GENES AND GENETIC INTERACTIONS UNDERLYING PHARMACOLOGICAL VARIATION IN YEAST
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
- A combined-cross analysis reveals genes with drug-specific and background-dependent effects on drug sensitivity in Saccharomyces cerevisiaeHyun 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...
- Identification of microRNAs and natural antisense transcript-originated endogenous siRNAs from small-RNA deep sequencing dataWeixiong 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...
- A noncomplementation screen for quantitative trait alleles in saccharomyces cerevisiaeHyun 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...
- Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networksMonika 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...
- Weighing the evidence for adaptation at the molecular levelJustin 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...
- SeqTar: an effective method for identifying microRNA guided cleavage sites from degradome of polyadenylated transcripts in plantsYun 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...
- Genome-wide analysis of plant nat-siRNAs reveals insights into their distribution, biogenesis and functionXiaoming 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...
- Structural features based genome-wide characterization and prediction of nucleosome organizationYanglan 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...