Yang, FanSun, SongTan, GuihongCostanzo, MichaelHill, DavidVidal, MarcAndrews, Brenda J.Boone, CharlesRoth, Frederick P.2017-07-242017Yang, Fan, Song Sun, Guihong Tan, Michael Costanzo, David E. Hill, Marc Vidal, Brenda J. Andrews, Charles Boone, and Frederick P. Roth. 2017. “Identifying pathogenicity of human variants via paralog-based yeast complementation.” PLoS Genetics 13 (5): e1006779. doi:10.1371/journal.pgen.1006779. http://dx.doi.org/10.1371/journal.pgen.1006779.http://nrs.harvard.edu/urn-3:HUL.InstRepos:33490787To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the number of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify pathogenic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complementation more than doubled the number of human disease genes with a yeast-based complementation assay for disease variation.en-USBiology and Life SciencesBiochemistryProteinsProtein DomainsOrganismsFungiYeastExperimental Organism SystemsModel OrganismsSaccharomyces CerevisiaeSaccharomycesYeast and Fungal ModelsMedicine and Health SciencesPathology and Laboratory MedicinePathogenesisDatabase and Informatics MethodsBioinformaticsSequence AnalysisSequence AlignmentEnzymologyEnzymesProtein KinasesPathogensImmunologyImmune System ProteinsImmune ReceptorsComplement ReceptorsCell BiologySignal TransductionJournal Article2017-07-2410.1371/journal.pgen.1006779