Genome-wide detection and quantification of genetic background effects using double-barcoded CRISPRi perturbations
Authors: Ilan Goldstein 1; Joseph Hale 1; Takeshi Matsui 2,3; Kevin Roy 3,4; Lars M. Steinmetz 3,4,5; Ian M. Ehrenreich 1
Affiliations: 1) University of Southern California; 2) SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA; 3) Department of Genetics, Stanford University, Stanford, CA 94305, USA; 4) Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA; 5) Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidlberg, Germany
Keywords: Genetic interactions
The phenotypic effects of mutations often depend on the genetic backgrounds in which they occur. Our goal is to move towards a fundamental understanding of the genetic and molecular mechanisms producing these background effects using the budding yeast model system. Here, we will measure the fitness effects of ~20,0000 CRISPR interference knockdowns targeting ~5,400 genes across 14 genetically diverse haploid and diploid S. cerevisiae strains. We leverage a double-barcode sequencing strategy in which one barcode denotes a strain genotype and the second barcode denotes a gRNA. The relative fitness of each genotype-gRNA combination will be measured by sequencing the double barcodes for all strains growing in a single pooled competition. By including ≥20 barcodes per gRNA and ≥3 barcodes per strain, we employ a high degree of internal replication that provides the statistical power to detect gRNAs that have different phenotypic effects across strains. With these data, we will determine the prevalence, extent, and character of genetic background effects across diverse genetic perturbations and genotypic contexts.