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Population genomics of domestic and wild yeasts

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      Abstract

      Since the completion of the genome sequence of Saccharomyces cerevisiae in 19961,2, there has been an exponential increase in complete genome sequences accompanied by great advances in our understanding of genome evolution. Although little is known about the natural and life histories of yeasts in the wild, there are an increasing number of studies looking at ecological and geographic distributions3,4, population structure5-8, and sexual versus asexual reproduction9,10. Less well understood at the whole genome level are the evolutionary processes acting within populations and species leading to adaptation to different environments, phenotypic differences and reproductive isolation. Here we present one- to four-fold or more coverage of the genome sequences of over seventy isolates of the baker's yeast, S. cerevisiae, and its closest relative, S. paradoxus. We examine variation in gene content, SNPs, indels, copy numbers and transposable elements. We find that phenotypic variation broadly correlates with global genome-wide phylogenetic relationships. Interestingly, S. paradoxus populations are well delineated along geographic boundaries while the variation among worldwide S. cerevisiae isolates shows less differentiation and is comparable to a single S. paradoxus population. Rather than one or two domestication events leading to the extant baker's yeasts, the population structure of S. cerevisiae consists of a few well-defined geographically isolated lineages and many different mosaics of these lineages, supporting the idea that human influence provided the opportunity for cross-breeding and production of new combinations of pre-existing variation.

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      Most cited references 31

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      Inference of population structure using multilocus genotype data.

      We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.
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        Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

         F Tajima (1989)
        The relationship between the two estimates of genetic variation at the DNA level, namely the number of segregating sites and the average number of nucleotide differences estimated from pairwise comparison, is investigated. It is found that the correlation between these two estimates is large when the sample size is small, and decreases slowly as the sample size increases. Using the relationship obtained, a statistical method for testing the neutral mutation hypothesis is developed. This method needs only the data of DNA polymorphism, namely the genetic variation within population at the DNA level. A simple method of computer simulation, that was used in order to obtain the distribution of a new statistic developed, is also presented. Applying this statistical method to the five regions of DNA sequences in Drosophila melanogaster, it is found that large insertion/deletion (greater than 100 bp) is deleterious. It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.
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          Adaptive protein evolution at the Adh locus in Drosophila.

          Proteins often differ in amino-acid sequence across species. This difference has evolved by the accumulation of neutral mutations by random drift, the fixation of adaptive mutations by selection, or a mixture of the two. Here we propose a simple statistical test of the neutral protein evolution hypothesis based on a comparison of the number of amino-acid replacement substitutions to synonymous substitutions in the coding region of a locus. If the observed substitutions are neutral, the ratio of replacement to synonymous fixed differences between species should be the same as the ratio of replacement to synonymous polymorphisms within species. DNA sequence data on the Adh locus (encoding alcohol dehydrogenase, EC 1.1.1.1) in three species in the Drosophila melanogaster species subgroup do not fit this expectation; instead, there are more fixed replacement differences between species than expected. We suggest that these excess replacement substitutions result from adaptive fixation of selectively advantageous mutations.
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            Author and article information

            Affiliations
            [1 ]Institute of Genetics, Queen's Medical Centre, University of Nottingham, Nottingham NG7 2UH, UK
            [2 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
            [3 ]Department of Cell & Systems Biology, University of Toronto, Canada, M5S 2J4
            [4 ]Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390 Gothenburg, Sweden
            [5 ]National Collection of Yeast Cultures, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK
            [6 ]Division of Biology, Imperial College London, Silwood Park, Ascot, Berks., SL5 7PY, UK
            [7 ]Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK
            [8 ]Department of Physics, Massachusetts Institute of Technology, Cambridge MA 02139, USA
            Author notes
            [*]

            These authors contributed equally to this work

            [‡]

            Present address: School of Biological Sciences, University of Liverpool, Liverpool, LG9 3BX

            Author Contributions R.D. and E.J.L. conceived and designed the project. G.L. selected and manipulated yeast strains and extracted DNA samples. M.J., M.A.Q., I.G., S.S., F.S. performed the subcloning and sequencing. D.M.C. did the reference comparison and assembly of the sequences. D.M.C. and G.L. coordinated the collection of data. D.M.C. and R.D. performed much of the global analysis, which was the basis for specific analyses performed by the rest. A.M.M. did the selection studies. E.J.L., G.L. D.M.C., L.B. did the population structure and novel genes analysis. C.M.B. and D.B. performed the analysis of Ty elements abundance. S.A.J., R.P.D., M.J.T.O., A.V. and I.N.R. analysed the rDNA. A.B., V.K. and I.J.T. did the sequence variation and recombination analyses. A.M.M. and A.N.N.B. created a BLAST server. J.W. and A.B. generated the phenomics data. E.J.L. and G.L. wrote the paper, coordinating everyone's contributions.

            Correspondence and requests for materials should be addressed to R. D. ( rd@ 123456sanger.ac.uk ) or E. J. L. ( ed.louis@ 123456nottingham.ac.uk ).
            Journal
            0410462
            6011
            Nature
            Nature
            Nature
            0028-0836
            1476-4687
            18 March 2009
            11 February 2009
            19 March 2009
            19 September 2009
            : 458
            : 7236
            : 337-341
            19212322
            2659681
            10.1038/nature07743
            UKMS4241
            Funding
            Funded by: Wellcome Trust :
            Award ID: 084507 || WT
            Funded by: Wellcome Trust :
            Award ID: 067008 || WT
            Funded by: Biotechnology and Biological Sciences Research Council :
            Award ID: G10415 || BB_
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