bioRxivGenomicsPreprint
Role of aridity in shaping adaptive genomic divergence and population connectivity in a Southern African rodent
Hamilcar KeilaniiD, Anna-Sophie Fiston-LavieriD, Pierre Caminade, Anne LoiseauiD, Maxime GalaniD, Hugues ParrinelloiD, Carine BrouatiD, Raphaël LebloisiD, Nico L. AvenantiD, Neville PillayiD, Guila GanemiD, Carole M. SmadjaiD
Abstract
Elucidating the drivers of evolution in dry environments is central to understanding how organisms respond to climate change. While research on the genomics of adaptation is growing, aridity-driven intraspecific divergence remains poorly quantified. Here, we address this gap by using genomic data from 230 individuals of the arid-adapted four-striped mouse Rhabdomys bechuanae, sampled across an aridity gradient in southern Africa, a region facing increasing aridification. Combining these data with palaeoclimatic reconstructions and present-day aridity indices, we investigate, from a spatio-temporal perspective, how intraspecific genetic variation relates to aridity. Inference of past effective population size revealed a sharp decline in the late Pleistocene, coinciding with regional aridification and potentially reflecting changes in connectivity during dry periods. Current population structure followed a pattern of isolation by distance and mirrored the aridity gradient. Genotype-Environment Association analyses identified SNPs and genes significantly associated with aridity and genetically differentiated among populations, with functions related to water and energy conservation - as expected under arid conditions - as well as neurotransmission. These findings highlight the underappreciated role for neurological processes in coping with water and resource scarcity. More broadly, our integrative genomics approach suggests that aridity shapes population connectivity and adaptation, with implications for climate resilience.
- organism: mouse
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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