GENOMIC BASIS OF AGING AND LIFE-HISTORY EVOLUTION IN DROSOPHILA MELANOGASTER
Version of Record online: 27 JUN 2012
© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.
Volume 66, Issue 11, pages 3390–3403, November 2012
How to Cite
Remolina, S. C., Chang, P. L., Leips, J., Nuzhdin, S. V. and Hughes, K. A. (2012), GENOMIC BASIS OF AGING AND LIFE-HISTORY EVOLUTION IN DROSOPHILA MELANOGASTER. Evolution, 66: 3390–3403. doi: 10.1111/j.1558-5646.2012.01710.x
- Issue online: 25 OCT 2012
- Version of Record online: 27 JUN 2012
- Accepted manuscript online: 1 JUN 2012 02:35PM EST
- Received February 9, 2012 Accepted May 4, 2012 Data Archived: Dryad doi:10.5061/dryad.94pv0
- quantitative genetics;
Natural diversity in aging and other life-history patterns is a hallmark of organismal variation. Related species, populations, and individuals within populations show genetically based variation in life span and other aspects of age-related performance. Population differences are especially informative because these differences can be large relative to within-population variation and because they occur in organisms with otherwise similar genomes. We used experimental evolution to produce populations divergent for life span and late-age fertility and then used deep genome sequencing to detect sequence variants with nucleotide-level resolution. Several genes and genome regions showed strong signatures of selection, and the same regions were implicated in independent comparisons, suggesting that the same alleles were selected in replicate lines. Genes related to oogenesis, immunity, and protein degradation were implicated as important modifiers of late-life performance. Expression profiling and functional annotation narrowed the list of strong candidate genes to 38, most of which are novel candidates for regulating aging. Life span and early age fecundity were negatively correlated among populations; therefore, the alleles we identified also are candidate regulators of a major life-history trade-off. More generally, we argue that hitchhiking mapping can be a powerful tool for uncovering the molecular bases of quantitative genetic variation.