Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens

• Published in: BMC genomics Vol. 25; no. 1; pp. 284 - 20
• Main Authors: Kebede, Fasil Getachew, Derks, Martijn F L, Dessie, Tadelle, Hanotte, Olivier, Barros, Carolina Pita, Crooijmans, Richard P M A, Komen, Hans, Bastiaansen, John W M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18-03-2024; BioMed Central; BMC
• Subjects: Adaptability; Adaptation; Analysis; Animals; Biodiversity; Calcium; Calcium signalling; Chickens; Chickens - genetics; Climate change; Datasets; Diseases; DNA binding proteins; Domestication; Ecology; Environmental conditions; Environmental predictors; Gene sequencing; Genes; Genetic aspects; Genetic diversity; Genetic improvement; Genetic Variation; Genome; Genomes; Genomics; Genomics - methods; Genotype; Genotype & phenotype; Genotypes; Global temperature changes; Gradients; Growth; High altitude; Humans; Hypoxia; Hypoxia-inducible factors; Landscape; Livelihoods; Livestock; Livestock production; Local adaptation; Machine learning; Mating; Natural selection; Outliers (statistics); Phenotypes; Phenotypic variation; Phenotypic variations; Polymorphism, Single Nucleotide; Population studies; Populations; Poultry; Poultry production; Quantitative traits; Redundancy; Redundancy analysis; Reproductive behaviour; Selection, Genetic; Signal transduction; Signatures; Signatures of selection; Single-nucleotide polymorphism; Statistical analysis; Sustainable production; Transcription; Transcription factors; Tropical environments; Vegetation; Water availability; South Africa; China; Ethiopia; Environmental predictors; Local adaptation; Genetic improvement; Signatures of selection; Quantitative traits; Redundancy analysis; Poultry production
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-024-10193-6

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.