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

Wei, Zhi (author)
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
Wang, Wei (author)
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
Bradfield, Jonathan (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia PA, USA
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Li, Jin (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia PA, USA
Cardinale, Christopher (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia PA, USA
Frackelton, Edward (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia PA, USA
Kim, Cecilia (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia PA, USA
Mentch, Frank (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia PA, USA
Van Steen, Kristel (author)
Systems andModeling Unit, Montefiore Institute, University of Liege, Liege, Belgium; Bioinformatics and Modeling, GIGA-R, University of Liege, Liege, Belgium
Visscher, Peter M. (author)
Queensland Brain Institute, The University of Queensland, Brisbane QLD, Australia; University of Queensland Diamantina Institute, The University of Queensland, Brisbane QLD, Australia
Baldassano, Robert N. (author)
Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia PA, USA
Hakonarson, Hakon (author)
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia PA, USA
IIBDGC the International IBD Genetics, Consortium (author)
Halfvarson, Jonas, 1970- (contributor)
Örebro universitet,Institutionen för medicinska vetenskaper,Department of Medicine, Örebro University Hospital, Örebro, Sweden
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 (creator_code:org_t)
University of Chicago Press, 2013
2013
English.
In: American Journal of Human Genetics. - : University of Chicago Press. - 0002-9297 .- 1537-6605. ; 92:6, s. 1008-12
Related links:
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Gastroenterologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Gastroenterology and Hepatology (hsv//eng)

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