Genomic Analysis of European Drosophila melanogaster Populations Reveals Longitudinal Structure, Continent-Wide Selection, and Previously Unknown DNA Viruses

Abstract

<jats:title>Abstract</jats:title> <jats:p>Genetic variation is the fuel of evolution, with standing genetic variation especially important for short-term evolution and local adaptation. To date, studies of spatiotemporal patterns of genetic variation in natural populations have been challenging, as comprehensive sampling is logistically difficult, and sequencing of entire populations costly. Here, we address these issues using a collaborative approach, sequencing 48 pooled population samples from 32 locations, and perform the first continent-wide genomic analysis of genetic variation in European Drosophila melanogaster. Our analyses uncover longitudinal population structure, provide evidence for continent-wide selective sweeps, identify candidate genes for local climate adaptation, and document clines in chromosomal inversion and transposable element frequencies. We also characterize variation among populations in the composition of the fly microbiome, and identify five new DNA viruses in our samples.</jats:p>

Cuticle darkening correlates with increased body copper content in Drosophila melanogaster

“…A discussion of why in some populations two traits may be co-segregating, whereas in others not, has already been offered (Rajpurohit et al 2016a). The argument boils down to the fact that adaptation under selective pressure takes different routes in most settings and is largely influenced by the pre-existing genetic variability in any given population (Gibbs 1999;Kapun et al 2020).…”

Locally adaptive inversions modulate genetic variation at different geographic scales in a seaweed fly

“…Despite more than 1,500km (or 3,000 km of coastal distance) between the most distant populations, geographic genetic differentiation was very weak (Maximal FST <0.02). This is much lower than other coastal specialised insects such as the saltmarsh beetle Pogonus chalceus (FST ~0.2, (Van Belleghem et al 2018) but comparable to small Diptera with large distributions like Drosophila melanogaster or D. simulans which typically exhibit Fst around 0.01-0.03, likely resulting from both high migration rate and large effective population size Ne (Machado et al 2016;Kapun et al 2020). Despite this weak genetic structure, we detected a strong signal of isolation-by-distance indicating that dispersal among populations and subsequent gene flow decreases with distance.…”

Thermal Plasticity in Insects’ Response to Climate Change and to Multifactorial Environments

“…Heat-shock genes also come up as significant hits in some (e.g., but not all (e.g., Mallard et al, 2018) genomic-level searches. Accumulating genomic studies in Drosophila melanogaster, using different natural and experimental populations and different approaches (Klepsatel et al, 2013;Tobler et al, 2014;Gerken et al, 2015;Machado et al, 2016;Porcelli et al, 2016;Fabian et al, 2017;Lafuente et al, 2018;Rolandi et al, 2018;Kapun et al, 2020), are building an unprecedently powerful body of data to assess the genomic basis of thermal adaptation, and its repeatability and relationship to thermal plasticity and thermal tolerance. In the future, integration of studies covering different species, different geographical and temporal scales, and different approaches will undoubtedly help shed much needed light onto the genomics of thermal plasticity, as well as its overlap with the genomics of thermal tolerance and thermal adaptation.…”

“…Our understanding of the phenotypic and genotypic change that accompanies adaptation of insects to complex environments relies on different types of studies. Studies of natural populations include both "snap-shot" and longitudinal comparisons between populations living in different environments (Reinhardt et al, 2014;Manenti et al, 2017;Lerat et al, 2019;Kapun et al, 2020). While studies of natural populations make it possible to detect genetic and phenotypic differentiation and, sometimes, associate the two, it is generally very difficult to know exactly which environmental variables explain divergence and how.…”