For most immune-mediated diseases, the main determinant of patient well-being is not the diagnosis itself but instead the course that the disease takes over time (prognosis)1,2,3. Prognosis may vary substantially between patients for reasons that are poorly understood. Familial studies support a genetic contribution to prognosis4,5,6, but little evidence has been found for a proposed association between prognosis and the burden of susceptibility variants7,8,9,10,11,12,13. To better characterize how genetic variation influences disease prognosis, we performed a within-cases genome-wide association study in two cohorts of patients with Crohn's disease. We identified four genome-wide significant loci, none of which showed any association with disease susceptibility. Conversely, the aggregated effect of all 170 disease susceptibility loci was not associated with disease prognosis. Together, these data suggest that the genetic contribution to prognosis in Crohn's disease is largely independent of the contribution to disease susceptibility and point to a biology of prognosis that could provide new therapeutic opportunities.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.



Gene Expression Omnibus


  1. 1.

    et al. Changes in clinical characteristics, course, and prognosis of inflammatory bowel disease during the last 5 decades: a population-based study from Copenhagen, Denmark. Inflamm. Bowel Dis. 13, 481–489 (2007).

  2. 2.

    Long-term outcomes in rheumatoid arthritis. Br. J. Rheumatol. 34 (Suppl. 2), 59–73 (1995).

  3. 3.

    et al. The natural history of multiple sclerosis: a geographically based study. I. Clinical course and disability. Brain 112, 133–146 (1989).

  4. 4.

    , , & Clinical patterns of familial inflammatory bowel disease. Gut 38, 738–741 (1996).

  5. 5.

    et al. Multiple sclerosis in sibling pairs: an analysis of 250 families. J. Neurol. Neurosurg. Psychiatry 71, 757–761 (2001).

  6. 6.

    , , , & Clustering of disease features within 512 multicase rheumatoid arthritis families. Arthritis Rheum. 50, 736–741 (2004).

  7. 7.

    et al. Molecular prediction of disease risk and severity in a large Dutch Crohn's disease cohort. Gut 58, 388–395 (2009).

  8. 8.

    , , & Burden of risk variants correlates with phenotype of multiple sclerosis. Mult. Scler. 21, 1670–1680 (2015).

  9. 9.

    et al. Genetic risk score predicting risk of rheumatoid arthritis phenotypes and age of symptom onset. PLoS One 6, e24380 (2011).

  10. 10.

    et al. Differential effect of genetic burden on disease phenotypes in Crohn's disease and ulcerative colitis: analysis of a North American cohort. Am. J. Gastroenterol. 109, 395–400 (2014).

  11. 11.

    et al. Genotype/phenotype analyses for 53 Crohn's disease associated genetic polymorphisms. PLoS One 7, e52223 (2012).

  12. 12.

    et al. Multiple sclerosis susceptibility-associated SNPs do not influence disease severity measures in a cohort of Australian MS patients. PLoS One 5, e10003 (2010).

  13. 13.

    et al. Do genetic susceptibility variants associate with disease severity in early active rheumatoid arthritis? J. Rheumatol. 42, 1131–1140 (2015).

  14. 14.

    , & Common disorders are quantitative traits. Nat. Rev. Genet. 10, 872–878 (2009).

  15. 15.

    et al. CARD15/NOD2 gene variants are associated with familially occurring and complicated forms of Crohn's disease. Gut 52, 558–562 (2003).

  16. 16.

    et al. Inherited determinants of Crohn's disease and ulcerative colitis phenotypes: a genetic association study. Lancet 387, 156–167 (2016).

  17. 17.

    , , , & T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 523, 612–616 (2015).

  18. 18.

    et al. Human SNP links differential outcomes in inflammatory and infectious disease to a FOXO3-regulated pathway. Cell 155, 57–69 (2013).

  19. 19.

    , , , & Power of selective genotyping in genetic association analyses of quantitative traits. Behav. Genet. 30, 141–146 (2000).

  20. 20.

    Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  21. 21.

    et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).

  22. 22.

    et al. Erosion of X chromosome inactivation in human pluripotent cells initiates with XACT coating and depends on a specific heterochromatin landscape. Cell Stem Cell 16, 533–546 (2015).

  23. 23.

    et al. Genes involved in immune response/inflammation, IGF1/insulin pathway and response to oxidative stress play a major role in the genetics of human longevity: the lesson of centenarians. Mech. Ageing Dev. 126, 351–361 (2005).

  24. 24.

    et al. A genome-wide association study suggests contrasting associations in ACPA-positive versus ACPA-negative rheumatoid arthritis. Ann. Rheum. Dis. 70, 259–265 (2011).

  25. 25.

    et al. The cellular basis for lack of antibody response to hepatitis B vaccine in humans. J. Exp. Med. 173, 531–538 (1991).

  26. 26.

    , & HLA-B8,DR3 phenotype and lymphocyte responses to phytohaemagglutinin. J. Immunogenet. 17, 101–107 (1990).

  27. 27.

    et al. T-cell activation in HLA-B8, DR3-positive individuals. Early antigen expression defect in vitro. Hum. Immunol. 42, 289–294 (1995).

  28. 28.

    et al. High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis. Nat. Genet. 47, 172–179 (2015).

  29. 29.

    et al. Risk of early surgery for Crohn's disease: implications for early treatment strategies. Am. J. Gastroenterol. 98, 2712–2718 (2003).

  30. 30.

    , , , & An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics 14, 632 (2013).

  31. 31.

    et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

  32. 32.

    et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

  33. 33.

    et al. Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease. Am. J. Hum. Genet. 92, 1008–1012 (2013).

  34. 34.

    et al. Data quality control in genetic case–control association studies. Nat. Protoc. 5, 1564–1573 (2010).

  35. 35.

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

  36. 36.

    1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  37. 37.

    , & A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

  38. 38.

    et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 10, e1004234 (2014).

  39. 39.

    , & Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

  40. 40.

    et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat. Genet. 42, 436–440 (2010).

  41. 41.

    , & Regression models for count data in R. J. Stat. Softw. 27, 1–25 (2008).

  42. 42.

    et al. Genetic factors conferring an increased susceptibility to develop Crohn's disease also influence disease phenotype: results from the IBDchip European Project. Gut 62, 1556–1565 (2013).

  43. 43.

    A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).

  44. 44.

    et al. Multi-population classical HLA type imputation. PLoS Comput. Biol. 9, e1002877 (2013).

  45. 45.

    & Cochran–Armitage test versus logistic regression in the analysis of genetic association studies. Hum. Hered. 73, 14–17 (2012).

  46. 46.

    et al. Identification of expressed and conserved human noncoding RNAs. RNA 20, 236–251 (2014).

  47. 47.

    et al. STAR: ultrafast universal RNA–seq aligner. Bioinformatics 29, 15–21 (2013).

  48. 48.

    et al. Transcript assembly and quantification by RNA–Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

  49. 49.

    , & SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30, 2496–2497 (2014).

  50. 50.

    et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

  51. 51.

    , , & PredictABEL: an R package for the assessment of risk prediction models. Eur. J. Epidemiol. 26, 261–264 (2011).

  52. 52.

    et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  53. 53.

    et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics (2016).

Download references


We thank L. Hildyard, E. Gray and other members of the Wellcome Trust Sanger Institute DNA team for their help with sample coordination and A. Groff and C. Weiner for critical reading of the manuscript. This work was supported by NIHR Biomedical Research Centres in Cambridge and Guy's and St Thomas' (in particular, J. Todd and the NIHR Cambridge BRC Genomics Theme), Crohn's and Colitis UK (Medical Research Award M/14/2), the Evelyn Trust (17/07), and the Medical Research Council (programme grant MR/L019027/1). J.C.L. is supported by a Wellcome Trust Intermediate Clinical Fellowship (105920/Z/14/Z), and D.B. is supported by a Marie Curie PhD Fellowship (TranSVIR FP7-PEOPLE-ITN-2008 238756). N.J.P. is supported by a Wellcome Trust University Award (094491/Z/10/Z), and J.A.T. is supported by the European Research Council (695551). C.A.A. is supported by the Wellcome Trust (098051). K.G.C.S. is an NIHR Senior Investigator. This study makes use of data generated by the UK10K Consortium, derived from samples from the ALSPAC and DTR cohorts. A full list of the investigators who contributed to the generation of the data is available from http://www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust (WT091310).

Author information

Author notes

    • James C Lee
    •  & Daniele Biasci

    These authors contributed equally to this work.


  1. Department of Medicine, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK.

    • James C Lee
    • , Daniele Biasci
    • , Paul A Lyons
    • , Miles Parkes
    •  & Kenneth G C Smith
  2. Department of Medicine, University of Otago, Christchurch, New Zealand.

    • Rebecca Roberts
    •  & Richard B Gearry
  3. Institute of Genetic Medicine, Newcastle University, Newcastle-upon-Tyne, UK.

    • John C Mansfield
  4. University of Exeter Medical School, Exeter, UK.

    • Tariq Ahmad
  5. Department of Medical and Molecular Genetics, Faculty of Life Science and Medicine, King's College London, London, UK.

    • Natalie J Prescott
  6. Gastrointestinal Unit, Division of Medical Sciences, School of Molecular and Clinical Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.

    • Jack Satsangi
  7. Paediatric Gastroenterology and Nutrition, Child Life and Health, College of Medicine and Veterinary Medicine, University of Edinburgh, Royal Hospital for Sick Children, Edinburgh, UK.

    • David C Wilson
  8. Wellcome Trust Centre for Human Genetics, University of Oxford, Headington, UK.

    • Luke Jostins
  9. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.

    • Carl A Anderson
  10. Department of Pathology, University of Cambridge, Cambridge, UK.

    • James A Traherne


  1. UK IBD Genetics Consortium

    A full list of members and affiliations appears in the Supplementary Note.


  1. Search for James C Lee in:

  2. Search for Daniele Biasci in:

  3. Search for Rebecca Roberts in:

  4. Search for Richard B Gearry in:

  5. Search for John C Mansfield in:

  6. Search for Tariq Ahmad in:

  7. Search for Natalie J Prescott in:

  8. Search for Jack Satsangi in:

  9. Search for David C Wilson in:

  10. Search for Luke Jostins in:

  11. Search for Carl A Anderson in:

  12. Search for James A Traherne in:

  13. Search for Paul A Lyons in:

  14. Search for Miles Parkes in:

  15. Search for Kenneth G C Smith in:


The experiment was conceived by J.C.L., M.P., and K.G.C.S. J.C.L., D.B., and P.A.L. designed the analysis. D.B. performed the analysis with input from J.C.L., L.J., C.A.A., J.A.T., and P.A.L. Patient samples and phenotype data were provided by J.C.L., R.R., R.B.G., J.C.M., T.A., N.J.P., J.S., D.C.W., M.P., and other members of the UK IBD Genetics Consortium. J.C.L. and K.G.C.S. wrote the manuscript with input from D.B., P.A.L., and M.P. All authors reviewed and approved the manuscript prior to submission.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to James C Lee or Kenneth G C Smith.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Tables 1–6 and 9, and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 7

    SNPsea results for enrichment of prognosis-associated genes in known biological pathways (Gene Ontology).

  2. 2.

    Supplementary Table 8

    SNPsea results for enrichment of prognosis-associated genes in primary human cell types.

  3. 3.

    Supplementary Table 10

    Association statistics for 170 Crohn's disease susceptibility SNPs in GWAS of prognosis.

About this article

Publication history






Further reading