The evolutionary dynamics of transposable elements (TEs) are still poorly understood. One reason is that TE abundance needs to be studied at the population level, but sequencing individuals on a population scale is still too expensive to characterize TE abundance in multiple populations. Although sequencing pools of individuals dramatically reduces sequencing costs, a comparison of TE abundance between pooled samples has been difficult, if not impossible, due to various biases. Here, we introduce a novel bioinformatic tool, PoPoolationTE2, which is specifically tailored for the comparison of TE abundance among pooled population samples or different tissues. Using computer simulations, we demonstrate that PoPoolationTE2 not only faithfully recovers TE insertion frequencies and positions but, by homogenizing the power to identify TEs across samples, it provides an unbiased comparison of TE abundance between pooled population samples. We anticipate that PoPoolationTE2 will greatly facilitate the analysis of TE insertion patterns in a broad range of applications.
The cost-effectiveness of sequencing pools of individuals (Pool-Seq) provides the basis for the popularity and wide-spread use of this method for many research questions, ranging from unravelling the genetic basis of complex traits to the clonal evolution of cancer cells. Because the accuracy of Pool-Seq could be affected by many potential sources of error, several studies determined, for example, the influence of the sequencing technology, the library preparation protocol, and mapping parameters. Nevertheless, the impact of the mapping tools has not yet been evaluated. Using simulated and real Pool-Seq data, we demonstrate a substantial impact of the mapping tools leading to characteristic false positives in genome-wide scans. The problem of false positives was particularly pronounced when data with different read lengths and insert sizes were compared. Out of 14 evaluated algorithms novoalign, bwa mem and clc4 are most suitable for mapping Pool-Seq data. Nevertheless, no single algorithm is sufficient for avoiding all false positives. We show that the intersection of the results of two mapping algorithms provides a simple, yet effective strategy to eliminate false positives. We propose that the implementation of a consistent Pool-seq bioinformatics pipeline building on the recommendations of this study can substantially increase the reliability of Pool-Seq results, in particular when libraries generated with different protocols are being compared. * contributed equally
The genetic architecture of adaptive traits is of key importance to predict evolutionary responses. Most adaptive traits are polygenic -i.e. result from selection on a large number of genetic loci -but most molecularly characterized traits have a simple genetic basis. This discrepancy is best explained by the difficulty in detecting small allele frequency changes across many contributing loci. To resolve this, we use laboratory natural selection, a framework that is powerful enough to detect signatures for selective sweeps and polygenic adaptation. We exposed 10 replicates of a Drosophila simulans population to a new temperature regime and uncovered a polygenic architecture of an adaptive trait with high genetic redundancy among adaptive alleles. We observed convergent phenotypic responses, e.g. fitness, metabolic rate and fat content, and a strong polygenic response (99 selected alleles; mean s=0.061). However, each of these selected alleles increased in frequency only in a subset of the evolving replicates. Our results show that natural D. simulans populations harbor a vast reservoir of adaptive variation facilitating rapid evolutionary responses. The observed genetic redundancy potentiates this genotypic variation through multiple genetic pathways leading to phenotypic convergence. This key property of adaptive alleles requires the modification of testing strategies in natural populations beyond the search for convergence on the molecular level.
Evolve and re-sequencing (E&R) studies investigate the genomic responses of adaptation during experimental evolution. Because replicate populations evolve in the same controlled environment, consistent responses to selection across replicates are frequently used to identify reliable candidate regions that underlie adaptation to a new environment. However, recent work demonstrated that selection signatures can be restricted to one or a few replicate(s) only. These selection signatures frequently have a weak statistical support, and given the difficulties of functional validation, additional evidence is needed before considering them as candidates for functional analysis. Here, we introduce an experimental procedure to validate candidate loci with weak or replicate-specific selection signature(s). Crossing an evolved population from a primary E&R experiment to the ancestral founder population reduces the frequency of candidate alleles that have reached a high frequency. We hypothesize that genuine selection targets will experience a repeatable frequency increase after the mixing with the ancestral founders if they are exposed to the same environment (secondary E&R experiment). Using this approach, we successfully validate two overlapping selection targets, which showed a mutually exclusive selection signature in a primary E&R experiment of Drosophila simulans adapting to a novel temperature regime. We conclude that secondary E&R experiments provide a reliable confirmation of selection signatures that are either not replicated or show only a low statistical significance in a primary E&R experiment. Such experiments are particularly helpful to prioritize candidate loci for time-consuming functional follow-up investigations.
The invasion of transposable elements (TEs) in mammals and invertebrates, is likely stopped by piRNAs that emerge after insertion of the TE into a piRNA cluster (the trap model). It remains, however, still unclear which factors influence the dynamics of TE invasions. The activity of the TE (i.e. transposition rate) is one of the frequently discussed key factors. Here we take advantage of the temperature-dependent activity of the P-element, a widely studied eukaryotic TE, to test how TE activity affects the dynamics of a TE invasion. Taking advantage of the temperature-specific activity of the P-element, we monitored its invasion dynamics in experimental Drosophila simulans populations at hot and cold culture conditions. Despite marked differences in transposition rates, the P-element reached very similar copy numbers at both temperatures. The reduction of the insertion rate upon approaching the copy number plateau was accompanied by the emergence of similar amounts of piRNAs against the P-element at both temperatures. Interestingly, the ping-pong cycle, which degrades TE transcripts, becomes only active after the copy number has reached the plateau. We show that the P-element abundance is an order of magnitude lower and fewer P-element insertions in piRNA clusters were observed than expected under the trap model. We conclude that the transposition rate has at the most only a minor influence on TE abundance, but other factors, such as paramutations or selection against TE insertions are shaping the TE composition of organism.
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