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Genetic differentiation of mainland-island sheep of Greece: Implications for identifying candidate genes for long-term local adaptation

Antonios Kominakis, Eirini Tarsani, Ariadne L. Hager‐Theodorides, Ioannis Mastranestasis, Dimitra Gkelia, I. Hadjigeorgiou

2021PLoS ONE16 citationsDOIOpen Access PDF

Abstract

In Greece, a number of local sheep breeds are raised in a wide range of ecological niches across the country. These breeds can be used for the identification of genetic variants that contribute to local adaptation. To this end, 50k genotypes of 90 local sheep from mainland Greece (Epirus, n = 35 and Peloponnesus, n = 55) were used, as well as 147 genotypes of sheep from insular Greece (Skyros, n = 21), Lemnos, n = 36 and Lesvos, n = 90). Principal components and phylogenetic analysis along with admixture and spatial point patterns analyses suggested genetic differentiation of 'mainland-island' populations. Genome scans for signatures of selection and genome-wide association analysis (GWAS) pointed to one highly differentiating marker on OAR4 (FST = 0.39, FLK = 21.93, FDR p-value = 0.10) that also displayed genome wide significance (FDR p-value = 0.002) during GWAS. A total number of 6 positional candidate genes (LOC106990429, ZNF804B, TEX47, STEAP4, SRI and ADAM22) were identified within 500 kb flanking regions around the significant marker. In addition, two QTLs related to fat tail deposition are reported in genomic regions 800 kb downstream the significant marker. Based on gene ontology analysis and literature evidence, the identified candidate genes possess biological functions relevant to local adaptation that worth further investigation.

Topics & Concepts

BiologyCandidate geneGeneticsGenomeGenome-wide association studyGeneAdaptation (eye)Phylogenetic treeEvolutionary biologyLocal adaptationGenotypeSingle-nucleotide polymorphismPopulationMedicineNeuroscienceEnvironmental healthGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsCancer-related molecular mechanisms research