Abstract
Breeding for climate resilience is currently an important goal for sustainable livestock production. Local adaptations exhibited by indigenous livestock allow investigating the genetic control of this resilience. Ecological niche modeling (ENM) provides a powerful avenue to identify the main environmental drivers of selection. Here, we applied an integrative approach combining ENM with genome-wide selection signature analyses (XPEHH and Fst) and genotype−environment association (redundancy analysis), with the aim of identifying the genomic signatures of adaptation in African village chickens. By dissecting 34 agro-climatic variables from the ecosystems of 25 Ethiopian village chicken populations, ENM identified six key drivers of environmental challenges: One temperature variable—strongly correlated with elevation, three precipitation variables as proxies for water availability, and two soil/land cover variables as proxies of food availability for foraging chickens. Genome analyses based on whole-genome sequencing (n = 245), identified a few strongly supported genomic regions under selection for environmental challenges related to altitude, temperature, water scarcity, and food availability. These regions harbor several gene clusters including regulatory genes, suggesting a predominantly oligogenic control of environmental adaptation. Few candidate genes detected in relation to heat-stress, indicates likely epigenetic regulation of thermo-tolerance for a domestic species originating from a tropical Asian wild ancestor. These results provide possible explanations for the rapid past adaptation of chickens to diverse African agro-ecologies, while also representing new landmarks for sustainable breeding improvement for climate resilience. We show that the pre-identification of key environmental drivers, followed by genomic investigation, provides a powerful new approach for elucidating adaptation in domestic animals.
Highlights
The global livestock sector is facing a major threat from climate change
By dissecting 34 agro-climatic variables from the ecosystems of 25 Ethiopian village chicken populations, Ecological Niche Modelling (ENM) identified six key drivers of environmental challenges: one temperature variable - strongly correlated with elevation, three precipitation variables as proxies for water availability, and two soil/land cover variables as proxies of food availability for foraging chickens
We show that preidentification of key environmental drivers, followed by genomic investigation, provides a powerful new approach for elucidating adaptation in domestic animals
Summary
The global livestock sector is facing a major threat from climate change. Extreme weather and global warming are challenging the physiological tolerance of animals and adversely affecting their ecosystems, leading to changes in the quality and quantity of livestock feed or forage, water availability, and disease prevalence (Rojas-Downing, et al 2017; Rashamol and Sejian 2018). From the Low LandUse group, several genes overlapped with strong signalling SRs from a single approach (ZFst > 8 or |XPEHH_std|> 4) (Supplementary Figure S13), including AGMO which has roles in lipid metabolism and feed efficiency in chicken (Izadnia, et al 2019), MED8 – involved in transcriptional regulation, and SZT2 – involved in cellular response to amino-acid and glucose starvation (Uniprot). In many instances, we have found important biologically relevant candidate genes residing in the same selection region or at close proximity; e.g. we detected nine major candidate genes associated with hypoxia, low temperature, and thrombosis from a single SR in chr, cluster of eight genes from chr in relation to water scarcity stress, and cluster of four heme-binding genes detected in chr from the SoilOrgC analysis Such gene clusters may be at the root of rapid adaptation to extreme environment by being under the genetic control of one or a few regulatory variants only. These can be extremely valuable in conservation of important livestock genetic resources to meet future demand
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