Ecological niche modelling for delineating livestock ecotypes and exploring environmental genomic adaptation: the example of Ethiopian village chicken

In evolutionary ecology, an “ecotype” is a population that is genetically adapted to specific environmental conditions. Environmental and genetic characterisation of livestock ecotypes can play a crucial role in conservation and breeding improvement, particularly to achieve climate resilience. However, livestock ecotypes are often arbitrarily defined without a detailed characterisation of their agro-ecologies. In this study, we employ a novel integrated approach, combining ecological niche modelling (ENM) with genomics, to delineate ecotypes based on environmental characterisation of population habitats and unravel the signatures of adaptive selection in the ecotype genomes. The method was applied on 25 Ethiopian village chicken populations representing diverse agro-climatic conditions. ENM identified six key environmental drivers of adaptation and delineated 12 ecotypes. Within-ecotype selection signature analyses (using Hp and iHS methods) identified 1,056 candidate sweep regions (SRs) associated with diverse biological processes. While most SRs are ecotype-specific, the biological pathways perturbed by overlapping genes are largely shared among ecotypes. A few biological pathways were shared amongst most ecotypes and the genes involved showed functions important for scavenging chickens, e.g., neuronal development/processes, immune response, vision development, and learning. Genotype-environment association using redundancy analysis (RDA) allowed for correlating ∼33% of the SRs with major environmental drivers. Inspection of some strong candidate genes from selection signature analysis and RDA showed highly relevant functions in relation to the major environmental drivers of corresponding ecotypes. This integrated approach offers a powerful tool to gain insight into the complex processes of adaptive evolution including the genotype × environment (G × E) interactions