GENES AND GENETIC INTERACTIONS UNDERLYING PHARMACOLOGICAL VARIATION IN YEAST

Summary

Principal Investigator: J C Fay
Abstract: DESCRIPTION (provided by applicant): Identification of even a single gene that contributes to a complex trait provides insight into its molecular basis. However, multiple genes need to be identified if different genes affect a trait through different biological processes or pathways. In the context of pharmacological treatments, each gene is relevant to attaining effective therapy and avoiding adverse drug reactions. While both linkage mapping and association studies have proved to be effective tools for analysis of complex traits, they rarely identify more than one or a small number of genes per study and each gene requires considerable effort to fine-map. The goal of the proposed research is to use S. cerevisiae as a model system to understand the diversity of molecular mechanisms by which different genes affect colony color, a drug-dependent quantitative trait. The first aim of the proposed research will determine whether multiple genes can be identified by a quantitative non-complementation screen using the yeast deletion collection. Reciprocal hemizygosity analysis will be used to estimate the rate of false positives due to haplo-insufficiency and second site mutations within the deletion collection. The second aim of the proposed research will characterize the relationship between colony color alleles by identifying downstream changes in gene expression. A quantitative model of colony color will be generated based on changes in gene expression associated with colony color and the model's ability to predict the effects of genetic background will be tested using recombinant strains segregating both known and unknown colony color alleles. Together, these aims will provide insight into the diversity of molecular mechanisms by which different genes influence a trait and how their effects are modified by genetic background. PUBLIC HEALTH RELEVANCE: Complex traits are the product of multiple genes, their interactions with each other, and their interactions with the environment. Although a number of methods are available to identify one or a few genes involved in a complex trait, it has been difficult to systematically identify every gene underlying a trait and the mechanisms by which they influence a trait. The proposed research will develop and evaluate a method of identifying multiple genes that contribute to complex traits and a model to predict their dependence on genetic background using gene expression data.
Funding Period: ----------------2009 - ---------------2011-
more information: NIH RePORT

Top Publications

  1. pmc A combined-cross analysis reveals genes with drug-specific and background-dependent effects on drug sensitivity in Saccharomyces cerevisiae
    Hyun Seok Kim
    Computational Biology Program, Washington University, St Louis, Missouri 63108, USA
    Genetics 183:1141-51. 2009
  2. ncbi Identification of microRNAs and natural antisense transcript-originated endogenous siRNAs from small-RNA deep sequencing data
    Weixiong Zhang
    Department of Computer Science and Engineering, Fudan University, Shanghai, China
    Methods Mol Biol 883:221-7. 2012
  3. pmc A noncomplementation screen for quantitative trait alleles in saccharomyces cerevisiae
    Hyun Seok Kim
    Department of Genetics, Washington University, St Louis, Missouri 63108, USA
    G3 (Bethesda) 2:753-60. 2012
  4. pmc Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks
    Monika Ray
    Washington University School of Engineering, Dept of Computer Science and Engineering, Saint Louis, MO 63130, USA
    BMC Syst Biol 4:136. 2010
  5. pmc Weighing the evidence for adaptation at the molecular level
    Justin C Fay
    Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University, St Louis, MO, USA
    Trends Genet 27:343-9. 2011
  6. pmc SeqTar: an effective method for identifying microRNA guided cleavage sites from degradome of polyadenylated transcripts in plants
    Yun Zheng
    Institute of Developmental Biology and Molecular Medicine, School of Life Sciences, Fudan University, 220 Handan Rd, Shanghai 200433, China
    Nucleic Acids Res 40:e28. 2012
  7. pmc Genome-wide analysis of plant nat-siRNAs reveals insights into their distribution, biogenesis and function
    Xiaoming Zhang
    Department of Plant Pathology and Microbiology, Center for Plant Cell Biology, University of California, Riverside, CA 92521, USA
    Genome Biol 13:R20. 2012
  8. pmc Structural features based genome-wide characterization and prediction of nucleosome organization
    Yanglan Gan
    Department of Computer Science and Technology, Tongji University, Shanghai, China
    BMC Bioinformatics 13:49. 2012

Scientific Experts

Detail Information

Publications8

  1. pmc A combined-cross analysis reveals genes with drug-specific and background-dependent effects on drug sensitivity in Saccharomyces cerevisiae
    Hyun Seok Kim
    Computational Biology Program, Washington University, St Louis, Missouri 63108, USA
    Genetics 183:1141-51. 2009
    ..Our results suggest that drug response may often depend on interactions between genes with multi-drug and drug-specific effects...
  2. ncbi Identification of microRNAs and natural antisense transcript-originated endogenous siRNAs from small-RNA deep sequencing data
    Weixiong Zhang
    Department of Computer Science and Engineering, Fudan University, Shanghai, China
    Methods Mol Biol 883:221-7. 2012
    ..We discuss here the computational procedures and major steps for identification of microRNAs and natural antisense transcript-originated small interfering RNAs from NGS small-RNA profiling data...
  3. pmc A noncomplementation screen for quantitative trait alleles in saccharomyces cerevisiae
    Hyun Seok Kim
    Department of Genetics, Washington University, St Louis, Missouri 63108, USA
    G3 (Bethesda) 2:753-60. 2012
    ..Our results highlight the difficulty of identifying small-effect alleles but support the use of noncomplementation as a rapid means of identifying quantitative trait alleles of large effect...
  4. pmc Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks
    Monika Ray
    Washington University School of Engineering, Dept of Computer Science and Engineering, Saint Louis, MO 63130, USA
    BMC Syst Biol 4:136. 2010
    ..We developed a novel method involving the differential topology of gene coexpression networks to understand the association among affected regions and disease severity...
  5. pmc Weighing the evidence for adaptation at the molecular level
    Justin C Fay
    Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University, St Louis, MO, USA
    Trends Genet 27:343-9. 2011
    ..Based on these considerations it is argued that the common assumption of independence among sites must be relaxed before abandoning the neutral theory of molecular evolution...
  6. pmc SeqTar: an effective method for identifying microRNA guided cleavage sites from degradome of polyadenylated transcripts in plants
    Yun Zheng
    Institute of Developmental Biology and Molecular Medicine, School of Life Sciences, Fudan University, 220 Handan Rd, Shanghai 200433, China
    Nucleic Acids Res 40:e28. 2012
    ..Thus, SeqTar is an effective method for identifying miRNA targets in plants using degradome data sets...
  7. pmc Genome-wide analysis of plant nat-siRNAs reveals insights into their distribution, biogenesis and function
    Xiaoming Zhang
    Department of Plant Pathology and Microbiology, Center for Plant Cell Biology, University of California, Riverside, CA 92521, USA
    Genome Biol 13:R20. 2012
    ..A few so-called nat-siRNAs have been reported in plants, mammals, Drosophila, and yeasts. However, many questions remain regarding the features and biogenesis of nat-siRNAs...
  8. pmc Structural features based genome-wide characterization and prediction of nucleosome organization
    Yanglan Gan
    Department of Computer Science and Technology, Tongji University, Shanghai, China
    BMC Bioinformatics 13:49. 2012
    ..Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae...