Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease

International Inflammatory Bowel Disease Genetics Consortium, Ian C. Lawrance

    Research output: Contribution to journalArticle

    80 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1008-1012
    Number of pages5
    JournalAmerican Journal of Human Genetics
    Volume92
    Issue number6
    DOIs
    Publication statusPublished - 6 Jun 2013

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