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      Trait Variation in Yeast Is Defined by Population History

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          Abstract

          A fundamental goal in biology is to achieve a mechanistic understanding of how and to what extent ecological variation imposes selection for distinct traits and favors the fixation of specific genetic variants. Key to such an understanding is the detailed mapping of the natural genomic and phenomic space and a bridging of the gap that separates these worlds. Here we chart a high-resolution map of natural trait variation in one of the most important genetic model organisms, the budding yeast Saccharomyces cerevisiae, and its closest wild relatives and trace the genetic basis and timing of major phenotype changing events in its recent history. We show that natural trait variation in S. cerevisiae exceeds that of its relatives, despite limited genetic variation, and follows the population history rather than the source environment. In particular, the West African population is phenotypically unique, with an extreme abundance of low-performance alleles, notably a premature translational termination signal in GAL3 that cause inability to utilize galactose. Our observations suggest that many S. cerevisiae traits may be the consequence of genetic drift rather than selection, in line with the assumption that natural yeast lineages are remnants of recent population bottlenecks. Disconcertingly, the universal type strain S288C was found to be highly atypical, highlighting the danger of extrapolating gene-trait connections obtained in mosaic, lab-domesticated lineages to the species as a whole. Overall, this study represents a step towards an in-depth understanding of the causal relationship between co-variation in ecology, selection pressure, natural traits, molecular mechanism, and alleles in a key model organism.

          Author Summary

          An overall aim in modern biology is to achieve an in-depth understanding of an organism's physiology in the context of its ecology and historic selective pressures that have been acting on its genome. The baker's yeast, Saccharomyces cerevisiae, has a peculiar life history completely dominated by clonal reproduction and self-fertilization, prompting the suggestion that natural yeasts are remnants of repeated population bottlenecks in essentially clonal lineages. Such a life history dominated by mitotic proliferation purports a strong evolutionary influence of genetic drift and predicts trait variation to be high and largely defined by the genetic history of each population. Here we chart a highly resolved map of natural trait variation in S. cerevisiae and its closest non-domesticated relative, Saccharomyces paradoxus, and confirm this prediction. We found that trait variation in budding yeast is indeed high and largely defined by population rather than source environment. In particular, the West African population was found to be phenotypically unique with an extreme abundance of low-performance alleles. Our findings support the idea of population bottlenecks in the recent yeast evolutionary history and a large influence of genetic drift.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Cluster analysis and display of genome-wide expression patterns.

            A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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              The genetic landscape of a cell.

              A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                June 2011
                June 2011
                16 June 2011
                : 7
                : 6
                : e1002111
                Affiliations
                [1 ]Department of Cell and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
                [2 ]Centre for Integrative Genetics (CIGENE), Animal and Aquacultural Sciences, Norwegian University of Life Sciences (UMB), Ås, Norway
                [3 ]Centre for Genetics and Genomics, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
                [4 ]Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
                [5 ]Centre for Integrative Genetics (CIGENE), Mathematical Sciences and Technology, Norwegian University of Life Sciences (UMB), Ås, Norway
                [6 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
                Princeton University, United States of America
                Author notes

                Conceived and designed the experiments: JW RD SWO EJL GL AM AB. Performed the experiments: JW EZ FAC AF. Analyzed the data: JW AZ AG JTS AF AM. Contributed reagents/materials/analysis tools: FAC EJL GL. Wrote the paper: JW RD SWO EJL GL AM AB.

                Article
                PGENETICS-D-10-00228
                10.1371/journal.pgen.1002111
                3116910
                21698134
                c2155574-32a0-477f-978f-2e89322ad8bb
                Warringer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 1 November 2010
                : 13 April 2011
                Page count
                Pages: 15
                Categories
                Research Article
                Biology
                Genetics
                Population Genetics
                Genetic Drift
                Genetic Polymorphism
                Natural Selection
                Genomics
                Comparative Genomics
                Functional Genomics
                Genome Evolution
                Microbiology
                Mycology
                Fungal Evolution
                Yeast
                Microbial Evolution

                Genetics
                Genetics

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