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Estimation of genomic inbreeding coefficient and effective population size in Zandi sheep breed using density SNP markers (50K SNPChip)

Document Type : Research Paper

Authors

1 Department of animal science, University of Tabriz

2 animal science- university of Tabriz

3 Department of Animal Sciences, University College of Agriculture and Natural Resources, University of Tehran.

4 Department of Animal Science, Faculty of Agricultural Sciences, University of Tabriz

5 Department of Animal Sciences, Faculty of Agriculture and Natural Resources, University of Arak

Abstract

The aim of this study was to investigate genome-wide Inbreeding and effective population size using the information obtained from 96 Zandi sheep breed using a density SNP panel (50K SNPChip). For this purpose, after quality control of SNP markers data, 40,879 SNPs were remained for computing inbreeding and effective population size. Effective number of breeders was estimated per each chromosome using NEESTIMATOR software based on heterozygote-excess method., and inbreeding coefficient was derived using four methods including, genomic relationship matrix (FGRM), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI) using GCTA software and run of homozygosity (FROH) using PLINK software. Average expected and observed heterozygosity ranged 0.393 and 0.407 respectively. Average chromosome-wise effective number of breeders was equal to 69 and corresponding average confidence interval was between 40.0 and 93.26. The magnitude of inbreeding coefficient using FGRM, FHOM, and FUNI was similar (0.064) and it was estimated 0.053 using Run of homozygosity. Generally, the results indicated that although a considerable genetic variation exists in Zandi population in case study, however effective population has been decreased strongly in Zandi sheep breed during recent years and designing of appropriate programs is necessary to conserve remaining purebred animals of this indigenous sheep breed.

Keywords

References
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Research Journal of Livestock Science
Volume 31, Issue 119
September 2018
Pages 129-142
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