Integrated environmental and genomic analysis reveals the drivers of local adaptation in African indigenous chickens

Abstract

Elucidating the genetic basis of environmental adaptation in indigenous livestock populations has important implications for sustainable breeding improvement. It requires a detailed untangling of relevant environmental pressures and fine resolution detection of genomic signatures of selection associated with these environmental parameters. Here, we uniquely employed an integrative approach, combining Ecological Niche Modelling (ENM) with (i) genome-wide analyses of positive signatures of selection (SSA) and (ii) genotype-environment association (GEA) analyses to unravel the environmental adaptation of Ethiopian indigenous chickens. We first examined 34 agro-ecological and climatic variables and identified six main environmental selection drivers (one temperature - strongly correlated to elevation, three precipitation, and two soil/land variables). We then performed genomic analyses using high-density SNP data from whole-genome sequencing of 245 Ethiopian chickens from 25 different populations. Environmental genomic association analyses (SSA and GEA) identify a few strongly supported selected genomic regions, often with clusters of candidate genes, related to altitude-induced stresses (hypoxia, thrombosis, and cold temperatures), water scarcity, and the challenges of scavenging feeding behaviour. These analyses support a predominantly oligogenic control of environmental adaptation and possible regulation by one or few genetic elements only. Our study shows that the pre-identification of the key environmental drivers of adaptation, followed by a detailed genomic investigation of the associated genetic mechanisms provides a powerful new approach for elucidating the effect of natural selection in domestic animals. These results represent new landmarks to inform sustainable poultry breeding improvement.

Whole-Genome Sequence Data Suggest Environmental Adaptation of Ethiopian Sheep Populations

“…The protein encoded by SDK1 is a member of the immunoglobulin superfamily and is highly expressed in artery-aorta and artery-tibial tissues (GTEx; Lonsdale et al 2013 ) (it is not present in the Sheep Gene Expression Atlas). SNPs near/in this gene were previously associated with low oxygen saturation in humans from Ethiopian high-altitude regions ( Alkorta-Aranburu et al 2012 ) and it was identified as a candidate for high altitude/low temperature adaptation in chickens sampled across Ethiopia ( Gheyas et al 2020 ). It has been associated with a wide range of phenotypes/conditions in humans, including retinal development, brain activity and certain cancers, and with several other traits in livestock, including feed conversion rate in cattle ( Barendse et al 2007 ), coat color in goats ( Nazari-Ghadikolaei et al 2018 ) and meat quality of pork (juiciness, intramuscular fat) ( Ji et al 2018 ).…”

Species and Phenotypic Distribution Models Reveal Population Differentiation in Ethiopian Indigenous Chickens

“…Ecological variables vary in terms of their influences on organisms as inducers of local adaptation. Knowledge of ecological factors responsible for adaptive variation should be the first step to design selective breeding programs on indigenous livestock, plan crossbreeding with improved genotypes, or introduce new genotypes from a different environment (Fleming et al, 2017;Bettridge et al, 2018;Birhanu et al, 2021;Gheyas et al, 2021).…”

“…For every population, a single geographic coordinate was taken at the center of the village during sampling of chickens. Coordinates from nine additional grids (1.44 km 2 ), covering a total of 12.96 km 2 , were then drawn around a recorded location and extracted using Google Earth Pro v 7.3.2 to ensure high representation of environmental variability affecting the population ( Gheyas et al, 2021 ). This way the total number of “presence” or “occurrence” points used in SDMs for the 26 sample populations comprised 260 coordinates.…”

“…Species distribution models (SDMs), implemented in MaxEnt ( Phillips et al, 2006 ) and similar software, predict distribution of a species based on presence-only data, estimate the contribution of environmental variables, and help identify suitable habitats in current and future environments. Gheyas et al (2021) and Lozano-Jaramillo M. et al (2019) applied SDMs to produce suitability maps of Ethiopian chickens and identify important environmental variables associated with habitat suitability in chickens, without relating ecological differences with phenotypic variation among study populations. When used alone, SDMs treat a species as an evolutionarily homogenous entity and fail to consider possible population differences pertaining to local adaptation ( Hampe, 2004 ).…”

Overview of Poultry Management as a Key Factor for Solving Food and Nutritional Security with a Special Focus on Chicken Breeding in East African Countries

“…Nevertheless, it is considered highly probable that these traits are not encoded by single major genes but are the result of the interaction of multiple genes [ 101 ], thus it could also be highly desirable to be incorporated into the development of high-performance IC for the tropics. For instance, recent evidence revealed some genomic regions under positive selection which are associated with water scarcity, scavenging challenges, feeding behavior, and altitude-induced stresses from a population of 245 Ethiopian IC selected from 34 different agro-ecological zones [ 108 ]. Despite the increased awareness about the importance of indigenous animal breeds, which includes their long history of adaptation to extreme habitats, little effort has been done to harness the genetic potential of African IC ecotypes in general.…”