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Published on: August 12, 2019
Genome-wide selection signatures in Ramlıç sheep identified by complementary ROH and iHS analyses
Orhan Eser1, Mustafa Tekerli1, Mustafa Demirtaş1
1Department of Animal Science, Faculty of Veterinary Medicine, Afyon Kocatepe University, Afyonkarahisar, Türkiye.
Introduction:
Ramlıç is a composite sheep breed developed in Türkiye through crossbreeding between Rambouillet and Dağlıç sheep followed by selection for economically important traits. This study aimed to characterize genomic autozygosity and identify putative selection signatures in Ramlıç sheep by integrating runs of homozygosity (ROH) and integrated haplotype score (iHS) analyses.
Methods:
A total of 96 animals were genotyped using the Illumina OvineSNP50K BeadChip, and 44,171 autosomal SNPs were retained after quality control.
Results:
Overall, 1,956 ROH segments were identified, with an average of 20.38 ROH per individual and a mean ROH-based genomic inbreeding coefficient (FROH of 4.57%). Short-to-intermediate ROH predominated, with segments of 2-<8 Mb accounting for approximately 78% of all detected ROH. Genome-wide analysis identified 66 ROH islands, with the highest maximum ROH incidence observed on OAR6. Haplotype-based analysis identified 48 candidate 500-kb windows containing at least three SNPs exceeding the empirical top 1% |iHS| threshold. Integrating both approaches revealed 15 concordant ROH-iHS regions on OAR1, OAR2, OAR6, and OAR11. The most prominent concordance was observed on OAR6 at 36.5-38.0 Mb, where the ROH island with the highest maximum incidence coincided with a cluster of extreme |iHS| signals and encompassed candidate genes including ABCG2, SPP1, MEPE, LAP3, and NCAPG, which have previously been associated with economically relevant traits in sheep.
Discussion:
These findings provide new insights into genomic autozygosity and the potential selection history of Ramlıç sheep and highlight candidate regions that warrant further investigation in larger populations using higher-resolution genomic data and functional validation.
