DOI: 10.5061/dryad.gc57610
title: Distinct genomic signals of lifespan and life history evolution in response to postponed reproduction and larval diet in Drosophila

description: Reproduction and diet are two major factors controlling the physiology of
 aging and life history, but how they interact to affect the evolution of
 longevity is unknown. Moreover, while studies of large-effect mutants
 suggest an important role of nutrient sensing pathways in regulating
 aging, the genetic basis of evolutionary changes in lifespan remains
 poorly understood. To address these questions, we analyzed the genomes of
 experimentally evolved Drosophila melanogaster populations subjected to a
 factorial combination of two selection regimes: reproductive age (early
 versus postponed), and diet during the larval stage (‘low’, ‘control’,
 ‘high’), resulting in six treatment combinations with four replicate
 populations each. Selection on reproductive age consistently affected
 lifespan, with flies from the postponed reproduction regime having evolved
 a longer lifespan. In contrast, larval diet affected lifespan only in
 early-reproducing populations: flies adapted to the ‘low’ diet lived
 longer than those adapted to control diet. Here we find genomic evidence
 for strong independent evolutionary responses to either selection regime,
 as well as loci that diverged in response to both regimes, thus
 representing genomic interactions between the two. Overall, we find that
 the genomic basis of longevity is largely independent of dietary
 adaptation. Differentiated loci were not enriched for ‘canonical’
 longevity genes, suggesting that naturally occurring genic targets of
 selection for longevity differ qualitatively from variants found in mutant
 screens. Comparing our candidate loci to those from other
 ‘evolve-and-resequence’ studies of longevity demonstrated significant
 overlap among independent experiments. This suggests that the evolution of
 longevity, despite its presumed complex and polygenic nature, might be to
 some extent convergent and predictable.
description: GLM.pyThis script takes count
 data from multiple populations in sync file format (Kofler et al. 2011) as
 input and calulates two-way generalized linear models with a binomial
 error structure for the two factors "food" and dev" and the
 interaction between them.GLM_qGLM_GLMM_ANOVA.pyThis script takes count data from multiple populations in sync file format (Kofler et al. 2011) as input and calulates (1) two-way generalized linear models with a binomial error structure, (2) two-way generalized linear models with a quasibinomial error structure, (3) generalized linear mixed models with a binomial error structure (with EE population as random factor), and (4) two-way ANOVA on arcsine square root transformed allele frequencies, for the two factors "food" and dev" and the interaction between them.FDR.pyThis script caluclates an empirical FDR based on permuted p-values as described in Jha et al. (2015).Hologenome_dmel6Hologenome_dmel6.sh provides an overview of all the genomes (Dmel + microbes) that were obtained to generate the hologenome, which was used to map the sequence reads against. This is an updated version of the script published by Casey M. Bergman.Dmel_6.04_hologenome_final.fastaReference hologenome file used in this study