genotyping array

Copy number variation (CNV), one of the most important structural changes in the genome, has been known as an important source of genetic and phenotypic variations. The purpose of this study was to compare the two different algorithms in CNV detection consisting PennCNV and QuantiSNP in Baluchi sheep.Data analysis was performed using the Illumina OvineSNP50k BeadChip on 96 Baluchi sheep.After CNV calling, the copy number variation regions (CNVRs) were determined using the CNVRuler program.91 CNVRs with a length range of 18.75 up to 511.7 kbp were identified by the PennCNV algorithm, covering 0.46% of whole sheep genome.Also, 316 CNVRs with the length range of 7.5 up to 1280 kbp were obtained using QuantiSNP algorithm, covering 1.33% of whole sheep genome. The number of loss events was about five and three times more than the number of gain events for QuantiSNP and PennCNV algorithms, respectively.Also, the number of CNVs detected by QuantiSNP was about four times higherthan PennCNV.Also, 86.6% of total CNVs (174 CNVs with average length of 12.62 kb) identified by PennCNV were common with CNVs detected by QuantiSNP. In general, the results showed that the use of several algorithms could improve the accuracy for detecting the structural variation in the genome and led to a better understanding of the sheep genome.

In order to detect signature of selection on buffalo genome, a set of 287 water buffalo samples from 260 Azari and 27 Mazandarani buffalo breeds were genotyped using the Axiom® Buffalo Genotyping 90K Array. The unbiased fixation index method (FST) was used to detect signatures of selection. In total, 14 regions with outlier FST values (0.1%) were identified. Annotation of these regions using the UMD3.1 Bos taurus Genome Assembly was performed to find putative candidate genes and QTLs within the selected and 105 genes and 28 QTLs with selection signatures were detected. A high proportion of identified genes (N=27) in regions under selection were involved in olfactory receptor, also some of the detected genes were associated with growth and body development, metabolicand apoptosis possesses, immune system development, and mammary gland development. Some of the identified QTLs in regions under selection were associated with growth traits such as body weight at birth, weaning and mature, subcutaneous fat, meat yield and carcass weight. The detected QTL for milk traits were only associated with milk contents and somatic cell count. However, it is recommended to carry out association studies to show the actual function of these genes.