Volume 10, Issue 25 (11-2019)                   rap 2019, 10(25): 112-119 | Back to browse issues page


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Latifi M, Rashidi A, Abdollahi Arpanahi R, Razmkabir M. Comparison of Introgression and Synthetic Breed Strategies for Litter Size Trait in Sheep using Computer Simulation. rap. 2019; 10 (25) :112-119
URL: http://rap.sanru.ac.ir/article-1-1002-en.html
Department of Animal Science, University of Kurdistan
Abstract:   (763 Views)
The objective of this study was to compare introgression and synthetic breed strategies for litter size trait improvement in sheep using computer simulation. For this purpose, a trait with heritability of 0.1, consisting of two chromosomes was simulated. On chromosome 1, a single QTL as the major gene was created that accounted for 40% of the total genetic variance. The effect of favorable and unfavorable alleles for the QTL was fixed after seven generations in both A and B breeds, respectively. The introgression and synthetic breed strategies were compared using Classical and Classical with gene-assisted selection (GasClassical) methods. The genetic gain in introgression and synthetic breed strategies using GasClassical method was 39% and 16% higher than that of Classical method, respectively. The mean of inbreeding coefficient in the fifth generation in introgression strategy was 0.049 and 0.077 using the Classical and GasClassical methods, respectively, and in synthetic breed strategy was 0.11 and 0.008, respectively. The results of this study showed that the GasClassical method in comparison with the Classical method led to an increasing frequency of favorable allele (major gene) and genetic gain in both introgression and synthetic breed strategies. However, the genetic gain for one percent increase in inbreeding in the synthetic breed strategy was greater than that of introgression strategy, and as a result, the synthetic breed strategy performs better than introgression strategy.
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Type of Study: Research | Subject: ژنتیک و اصلاح نژاد دام
Received: 2019/04/11 | Revised: 2019/12/9 | Accepted: 2019/07/28 | Published: 2019/11/25

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