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International Journal of Genomics
Volume 2016 (2016), Article ID 2157494, 11 pages
http://dx.doi.org/10.1155/2016/2157494
Research Article

A Quantitative Genomic Approach for Analysis of Fitness and Stress Related Traits in a Drosophila melanogaster Model Population

1Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
2The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), 8000 Aarhus, Denmark
3Center for Integrative Sequencing (iSEQ), Aarhus University, 8000 Aarhus, Denmark
4Section for Genetics, Ecology and Evolution, Department of Bioscience, Aarhus University, 8000 Aarhus, Denmark
5Section of Zoophysiology, Department of Bioscience, Aarhus University, 8800 Aarhus, Denmark
6Section of Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark

Received 21 December 2015; Accepted 29 March 2016

Academic Editor: Aritz Ruiz-González

Copyright © 2016 Palle Duun Rohde et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The ability of natural populations to withstand environmental stresses relies partly on their adaptive ability. In this study, we used a subset of the Drosophila Genetic Reference Panel, a population of inbred, genome-sequenced lines derived from a natural population of Drosophila melanogaster, to investigate whether this population harbors genetic variation for a set of stress resistance and life history traits. Using a genomic approach, we found substantial genetic variation for metabolic rate, heat stress resistance, expression of a major heat shock protein, and egg-to-adult viability investigated at a benign and a higher stressful temperature. This suggests that these traits will be able to evolve. In addition, we outline an approach to conduct pathway associations based on genomic linear models, which has potential to identify adaptive genes and pathways, and therefore can be a valuable tool in conservation genomics.