Understanding the connection between genotypes and phenotypes using linkage analysis and CRISPR genetic engineering

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