Genetic architecture of natural variation in cuticular hydrocarbon composition in Drosophila melanogaster

  1. Lauren M Dembeck
  2. Katalin Böröczky
  3. Wen Huang
  4. Coby Schal
  5. Robert RH Anholt
  6. Trudy FC Mackay  Is a corresponding author
  1. Okinawa Institute of Science and Technology Graduate University, Japan
  2. Cornell University, United States
  3. North Carolina State University, United States

Abstract

Insect cuticular hydrocarbons (CHCs) prevent desiccation and serve as chemical signals that mediate social interactions. Drosophila melanogaster CHCs have been studied extensively, but the genetic basis for individual variation in CHC composition is largely unknown. We quantified variation in CHC profiles in the D. melanogaster Genetic Reference Panel (DGRP) and identified novel CHCs. We used principal component (PC) analysis to extract PCs that explain the majority of CHC variation and identified polymorphisms in or near 305 and 173 genes in females and males, respectively, associated with variation in these PCs. In addition, 17 DGRP lines contain the functional Desat2 allele characteristic of African and Caribbean D. melanogaster females (more 5,9-C27:2 and less 7,11-C27:2, female sex pheromone isomers). Disruption of expression of 24 candidate genes affected CHC composition in at least one sex. These genes are associated with fatty acid metabolism and represent mechanistic targets for individual variation in CHC composition.

Article and author information

Author details

  1. Lauren M Dembeck

    Ecology and Evolution Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Japan
    Competing interests
    The authors declare that no competing interests exist.
  2. Katalin Böröczky

    Department of Neurobiology and Behavior, Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Wen Huang

    Department of Biological Sciences, North Carolina State University, Raleigh, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Coby Schal

    Genetics Program, North Carolina State University, Raleigh, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Robert RH Anholt

    Department of Biological Sciences, North Carolina State University, Raleigh, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Trudy FC Mackay

    Department of Biological Sciences, North Carolina State University, Raleigh, United States
    For correspondence
    trudy_mackay@ncsu.edu
    Competing interests
    The authors declare that no competing interests exist.

Copyright

© 2015, Dembeck et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,840
    views
  • 714
    downloads
  • 126
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Lauren M Dembeck
  2. Katalin Böröczky
  3. Wen Huang
  4. Coby Schal
  5. Robert RH Anholt
  6. Trudy FC Mackay
(2015)
Genetic architecture of natural variation in cuticular hydrocarbon composition in Drosophila melanogaster
eLife 4:e09861.
https://doi.org/10.7554/eLife.09861

Share this article

https://doi.org/10.7554/eLife.09861

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Steven Henikoff, David L Levens
    Insight

    A new method for mapping torsion provides insights into the ways that the genome responds to the torsion generated by RNA polymerase II.