title: Computational assessment of feature combinations for pathogenic variant prediction.
abstract: BACKGROUND
  Although several methods have been proposed for predicting the effects of genetic variants and their role in disease, it is still a challenge to identify and prioritize pathogenic variants within sequencing studies.
  
  
  METHODS
  Here, we compare different variant and gene-specific features as well as existing methods and investigate their best combination to explore potential performance gains.
  
  
  RESULTS
  We found that combining the number of "biological process" Gene Ontology annotations of a gene with the methods PON-P2, and PROVEAN significantly improves prediction of pathogenic variants, outperforming all individual methods. A comprehensive analysis of the Gene Ontology feature suggests that it is not a variant-dependent annotation bias but reflects the multifunctional nature of disease genes. Furthermore, we identified a set of difficult variants where different prediction methods fail.
  
  
  CONCLUSIONS
  Existing pathogenicity prediction methods can be further improved.
authors.affiliation: Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)Viale Druso 139100BolzanoItaly; Affiliated Institute of the University of LübeckLübeckGermany.
authors.forename: Eva
authors.initials: E
authors.lastname: König
authors.affiliation: Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)Viale Druso 139100BolzanoItaly; Affiliated Institute of the University of LübeckLübeckGermany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Rainer
authors.affiliation: Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)Viale Druso 139100BolzanoItaly; Affiliated Institute of the University of LübeckLübeckGermany.
authors.forename: Francisco S
authors.initials: FS
authors.lastname: Domingues
keywords: Feature analysis; GO annotation bias; feature combination; gene features; pathogenic variant prediction; variant features