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Yang, Fan; Sun, Song; Tan, Guihong; Costanzo, Michael; Hill, David E.; Vidal, Marc; Andrews, Brenda J.; Boone, Charles; Roth, Frederick P. 2018-05-25 To 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.
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Kuzmin, Elena; VanderSluis, Benjamin; Wang, Wen; Tan, Guihong; Deshpande, Raamesh; Chen, Yiqun; Usaj, Matej; Balint, Attila; Mattiazzi Usaj, Mojca; van Leeuwen, Jolanda; Koch, Elizabeth N.; Pons, Carles; Dagilis, Andrius Jonas; Pryszlak, Michael; Wang, Jason Zi Yang; Hanchard, Julia; Riggi, Margot; Xu, Kaicong; Heydari, Hamed; San Luis, Bryan-Joseph; Shuteriqi, Ermira; Zhu, Hongwei; Van Dyk, Nydia; Sharifpoor, Sara; Costanzo, Michael; Loewith, Robbie; Caudy, Amy; Bolnick, Daniel; Brown, Grant W.; Andrews, Brenda J.; Boone, Charles; Myers, Chad L. 2019-03-30 To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.
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Kuzmin, Elena; VanderSluis, Benjamin; Nguyen Ba, Alex N.; Wang, Wen; Koch, Elizabeth N.; Usaj, Matej; Khmelinskii, Anton; Mattiazzi Usaj, Mojca; van Leeuwen, Jolanda; Kraus, Oren; Tresenrider, Amy; Pryszlak, Michael; Hu, Ming-Che; Varriano, Brenda; Costanzo, Michael; Knop, Michael; Moses, Alan; L. Myers, Chad; Andrews, Brenda J.; Boone, Charles 2020-08-05 <p>Whole-genome duplication<b> </b>has played a central role in genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated and factors that influence the retention of persisting duplicates remain poorly understood. Here, we describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping digenic interactions for a deletion mutant of each paralog and trigenic interactions for the double mutant provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs, a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors.</p>