quantisnp algorithms

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.