Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110573
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Type: Journal article
Title: Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method
Author: Chen, G.
Lee, S.
Montgomery, G.
Wray, N.
Visscher, P.
Gearry, R.
Lawrance, I.
Andrews, J.
Bampton, P.
Mahy, G.
Bell, S.
Walsh, A.
Connor, S.
Sparrow, M.
Bowdler, L.
Simms, L.
Krishnaprasad, K.
Radford-Smith, G.
Moser, G.
Citation: BMC Medical Genetics, 2017; 18(1):94-1-94-11
Publisher: BioMed Central Ltd.
Issue Date: 2017
ISSN: 1471-2350
1471-2350
Statement of
Responsibility: 
Guo-Bo Chen, Sang Hong Lee, Grant W. Montgomery, Naomi R. Wray, Peter M. Visscher, Richard B. Gearry, Ian C. Lawrance, Jane M. Andrews, Peter Bampton, Gillian Mahy, Sally Bell, Alissa Walsh, Susan Connor, Miles Sparrow, Lisa M. Bowdler, Lisa A. Simms, Krupa Krishnaprasad, the International IBD Genetics Consortium, Graham L. Radford-Smith, and Gerhard Moserk
Abstract: 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.
Keywords: Inflammatory bowel disease; Crohn’s disease; ulcerative colitis; case-control study, risk score, SNP array; complex trait
Rights: © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
RMID: 0030074883
DOI: 10.1186/s12881-017-0451-2
Grant ID: http://purl.org/au-research/grants/nhmrc/1080157
http://purl.org/au-research/grants/nhmrc/1028569
http://purl.org/au-research/grants/nhmrc/1078399
http://purl.org/au-research/grants/nhmrc/1011506
http://purl.org/au-research/grants/arc/DP160102126
http://purl.org/au-research/grants/arc/FT160100229
Appears in Collections:Medicine publications

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