Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

Michael Ku Yu; Michael Kramer; Janusz Dutkowski; Rohith Srivas; Katherine Licon; Jason Kreisberg; Cherie T Ng; Nevan Krogan; Roded Sharan; Trey Ideker

doi:10.1016/j.cels.2016.02.003

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

Cell Syst. 2016 Feb 24;2(2):77-88. doi: 10.1016/j.cels.2016.02.003.

Authors

Affiliations

¹ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA 92093, USA; Department of Medicine, University of California San Diego, La Jolla CA 92093, USA.
² Department of Medicine, University of California San Diego, La Jolla CA 92093, USA; Biomedical Sciences Program, University of California San Diego, La Jolla CA 92093, USA.
³ Department of Medicine, University of California San Diego, La Jolla CA 92093, USA; Data4Cure, La Jolla, CA 92037, USA.
⁴ Department of Medicine, University of California San Diego, La Jolla CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla CA 92093, USA.
⁵ Department of Medicine, University of California San Diego, La Jolla CA 92093, USA.
⁶ aTyr Pharmaceuticals, San Diego, CA 92121, USA.
⁷ Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco 94143, USA.
⁸ Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel.

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.

Abstract

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