title: Improving the in silico assessment of pathogenicity for compensated variants.
abstract: Understanding the functional sequelae of amino-acid replacements is of fundamental importance in medical genetics. Perhaps, the most intuitive way to assess the potential pathogenicity of a given human missense variant is by measuring the degree of evolutionary conservation of the substituted amino-acid residue, a feature that generally serves as a good proxy metric for the functional/structural importance of that residue. However, the presence of putatively compensated variants as the wild-type alleles in orthologous proteins of other mammalian species not only challenges this classical view of amino-acid essentiality but also precludes the accurate evaluation of the functional impact of this type of missense variant using currently available bioinformatic prediction tools. Compensated variants constitute at least 4% of all known missense variants causing human-inherited disease and hence represent an important potential source of error in that they are likely to be disproportionately misclassified as benign variants. The consequent under-reporting of compensated variants is exacerbated in the context of next-generation sequencing where their inappropriate exclusion constitutes an unfortunate natural consequence of the filtering and prioritization of the very large number of variants generated. Here we demonstrate the reduced performance of currently available pathogenicity prediction tools when applied to compensated variants and propose an alternative machine-learning approach to assess likely pathogenicity for this particular type of variant.
authors.affiliation: Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Population Genetics and Evolution Group, Porto, Portugal. lazevedo@ipatimup.pt.|IPATIMUP-Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal. lazevedo@ipatimup.pt.|Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal. lazevedo@ipatimup.pt.
authors.forename: Luisa
authors.initials: L
authors.lastname: Azevedo
authors.affiliation: Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK.
authors.forename: Matthew
authors.initials: M
authors.lastname: Mort
authors.affiliation: Instituto Superior de Engenharia do Porto, GECAD-ISEP Research Group, Porto, Portugal.
authors.forename: Antonio C
authors.initials: AC
authors.lastname: Costa
authors.affiliation: Department of Medical Sciences, iBiMED and IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal.
authors.forename: Raquel M
authors.initials: RM
authors.lastname: Silva
authors.affiliation: Biochemical Genetics Unit, Centro de Genética Médica Jacinto de Magalhães, Centro Hospitalar do Porto, Porto, Portugal.|Unit for Multidisciplinary Research in Biomedicine, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal.
authors.forename: Dulce
authors.initials: D
authors.lastname: Quelhas
authors.affiliation: Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Population Genetics and Evolution Group, Porto, Portugal.|IPATIMUP-Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal.|Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal.
authors.forename: Antonio
authors.initials: A
authors.lastname: Amorim
authors.affiliation: Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK.
authors.forename: David N
authors.initials: DN
authors.lastname: Cooper
mesh_terms: D000483:Alleles; D019943:Amino Acid Substitution / Q000235:genetics*; D003198:Computer Simulation; D030342:Genetic Diseases, Inborn; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D009154:Mutation / Q000235:genetics*; D060388:Support Vector Machine