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

Transcriptional signature of host shift in the seed beetle Zabrotes subfasciatus

In phytophagous insects, adaptation to a new host is a dynamic process, in which early and later steps may be underpinned by different features of the insect genome. Here, we tested the hypothesis that early steps of this process are underpinned by a shift in gene expression patterns. We set up a short-term artificial selection experiment (10 generations) for the use of an alternative host (Cicer arietinum) on populations of the bean beetle Zabrotes subfasciatus. Using Illumina sequencing on young adult females, we show the selected populations differ in the expression of genes associated to stimuli, signalling, and developmental processes. Particularly, the "C. arietinum" population shows upregulation of histone methylation genes, which may constitute a strategy for fine-tuning the insect global gene expression network. Using qPCR on body regions, we demonstrated that the "Phaseolus vulgaris" population upregulates the genes polygalacturonase and egalitarian and that the expression of an odorant receptor transcript variant changes over generations. Moreover, in this population we detected the existence of vitellogenin (Vg) variants in both males and females, possibly harbouring canonical reproductive function in females and extracellular unknown functions in males. This study provides the basis for future genomic investigations seeking to shed light on the nature of the proximate mechanisms involved in promoting differential gene expression associated to insect development and adaptation to new hosts.

Haplotype based testing for a better understanding of the selective architecture

BACKGROUND

The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies.

RESULTS

Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits.

CONCLUSIONS

Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Artificial selection reveals complex genetic architecture of shoot branching and its response to nitrate supply in Arabidopsis

Quantitative traits may be controlled by many loci, many alleles at each locus, and subject to genotype-by-environment interactions, making them difficult to map. One example of such a complex trait is shoot branching in the model plant Arabidopsis, and its plasticity in response to nitrate. Here, we use artificial selection under contrasting nitrate supplies to dissect the genetic architecture of this complex trait, where loci identified by association mapping failed to explain heritability estimates. We found a consistent response to selection for high branching, with correlated responses in other traits such as plasticity and flowering time. Genome-wide scans for selection and simulations suggest that at least tens of loci control this trait, with a distinct genetic architecture between low and high nitrate treatments. While signals of selection could be detected in the populations selected for high branching on low nitrate, there was very little overlap in the regions selected in three independent populations. Thus the regulatory network controlling shoot branching can be tuned in different ways to give similar phenotypes.

Evolution of phenotypic variance in response to a novel hot environment

Shifts in trait means are widely considered as evidence for adaptive responses, but the impact on phenotypic variance remains largely unexplored. Classic quantitative genetics provides a theoretical framework to predict how selection on phenotypic mean affects the variance. In addition to this indirect effect, it is also possible that the variance of the trait is the direct target of selection, but experimentally characterized cases are rare. 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 populations, the variance of 125 and 97 genes was significantly reduced. We propose that the drastic loss in environmental complexity from nature to the laboratory may have triggered selection for reduced variance. Our observation that selection could drive changes in the variance of gene expression could have important implications for studies of adaptation processes in natural and experimental populations.

Highly Parallel Genomic Selection Response in Replicated Drosophila melanogaster Populations with Reduced Genetic Variation

Many adaptive traits are polygenic and frequently more loci contributing to the phenotype are segregating than needed to express the phenotypic optimum. Experimental evolution with replicated populations adapting to a new controlled environment provides a powerful approach to study polygenic adaptation. Because genetic redundancy often results in nonparallel selection responses among replicates, we propose a modified evolve and resequence (E&R) design that maximizes the similarity among replicates. Rather than starting from many founders, we only use two inbred Drosophila melanogaster strains and expose them to a very extreme, hot temperature environment (29 °C). After 20 generations, we detect many genomic regions with a strong, highly parallel selection response in 10 evolved replicates. The X chromosome has a more pronounced selection response than the autosomes, which may be attributed to dominance effects. Furthermore, we find that the median selection coefficient for all chromosomes is higher in our two-genotype experiment than in classic E&R studies. Because two random genomes harbor sufficient variation for adaptive responses, we propose that this approach is particularly well-suited for the analysis of polygenic adaptation.

Genomic time-series data show that gene flow maintains high genetic diversity despite substantial genetic drift in a butterfly species

Effective population size affects the efficacy of selection, rate of evolution by drift, and neutral diversity levels. When species are subdivided into multiple populations connected by gene flow, evolutionary processes can depend on global or local effective population sizes. Theory predicts that high levels of diversity might be maintained by gene flow, even very low levels of gene flow, consistent with species long-term effective population size, but tests of this idea are mostly lacking. Here, we show that Lycaeides buttery populations maintain low contemporary (variance) effective population sizes (e.g., ~200 individuals) and thus evolve rapidly by genetic drift. In contrast, populations harbored high levels of genetic diversity consistent with an effective population size several orders of magnitude larger. We hypothesized that the differences in the magnitude and variability of contemporary versus long-term effective population sizes were caused by gene flow of sufficient magnitude to maintain diversity but only subtly affect evolution on generational time scales. Consistent with this hypothesis, we detected low but non-trivial gene flow among populations. Furthermore, using short-term population-genomic time-series data, we documented patterns consistent with predictions from this hypothesis, including a weak but detectable excess of evolutionary change in the direction of the mean (migrant gene pool) allele frequencies across populations, and consistency in the direction of allele frequency change over time. The documented decoupling of diversity levels and short-term change by drift in Lycaeides has implications for our understanding of contemporary evolution and the maintenance of genetic variation in the wild.

Fine Mapping without Phenotyping: Identification of Selection Targets in Secondary Evolve and Resequence Experiments

Evolve and Resequence (E&R) studies investigate the genomic selection response of populations in an Experimental Evolution setup. Despite the popularity of E&R, empirical studies in sexually reproducing organisms typically suffer from an excess of candidate loci due to linkage disequilibrium, and single gene or SNP resolution is the exception rather than the rule. Recently, so-called "secondary E&R" has been suggested as promising experimental follow-up procedure to confirm putatively selected regions from a primary E&R study. Secondary E&R provides also the opportunity to increase mapping resolution by allowing for additional recombination events, which separate the selection target from neutral hitchhikers. Here, we use computer simulations to assess the effect of different crossing schemes, population size, experimental duration, and number of replicates on the power and resolution of secondary E&R. We find that the crossing scheme and population size are crucial factors determining power and resolution of secondary E&R: A simple crossing scheme with few founder lines consistently outcompetes crossing schemes where evolved populations from a primary E&R experiment are mixed with a complex ancestral founder population. Regardless of the experimental design tested, a population size of at least 4,800 individuals, which is roughly five times larger than population sizes in typical E&R studies, is required to achieve a power of at least 75%. Our study provides an important step toward improved experimental designs aiming to characterize causative SNPs in Experimental Evolution studies.

A morphological trait involved in reproductive isolation between Drosophila sister species is sensitive to temperature

Male genitalia are usually extremely divergent between closely related species, but relatively constant within one species. Here we examine the effect of temperature on the shape of the ventral branches, a male genital structure involved in reproductive isolation, in the sister species Drosophila santomea and Drosophila yakuba. We designed a semi-automatic measurement machine learning pipeline that can reliably identify curvatures and landmarks based on manually digitized contours of the ventral branches. With this method, we observed that temperature does not affect ventral branches in D. yakuba but that in D. santomea ventral branches tend to morph into a D. yakuba-like shape at lower temperature. We found that male genitalia structures involved in reproductive isolation can be relatively variable within one species and can resemble the shape of closely related species' genitalia through plasticity to temperature. Our results suggest that reproductive isolation mechanisms can be dependent on the environmental context.

Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change

"Evolve and resequence" (E&R) studies combine experimental evolution and whole-genome sequencing to interrogate the genetics underlying adaptation. Due to ease of handling, E&R work with asexual organisms such as bacteria can employ optimized experimental design, with large experiments and many generations of selection. By contrast, E&R experiments with sexually reproducing organisms are more difficult to implement, and design parameters vary dramatically among studies. Thus, efforts have been made to assess how these differences, such as number of independent replicates, or size of experimental populations, impact inference. We add to this work by investigating the role of time sampling-the number of discrete time points sequence data are collected from evolving populations. Using data from an E&R experiment with outcrossing Saccharomyces cerevisiae in which populations were sequenced 17 times over ~540 generations, we address the following questions: (a) Do more time points improve the ability to identify candidate regions underlying selection? And (b) does high-resolution sampling provide unique insight into evolutionary processes driving adaptation? We find that while time sampling does not improve the ability to identify candidate regions, high-resolution sampling does provide valuable opportunities to characterize evolutionary dynamics. Increased time sampling reveals three distinct trajectories for adaptive alleles: one consistent with classic population genetic theory (i.e., models assuming constant selection coefficients), and two where trajectories suggest more context-dependent responses (i.e., models involving dynamic selection coefficients). We conclude that while time sampling has limited impact on candidate region identification, sampling eight or more time points has clear benefits for studying complex evolutionary dynamics.

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

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.