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Amir Rashidi

Amir Rashidi

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 23009961900
Faculty: Faculty of Agriculture
Address: Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.
Phone: 08733668512

Research

Title
Comparison of different selection methods for improving litter size in sheep using computer simulation
Type
JournalPaper
Keywords
major gene; synthetic breed; genomic evaluation
Year
2020
Journal Spanish Journal of Agricultural Research
DOI
Researchers Meysam Latifi ، Amir Rashidi ، Rostam Abdollahi Arpanahi ، Mohammad Razmkabir

Abstract

Aim of study: To assess selection methods via introgression to improve litter size in native and synthetic sheep breeds. Area of study: Sanandaj, Kurdistan, Iran. Material and methods: Selection approaches were performed using classical, genomic, gene-assisted classical (GasClassical) and gene-assisted genomic (GasGenomic) selection. Litter size trait with heritability of 0.1 including two chromosomes was simulated. On chromosome 1, a single QTL as the major gene was created to explain 40% of the total additive genetic variance. After simulation of a historical population, the animals from the last historical population were split into two populations. For the next 7 generations, animals were selected for favorable or unfavorable alleles to create distinct breeds of A or B, respectively. Then from the last generation, both males and females from breed B were selected to create a native population. On the other hand, males from breed A and females from breed B were mated to simulate a synthetic population. Finally, intra-population selections were carried out based on high breeding values during the last five generations. Main results: The genetic gain in the synthetic breed was higher than that of the native breed under all selection methods. The frequencies of favorable alleles after five generations in the classical, genomic, GasClassical and GasGenoimc selection approaches in the synthetic breed were 0.623, 0.730, 0.850 and 0.848, respectively. Research highlights: Combining gene-assisted selection with classical or genomic selection has the potential to improve genetic gain and increase the frequencies of favorable allele for litter size in sheep.