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June 30th, 2025
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University of Bonn
bioinformatics
biorxiv

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

Billmann, M.Open in Google Scholar•Costanzo, M.Open in Google Scholar•Rahman, M.Open in Google Scholar•Chan, K.Open in Google Scholar•Tong, A.Open in Google Scholar•Ward, H. N.Open in Google Scholar•Hassan, A. Z.Open in Google Scholar•Zhang, X.Open in Google Scholar•Brown, K. R.Open in Google Scholar•Rohde, T.Open in Google Scholaret al.

Genetic interaction (GI) networks in model organisms have revealed how combinations of genome variants can impact phenotypes and underscored the value of GI maps for functional genomics. 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 isogenic human cell lines. We found surprising systematic variation unrelated to genetic interactions in CRISPR screen data, including both genomically linked effects and functionally coherent covariation. Leveraging ~40 control screens and half a billion differential fitness effect measurements, we developed a CRISPR screen data processing and normalization pipeline to correct these artifacts and measure accurate, quantitative GIs. Finally, we comprehensively characterized GI reproducibility by recording 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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