Genetic directional dependencies

Single genes are rarely causative for complex cellular phenotypes. Instead, complex interactions of two or more genes individually contributing to a phenotype subsume their effects until a stage of phenotypic robustness (homeostasis) is achieved. These interactions can occur by additive effects triggered by the perturbation of multiple genes. In this context, genetic interactions (GI) are defined as a combination of two or more genes whose contribution to a phenotype cannot be explained by either gene’s single effect. Within GIs, genetic directional dependencies make up a subclass of interactions. In this context, the term directionality refers to combinatorial phenotypes in which the order of mutations directs the phenotype.

In order to screen for directional interaction on a larger scale, we used the recently introduced 3C CRISPR/Cas technology to perform a time dependent inactivation screen of gene pairs. We created a library for cancer druggable gene pairs and conducted a time dependent (14 and 28 days) NGS experiment. To computationally infer directionals from the experimental data, we propose a pipeline including a learning capable algorithm. The pipeline performs i) quality control steps, ii) replicate handling if applicable, iii) normalization and iv) scoring of the directional potential per gene pair. Our algorithm calculates and considers characteristics including positional effects, dual-edit over anchor phenotype, the single gene effects (main effects), and a gene’s essentiality.