Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeast

  1. Joshua M. Akey1,8
  1. 1Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA;
  2. 2Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA;
  3. 3Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA;
  4. 4Department of Statistics, University of Washington, Seattle, Washington 98195, USA;
  5. 5Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA;
  6. 6Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA
    • 7 Present address: HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, Alabama 35806, USA.

    Abstract

    To better understand the quantitative characteristics and structure of phenotypic diversity, we measured over 14,000 transcript, protein, metabolite, and morphological traits in 22 genetically diverse strains of Saccharomyces cerevisiae. More than 50% of all measured traits varied significantly across strains [false discovery rate (FDR) = 5%]. The structure of phenotypic correlations is complex, with 85% of all traits significantly correlated with at least one other phenotype (median = 6, maximum = 328). We show how high-dimensional molecular phenomics data sets can be leveraged to accurately predict phenotypic variation between strains, often with greater precision than afforded by DNA sequence information alone. These results provide new insights into the spectrum and structure of phenotypic diversity and the characteristics influencing the ability to accurately predict phenotypes.

    Footnotes

    • 8 Corresponding authors

      E-mail akeyj{at}uw.edu

      E-mail maitreya{at}uw.edu

      E-mail maccoss{at}uw.edu

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.155762.113.

      Freely available online through the Genome Research Open Access option.

    • Received February 1, 2013.
    • Accepted May 20, 2013.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported), as described at http://creativecommons.org/licenses/by-nc/3.0/.

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