engleski

Identification of selection signatures on the X chromosome in East Adriatic sheep

Identification of selection signatures on the X chromosome in East Adriatic sheep Sheep farming is one of the most important livestock sectors in the region of East Adriatic. Therefore, the identification of genomic regions showing signals of a positive selection signature is of economic importance. The aim of this study was to analyse regions showing selection signals in 59 sheep individuals representing the East Adriatic sheep populations (Dalmatian Pramenka ; Dubrovnik Ruda Sheep ; and Pag Island sheep) using the Ovine Infinium® HD SNP BeadChip. Our analysis was limited to the X chromosome only (21, 748 SNPs), as such analyses are rarely performed, with the exception of a few studies. Among sheep populations, the Balkan sheep group was never represented in such an analysis. After quality control, we performed two different methods, identification of extremely frequent SNPs in ROHs (eROHi) and Integrated Haplotype Score (iHS), based on the analysis of genomic variation within a large meta-population. Using iHS, we detected a weak selection signal in a narrow region (six SNPs at position 25.6 Mb) indicative of the interleukin-1 receptor accessory protein-like 1 (IL1RAPL1) gene. In humans, ILRAPL1 has been linked to the hippocampal memory system and learning ability. Using eROHi( 1Mb), we identify strong selection signals in a large region extending from position 69.9 to 74.6 Mb with a number of potential gene candidates (POU3F4, CYLC1, RPS6KA6, HDX, APOOL, ZNF711, POF1B, TRNAC- GCA, CHM and DACH2). CHM , DACH2, RPS6KA6, ZNF711, and IL1RAPL1 were also identified as selection signals in some Chinese and European breeds. Nevertheless, the pattern of selection signals in the X chromosome when comparing iHS and eROHi was somewhat different from analyses performed on autosomes and needs to be further analysed using other methods and on large samples.

Adaptation, Sheep and related species, Genetic Improvement, Genotyping, Evolutionary Genomics

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