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Genetic interactions between polymorphisms that affect gene expression in yeast

Author

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  • Rachel B. Brem

    (Program in Computational Biology, Fred Hutchinson Cancer Research Center)

  • John D. Storey

    (University of Washington)

  • Jacqueline Whittle

    (Infectious Disease Research Institute)

  • Leonid Kruglyak

    (Princeton University)

Abstract

Interactions between polymorphisms at different quantitative trait loci (QTLs) are thought to contribute to the genetics of many traits, and can markedly affect the power of genetic studies to detect QTLs1. Interacting loci have been identified in many organisms1,2,3,4,5. However, the prevalence of interactions6,7,8, and the nucleotide changes underlying them9,10, are largely unknown. Here we search for naturally occurring genetic interactions in a large set of quantitative phenotypes—the levels of all transcripts in a cross between two strains of Saccharomyces cerevisiae7. For each transcript, we searched for secondary loci interacting with primary QTLs detected by their individual effects. Such locus pairs were estimated to be involved in the inheritance of 57% of transcripts; statistically significant pairs were identified for 225 transcripts. Among these, 67% of secondary loci had individual effects too small to be significant in a genome-wide scan. Engineered polymorphisms in isogenic strains confirmed an interaction between the mating-type locus MAT and the pheromone response gene GPA1. Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions.

Suggested Citation

  • Rachel B. Brem & John D. Storey & Jacqueline Whittle & Leonid Kruglyak, 2005. "Genetic interactions between polymorphisms that affect gene expression in yeast," Nature, Nature, vol. 436(7051), pages 701-703, August.
    • Handle: RePEc:nat:nature:v:436:y:2005:i:7051:d:10.1038_nature03865
    DOI: 10.1038/nature03865
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    Cited by:

    1. Zheyang Wu & Hongyu Zhao, 2009. "Statistical Power of Model Selection Strategies for Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 5(7), pages 1-14, July.
    2. Wei Zhang & Jun Zhu & Eric E Schadt & Jun S Liu, 2010. "A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-10, January.
    3. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.
    4. Sourav Bandyopadhyay & Ryan Kelley & Nevan J Krogan & Trey Ideker, 2008. "Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-8, April.

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