Letter

Nature 464, 1039-1042 (15 April 2010) | doi:10.1038/nature08923; Received 10 November 2009; Accepted 16 February 2010

Dissection of genetically complex traits with extremely large pools of yeast segregants

Ian M. Ehrenreich1,2,3, Noorossadat Torabi1,4, Yue Jia1,3, Jonathan Kent1, Stephen Martis1, Joshua A. Shapiro1,2,3, David Gresham1,5, Amy A. Caudy1 & Leonid Kruglyak1,2,3

  1. Lewis-Sigler Institute for Integrative Genomics,
  2. Department of Ecology and Evolutionary Biology,
  3. Howard Hughes Medical Institute,
  4. Department of Molecular Biology, Princeton University, Princeton, New Jersey 08540, USA
  5. Present address: Center for Genomics and Systems Biology, New York University, New York, New York 10003, USA.

Correspondence to: Leonid Kruglyak1,2,3 Correspondence and requests for materials should be addressed to L.K. (Email: leonid@genomics.princeton.edu).

Most heritable traits, including many human diseases1, are caused by multiple loci. Studies in both humans and model organisms, such as yeast, have failed to detect a large fraction of the loci that underlie such complex traits2, 3. A lack of statistical power to identify multiple loci with small effects is undoubtedly one of the primary reasons for this problem. We have developed a method in yeast that allows the use of much larger sample sizes than previously possible and hence permits the detection of multiple loci with small effects. The method involves generating very large numbers of progeny from a cross between two Saccharomyces cerevisiae strains and then phenotyping and genotyping pools of these offspring. We applied the method to 17 chemical resistance traits and mitochondrial function, and identified loci for each of these phenotypes. We show that the level of genetic complexity underlying these quantitative traits is highly variable, with some traits influenced by one major locus and others by at least 20 loci. Our results provide an empirical demonstration of the genetic complexity of a number of traits and show that it is possible to identify many of the underlying factors using straightforward techniques. Our method should have broad applications in yeast and can be extended to other organisms.

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