The fundamental goal of genetics is to understand the functional effect of DNA sequence variations on a wide range of phenotypes, from basic biology to genetic diseases. Broadly, there are two major strategies to approach this goal: the first one is to find natural genetic variants underlying the trait of interest through linkage or association studies; the other is experimentally introducing genetic perturbations and assaying the effects of the perturbations in a high-throughput manner.

In this dissertation, both approaches were employed to understand the effect of genetic variants. Following the first approach, we used linkage analysis to find the genetic basis of mutation rate variation in yeast. We developed a high-throughput fluctuation assay to enable quantification of spontaneous mutation rate in hundreds of yeast for the first time. We measured the mutation rate of 1040 yeast segregants from a cross between two diverge yeast strains, BY and RM. Combined with the genotype data, we performed linkage analysis in the segregants and identified four quantitative trait loci (QTLs) that contribute to the mutation rate variation in the cross. We fine-mapped two QTLs to the underlying causal genes, RAD5 and MKT1, that contribute to mutation rate variation.

For the second approach, we developed three different systems to study the effect of natural variants using the genetic engineering tool CRISPR-Cas9. We constructed ten different CRISPR-Cas9 base editor systems for yeast, aiming to expand the targetable regions and the base converting types by using different base editors. We measured the efficiency of ten base editors in yeast from amplicon sequencing results at ten different sites along the genome and found one base editor that recognized the protospacer adjacent motif (PAM) site NGA with high efficiency. In addition to CRISPR base editor, we constructed a precise genome editing system with trackable genome integrated barcode using CRISPR-Cas9 with gRNA and donor DNA pairs. The integrated barcode enables precise tracking of edited strains with sequencing, ensuring robust downstream phenotyping. We also worked toward developing a CRISPR-directed mitotic recombination mapping panel in human cell lines to narrow down mapped out regions to causal genes by targeted creation of DNA double strand breaks along the chromosome.

Variation in nutrient availability between environments has led to the evolution of diverse metabolic pathways. These pathways differ across species, but are expected to be similar within a species. To identify large genetic differences in pathways within a single species, We performed a genome-wide scan for higher-order genetic interactions in segregants from 16 highly diverse S. cerevisiae crosses grown in 38 different conditions. We observed a large effect genetic interaction for growth in galactose among three loci in crosses involving the soil strain CBS2888. We used precisely engineered alleles to show that this genetic interac- tion arises from variation in the genes GAL2, GAL1/10/7, and PGM1 from the galactose metabolic pathway. The CBS2888 alleles of these galactose genes were highly diverged from the reference. We hereafter refer to the divergent galactose alleles found in CBS2888 as the alternative alleles, and the alleles found in other strains as the reference alleles. Strains with alternative alleles are found primarily in galactose-rich dairy environments, and they grow faster in galactose, but slower in glucose, revealing a tradeoff, on which population genetics analyses suggest that balancing selection could have acted on. Our results show that bal- ancing selection can preserve, functionally distinct states of a multi-locus genetic network, providing a general mechanism for maintenance of complex, interacting genetic variation at co-adapted alleles.