Systems genetics of the Drosophila metabolome

  1. Trudy F.C. Mackay1,6
  1. 1Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA;
  2. 2Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, JiangXi Agricultural University, JiangXi, China
  • Present addresses: 3Covance Companion Diagnostics, Morrisville, NC 27560, USA; 4Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA; 5University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27516, USA; 6Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC 29646, USA

  • Corresponding authors: ranholt{at}clemson.edu, tmackay{at}clemson.edu
  • Abstract

    How effects of DNA sequence variants are transmitted through intermediate endophenotypes to modulate organismal traits remains a central question in quantitative genetics. This problem can be addressed through a systems approach in a population in which genetic polymorphisms, gene expression traits, metabolites, and complex phenotypes can be evaluated on the same genotypes. Here, we focused on the metabolome, which represents the most proximal link between genetic variation and organismal phenotype, and quantified metabolite levels in 40 lines of the Drosophila melanogaster Genetic Reference Panel. We identified sex-specific modules of genetically correlated metabolites and constructed networks that integrate DNA sequence variation and variation in gene expression with variation in metabolites and organismal traits, including starvation stress resistance and male aggression. Finally, we asked to what extent SNPs and metabolites can predict trait phenotypes and generated trait- and sex-specific prediction models that provide novel insights about the metabolomic underpinnings of complex phenotypes.

    Footnotes

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.243030.118.

    • Freely available online through the Genome Research Open Access option.

    • Received August 14, 2018.
    • Accepted March 11, 2019.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

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