Discovering Copy Number Variation in Dual-Purpose XinJiang Brown Cattle - Zhou, Jinghang; Liu, Liyuan; Reynolds, Edwardo; Huang, Xixia; Garrick, Dorian; Shi, Yuangang (Frontiers in Genetics, 2022)
Copy number variants (CNVs), which are a class of structural variant, can be important in relating genomic variation to phenotype. Here, we aimed to discover the common CNV regions (CNVRs) in the dual-purpose XinJiang-Brown cattle population and to detect differences between CNVs inferred using the ARS-UCD 1.2 (ARS) or the UMD 3.1 (UMD) genome assemblies based on the GGP Bovine 150K SNP (Single Nucleotide Polymorphisms) Chip. PennCNV and CNVPartition methods were employed to calculate the deviation of standardized signal intensity of SNPs markers to detect CNV status. Using the R package HandyCNV in the post-analysis of CNVs, we generated and visualized CNVRs, compared CNVs and CNVRs, and using annotation resources found consensus genes. The number of SNPs available for the detection of CNVs are about 10% more in ARS map than default UMD map. The main reason CNVRs differed between the two reference genomes was due to differences in assembly quality as characterized by the number of unmapped markers on UMD. We identified 38 consensus CNVRs on the ARS assembly with 1.95% whole genome coverage and 33 consensus CNVRs on the UMD assembly with 1.46% whole genome coverage using the two methods. Among them, 1 CNVR (CNVR_146, BTA4:72,297,257-72,561,411, UMD version) was detected in 25 samples were found has no intersection with the DGVa and a Brown Swiss Cattle CNVR results. 37 genes were identified intersecting 13 common CNVs (>5% frequency) across ARS and UMD results. These results include functionally interesting genes such as GBP4 which exhibits copy numbers negatively related to cattle stature. The default UMD map file of GGP Bovine 150k Bead Chip has many SNPs with unknown genomic positions whereas the newly released ARS map is a better reference for CNV detection. We show different CNV methods can complement each other to generate a larger number of CNVRs than using a single approach and highlight more genes of interest.