L.C. et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat. Genet. 44, 1341–1348 (2012).

    Article  CAS  Google Scholar 

  3. Liu, Y. et al. A genome-wide association study of psoriasis and psoriatic arthritis identifies new disease loci. PLoS Genet. 4, e1000041 (2008).

    Article  Google Scholar 

  4. Strange, A. et al. A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1. Nat. Genet. 42, 985–990 (2010).

    Article  CAS  Google Scholar 

  5. Nair, R.P. et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-κB pathways. Nat. Genet. 41, 199–204 (2009).

    Article  CAS  Google Scholar 

  6. Zhang, X.J. et al. Psoriasis genome-wide association study identifies susceptibility variants within LCE gene cluster at 1q21. Nat. Genet. 41, 205–210 (2009).

    Article  CAS  Google Scholar 

  7. Ellinghaus, E. et al. Genome-wide association study identifies a psoriasis susceptibility locus at TRAF3IP2. Nat. Genet. 42, 991–995 (2010).

    Article  CAS  Google Scholar 

  8. Ellinghaus, E. et al. Genome-wide meta-analysis of psoriatic arthritis identifies susceptibility locus at REL. J. Invest. Dermatol. 132, 1133–1140 (2012).

    Article  CAS  Google Scholar 

  9. Stuart, P.E. et al. Genome-wide association analysis identifies three psoriasis susceptibility loci. Nat. Genet. 42, 1000–1004 (2010).

    Article  CAS  Google Scholar 

  10. Sun, L.D. et al. Association analyses identify six new psoriasis susceptibility loci in the Chinese population. Nat. Genet. 42, 1005–1009 (2010).

    Article  CAS  Google Scholar 

  11. Hüffmeier, U. et al. Common variants at TRAF3IP2 are associated with susceptibility to psoriatic arthritis and psoriasis. Nat. Genet. 42, 996–999 (2010).

    Article  Google Scholar 

  12. Capon, F. et al. Identification of ZNF313/RNF114 as a novel psoriasis susceptibility gene. Hum. Mol. Genet. 17, 1938–1945 (2008).

    Article  CAS  Google Scholar 

  13. Ellinghaus, D. et al. Combined analysis of genome-wide association studies for Crohn disease and psoriasis identifies seven shared susceptibility loci. Am. J. Hum. Genet. 90, 636–647 (2012).

    Article  CAS  Google Scholar 

  14. Zeggini, E. Next-generation association studies for complex traits. Nat. Genet. 43, 287–288 (2011).

    Article  CAS  Google Scholar 

  15. 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).

    Article  CAS  Google Scholar 

  16. Momozawa, Y. et al. Resequencing of positional candidates identifies low frequency IL23R coding variants protecting against inflammatory bowel disease. Nat. Genet. 43, 43–47 (2011).

    Article  CAS  Google Scholar 

  17. Rivas, M.A. et al. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet. 43, 1066–1073 (2011).

    Article  CAS  Google Scholar 

  18. Diogo, D. et al. Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis. Am. J. Hum. Genet. 92, 15–27 (2013).

    Article  CAS  Google Scholar 

  19. Lesage, S. et al. CARD15/NOD2 mutational analysis and genotype-phenotype correlation in 612 patients with inflammatory bowel disease. Am. J. Hum. Genet. 70, 845–857 (2002).

    Article  CAS  Google Scholar 

  20. Jordan, C.T. et al. Rare and common variants in CARD14, encoding an epidermal regulator of NF-κB, in psoriasis. Am. J. Hum. Genet. 90, 796–808 (2012).

    Article  CAS  Google Scholar 

  21. Ng, S.B. et al. Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. 42, 30–35 (2010).

    Article  CAS  Google Scholar 

  22. Nair, R.P. et al. Polymorphisms of the IL12B and IL23R genes are associated with psoriasis. J. Invest. Dermatol. 128, 1653–1661 (2008).

    Article  CAS  Google Scholar 

  23. Kumar, P., Henikoff, S. & Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  CAS  Google Scholar 

  24. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    Article  CAS  Google Scholar 

  25. Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    Article  CAS  Google Scholar 

  26. Maeda, S. et al. Structure of the connexin 26 gap junction channel at 3.5 Å resolution. Nature 458, 597–602 (2009).

    Article  CAS  Google Scholar 

  27. Nguyen, T.T. et al. Structural basis for antigenic peptide precursor processing by the endoplasmic reticulum aminopeptidase ERAP1. Nat. Struct. Mol. Biol. 18, 604–613 (2011).

    Article  CAS  Google Scholar 

  28. Kochan, G. et al. Crystal structures of the endoplasmic reticulum aminopeptidase-1 (ERAP1) reveal the molecular basis for N-terminal peptide trimming. Proc. Natl. Acad. Sci. USA 108, 7745–7750 (2011).

    Article  CAS  Google Scholar 

  29. Tanaka, T. et al. Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am. J. Hum. Genet. 84, 477–482 (2009).

    Article  CAS  Google Scholar 

  30. Hazra, A. et al. Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat. Genet. 40, 1160–1162 (2008).

    Article  CAS  Google Scholar 

  31. Jordan, C.T. et al. PSORS2 is due to mutations in CARD14. Am. J. Hum. Genet. 90, 784–795 (2012).

    Article  CAS  Google Scholar 

  32. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  Google Scholar 

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

  34. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

    Article  CAS  Google Scholar 

  35. Hunt, K.A. et al. Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498, 232–235 (2013).

    Article  CAS  Google Scholar 

  36. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  Google Scholar 

  37. Eichler, E.E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).

    Article  CAS  Google Scholar 

  38. Albert, T.J. et al. Direct selection of human genomic loci by microarray hybridization. Nat. Methods 4, 903–905 (2007).

    Article  CAS  Google Scholar 

  39. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  Google Scholar 

  40. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  Google Scholar 

  41. Li, R. et al. SNP detection for massively parallel whole-genome resequencing. Genome Res. 19, 1124–1132 (2009).

    Article  CAS  Google Scholar 

  42. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  Google Scholar 

  43. 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).

    Article  CAS  Google Scholar 

  44. Wallenstein, S. & Wittes, J. The power of the Mantel-Haenszel test for grouped failure time data. Biometrics 49, 1077–1087 (1993).

    Article  CAS  Google Scholar 

  45. Barrett, J.C., Fry, B., Maller, J. & Daly, M.J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    Article  CAS  Google Scholar 

  46. Purcell, S., Cherny, S.S. & Sham, P.C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150 (2003).

    Article  CAS  Google Scholar 

  47. Siepel, A., Pollard, K. & Haussler, D. in Research in Computational Molecular Biology, Vol. 3909 (eds. Apostolico, A., Guerra, C., Istrail, S., Pevzner, P. & Waterman, M.) 190–205 (Springer, Berlin, Heidelberg, 2006).

  48. Han, F. & Pan, W. A data-adaptive sum test for disease association with multiple common or rare variants. Hum. Hered. 70, 42–54 (2010).

    Article  Google Scholar 

  49. Madsen, B.E. & Browning, S.R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009).

    Article  Google Scholar 

  50. Dering, C., Hemmelmann, C., Pugh, E. & Ziegler, A. Statistical analysis of rare sequence variants: an overview of collapsing methods. Genet. Epidemiol. 35 (suppl. 1), S12–S17 (2011).

    Article  Google Scholar 

  51. Li, B. & Leal, S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).

    Article  CAS  Google Scholar 

  52. Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  Google Scholar 

  53. Price, A.L. et al. Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86, 832–838 (2010).

    Article  Google Scholar 

  54. Neale, B.M. et al. Testing for an unusual distribution of rare variants. PLoS Genet. 7, e1001322 (2011).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank the individuals who participated in this project and their families. We also want to thank L. Wu, D. Li, Y. Shi, J. Shen, L. Song, Y. Xue, J. Jv, Y. Sheng and J. Gao who participated in the analysis of exome sequencing data. We thank the State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology (Hefei, China). This study was funded by the Key Program of the National Natural Science Foundation of China (81130031), the National Science Fund for Excellent Young Scholars (81222022), the Outstanding Talents of Organization Department of the CPC (Communist Party of China) Central Committee program, the Local Universities Characteristics and Advantages of Discipline Development Program of the Ministry of Finance of China and the General Program of the National Natural Science Foundation of China (81072461, 30971644, 31171224, 31000528, 81000692, 81071285, 81172866, 81172591 and 31200939), New Century Excellent Talents in University (NCET-11-0889), and the Science and Technological Fund of Anhui Province for Outstanding Youth (1108085J10) as well as the Pre-National Basic Research Program of China (973 Plan; 2012CB722404), the National Basic Research Program of China (973 Plan; 2009CB825404), the State Key Development Program for Basic Research of China (973 Program; 2011CB809203), the Chinese High-Tech (863) Program (2012AA02A201), the Enterprise Key Laboratory, supported by Guangdong Province, and the Shenzhen Key Laboratory of Transomics Biotechnologies (CXB201108250096A), and the National High-Tech Research & Development Program (2012AA020206).

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Authors

Contributions

Xuejun Zhang conceived this study and obtained financial support. Xuejun Zhang, J.W., Yingrui Li and L.S. participated in study design and were responsible for project management. H.C., Y.Q., Q.C., C.Q., Y.C., F.T., H.L., F. Xiao, J.H., D.S., A.Z., C.Z., X.F., H. Tian, Z.W., F.W., B.Y., B.L., G.W., Y.S., L.D., J.S., T.L., Xiuyun Zhang, Yuzhen Li, C.H., A.X., L.W., Xiaohang Zhao, X.G., J.X., F. Zhang and J.Z. conducted sample selection and data management, undertook recruitment, collected phenotype data, undertook related data handling and calculations, managed recruitment and obtained biological samples. H. Tang, X.J., Yang Li, H.J., X.T., X.Y., J.M., R.W., X. Zuo, Y.Z., X. Yin, H.S., Xia Zhao, F. Xu, Q.L., L.L., H.F., S.H., X.X., Y.R., Q.G., X.W., M.X., L.Y. and R.C. designed the bioinformatics and experimental sections, coordinated the collection, maintained project procedures and performed data analysis. F. Zhou, G.C. and X. Zheng performed genotyping analysis. H. Tang, X.J., X. Zuo, X.T. and H.C. undertook data processing, statistical analysis and bioinformatics investigations. H. Tang, X.J., L.S., X.T., Yang Li and J.L. cowrote the manuscript. All authors contributed to the final version of the manuscript, with Xuejun Zhang, J.W., S.Y., L.S., Yingrui Li, H. Tang, X.J., X.T., H.J. and Yang Li having key roles.

Corresponding authors

Correspondence to Jun Wang or Xuejun Zhang.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1–6, 10–17 and 19 (PDF 4351 kb)

Supplementary Table 7

742 genes by gene-based analysis from exome sequencing and targeted sequencing data (XLSX 395 kb)

Supplementary Table 8

Targeted sequencing of 565 immune related genes (not included psoriasis GWAS loci) and results of gene-based test (XLSX 222 kb)

Supplementary Table 9

Targeted sequencing of 57 genes in psoriasis GWAS loci and results of gene-based test (XLSX 37 kb)

Supplementary Table 18

Depth and coverage per gene (XLSX 273 kb)

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Tang, H., Jin, X., Li, Y. et al. A large-scale screen for coding variants predisposing to psoriasis. Nat Genet 46, 45–50 (2014). https://doi.org/10.1038/ng.2827

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