Genomic Evaluation of Average Daily Gain Traits in a Mixture of Arian Line and Urmia Iranian Native Chickens

Document Type : Original Research

Authors
H. Asadollahi 1
S. Ansari Mahyari 1
R. Vaez Torshizi 1
‪. Emrani 2
A. Ehsani 3
1 Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, Islamic Republic of Iran.
2 Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), 31585 Karaj, Islamic Republic of Iran.
3 Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Islamic Republic of Iran.
10.22034/JAST.26.2.299
Abstract
The aims of this investigation were to compare the accuracy and bias of prediction of Estimated Breeding Values (EBV) for Average Daily Gain (ADG) at 2-4 weeks old by employing pedigree-based BLUP and single-step Genomic BLUP (ssGBLUP) techniques. Additionally, the study aimed to identify the optimal minor allele frequencies (MAF) threshold for pre-selecting SNPs for genetic prediction. The present investigation utilized a total of 488 F2 broiler chickens, which were derived from the crossbreeding of fast-growing Arian chickens and slow-growing native chickens from Urmia, Iran. These chickens were between 2-4 weeks old at the time of the study. Samples were genotyped using the Illumina 60K chicken Beadchip. In order to examine the impact of MAF on prediction accuracy, a total of 48,379 quality-controlled SNPs were categorized into five subgroups based on their MAF values: 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5. The findings substantiated the dominance of ssGBLUP over conventional BLUP techniques. The average accuracy of GP improved by 1.96, 3.87, and 2.12% using ssGBLUP compared to BLUP method for ADG at 2-4 weeks of age, respectively. Using a specific MAF bin and a subset of SNPs based on age group significantly enhanced the accuracy of genomic prediction for ADG traits. Current results highlighted that the pre-selection of SNPs based on allele frequency may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements.

Keywords

Subjects

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Journal of Agricultural Science and Technology
Volume 26, Issue 2
March and April 2024
Pages 299-312