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

@article{Wei2013LargeSS,
  title={Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease.},
  author={Zhi Wei and Wei Wang and Jonathan Bradfield and Jin Li and Christopher Cardinale and Edward C. Frackelton and Cecilia Kim and Frank D. Mentch and Kristel van Steen and Peter M. Visscher and Robert N Baldassano and Hakon Hakonarson},
  journal={American journal of human genetics},
  year={2013},
  volume={92 6},
  pages={1008-12}
}
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… CONTINUE READING

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