CAGI4 Crohn's exome challenge: Marker SNP versus exome variant models for assigning risk of Crohn disease

Hum Mutat. 2017 Sep;38(9):1225-1234. doi: 10.1002/humu.23256. Epub 2017 Jun 28.


Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn's Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of genome wide association studies marker SNPs. We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information. We achieved the best performance using Naïve Bayes and with a consensus machine learning method, obtaining an area under the curve of 0.72, larger than other methods used in CAGI4. We also developed a model that incorporated the contribution from rare missense variants in the exome data, but this performed less well. Future progress is expected to come from the use of whole genome data rather than exomes.

Keywords: CAGI; Crohn disease; GWAS data; Naïve Bayes; complex disease risk model; exome sequencing; machine learning model.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Area Under Curve
  • Crohn Disease / genetics*
  • Genetic Markers
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study
  • Humans
  • Machine Learning
  • Phenotype
  • Polymorphism, Single Nucleotide*
  • Whole Exome Sequencing / methods*


  • Genetic Markers