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Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
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2013 (English)In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 92, no 6, p. 1008-12Article in journal (Refereed) Published
Abstract [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.

Place, publisher, year, edition, pages
University of Chicago Press, 2013. Vol. 92, no 6, p. 1008-12
National Category
Medical and Health Sciences Gastroenterology and Hepatology
Identifiers
URN: urn:nbn:se:oru:diva-66638DOI: 10.1016/j.ajhg.2013.05.002ISI: 000320415300019PubMedID: 23731541Scopus ID: 2-s2.0-84878829383OAI: oai:DiVA.org:oru-66638DiVA, id: diva2:1198720
Note

Funding Agency:

NIGMS NIH HHS, P01 GM099568

Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2018-04-24Bibliographically approved

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Halfvarson, Jonas

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