A 24 h Age Difference Causes Twice as Much Gene Expression Divergence as 100 Generations of Adaptation to a Novel Environment

2020

Molecular Ecology

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Experimental evolution is becoming a popular approach to study the genomic selection response of evolving populations. Computer simulation studies suggest that the accuracy of the signature increases with the duration of the experiment. Since some assumptions of the computer simulations may be violated, it is important to scrutinize the influence of the experimental duration with real data. Here, we use a highly replicated Evolve and Resequence study in Drosophila simulans to compare the selection targets inferred at different time points. At each time point, approximately the same number of SNPs deviates from neutral expectations, but only 10% of the selected haplotype blocks identified from the full data set can be detected after 20 generations. Those haplotype blocks that emerge already after 20 generations differ from the others by being strongly selected at the beginning of the experiment and display a more parallel selection response. Consistent with previous computer simulations, our results demonstrate that only Evolve and Resequence experiments with a sufficient number of generations can characterize complex adaptive architectures.

Section: Methodsmentioning

confidence: 97%

“…10 replicate populations evolved in the laboratory at a “cycling hot” temperature regime (12 hr light and 28°C, 12 hr dark and 18°C). The census size of the replicates was 1,000 individuals with non‐overlapping generations (Barghi et al., 2019; Barghi, Tobler, Nolte, & Schlötterer, 2017; Hsu et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Low concordance of short‐term and long‐term selection responses in experimental Drosophila populations

Langmüller¹,

2020

Molecular Ecology

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“…A detailed description of the Drosophila simulans E&R experiment can be found in Barghi et al (2019) and Hsu et al (2019). Pooled individuals (Schlötterer et al, 2014) from the evolving populations were sequenced every 10 t h generation starting with the founder population (generation 0) until generation 60 resulting in seven sequenced time points.…”

Section: Materials and Methods Experimental Drosophila Simulans Populationsmentioning

confidence: 99%

“…10 replicate populations evolved in the laboratory at a "cycling hot" temperature regime (12 hours light and 28°C, 12 hours dark and 18°C). The census size of the replicates was 1 000 individuals with non-overlapping generations (Barghi et al, 2017(Barghi et al, , 2019Hsu et al, 2019).…”

Section: Materials and Methods Experimental Drosophila Simulans Populationsmentioning

confidence: 99%

Low concordance of short-term and long-term selection responses in experimentalDrosophilapopulations

Langmüller

2019

Preprint

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1Experimental evolution is becoming a popular approach to study genomic selection responses 2 of evolving populations. Computer simulation studies suggested that the accuracy of the sig-3 nature increases with the duration of the experiment. Since some assumptions of the com-4 puter simulations may be violated, it is important to scrutinize the influence of the experimen-5 tal duration with real data. Here, we use a highly replicated Evolve and Resequence study in 6 Drosophila simulans to compare the selection targets inferred at different time points. At each 7 time point approximately the same number of SNPs deviated from neutral expectations, but 8 only 10 % of the selected haplotype blocks identified from the full data set could be detected in 9 the first 20 generations. Those haplotype blocks that emerged already after 20 generations dif-10 fer from the others by being strongly selected at the beginning of the experiment and displaying 11 a more parallel selection response. Consistent with previous computer simulations, our results 12 confirm that only Evolve and Resequence experiments with a sufficient number of generations 13 can characterize complex adaptive architectures. 14 15 17 18 Deciphering the adaptive architecture is a long-term goal in evolutionary biology. In contrast 19 to natural populations, experimental evolution (EE) provides the possibility to replicate exper-20 iments under controlled, identical conditions and to study how evolution shapes populations 21 in real time (Kawecki et al. (2012); Schlötterer et al. (2015)). The combination of EE with next-22 23 (2015); Long et al. ( 2015)) -has become a popular approach to study the genomic response to se-24 lection and to identify adaptive loci. E&R has been applied to various selection regimes, such as 25 virus infection (Martins et al. (2014)), host-pathogen co-adaptation (Papkou et al. (2019)), ther-26 mal adaptation (Orozco-Terwengel et al. (2012); Barghi et al. (2019)), or body weight (Johansson 27 et al. (2010)). A wide range of experimental designs have been used, which vary in census popu-28 lation size, replication level, history of the ancestral populations, selection regime, and number and Hughes (2018); Turner and Miller (2012); Rêgo et al. (2019)), over a few dozen (Orozco-33 Terwengel et al. (2012); Johansson et al. (2010)), up to hundreds of generations (Burke et al. 34 (2010)). Computer simulations showed that the number of generations has a strong influence 35 on the power of E&R studies, and increasing the number of generations typically improved the 36 results (Baldwin-Brown et al. (2014); Kofler and Schlötterer (2014); Vlachos and Kofler (2019)). 37Since simulations make simplifying assumptions, it is important to scrutinize these conclu-38 sions with empirical data. Until recently no suitable data-sets were available, which included 39 multiple time points and replicates. We use an E&R experiment (Barghi et al. (2019)), which 40 reports allele frequency changes in 10 replicates over 60 generations in 10 generation interval...

“…The collection of samples from the evolution experiment for RNA-Seq was preceded by two generations of common garden (CGE). The common garden experiment was performed at generation 103 of the evolution in the hot environment and this CGE has been described in 5,[29][30][31] . In brief, an ancestral population was reconstituted by pooling five mated females from 184 founder isofemale lines 32 .…”

Section: Common Garden Experimentsmentioning

confidence: 99%

Evolution of phenotypic variance in response to a novel hot environment

Lai¹,

2021

Preprint

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Shifts in trait means are widely considered as evidence for adaptive responses, but the impact on phenotypic variance remains largely unexplored. Here, we studied gene expression variance of Drosophila simulans males before and after 100 generations of adaptation to a novel hot laboratory environment. In each of the two independently evolved replicate populations the variance of about 150 genes changed significantly (mostly reduction). Although different genes were affected in both replicates, these genes are related to digestion in the gut. This non-parallel selection response on the gene level in combination with a convergent response at a higher phenotypic level reflects genetic redundancy, a characteristic hallmark of polygenic adaptation. We propose that the constant and simple food source in the laboratory resulted in selection for reduced variance in digestive genes. In natural populations adaptation to diverse types of food may be beneficial, resulting in higher phenotypic variance. This empirical evidence of phenotypic variance being the direct target of selection during adaptation has important implications for strategies to identify selection signatures.