Finding Modulators of Stochasticity Levels by Quantitative Genetics

Although bakers and wine makers constantly select, compare, and hunt for new wild strains of Saccharomyces cerevisiae , yeast geneticists have long focused on a few “standard” strains to ensure reproducibility and easiness of experimentation. And so far, the wonderful natural resource of wild genetic variation has been poorly exploited in most academic laboratories. We describe here how one can use this resource to investigate the molecular sources of stochasticity in a gene regulatory network. The approach is general enough to be applied to any network of interest, as long as the experimental read-out offers robust statistics. For a given network, a typical study first identifies two backgrounds A and B displaying different levels of stochasticity and then study the network in A � B progeny. Taking advantage of microarrays or resequencing technologies, genotyping of appropriate segregants can then lead to the genomic regions housing modulators of stochasticity. The powerful toolbox available to manipulate the yeast genome offers several ways to narrow these regions further and to unambiguously demonstrate the regulatory consequences of DNA polymorphisms.