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Genetic Risk Prediction in IBD

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Molecular Genetics of Inflammatory Bowel Disease
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Abstract

An emerging objective in the IBD community is to gear the findings from genome-wide association studies towards achieving precision medicine. The main goal of this new model of disease management is to adapt clinical practice to the needs of each patient. This includes, among others, obtaining more detailed characterizations of disease presentation at diagnosis, better prediction of disease prognosis that permits to anticipate and adapt management to variations in symptomatology, and tailoring treatment to individual needs. In this chapter, we will introduce the methodology for estimation of disease risk using genetic data from individuals and discuss the potential and main challenges for using genomic risk profiling as a predictive tool that can pave the way for adoption of precision medicine-based approaches in the clinical management of IBD.

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References

  1. Wray NR, Visscher PM (2010) Narrowing the boundaries of the genetic architecture of schizophrenia. Schizophr Bull 36:14–23. https://doi.org/10.1093/schbul/sbp137

    Article  PubMed  Google Scholar 

  2. Visscher PM, Wray NR (2015) Concepts and misconceptions about the polygenic additive model applied to disease. Hum Hered 80:165–170. https://doi.org/10.1159/000446931

    Article  CAS  PubMed  Google Scholar 

  3. Marigorta UM, Rodriguez JA, Gibson G, Navarro A (2018) Replicability and prediction: lessons and challenges from GWAS. Trends Genet 34:504–517. https://doi.org/10.1016/j.tig.2018.03.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE (2009) Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers. PLoS Genet 5:e1000337. https://doi.org/10.1371/journal.pgen.1000337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zupancic K et al (2016) Multi-locus genetic risk score predicts risk for Crohn’s disease in Slovenian population. World J Gastroenterol 22:3777–3784. https://doi.org/10.3748/wjg.v22.i14.3777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Damas OM et al (2017) Genetic characterization and influence on inflammatory bowel disease expression in a diverse Hispanic South Florida cohort. Clin Transl Gastroenterol 8:e87. https://doi.org/10.1038/ctg.2017.13

    Article  PubMed  PubMed Central  Google Scholar 

  7. Marigorta UM et al (2017) Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn’s disease. Nat Genet 49:1517–1521. https://doi.org/10.1038/ng.3936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Evans DM, Visscher PM, Wray NR (2009) Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum Mol Genet 18:3525–3531. https://doi.org/10.1093/hmg/ddp295

    Article  CAS  PubMed  Google Scholar 

  9. Liu JZ et al (2015) Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet 47:979–986. https://doi.org/10.1038/ng.3359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Maher B (2008) Personal genomes: the case of the missing heritability. Nature 456:18–21. https://doi.org/10.1038/456018a

    Article  CAS  PubMed  Google Scholar 

  11. Yang J et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569. https://doi.org/10.1038/ng.608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shi H, Kichaev G, Pasaniuc B (2016) Contrasting the genetic architecture of 30 complex traits from summary association data. Am J Hum Genet 99:139–153. https://doi.org/10.1016/j.ajhg.2016.05.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gibson G (2010) Hints of hidden heritability in GWAS. Nat Genet 42:558–560. https://doi.org/10.1038/ng0710-558

    Article  CAS  PubMed  Google Scholar 

  14. Dudbridge F, Newcombe PJ (2015) Accuracy of gene scores when pruning markers by linkage disequilibrium. Hum Hered 80:178–186. https://doi.org/10.1159/000446581

    Article  CAS  PubMed  Google Scholar 

  15. Abraham G, Kowalczyk A, Zobel J, Inouye M (2013) Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease. Genet Epidemiol 37:184–195. https://doi.org/10.1002/gepi.21698

    Article  PubMed  Google Scholar 

  16. Vilhjalmsson BJ et al (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet 97:576–592. https://doi.org/10.1016/j.ajhg.2015.09.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dudbridge F (2016) Polygenic epidemiology. Genet Epidemiol 40:268–272. https://doi.org/10.1002/gepi.21966

    Article  PubMed  PubMed Central  Google Scholar 

  18. Khera AV et al (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50:1219–1224. https://doi.org/10.1038/s41588-018-0183-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wei Z et al (2013) 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. https://doi.org/10.1016/j.ajhg.2013.05.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen GB et al (2017) Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. BMC Med Genet 18:94. https://doi.org/10.1186/s12881-017-0451-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chatterjee N et al (2013) Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet 45:400–5, 405e401–403. https://doi.org/10.1038/ng.2579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ashton JJ, Mossotto E, Ennis S, Beattie RM (2019) Personalising medicine in inflammatory bowel disease-current and future perspectives. Transl Pediatr 8:56–69. https://doi.org/10.21037/tp.2018.12.03

    Article  PubMed  PubMed Central  Google Scholar 

  23. O’Toole A, Korzenik J (2014) Environmental triggers for IBD. Curr Gastroenterol Rep 16:396. https://doi.org/10.1007/s11894-014-0396-y

    Article  PubMed  Google Scholar 

  24. van der Sloot KWJ, Amini M, Peters V, Dijkstra G, Alizadeh BZ (2017) Inflammatory bowel diseases: review of known environmental protective and risk factors involved. Inflamm Bowel Dis 23:1499–1509. https://doi.org/10.1097/MIB.0000000000001217

    Article  PubMed  Google Scholar 

  25. Martens FK, Tonk EC, Kers JG, Janssens AC (2016) Small improvement in the area under the receiver operating characteristic curve indicated small changes in predicted risks. J Clin Epidemiol 79:159–164. https://doi.org/10.1016/j.jclinepi.2016.07.002

    Article  PubMed  Google Scholar 

  26. Uhlig HH, Powrie F (2018) Translating immunology into therapeutic concepts for inflammatory bowel disease. Annu Rev Immunol 36:755–781. https://doi.org/10.1146/annurev-immunol-042617-053055

    Article  CAS  PubMed  Google Scholar 

  27. Rogler G (2017) Resolution of inflammation in inflammatory bowel disease. Lancet Gastroenterol Hepatol 2:521–530. https://doi.org/10.1016/S2468-1253(17)30031-6

    Article  PubMed  Google Scholar 

  28. Rogler G (2013) The history and philosophy of inflammatory bowel disease. Dig Dis 31:270–277. https://doi.org/10.1159/000354676

    Article  PubMed  Google Scholar 

  29. Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM (2018) Common disease is more complex than implied by the Core gene Omnigenic model. Cell 173:1573–1580. https://doi.org/10.1016/j.cell.2018.05.051

    Article  CAS  PubMed  Google Scholar 

  30. Abraham G, Rohmer A, Tye-Din JA, Inouye M (2015) Genomic prediction of celiac disease targeting HLA-positive individuals. Genome Med 7:72. https://doi.org/10.1186/s13073-015-0196-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Choung RS et al (2016) Serologic microbial associated markers can predict Crohn’s disease behaviour years before disease diagnosis. Aliment Pharmacol Ther 43:1300–1310. https://doi.org/10.1111/apt.13641

    Article  CAS  PubMed  Google Scholar 

  32. Kennedy NA et al (2015) Clinical utility and diagnostic accuracy of faecal calprotectin for IBD at first presentation to gastroenterology services in adults aged 16-50 years. J Crohns Colitis 9:41–49. https://doi.org/10.1016/j.crohns.2014.07.005

    Article  PubMed  Google Scholar 

  33. Dzau VJ, Ginsburg GS, Van Nuys K, Agus D, Goldman D (2015) Aligning incentives to fulfil the promise of personalised medicine. Lancet 385:2118–2119. https://doi.org/10.1016/S0140-6736(15)60722-X

    Article  PubMed  PubMed Central  Google Scholar 

  34. Hollands GJ et al (2016) The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. BMJ 352:i1102. https://doi.org/10.1136/bmj.i1102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Essers JB et al (2009) Established genetic risk factors do not distinguish early and later onset Crohn’s disease. Inflamm Bowel Dis 15:1508–1514. https://doi.org/10.1002/ibd.20922

    Article  PubMed  Google Scholar 

  36. Ananthakrishnan AN et al (2014) 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. https://doi.org/10.1038/ajg.2013.464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cleynen I et al (2016) Inherited determinants of Crohn’s disease and ulcerative colitis phenotypes: a genetic association study. Lancet 387:156–167. https://doi.org/10.1016/S0140-6736(15)00465-1

    Article  PubMed  PubMed Central  Google Scholar 

  38. Li D et al (2018) Late-onset Crohn’s disease is a subgroup distinct in genetic and Behavioral risk factors with UC-like characteristics. Inflamm Bowel Dis 24:2413–2422. https://doi.org/10.1093/ibd/izy148

    Article  PubMed  PubMed Central  Google Scholar 

  39. Imhann F et al (2018) Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67:108–119. https://doi.org/10.1136/gutjnl-2016-312135

    Article  CAS  PubMed  Google Scholar 

  40. Kugathasan S et al (2017) Prediction of complicated disease course for children newly diagnosed with Crohn’s disease: a multicentre inception cohort study. Lancet 389:1710–1718. https://doi.org/10.1016/S0140-6736(17)30317-3

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lee JC et al (2017) Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn’s disease. Nat Genet 49:262–268. https://doi.org/10.1038/ng.3755

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Furey TS, Sethupathy P, Sheikh SZ (2019) Redefining the IBDs using genome-scale molecular phenotyping. Nat Rev Gastroenterol Hepatol 16:296–311. https://doi.org/10.1038/s41575-019-0118-x

    Article  PubMed  PubMed Central  Google Scholar 

  43. Baert F, Caprilli R, Angelucci E (2007) Medical therapy for Crohn’s disease: top-down or step-up? Dig Dis 25:260–266. https://doi.org/10.1159/000103897

    Article  PubMed  Google Scholar 

  44. Baumgart DC, Sandborn WJ (2012) Crohn’s disease. Lancet 380:1590–1605. https://doi.org/10.1016/S0140-6736(12)60026-9

    Article  PubMed  Google Scholar 

  45. Paramsothy S, Rosenstein AK, Mehandru S, Colombel JF (2018) The current state of the art for biological therapies and new small molecules in inflammatory bowel disease. Mucosal Immunol 11:1558–1570. https://doi.org/10.1038/s41385-018-0050-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Peyrin-Biroulet L et al (2015) Selecting therapeutic targets in inflammatory bowel disease (STRIDE): determining therapeutic goals for treat-to-target. Am J Gastroenterol 110:1324–1338. https://doi.org/10.1038/ajg.2015.233

    Article  CAS  PubMed  Google Scholar 

  47. Barber GE et al (2016) Genetic markers predict primary non-response and durable response to anti-TNF biologic therapies in Crohn’s disease. Am J Gastroenterol 111:1816–1822. https://doi.org/10.1038/ajg.2016.408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Burke KE et al (2018) Genetic markers predict primary nonresponse and durable response to anti-tumor necrosis factor therapy in ulcerative colitis. Inflamm Bowel Dis 24:1840–1848. https://doi.org/10.1093/ibd/izy083

    Article  PubMed  PubMed Central  Google Scholar 

  49. D’Haens GR (2010) Top-down therapy for IBD: rationale and requisite evidence. Nat Rev Gastroenterol Hepatol 7:86–92. https://doi.org/10.1038/nrgastro.2009.222

    Article  PubMed  Google Scholar 

  50. Colombel JF et al (2010) Infliximab, azathioprine, or combination therapy for Crohn’s disease. N Engl J Med 362:1383–1395. https://doi.org/10.1056/NEJMoa0904492

    Article  CAS  PubMed  Google Scholar 

  51. Gibson G (2019) Going to the negative: genomics for optimized medical prescription. Nat Rev Genet 20:1–2. https://doi.org/10.1038/s41576-018-0061-7

    Article  CAS  PubMed  Google Scholar 

  52. Cholesterol Treatment Trialists’ (CTT) Collaborators et al (2012) The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 380:581–590. https://doi.org/10.1016/S0140-6736(12)60367-5

    Article  CAS  Google Scholar 

  53. Natarajan P et al (2017) Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135:2091–2101. https://doi.org/10.1161/CIRCULATIONAHA.116.024436

    Article  PubMed  PubMed Central  Google Scholar 

  54. Bebb JR, Scott BB (2004) How effective are the usual treatments for Crohn’s disease? Aliment Pharmacol Ther 20:151–159. https://doi.org/10.1111/j.1365-2036.2004.02019.x

    Article  CAS  PubMed  Google Scholar 

  55. Ford AC et al (2011) Efficacy of biological therapies in inflammatory bowel disease: systematic review and meta-analysis. Am J Gastroenterol 106:644–659, quiz 660. https://doi.org/10.1038/ajg.2011.73

    Article  CAS  PubMed  Google Scholar 

  56. Cottone M, Criscuoli V (2011) Infliximab to treat Crohn’s disease: an update. Clin Exp Gastroenterol 4:227–238. https://doi.org/10.2147/CEG.S6440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ventham NT et al (2016) Integrative epigenome-wide analysis demonstrates that DNA methylation may mediate genetic risk in inflammatory bowel disease. Nat Commun 7:13507. https://doi.org/10.1038/ncomms13507

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Howell KJ et al (2018) DNA methylation and transcription patterns in intestinal epithelial cells from Pediatric patients with inflammatory bowel diseases differentiate disease subtypes and associate with outcome. Gastroenterology 154:585–598. https://doi.org/10.1053/j.gastro.2017.10.007

    Article  CAS  PubMed  Google Scholar 

  59. Lee JC et al (2011) Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis. J Clin Invest 121:4170–4179. https://doi.org/10.1172/JCI59255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Haberman Y et al (2019) Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response. Nat Commun 10:38. https://doi.org/10.1038/s41467-018-07841-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. West NR et al (2017) Oncostatin M drives intestinal inflammation and predicts response to tumor necrosis factor-neutralizing therapy in patients with inflammatory bowel disease. Nat Med 23:579–589. https://doi.org/10.1038/nm.4307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gaujoux R et al (2019) Cell-centred meta-analysis reveals baseline predictors of anti-TNFalpha non-response in biopsy and blood of patients with IBD. Gut 68:604–614. https://doi.org/10.1136/gutjnl-2017-315494

    Article  CAS  PubMed  Google Scholar 

  63. Arijs I, Cleynen I (2017) RISK stratification in paediatric Crohn’s disease. Lancet 389:1672–1674. https://doi.org/10.1016/S0140-6736(17)30634-7

    Article  PubMed  Google Scholar 

  64. Gibson G, Powell JE, Marigorta UM (2015) Expression quantitative trait locus analysis for translational medicine. Genome Med 7:60. https://doi.org/10.1186/s13073-015-0186-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Somineni HK et al (2019) Blood-derived DNA methylation signatures of Crohn’s disease and severity of intestinal inflammation. Gastroenterology 56:2254–2265.e3. https://doi.org/10.1053/j.gastro.2019.01.270

    Article  CAS  Google Scholar 

  66. Mo A et al (2018) Disease-specific regulation of gene expression in a comparative analysis of juvenile idiopathic arthritis and inflammatory bowel disease. Genome Med 10:48. https://doi.org/10.1186/s13073-018-0558-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Urko M. Marigorta .

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Marigorta, U.M. (2019). Genetic Risk Prediction in IBD. In: Hedin, C., Rioux, J., D'Amato, M. (eds) Molecular Genetics of Inflammatory Bowel Disease. Springer, Cham. https://doi.org/10.1007/978-3-030-28703-0_7

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