Genome-wide identification of genetic interactions in human cells using CRISPR/Cas9
Biochemistry
Bioinformatics and Theoretical Biology
Cell Biology
Final Report Abstract
While genome sequencing efforts provided a comprehensive list of genetic elements in the human genome, the function of those genetic elements remains to be systematically explored. Genetic interaction networks in model organisms have revealed how combinations of variants in genetic elements can affect phenotypes and highlighted the impact of reference genetic networks for understanding gene function. To build a human reference genetic network, we performed around 200 genome-wide screens using the TKOv3 library in HAP1 query cell lines carrying loss-of-function mutations in genes in diverse bioprocesses. To allow robust identification of genetic interactions from those screens, we also screened more than 30 screens in wildtype (wt) HAP1 cells. During the fellowship period, I have developed a computational pipeline to identify quantitative genetic interactions (qGI) from these data. I identified several unexpected statistical artifacts in loss-of-function screens including frequent interactions caused by variation between wt HAP1 screens and potential clonal effects of HAP1 cells harboring a loss-offunction mutation. I developed normalization steps in the qGI pipeline that, among others, utilize singular value decomposition to correct for these effects. The development of the qGI score and a proof-of-concept set of screens showing how genetic interaction analysis functionally associates genes including the previously uncharacterized gene LUR1/C12orf49 was recently published (Nat Metabolism 2020). This work also details how qGIs are structured in known functional hierarchies and physical protein-protein interactions, insights generated during the fellowship period. To systematically integrate the human reference genetic network with orthogonal omics data, we developed a computational framework called functional evaluation of experimental perturbations (FLEX). Finally, my work has guided the intelligent screening of genetic backgrounds to cover diverse biological neighborhoods leading to a reference genetic network. In summary, the work conducted throughout the fellowship period developed computational tools for the generation of a genome-wide reference genetic interaction network in human cells, which will impact the broader community’s efforts to understand the biology of human cells and how genetic variants lead to disease.
Publications
- Systematic mapping of genetic interactions for de novo fatty acid synthesis. Nature Metabolism (2020)
Aregger M, Lawson K, Billmann M, Costanzo M, Tong AHY, Chan K, Rahman M, Brown KR, Ross C, Usaj M, Nedyalkova L, Sizova O, Habsid A, Pawling J, Lin YZ, Abdouni H, Wong CJ, Weiss A, Mero P, Dennis JW, Gingras AC, Myers CL, Andrews B, Boone C & Moffat J
(See online at https://doi.org/10.1038/s42255-020-0211-z) - A method for benchmarking genetic screens reveals a predominant mitochondrial bias. Molecular Systems Biology (2021)
Rahman M, Billmann M, Ward H, Brown KR, Costanzo M, Boone C, Moffat J & Myers CL
(See online at https://doi.org/10.15252/msb.202010013)
