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

Zhi Wei | Wei Wang | Jonathan Bradfield | Jin Li | Christopher Cardinale | Edward Frackelton | Cecilia Kim | Frank Mentch | Kristel Van Steen | Peter M Visscher | Robert N Baldassano | Hakon Hakonarson | International IBD Genetics Consortium
American journal of human genetics | 2013

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.

Pubmed ID: 23731541

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Associated grants

  • Agency: NIGMS NIH HHS, United States
    Id: P01 GM099568

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OMIM (tool)

RRID:SCR_006437

Online catalog of human genes and genetic disorders, for clinical features, phenotypes and genes. Collection of human genes and genetic phenotypes, focusing on relationship between phenotype and genotype. Referenced overviews in OMIM contain information on all known mendelian disorders and variety of related genes. It is updated daily, and entries contain copious links to other genetics resources.

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