DNA Motifs Are Not General Predictors of Recombination in Two Drosophila Sister Species

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: Data Availability Statementmentioning

confidence: 99%

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

Langmüller¹,

2020

Molecular Ecology

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“…We refer to the haplotype blocks in this cluster as early detected haplotype blocks (EDHAs). We investigated whether EDHAs have distinct characteristics that might explain why they are early detectable by comparing the following features between EDHAs, and non-EDHAs: haplotype block length, median starting allele frequency, average recombination rate (Howie et al, 2019), selection coefficient (s, estimated with poolSeq (Taus et al, 2017)…”

Section: Early Detected Haplotype Blocks (Edhas)mentioning

confidence: 99%

“…Because the distinction between true causative SNPs and linked polymorphisms is challenging (Nuzhdin and Turner, 2013;Tobler et al, 2014), we obtained a simulated data set that models linkage and resembles the Barghi et al (2019) study using MimicrEE2 (v. 206) (Vlachos and Kofler, 2018). Our ancestral population was built from 189 ancestral haplotypes published in Barghi et al (2019) and Howie et al (2019). We simulated a selective sweep scenario with 99 independent selection targets, where each of them is located in the inferred selected haplotype block, has the same starting frequency, and selection coefficient (both estimated as the median from all selected SNPs in the block) (Barghi et al, 2019).…”

Section: Simulation Of An Eandr Experiments Simulation With Linkagementioning

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...

“…Reduction of haplotype diversity in populations maintained for many generations without selection. We simulated 1,037,324 SNPs on chromosome 2L in a population of 1,000 diploid individuals for 500 generations using 189 founder haplotypes Howie et al, () and D. melanogaster recombination rate (Comeron et al, ). Computer simulations were performed using MimicrEE2 (Vlachos & Kofler, ).…”

mentioning

confidence: 99%

“…Given the apparent problems caused by segregating inversions in D. melanogaster , we recently proposed to establish Drosophila simulans as an alternative model for experimental evolution (Barghi, Tobler, Nolte, & Schlötterer, ). This species lacks segregating inversions and has a higher recombination rate, which remains almost uniform across entire chromosome arms (Howie, Mazzucco, Taus, & Schlötterer, ), providing a higher resolution in E&R studies. The advantage of D. simulans was confirmed in a recent E&R experiment studying temperature adaptation in the species, which resulted in identification of a few distinct selection signatures (Mallard, Nolte, Tobler, Kapun, & Schlötterer, ).…”

mentioning

confidence: 99%

Shifting the paradigm in Evolve and Resequence studies: From analysis of single nucleotide polymorphisms to selected haplotype blocks

Barghi¹,

2019

Molecular Ecology

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For almost a decade the combination of whole genome sequencing with experimental evolution (Evolve and Resequence, E&R; Turner, Stewart, Fields, Rice, & Tarone, 2011) has been used to study adaptation in outcrossing organisms. However, complications caused by inversions and hitchhiking variants have prevented this powerful approach from living up to its potential. In this issue of Molecular Ecology, Michalak, Kang, Schou, Garner, and Loeschke (2018), provide an important step ahead by using a population of Drosophila melanogaster devoid of segregating inversions to identify the genetic basis of resistance to five environmental stressors. They further address the challenge of hitchhiking variants by reconstructing selected haplotype blocks. While it is apparent that the haplotype block reconstruction needs further refinements, their work underpins the potential of E&R studies in Drosophila to address fundamental questions in evolutionary biology.