Efficiency of genotyping by sequencing technology and its comparison with single nucleotide polymorphism array in an F2 chickens population
For researches, there is a wide variety of available technologies to collect molecular information in the field of chicken genomics. These technologies, which consist of single-nucleotide polymorphism (SNP) arrays and genotyping by sequencing (GBS), depending on the goals of the study, can have different applications. The aim of this study was to compare the results of markers genotyped by two technologies, namely, 60 K SNP BeadChip and genotyping by sequencing, using data collected on F2 chicken population resulting from a reciprocal crosses between a native bird of Urmia and a fast-growing commercial Arian line. In genotyping by GBS, 882,918 SNPs were identified, of which 815,613 SNPs (92.40%) were located on chromosomes 1 to 28. In 60 K SNP array, the number of SNPs for each sample were 51347, which were distributed on chromosomes 1 to 28. The GBS data identified more markers and haplotype blocks than the 60 K SNP array. The rate of linkage disequilibrium (LD) in the physical distances of 10, 100 and 1000 kbp in GBS was less than that of SNP array. The large variety of SNPs in the GBS resulted in a uniform population structures and kinship. Also, in addition to the high performance for identifying single nucleotide polymorphisms, the technology of GBS also reduced the costs of the genotyping for each sample, therefore, it seems that the use of genotyping by sequencing technology could be a suitable alternative method to the 60 K SNP BeadChip array technology for genome-wide association studies in chicken population.
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Genomic association study to identify candidate genes for early growth traits in chickens
Zeinab Asgari *, , Aliakbar Masoudi,
Iranian Journal of Animal Science, -
Comparing machine learning algorithms and linear model for detecting significant SNPs for genomic evaluation of growth traits in F2 chickens
H. Bani Saadat, R. Vaez Torshizi*, Gh. Manafiazar, A. A. Masoudi, A. Ehsani, S. Shahinfar
Journal of Agricultural Science and Technology, Nov 2024