Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method.

Autor:	Chen GB; Queensland Brain Institute, The University of Queensland, Brisbane, Australia., Lee SH; Queensland Brain Institute, The University of Queensland, Brisbane, Australia.; School of Environmental and Rural Science, The University of New England, Armidale, Australia., Montgomery GW; Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia., Wray NR; Queensland Brain Institute, The University of Queensland, Brisbane, Australia., Visscher PM; Queensland Brain Institute, The University of Queensland, Brisbane, Australia.; University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, Brisbane, Australia., Gearry RB; Department of Medicine, University of Otago, Christchurch, New Zealand.; Department of Gastroenterology, Christchurch Hospital, Christchurch, New Zealand., Lawrance IC; Harry Perkins Institute of Medical Research, School of Medicine and Pharmacology, University of Western Australia, Murdoch, Australia.; Centre for Inflammatory Bowel Diseases, Saint John of God Hospital, Subiaco, Australia., Andrews JM; Inflammatory Bowel Disease Service, Department of Gastroenterology and Hepatology, Royal Adelaide Hospital, School of Medicine, University of Adelaide, Adelaide, Australia., Bampton P; Department of Gastroenterology and Hepatology, Flinders Medical Centre, Adelaide, Australia., Mahy G; Department of Gastroenterology, Townsville Hospital, Townsville, Australia., Bell S; Department of Gastroenterology, St Vincent's Hospital, Melbourne, Australia., Walsh A; Department of Gastroenterology and Hepatology, St Vincent's Hospital, Sydney, Australia., Connor S; Department of Gastroenterology and Hepatology, Liverpool Hospital, Sydney, Australia.; University of NSW, Sydney, Australia., Sparrow M; Department of Gastroenterology, Alfred Health, Melbourne, Australia., Bowdler LM; Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia., Simms LA; Inflammatory Bowel Disease Research Group, Immunology Division, QIMR Berghofer Medical Research Institute, Brisbane, Australia., Krishnaprasad K; Inflammatory Bowel Disease Research Group, Immunology Division, QIMR Berghofer Medical Research Institute, Brisbane, Australia., Radford-Smith GL; School of Medicine, The University of Queensland, Brisbane, Australia.; Inflammatory Bowel Disease Research Group, Immunology Division, QIMR Berghofer Medical Research Institute, Brisbane, Australia.; Department of Gastroenterology, Royal Brisbane and Women's Hospital, Brisbane, Australia., Moser G; Queensland Brain Institute, The University of Queensland, Brisbane, Australia. gerhard.moser@bigpond.com.
Jazyk:	angličtina
Zdroj:	BMC medical genetics [BMC Med Genet] 2017 Aug 29; Vol. 18 (1), pp. 94. Date of Electronic Publication: 2017 Aug 29.
DOI:	10.1186/s12881-017-0451-2
Abstrakt:	Background: Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn's disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach. Methods: We performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation. Results: On average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis. Conclusions: Our analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.
Databáze:	MEDLINE
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