Genome-scale analysis of interactions between genetic perturbations and natural variation

Joseph J. Hale, Takeshi Matsui, Ilan Goldstein, Martin N. Mullis, Kevin R. Roy, Christopher Ne Ville, Darach Miller, Charley Wang, Trevor Reynolds, Lars M. Steinmetz, Sasha F. Levy () and Ian M. Ehrenreich ()
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Joseph J. Hale: University of Southern California
Takeshi Matsui: SLAC National Accelerator Laboratory
Ilan Goldstein: University of Southern California
Martin N. Mullis: University of Southern California
Kevin R. Roy: Stanford University
Christopher Ne Ville: University of Southern California
Darach Miller: SLAC National Accelerator Laboratory
Charley Wang: University of Southern California
Trevor Reynolds: University of Southern California
Lars M. Steinmetz: Stanford University
Sasha F. Levy: SLAC National Accelerator Laboratory
Ian M. Ehrenreich: University of Southern California

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual’s response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Date: 2024
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DOI: 10.1038/s41467-024-48626-1

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