Reference: Yu MK, et al. (2016) Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst 2(2):77-88

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Abstract


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.

Reference Type
Journal Article
Authors
Yu MK, Kramer M, Dutkowski J, Srivas R, Licon K, Kreisberg J, Ng CT, Krogan N, Sharan R, Ideker T
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Gene Ontology Annotations


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Gene/Complex Qualifier Gene Ontology Term Aspect Annotation Extension Evidence Method Source Assigned On Reference

Phenotype Annotations


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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

Disease Annotations


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Gene Disease Ontology Term Qualifier Evidence Method Source Assigned On Reference

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Post-translational Modifications


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Interactor Interactor Allele Assay Annotation Action Phenotype SGA score P-value Source Reference

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Gene Species Gene ID Strain background Direction Details Source Reference