bioRxivEvolutionary Biology
Genome-wide patterns of local adaptation in Drosophila melanogaster: adding intra European variability to the map
Abstract
Signatures of spatially varying selection have been investigated both at the genomic and transcriptomic level in several organisms. In Drosophila melanogaster, the majority of these studies have analyzed North American and Australian populations, leading to the identification of several loci and traits under selection. However, populations in these two continents showed evidence of admixture that likely contributed to the observed population differentiation patterns. Thus, disentangling demography from selection is challenging when analyzing these populations. European populations could be a suitable system to identify loci under spatially varying selection provided that no recent admixture from African populations would have occurred. In this work, we individually sequence the genome of 42 European strains collected in populations from contrasting environments: Stockholm (Sweden), and Castellana Grotte, (Southern Italy). We found low levels of population structure and no evidence of recent African admixture in these two populations. We thus look for patterns of spatially varying selection affecting individual genes and gene sets. Besides single nucleotide polymorphisms, we also investigate the role of transposable elements in local adaptation. We concluded that European populations are a good dataset to identify loci under spatially varying selection. The analysis of the two populations sequenced in this work in the context of all the available D. melanogaster data allowed us to pinpoint genes and biological processes relevant for local adaptation. Identifying and analyzing populations with low levels of population structure and admixture should help to disentangle selective from non-selective forces underlying patterns of population differentiation in other species as well.
- organism: Drosophila
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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