Large Sample Size, Wide Variant Spectrum, and Advanced Machine-Learning Technique Boost Risk Prediction for Inflammatory Bowel Disease

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• 作者：Zhi Wei¹ ; ^{zhiwei04@gmail.com} ; Wei Wang¹ ; Jonathan Bradfield² ; Jin Li² ; Christopher Cardinale² ; Edward Frackelton² ; Cecilia Kim² ; Frank Mentch² ; Kristel Van Steen³ ; ⁴ ; Peter M. Visscher⁵ ; ⁶ ; Robert N. Baldassano⁷ ; Hakon Hakonarson² ; ⁷ ; ^{hakonarson@email.chop.edu} ; the International IBD Genetics Consortium 8
• 刊名：The American Journal of Human Genetics
• 出版年：2013
• 出版时间：6 June, 2013
• 年：2013
• 卷：92
• 期：6
• 页码：1008-1012
• 全文大小：616 K

文摘

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.