title: Crohn disease risk prediction-Best practices and pitfalls with exome data.
abstract: The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.
authors.affiliation: Institute of Structural and Molecular Biology, University College London, London, United Kingdom.
authors.forename: Manuel
authors.initials: M
authors.lastname: Giollo
authors.affiliation: Institute of Structural and Molecular Biology, University College London, London, United Kingdom.
authors.forename: David T
authors.initials: DT
authors.lastname: Jones
authors.affiliation: Department of Biomedical Sciences, University of Padova, Padova, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Carraro
authors.affiliation: Department of Woman and Child Health, University of Padova, Padova, Italy.
authors.forename: Emanuela
authors.initials: E
authors.lastname: Leonardi
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Carlo
authors.initials: C
authors.lastname: Ferrari
authors.affiliation: Department of Woman and Child Health, University of Padova, Padova, Italy.|CNR Institute of Neuroscience, Padova, Italy.
authors.forename: Silvio C E
authors.initials: SCE
authors.lastname: Tosatto
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003424:Crohn Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D017410:Practice Guidelines as Topic; D000073359:Exome Sequencing
keywords: Crohn disease; SNV evaluation; disease risk prediction; exome data; genetic analysis; linear models; machine learning; methods comparison; next-generation sequencing; variants prioritization