title: Smoking Gun or Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants.
abstract: Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.
authors: Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.|Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.|Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
authors: Sarah A
authors: SA
authors: Gagliano
authors: Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
authors: Reena
authors: R
authors: Ravji
authors: William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
authors: Michael R
authors: MR
authors: Barnes
authors: Department of Medical &Molecular Genetics, King's College London, Guy's Hospital, London, UK.
authors: Michael E
authors: ME
authors: Weale
authors: Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.|Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.|Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.|Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
authors: Jo
authors: J
authors: Knight
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D030541:Databases, Genetic; D055106:Genome-Wide Association Study; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D009154:Mutation / Q000235:genetics*; D012372:ROC Curve; D012307:Risk Factors; D012559:Schizophrenia / Q000235:genetics; D013223:Statistics as Topic