Temperature, rainfall and wind variables underlie environmental adaptation in natural populations of Drosophila melanogaster

Abstract

While several studies in a diverse set of species have shed light on the genes underlying adaptation, our knowledge on the selective pressures that explain the observed patterns lags behind. Drosophila melanogaster is a valuable organism to study environmental adaptation because this species originated in Southern Africa and has recently expanded worldwide, and also because it has a functionally well‐annotated genome. In this study, we aimed to decipher which environmental variables are relevant for adaptation of D. melanogaster natural populations in Europe and North America. We analysed 36 whole‐genome pool‐seq samples of D. melanogaster natural populations collected in 20 European and 11 North American locations. We used the BayPass software to identify single nucleotide polymorphisms (SNPs) and transposable elements (TEs) showing signature of adaptive differentiation across populations, as well as significant associations with 59 environmental variables related to temperature, rainfall, evaporation, solar radiation, wind, daylight hours, and soil type. We found that in addition to temperature and rainfall, wind related variables are also relevant for D. melanogaster environmental adaptation. Interestingly, 23%–51% of the genes that showed significant associations with environmental variables were not found overly differentiated across populations. In addition to SNPs, we also identified 10 reference transposable element insertions associated with environmental variables. Our results showed that genome‐environment association analysis can identify adaptive genetic variants that are undetected by population differentiation analysis while also allowing the identification of candidate environmental drivers of adaptation.

Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila

“…Our ability to detect a shared genetic basis of seasonal adaptation across these broadly distributed populations implies that there are generic features in the environment that exert consistent selective pressures despite the environmental heterogeneity across sampling times and locations. It is possible that such generic selective pressures relate to temperature, humidity, population density, resource availability, or other biotic and abiotic factors (Bogaerts-Márquez et al, 2020). The seasonal and latitudinal parallelism in allele frequency change that we observe (Figure 2E) suggests that the environmental factors that drive these patterns must both shift between seasons locally and vary spatially from south to north.…”

Population-scale long-read sequencing uncovers transposable elements associated with gene expression variation and adaptive signatures in Drosophila

“…We next performed GO enrichment analysis with all the genes located nearby candidate adaptive TE insertions identified so far in D. melanogaster , including 84 TEs reported in 33 , five other insertions recently described by Bogaerts-Márquez et al 71 , and the 18 TEs identified in this work, including the previously described Accord insertion (107 insertions in total). Biological process GO term analysis identified clusters enriched for response to stimulus, behavior, and development and morphogenesis as the ones showing the highest enrichment scores (Fig.…”

Influence of environmental conditions at spawning sites and migration routes on adaptive variation and population connectivity in Chinook salmon

“…Since AutoLM provided just a few candidate SNPs (total of eight SNPs) across all four environmental datasets tested and which were not consistent with other GEA results, we interpreted those results as false positives and did not include them in downstream analyses. Among our seven populations, which correspond to seven data points for each variable, we may not have represented the full extent of the variation in environmental predictors (Bogaerts‐Márquez et al, 2021). This loss of statistical power could explain why the RDA multivariate approach and the AutoLM univariate approach were not effective for our GEA analyses.…”