Quantitative analysis of genetic interactions in human cells from genome-wide CRISPR-Cas9 screens

Maximilian Billmann; Michael Costanzo; Mahfuzur Rahman; Katherine Chan; Amy H.Y. Tong; Henry N. Ward; Arshia Zernab Hassan; Xiang Zhang; Kevin R. Brown; Thomas Rohde; Angela Helen Shaw; Catherine Ross; Jolanda S. van Leeuwen; Michael Aregger; Keith A. Lawson; Barbara Mair; Patricia Mero; Matej Ušaj; Brenda Andrews; Charles M. Boone; Jason Moffat; Chad L. Myers

doi:10.1101/2025.06.30.662330

BioinformaticsbioRxivJun 30, 2025

Preprint

Quantitative analysis of genetic interactions in human cells from genome-wide CRISPR-Cas9 screens

Maximilian Billmann iD, Michael Costanzo iD, Mahfuzur Rahman iD, Katherine Chan iD, Amy H.Y. Tong iD, Henry N. Ward iD, Arshia Zernab Hassan iD, Xiang Zhang iD, Kevin R. Brown iD, Thomas Rohde iD, Angela Helen Shaw iD, Catherine Ross, Jolanda S. van Leeuwen iD, Michael Aregger iD, Keith A. Lawson iD, Barbara Mair iD, Patricia Mero, Matej Ušaj iD, Brenda Andrews iD, Charles M. Boone iD, Jason Moffat iD, Chad L. Myers iD

Abstract

Genetic interaction (GI) networks in model organisms have revealed how combinations of genome variants can impact phenotypes. To advance efforts toward a reference human GI network, we developed the quantitative Genetic Interaction (qGI) score, a method for precise GI measurement from genome-wide CRISPR-Cas9 screens in different query mutants constructed in a single human cell line. We found surprising prevalent systematic variation unrelated to GIs in CRISPR screen data, including both genomically linked effects and functionally coherent covariation. Leveraging [~]40 control screens in wild-type cells and half a billion differential fitness effect measurements, we developed a pipeline for CRISPR screen data processing and normalization to correct these artifacts and measure accurate, quantitative GIs. We also comprehensively characterized GI reproducibility by characterizing 4 - 5 biological replicates for [~]125,000 unique gene pairs. The qGI framework enables systematic identification of human GIs and provides broadly applicable strategies for analyzing context-specific CRISPR screen data.

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