Genetic complexity and quantitative trait loci mapping of yeast morphological traits.

Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan.
PLoS Genetics (Impact Factor: 8.52). 03/2007; 3(2):e31. DOI: 10.1371/journal.pgen.0030031
Source: PubMed

ABSTRACT Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.

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    ABSTRACT: Dissecting the molecular basis of quantitative traits is a significant challenge, and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes, and in detecting epistasis or linked quantitative trait loci. These difficulties are exacerbated by limitations in experimental design, such as low numbers of analyzed individuals, and polymorphisms between parental genomes. We address these challenges by applying three independent high-throughput approaches for QTL mapping to map the genetic variants underlying eleven phenotypes in two genetically distant Saccharomyces cerevisiae strains, namely: 1) individual analysis of over 700 meiotic segregants, 2) bulk segregant analysis, and 3) reciprocal hemizygosity analysis, a new genome-wide method we developed. We identified differences in the performance of each approach and, by combining them, identified eight polymorphic genes that affect eight different phenotypes: colony shape, flocculation, growth on non-fermentable carbon sources, and resistance to drugs, salt, and heat. Our results demonstrate the power of individual segregant analysis to dissect quantitative trait loci and address the underestimated contribution of interactions between variants. We also reveal confounding factors like mutations and aneuploidy in pooled approaches, providing valuable lessons for future designs of complex trait mapping studies.
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    BMC Genomics 06/2014; 15:495. · 4.40 Impact Factor
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    ABSTRACT: The vitality of brewing yeasts has been used to monitor their physiological state during fermentation. To investigate the fermentation process, we used the image processing software, CalMorph, which generates morphological data on yeast mother cells and bud shape, nuclear shape and location, and actin distribution. We found that 248 parameters changed significantly during fermentation. Successive use of principal component analysis (PCA) revealed several important features of yeast, providing insight into the dynamic changes in the yeast population. First, PCA indicated that much of the observed variability in the experiment was summarized in just two components: a change with a peak and a change over time. Second, PCA indicated the independent and important morphological features responsible for dynamic changes: budding ratio, nucleus position, neck position, and actin organization. Thus, the large amount of data provided by imaging analysis can be used to monitor the fermentation processes involved in beer and bioethanol production.
    Journal of Bioscience and Bioengineering 09/2013; · 1.74 Impact Factor

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