Human Genetics

, Volume 133, Issue 5, pp 547–558 | Cite as

Gene–gene and gene–environment interactions in ulcerative colitis

  • Ming-Hsi Wang
  • Claudio Fiocchi
  • Xiaofeng Zhu
  • Stephan Ripke
  • M. Ilyas Kamboh
  • Nancy Rebert
  • Richard H. Duerr
  • Jean-Paul Achkar
Original Investigation

Abstract

Genome-wide association studies (GWAS) have identified at least 133 ulcerative colitis (UC) associated loci. The role of genetic factors in clinical practice is not clearly defined. The relevance of genetic variants to disease pathogenesis is still uncertain because of not characterized gene–gene and gene–environment interactions. We examined the predictive value of combining the 133 UC risk loci with genetic interactions in an ongoing inflammatory bowel disease (IBD) GWAS. The Wellcome Trust Case–Control Consortium (WTCCC) IBD GWAS was used as a replication cohort. We applied logic regression (LR), a novel adaptive regression methodology, to search for high-order interactions. Exploratory genotype correlations with UC sub-phenotypes [extent of disease, need of surgery, age of onset, extra-intestinal manifestations and primary sclerosing cholangitis (PSC)] were conducted. The combination of 133 UC loci yielded good UC risk predictability [area under the curve (AUC) of 0.86]. A higher cumulative allele score predicted higher UC risk. Through LR, several lines of evidence for genetic interactions were identified and successfully replicated in the WTCCC cohort. The genetic interactions combined with the gene-smoking interaction significantly improved predictability in the model (AUC, from 0.86 to 0.89, P = 3.26E−05). Explained UC variance increased from 37 to 42 % after adding the interaction terms. A within case analysis found suggested genetic association with PSC. Our study demonstrates that the LR methodology allows the identification and replication of high-order genetic interactions in UC GWAS datasets. UC risk can be predicted by a 133 loci and improved by adding gene–gene and gene–environment interactions.

Supplementary material

439_2013_1395_MOESM1_ESM.tif (89 kb)
Supplementary Figure 2A Tree1 and its genetic interactions illustrated by further pairwise stratified analyses (TIFF 89 kb)
439_2013_1395_MOESM2_ESM.tif (92 kb)
Supplementary Figure 2B Tree2 and its genetic interactions illustrated by further stratified analyses (TIFF 91 kb)
439_2013_1395_MOESM3_ESM.tif (92 kb)
Supplementary Figure 2C Tree3 and its genetic interactions illustrated by further pairwise stratified analyses (TIFF 91 kb)
439_2013_1395_MOESM4_ESM.tif (151 kb)
Supplementary Figure 2D Tree4 and its genetic interactions illustrated by further stratified analyses (TIFF 150 kb)
439_2013_1395_MOESM5_ESM.tif (77 kb)
Supplementary Figure 2E Tree5 and its gene–gene and gene-smoking interactions illustrated by further stratified analyses (TIFF 77 kb)
439_2013_1395_MOESM6_ESM.doc (208 kb)
Supplementary material 6 (DOC 207 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ming-Hsi Wang
    • 1
    • 2
  • Claudio Fiocchi
    • 1
    • 2
  • Xiaofeng Zhu
    • 3
  • Stephan Ripke
    • 4
    • 5
  • M. Ilyas Kamboh
    • 6
  • Nancy Rebert
    • 2
  • Richard H. Duerr
    • 6
    • 7
  • Jean-Paul Achkar
    • 1
    • 2
  1. 1.Department of Gastroenterology and Hepatology, Digestive Disease InstituteCleveland ClinicClevelandUSA
  2. 2.Department of Pathobiology, Lerner Research InstituteCleveland ClinicClevelandUSA
  3. 3.Department of Epidemiology and BiostatisticsCase Western Reserve UniversityClevelandUSA
  4. 4.Analytic and Translational Genetics UnitMassachusetts General HospitalBostonUSA
  5. 5.Stanley Center for Psychiatric ResearchBroad Institute of Harvard and MITCambridgeUSA
  6. 6.Department of Human GeneticsUniversity of Pittsburgh, Graduate School of Public HealthPittsburghUSA
  7. 7.Division of Gastroenterology, Hepatology, and Nutrition, Department of MedicineUniversity of Pittsburgh School of MedicinePittsburghPA

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