Identification of the pseudoautosomal region of the X chromosome in sheep and sex prediction using the ARS-UI_Ramb_v2.0 genome assembly
Sex determination through genotyping is an efficient tool for verifying recorded sex in sheep. Since males possess only one X chromosome, heterozygous genotypes should not be present in the non-pseudoautosomal region (nPAR) of the X chromosome. Therefore, determining the boundaries of the pseudoauto...
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| Other Authors: | , , |
| Format: | article |
| Language: | English |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.12008/52498 |
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| Summary: | Sex determination through genotyping is an efficient tool for verifying recorded sex in sheep. Since males possess only one X chromosome, heterozygous genotypes should not be present in the non-pseudoautosomal region (nPAR) of the X chromosome. Therefore, determining the boundaries of the pseudoautosomal region (PAB) is necessary for a reliable sex prediction. Although it is recommended to use SNPs from both the X and Y chromosomes, the SNP chip we used did not contain Y chromosome markers. This study aimed to determine the pseudoautosomal region (PAR) on the X chromosome in sheep using the ARS-UI_Ramb_v2.0 genome assembly and test a method for sex prediction based solely on X chromosome SNPs. The training dataset was composed of 210 sheep from various breeds and crossbreeds, genotyped with the Ovine Infinium® HD SNP BeadChip to determine the PAR region. A validation dataset of 229 sheep was used to assess the accuracy of sex determination using the OvineSNP50 BeadChip. After quality control, the PAR region was found to span from 0 to 7.24 Mb and was identified by SNPs that exhibited high heterozygosity rates in males. The proposed approach, that uses only X chromosome SNPs, achieved a sex prediction accuracy of 94% with precision rates of 99% for males and 89% for females. This study showed that it is possible to predict sex in sheep using only X chromosome nPAR SNPs. This is the first study that has used this approach specifically using the ARS-UI_Ramb_v2.0 genome assembly. |
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