[en] We performed risk assessment for Crohn's disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), by using data from the International IBD Genetics Consortium's Immunochip project. This data set contains ?17,000 CD cases, ?13,000 UC cases, and ?22,000 controls from 15 European countries typed on the Immunochip. This custom chip provides a more comprehensive catalog of the most promising candidate variants by picking up the remaining common variants and certain rare variants that were missed in the first generation of GWAS. Given this unprecedented large sample size and wide variant spectrum, we employed the most recent machine-learning techniques to build optimal predictive models. Our final predictive models achieved areas under the curve (AUCs) of 0.86 and 0.83 for CD and UC, respectively, in an independent evaluation. To our knowledge, this is the best prediction performance ever reported for CD and UC to date.
Disciplines :
Genetics & genetic processes
Author, co-author :
Wei, Zhi
Wang, Wei
Bradfield, Jonathan
Li, Jin
Cardinale, Christopher
Frackelton, Edward
Kim, Cecilia
Mentch, Frank
Van Steen, Kristel ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
Visscher, Peter M.
Baldassano, Robert N.
Hakonarson, Hakon
Language :
English
Title :
Large Sample Size, Wide Variant Spectrum, and Advanced Machine-Learning Technique Boost Risk Prediction for Inflammatory Bowel Disease.
Publication date :
2013
Journal title :
American Journal of Human Genetics
ISSN :
0002-9297
eISSN :
1537-6605
Publisher :
University of Chicago Press, Chicago, United States - Illinois
Peer reviewed :
Peer Reviewed verified by ORBi
Commentary :
Copyright (c) 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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