Browsing by Subject "Local adaptation"

Now showing 1 - 2 of 2

Genomic landscape of high‐altitude adaptation in East African mountain honey bees (Apis mellifera)
(2025) Mazzoni, Marco; Loidolt, Florian; Kersten, Sonja; Amulen, Deborah Ruth; Vudriko, Patrick; Meyer, Philipp; Scharnhorst, Victor Sebastian; Scheiner, Ricarda; Hasselmann, Martin
Understanding the evolutionary processes leading to differentiation within species is a central goal in population biology. A key process is local adaptation, for which organisms evolve traits enhancing the survival and reproduction in specific environments. Honey bees ( Apis mellifera ) in East Africa are well adapted to highland environments, showing different phenotypes, including behavior, compared to lowland bees. Despite these differences, highland and lowland honey bees show very low genetic differentiation, with the exception of two segments on chromosome 7 (r7) and chromosome 9 (r9), which were previously identified as chromosomal inversions. These inversions are rare in lowland populations, suggesting a key role in adaptation to high‐elevation habitats. In this study, we obtained 24 whole genomes from honey bees of Western Uganda and compared these with existing data from Kenya. We show that the chromosomal inversions play a pivotal role in local adaptation in both regions but with substantial differentiation. Genome‐wide analysis of polymorphism revealed additional genomic regions potentially involved in high‐altitude adaptation. The acquisition of transcriptome data from highland and lowland honey bees in Uganda has enabled the first insights into the differential expression of genes between these bees. Our findings elucidate the involvement of genes in behavioral and oxygen consumption processes. This paves the way to clarify the interplay of r7 and r9 with gene expression and to unravel the regulatory network underlying A. mellifera adaptation to high‐elevation habitats. Our study will contribute to a better understanding of the evolutionary processes in honey bee populations driven by environmental conditions.
Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions
(2025) Haupt, Max; Schmid, Karl
Background: Understanding how crops adapt to specific environmental conditions is becoming increasingly important in the face of accelerating climate change, but the genetics of local adaptation remains little understood for many crops. Landscape genomics can reveal patterns of genetic variation that indicate adaptive diversification during crop evolution and dispersal. Here, we examine genetic differentiation and association signatures with environmental gradients in soybean ( Glycine max ) germplasm groups from China that were inferred from the USDA Soybean Germplasm Collection ( N = 17, 019 accessions) based on population structure and passport information. Results: We recover genes previously known to be involved in soybean environmental adaptation and report numerous new candidate genes in adaptation signatures implicated by genomic resources such as the genome annotation and gene expression datasets to function in flowering regulation, photoperiodism and stress reaction cascades. Linkage disequilibrium network analysis suggested functional relationships between genomic regions with signatures of genetic differentiation, consistent with a polygenic nature of environmental adaptation. We tested whether haplotypes associated with environmental adaptation in China were present in 843 North American and 160 European soybean cultivars and found that haplotypes in major genes for early maturity have been selected during breeding, but also that a large number of haplotypes exhibiting putative adaptive variation for cold regions at high latitudes are underrepresented in modern cultivars. Conclusions: Our results demonstrate the value of landscape genomics analysis of genebank accessions studying crop environmental adaptation and to inform future research and breeding efforts for improved adaptation of soybean and other crops to future climates.