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Genomic Patterns of Homozygosity and Genetic Diversity in the Rhenish German Draught Horse

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Background/Objectives: The Rhenish German draught horse is an endangered German horse breed, originally used as working horse in agriculture. Therefore, the objective of this study was to evaluate the breed’s genetic diversity using pedigree and genomic data in order to analyze classical and ancestral pedigree-based inbreeding, runs of homozygosity, ROH islands, and consensus ROH. Methods: We studied the genome-wide genotype data of 675 Rhenish German draught horses and collated pedigree-based inbreeding coefficients for these horses. The final dataset contained 64,737 autosomal SNPs. Results: The average number of ROH per individual was 43.17 ± 9.459 with an average ROH length of 5.087 Mb ± 1.03 Mb. The average genomic inbreeding coefficient FROH was 0.099 ± 0.03, the pedigree-based classical inbreeding coefficient FPED 0.016 ± 0.021, and ancestral inbreeding coefficients ranged from 0.03 (Fa_Kal) to 0.51 (Ahc). Most ROH (55.85%) were classified into the length category of 2–4 Mb, and the minority (0.43%) into the length category of >32 Mb. The effective population size (Ne) decreased in the last seven generations (~65 years) from 189.43 to 58.55. Consensus ROH shared by 45% of the horses were located on equine chromosomes 3 and 7, while ROH islands exceeding the 99th percentile threshold were identified on chromosomes 2, 3, 5, 7, 9, 10, and 11. These ROH islands contained genes associated with morphological development (HOXB cluster), fertility (AURKC, NLRP5, and DLX3), muscle growth, and skin physiology (ZNF gene cluster). Conclusions: This study highlights how important it is to monitor genetic diversity in endangered populations with genomic data. The results of this study will help to develop breeding strategies to ensure the conservation of the German Rhenish draught horse population and show whether favorable alleles from the overrepresented candidate genes within ROH were transmitted to the next generation.
Title: Genomic Patterns of Homozygosity and Genetic Diversity in the Rhenish German Draught Horse
Description:
Background/Objectives: The Rhenish German draught horse is an endangered German horse breed, originally used as working horse in agriculture.
Therefore, the objective of this study was to evaluate the breed’s genetic diversity using pedigree and genomic data in order to analyze classical and ancestral pedigree-based inbreeding, runs of homozygosity, ROH islands, and consensus ROH.
Methods: We studied the genome-wide genotype data of 675 Rhenish German draught horses and collated pedigree-based inbreeding coefficients for these horses.
The final dataset contained 64,737 autosomal SNPs.
Results: The average number of ROH per individual was 43.
17 ± 9.
459 with an average ROH length of 5.
087 Mb ± 1.
03 Mb.
The average genomic inbreeding coefficient FROH was 0.
099 ± 0.
03, the pedigree-based classical inbreeding coefficient FPED 0.
016 ± 0.
021, and ancestral inbreeding coefficients ranged from 0.
03 (Fa_Kal) to 0.
51 (Ahc).
Most ROH (55.
85%) were classified into the length category of 2–4 Mb, and the minority (0.
43%) into the length category of >32 Mb.
The effective population size (Ne) decreased in the last seven generations (~65 years) from 189.
43 to 58.
55.
Consensus ROH shared by 45% of the horses were located on equine chromosomes 3 and 7, while ROH islands exceeding the 99th percentile threshold were identified on chromosomes 2, 3, 5, 7, 9, 10, and 11.
These ROH islands contained genes associated with morphological development (HOXB cluster), fertility (AURKC, NLRP5, and DLX3), muscle growth, and skin physiology (ZNF gene cluster).
Conclusions: This study highlights how important it is to monitor genetic diversity in endangered populations with genomic data.
The results of this study will help to develop breeding strategies to ensure the conservation of the German Rhenish draught horse population and show whether favorable alleles from the overrepresented candidate genes within ROH were transmitted to the next generation.

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