Secondary Evolve and Resequencing: An Experimental Confirmation of Putative Selection Targets without Phenotyping

Burny, Claire; Nolte, Viola; Nouhaud, Pierre; Dolezal, Marlies; Schlötterer, Christian

doi:10.1093/gbe/evaa036

Cited by 14 publications

(31 citation statements)

References 36 publications

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“…Simulations of polygenic adaptation can become quite complex and rich in parameters (Thornton, 2019), which limits the power of computer simulations to scrutinize this result further. Rather, experimental validation of selection targets in secondary E&R studies (Burny et al., 2020) may be used to confirm selection signatures beyond statistical testing and could serve an important role to test early selection signatures that cannot be confirmed at later time points.…”

Section: Resultsmentioning

confidence: 99%

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

Langmüller¹,

Schlötterer²

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.

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Section: Resultsmentioning

confidence: 99%

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

Langmüller¹,

Schlötterer²

2020

Molecular Ecology

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“…Simulations of polygenic adaptation can become quite complex and rich in parameters (Thornton, 2019), which limits the power of computer simulations to scrutinize this result further. Rather, experimental validation of selection targets in secondary E&R studies (Burny et al, 2020) may be used to confirm selection signatures beyond statistical testing and could serve an important role to test early selection signatures that cannot be confirmed at later time points.…”

Section: Resultsmentioning

confidence: 99%

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

Langmüller

Schlötterer²

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...

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“…Recent methods focus on haplotype block estimations and analyses (Franssen et al 2017;Burny et al 2020;Otte & Schlötterer 2020) so much so that the paradigm is shifting from analysis of single nucleotide polymorphisms to selected haplotype blocks (Barghi & Schlötterer 2019).…”

Section: Discussionmentioning

confidence: 99%

Individual-based simulations of genome evolution with ancestry: the GenomeAdmixR R package

Janzen

Dı́az

2020

Preprint

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Hybridization between populations or species results in a mosaic of the two parental genomes. Genome admixture has received increasing attention for its implications in speciation, human evolution, Evolve and Resequencing (E&R) and genetic mapping. However, a thorough understanding of how local ancestry changes after admixture, and how selection affects patterns of local ancestry remains elusive. The complexity of these questions limits analytical treatment, but these scenarios are specifically suitable for simulation. Currently, there is no simulation framework that models whole-genome evolution of local ancestry following admixture. Here, we present the R package GENOMEADMIXR, which uses an individual-based model to simulate genomic patterns of local ancestry following admixture forward in time. GENOMEADMIXR provides user-friendly functions to set up and analyze simulations under evolutionary scenarios with selection, linkage and migration. We show the flexible functionality of the GENOMEADMIXR workflow by demonstrating 1) how to design an E&R simulation using GENOMEADMIXR and 2) how to use GENOMEADMIXR to verify analytical expectations following from the theory of junctions. GENOMEADMIXR provides a mechanistic approach to explore expected genome responses to realistic admixture scenarios. With this package, we aim to aid researchers in testing specific hypotheses based on empirical findings involving admixing populations.

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Secondary Evolve and Resequencing: An Experimental Confirmation of Putative Selection Targets without Phenotyping

Cited by 14 publications

References 36 publications

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

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

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

Individual-based simulations of genome evolution with ancestry: the GenomeAdmixR R package

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