Genomic Diversity and Autozygosity-Based Signatures of Selection in Kangal Akkaraman Sheep via Genotyping-by-Sequencing

• Published in: Veteriner fakultesi dergisi Vol. 30; no. 5; pp. 669 - 675
• Main Author: DEMİR, Eymen
• Format: Journal Article
• Language: English
• Published: Kafkas University, Faculty of Veterinary Medicine 01.09.2024
• Subjects: genetic variation; genotyping-by-sequencing; kangal akkaraman; molecular genotyping; selection signals; selective breeding
• ISSN: 1300-6045; 1309-2251
• DOI: 10.9775/kvfd.2024.32339

In this study, genome-wide variability and selection signatures in Kangal Akkaraman sheep were assessed by 238.103 bi-allelic single nucleotide polymorphisms (SNPs) recovered from genotyping-by-sequencing (GBS) libraries processed in Illumina HiSeq X Ten instrument. Summary statistics of genetic diversity such as minor allele frequency (MAF), observed (HO) and expected (HE) heterozygosity were estimated at 0.32, 0.29, and 0.30, respectively. A declining trend in effective population size was observed through generations in which the current population was estimated at 978 individuals 150 generations ago. 608 of 647 runs of homozygosity (ROH) islands were classified into ≤2 Mb. Strong selection signals were identified in thirteen genomic intervals overlapped with 17 protein-coding genes. The sheep quantitative trait locus (QTL) database confirmed that these genomic regions were associated with economically important traits such as milk content (KCNH5, KCNH7, LRP1B, SNAPC1, and SYT16) and fleece yield (CCDC85A, EFEMP1, and PPP4R3B), parasite resistance (MMS22L and KLHL32), fat deposition in the tail (JAZF1, TAXIBP1, EVX1, and HOXA13), and water-holding capacity (KLHL1 and DACH1). This study implies that the Kangal Akkaraman sheep will play a vital role in developing some genotypes tolerant to environmental challenges, parasite infections, fat deposition, and water-holding capacity in the future. Still, the other native sheep should be screened to identify genomic regions under selection practices using high-density genetic data obtained from next-generation sequencing (NGS) platforms.