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  • Dataset for "Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae"

    work
    Creator:
    Hodgins-Davis, Andrea, Duveau, Fabien, Walker, Elizabeth, and Wittkopp, Patricia J
    Description:
    Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the ten genes surveyed and are inconsistent with normality. For example, all ten distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene’s expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.
    Keyword:
    gene expression, evolution, mutation, mutagenesis, regulatory evolution, YFP, reporter construct, yeast, TDH1, TDH2, TDH3, GPD1, OST1, PFY1, STM1, RNR1, and RNR2
    Citation to related publication:
    Hodgins-Davis, A., Duveau, F., Walker, E. A., & Wittkopp, P. J. (2019). Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae. BioRxiv, 551804.  https://doi.org/10.1101/551804
    Discipline:
    Science