Genetic Dissection Of Quantitative Trait In Yeast And Discovery Of Single Feature Polymorphisms In Gene Expression Profiling Microarray Data

Identification of genes affecting complex traits is considered to be one of the most difficult and challenging tasks in genetics today. The ultimate goal of a genetic mapping experiment is to detect and localize the genetic variation responsible for the trait in interest. The primary difficulty toward unraveling genetic causal is because of the low resolution of quantitative trait locus (QTL) mapping. Traditional analytical methods yield a minimal interval of QTL to be restricted to no less than 10 to 30 cM in primary genome screens which is far from being adequate in dissecting complex genetic architecture at molecular level.In the present thesis, two projects addressed respectively on genetic dissection of a quantitative trait and on the use of microarray technology for polymorphism discovery have been followed.The first one takes the ethanol tolerance variation in budding yeast Saccharomyces cerevisiae as a model for quantitative trait, to explore the efficiency of QTL mapping techniques in uncovering information on genome locations of QTL for gene targeting. We selected two yeast strains with a highly divergent trait phenotype. We developed a set of STR/SNP markers evenly covering the whole genome. The two divergent strains were crossed to establish a conventional F₂ segregating population and further a two-way recurrent selection and backcross (RSB) population. By mapping in F₂ segregating population, five QTLs were detected and together explained～50% of phenotypic variation; in particular, the major QTL mapped on yeast chromosome 9 has accounted for a quarter of the phenotypic variation. While in RSB population, more QTLs were identified using a set of high density markers. The genome regions that showed extremely significant linkage with the trait phenotype have been narrowed down even confined to several candidate genes.Genome-wide characterization of sequence variation at coding region is of great importance for its potential to understanding gene function and for screening high-density genetic markers in QTL mapping. Therefore, the goal of the second project is to develop a robust algorithm able to detect sequence differences using expression profiling microarray data. The sequence polymorphism discovered on microarray has been termed single feature polymorphism (SFP). SFP can be used as genetic marker to genotype a cross population. By detailed comparison with 4 existing methods, our approach is proved to (1) be robust to differentially expressed genes, (2) detect and genotype equally efficiently from genomic DNA and RNA microarray data, (3) have a high mutual predictability, and (4) have a high false negative rate and a low and comparable false positive rate. Furthermore, we used SFPs detected by our approach to construct genetic linkage map in barley. We found that genetic maps constructed from SFPs would be equally reliable to those from SNPs. The SFPs identified in yeast RSB population succeeded in improving the mapping resolution.