Chemogenomic profiling of diverse Saccharomyces cerevisiae strains using the novel CRISPR-Cas9 markerless barcoded library BarMix

Natural populations are rich reservoirs of genetic variation, and incorporating this diversity into functional genomic screens can reveal the genetic and phylogenetic determinants underlying trait differences. Genetic barcoding is a powerful tool for yeast functional genomics, enabling scalable fitness profiling and high-throughput gene-trait interaction mapping. Nevertheless, few barcoded libraries capture the natural diversity of Saccharomyces cerevisiae . Here, we introduce BarMix, a novel barcoded library of diverse S. cerevisiae strains designed and validated for high-throughput fitness profiling. Using CRISPR-Cas9, we integrate distinct markerless genetic barcodes into the genomes of 262 strains representing multiple phylogenetic clades of wild, domesticated, and admixed origins. To evaluate its utility for genotype–phenotype mapping, we quantitatively profile the pooled library by barcode-sequencing against 33 small-molecule compounds with diverse bioactivities and identify 281 chemogenomic interactions through genome-wide association analysis. Chemical fitness signatures differ between domesticated and wild clades, particularly under stresses affecting ergosterol biosynthesis, DNA alkylation, microtubule depolymerisation, TORC1 inactivation, and membrane permeability. We validate seven associated alleles through allele-swap experiments, including variants in genes that modulate multidrug export (Yrr1), tRNA-specificity (ArgRS), and small-molecule methylation (Crg1). Several alleles display variable penetrance across strains, suggesting a complex genetic basis for trait variation. Importantly, including admixture into the genome-wide association design enhances variant detection in S. cerevisiae . These results establish BarMix as a powerful platform for genotype–phenotype discovery with diverse applications in functional genomics and biotechnology, and highlight the value of incorporating natural variation into model-systems biology.