Abstract
Copy number variants (CNVs), which are a class of structural variant, can be important in relating genomic variation to phenotype. The primary aims of this study were 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 150K SNP (Single Nucleotide Polymorphisms) Chip. PennCNV and CNVPartition methods were applied to calculate the deviation of the standardized signal intensity of SNPs markers to detect CNV status. Following the discovery of CNVs, we used the R package HandyCNV to generate and visualize CNVRs, compare CNVs and CNVRs between genome assemblies, and identify consensus genes using annotation resources. We identified 38 consensus CNVRs using the ARS assembly with 1.95% whole genome coverage, and 33 consensus CNVRs using the UMD assembly with 1.46% whole genome coverage using PennCNV and CNVPartition. We identified 37 genes that intersected 13 common CNVs (>5% frequency), these included functionally interesting genes such as GBP4 for which an increased copy number has been negatively associated with cattle stature, and the BoLA gene family which has been linked to the immune response and adaption of cattle. The ARS map file of the GGP Bovine 150K Bead Chip maps the genomic position of more SNPs with increased accuracy compared to the UMD map file. Comparison of the CNVRs identified between the two reference assemblies suggests the newly released ARS reference assembly is better for CNV detection. In spite of this, different CNV detection methods can complement each other to generate a larger number of CNVRs than using a single approach and can highlight more genes of interest.
Highlights
Copy number variants are structural variants caused by insertions, deletions, duplications, and translocations of DNA fragments (Stankiewicz and Lupski, 2010)
After comparing the results of this study with the Database of Genomic Varia (DGVa) or Brown Swiss datasets, we identified a large number of population-specific copy number variant region (CNVR), but we could not confirm that these regions were XJ-Brown specific copy number variant (CNV)
Two CNV detection methods were employed in this study to infer CNVs on each of the ARS-UCD 1.2 reference genome (ARS)-UCD 1.2 and UMD 3.1 reference genome (UMD) 3.1 genome assemblies
Summary
Copy number variants are structural variants caused by insertions, deletions, duplications, and translocations of DNA fragments (Stankiewicz and Lupski, 2010). The reported genome coverage of CNVs ranges from 1 to more than 10% depending on the detection strategies and the breed of cattle investigated in the study (Prinsen et al, 2016; Letaief et al, 2017; Kommadath et al, 2019). CNVs can intersect genes which can alter their structure and expression, and many investigations on human disease suggest that copy number variation could be the source of pathogenesis (Stankiewicz and Lupski, 2010). CNVs can be inherited and can contribute to genomic diversity that could be informative in evaluating the evolution of different animal breeds (Bickhart et al, 2016). The inclusion of CNV information in genomic evaluation models might improve the accuracy of animal evaluation (Hay et al, 2018). Studying CNVs in a breed could help us to better understand this population
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
