We propose to use yeast as a model system to evaluate methodologies for characterizing quantitative trait genes and to understand the complexity of the genetics that underlies this class of traits. Because quantitative traits are poorly understood, we have developed S. cerevisiae as a model for the study of quantitative traits and have chosen sporulation efficiency because it is readily quantifiable and as described previously, the sporulation process is an important developmental program in yeast. Our combination of technologies designed, developed and tested in our laboratory and applied to dissect two quantitative traits so far, growth at high-temperature and efficiency of sporulation, will be extended here to a larger scale to identify all major- effect genes that condition the sporulation phenotype. New quantitative trait loci (QTL) will be identified by genome-wide mapping of backcross progeny using direct allelic variation scanning on tiling microarrays, our high-throughput array based genotyping assay that genotypes over 50,000 markers in a single genomic DNA hybridization. Fine structure mapping, sequence analysis, mRNA expression analysis, and reciprocal hemizygosity analysis will be applied to identify and evaluate the responsible genes in each mapped QTL. In conjunction, a new application named reciprocal hemizygosity scanning will be developed and applied to identify quantitative trait genes directly on a genome-scale. Reciprocal hemizygosity scanning is a tool for comparing two genomes and identifying all phenotypically relevant allelic differences in a single tube assay using a hybrid strain. We expect that this combination of approaches will yield the most comprehensive list to date of genetic factors underlying a quantitative trait. Since a major question is how QTLs mediate their effects on phenotype, allele replacement strains will be constructed for all combinations of major effect QTLs, and the consequences will be monitored at the molecular level by transcriptome profiling on tiling microarrays. The establishment of tools for identifying quantitative trait genes and results on their functional reconstitution and genetic engineering will provide the best data for approaching complex traits for medical studies.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Krasnewich, Donna M
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Stanford University
Schools of Medicine
United States
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