Massive Habitat-Specific Genomic Response in D. melanogaster Populations during Experimental Evolution in Hot and Cold Environments

Tobler, Raymond; Franssen, Susanne U.; Kofler, Robert; Orozco‐terWengel, Pablo; Nolte, Viola; Hermisson, Joachim; Schlötterer, Christian

doi:10.1093/molbev/mst205

Cited by 145 publications

(213 citation statements)

References 51 publications

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“…These results confirm the power of the E&R approach in the identification of targets of selection (32). This methodology has been used to identify changes in allele frequencies following selection in complex traits such as developmental time (7), body size (8), hypoxia tolerance (6), increased life span (33), adaptation to high/low temperatures (9,34), and courtship behavior (10,31). These studies have identified a polygenic basis for the studied traits, hampering the identification of candidate genes and a subsequent functional analysis.…”

Section: Discussionsupporting

confidence: 55%

Host adaptation to viruses relies on few genes with different cross-resistance properties

Martins

Faria

Nolte

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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125

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Significance Despite ample knowledge of the genetics and physiology of host responses to parasites, little is known about the genetic basis of host adaptation to parasites. Moreover, adaptation to one parasite is likely to impact the outcome of different infections. Yet these correlated responses, seminal to the understanding of host evolution in multiparasite environments, remain poorly studied. We determined the genetic and phenotypic changes underlying adaptation upon experimental evolution of a Drosophila melanogaster population under viral infection [ Drosophila C virus (DCV)]. After 20 generations, selected flies showed increased survival upon infection with DCV and two other viruses. Using whole-genome sequencing and through RNAi, we identified and functionally validated three genes underlying the adaptive process and revealed their differential roles in the correlated responses observed.

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Section: Discussionsupporting

confidence: 55%

Host adaptation to viruses relies on few genes with different cross-resistance properties

Martins

Faria

Nolte

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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125

208

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“…results are consistent with those of Tobler et al (2014), who observed a massive amount of outlier SNPs around the centromere of chromosome 2 and on 3R. Interestingly, certain regions of the genome show extensive differences in b N e between the replicates, which might be reflecting different selection histories or differences in demography, such as replicatespecific bottlenecks.…”

Section: Heterogeneous Bsupporting

confidence: 89%

“…Nevertheless, systematic forces that result in locally different values of b N e can be detected with a slidingwindow approach, as illustrated with simulations under selection ( Figure S15). The D. melanogaster data set also illustrates this point; i.e., the hypothesized regions under selection coincide with regions of reduced N e (Orozco-terWengel et al 2012;Tobler et al 2014;Franssen et al 2015). For this to be detected, however, most of the allele frequency change has to occur over the sampled time span.…”

Section: Recommendations For Genome-wide Data Setsmentioning

confidence: 81%

Estimating effective population size from temporal allele frequency changes in experimental evolution

Jónás¹,

Taus²,

Kosiol³

et al. 2016

Preprint

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The effective population size (N e ) is a major factor determining allele frequency changes in natural and experimental populations. Temporal methods provide a powerful and simple approach to estimate short-term N e : They use allele frequency shifts between temporal samples to calculate the standardized variance, which is directly related to N e : Here we focus on experimental evolution studies that often rely on repeated sequencing of samples in pools (Pool-seq). Pool-seq is cost-effective and often outperforms individual-based sequencing in estimating allele frequencies, but it is associated with atypical sampling properties: Additional to sampling individuals, sequencing DNA in pools leads to a second round of sampling, which increases the variance of allele frequency estimates. We propose a new estimator of N e ; which relies on allele frequency changes in temporal data and corrects for the variance in both sampling steps. In simulations, we obtain accurate N e estimates, as long as the drift variance is not too small compared to the sampling and sequencing variance. In addition to genome-wide N e estimates, we extend our method using a recursive partitioning approach to estimate N e locally along the chromosome. Since the type I error is controlled, our method permits the identification of genomic regions that differ significantly in their N e estimates. We present an application to Pool-seq data from experimental evolution with Drosophila and provide recommendations for whole-genome data. The estimator is computationally efficient and available as an R package at https://github.com/ThomasTaus/Nest. KEYWORDS effective population size; genetic drift; Pool-seq; experimental evolution D URING experimental evolution studies, populations are maintained under specific laboratory conditions (Kawecki et al. 2012;Long et al. 2015;Schlötterer et al. 2015). In sexually reproducing organisms, the census population size is typically kept fixed at fairly low numbers, rarely exceeding 2000 individuals. With such small population sizes, genetic drift causes stochastic fluctuations in allele frequencies. Under neutrality, the level of random frequency changes is determined by the effective population size (N e ) (Wright 1931). Furthermore, the efficacy of selection is influenced by N e : For weakly selected alleles, the probability of fixation is directly proportional to the product of N e and the intensity of selection (Fisher 1930;Kimura 1964). As changes in allele frequency are greatly affected by the population size, it is fundamental to estimate N e accurately to understand molecular variation in experimental evolution studies. Krimbas and Tsakas (1971) estimated N e using the standardized variance of allele frequency (F, see also Falconer and Mackay 1996) from longitudinal samples in natural populations of olive flies. As F was calculated from these samples, they accounted for the sampling variance that also contributed to the true allele frequency variance. This approach was further improved and used by sev...

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“…was studied using both long-term and realized N e . Variation in N e was reported at large scale such as entire chromosome arm (Orozco-terWengel et al 2012;Tobler et al 2014), as well as between gene fragments (Gossmann et al 2011). The other nine species studied in Gossmann et al (2011) also reported statistically significant variation in N e between gene fragments.…”

Section: Causes For the Heterogeneity In N Ementioning

confidence: 85%

“…They observed different rates of genetic drift, as estimated by loss of heterozygosity, among the chromosomes; these rates ranged from very low levels of genetic drift found in the sexual X chromosome to very high levels presented by chromosome 3. Tobler et al (2014) combined experimental evolution and next-generation sequencing (NGS) to detect candidate SNPs associated with thermal adaptation in D. melanogaster. They estimated a chromosome-specific N e for each chromosome, using a total of 1.45 million SNPs.…”

Section: Different Methods Can Be Used To Detect Heterogeneity In N Ementioning

confidence: 99%

Heterogeneity in effective population size and its implications in conservation genetics and animal breeding

Jiménez‐Mena

Bataillon

2015

Conservation Genet Resour

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Effective population size (N e ) is defined as the size of an idealized population undergoing the same rate of genetic drift as the population under study. It is a central concept in population genetics and used extensively in the design and monitoring of conservation and breeding programs. It is most often assumed that genetic drift effects are homogeneous across the genome and thus, so is N e . However, theory predicts that various processes can modify rates of genetic drift experienced by a locus in the genome. In particular, selection affecting linked neutral sites and rates of recombination can modulate the intensity of genetic drift throughout the genome. The increasing availability of sequence data has made it possible to test whether a single N e can account for the evolution of the genome. In this article, we discuss reasons why a heterogeneous N e can be expected theoretically and review what evidence is available from empirical studies. We finish by discussing potential implications for conservation and breeding practices. The evidence suggests that heterogeneity in N e can be just as important as heterogeneity in mutation rates in determining levels of genetic diversity throughout the genome.

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Massive Habitat-Specific Genomic Response in D. melanogaster Populations during Experimental Evolution in Hot and Cold Environments

Cited by 145 publications

References 51 publications

Host adaptation to viruses relies on few genes with different cross-resistance properties

Host adaptation to viruses relies on few genes with different cross-resistance properties

Estimating effective population size from temporal allele frequency changes in experimental evolution

Heterogeneity in effective population size and its implications in conservation genetics and animal breeding

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