Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

By Michael Ku Yu, Michael Kramer, Janusz Dutkowski, R. Srivas, Katherine Licon, Jason F. Kreisberg, C. Ng, Nevan J. Krogan, Roded Sharan, Trey Ideker. 2016

77 citations recorded by the source corpus.

Summary Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology’s hierarchical structure, we organize genotype data into an “ontotype,” that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Predicting multicellular function through multi-layer tissue networks (2017, background): “• Finally,thehierarchyisanaturalwaytorepresentandmodelbiological systems spanning many different biological scales (Carvunis and Ideker, 2014; Greene et al. , 2015; Yu et al. , 2016).”
Empowering Biomedical Discovery with AI Agents (2024, unknown): “These models have advanced modeling of proteins [22–26], genes [27], phenotypes [28], clinical outcomes [29–31], and chemical compounds [32, 33] through mining of biomedical data.”
Using deep learning to model the hierarchical structure and function of a cell (2018, methodology): “For example, our earlier approach 13 used hierarchical knowledge of subsystems to create new features based on the number of gene disruptions in a subsystem, but these features were predetermined”