|HOU, YALI - University Of Maryland|
|HVINDEN, MIRANDA - Pfizer Animal Health|
|Li, Congjun - Cj|
|SONG, JIUZHOU - University Of Maryland|
|BOICHARD, DIDIER - Institut National De La Recherche Agronomique (INRA)|
|FRITZ, SEBASTIEN - Collaborator|
|EGGEN, ANDRE - Collaborator|
|DENISE, SUE - Pfizer Animal Health|
|Van Tassell, Curtis - Curt|
|Liu, Ge - George|
Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/25/2012
Publication Date: 8/6/2012
Citation: Hou, Y., Bickhart, D.M., Hvinden, M.L., Li, C., Song, J., Boichard, D.A., Fritz, S., Eggen, A., Denise, S., Wiggans, G.R., Sonstegard, T.S., Van Tassell, C.P., Liu, G. 2012. Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array. Biomed Central (BMC) Genomics. 13:376.
Interpretive Summary: Genome assemblies are the foundation of genome research. There exist two distinct cattle assemblies: Btau_4.0 and UMD3.1. We compared these two assemblies and their impacts on the ability to identify gene copy number variation (CNV) within the cattle genome. We observed about 50% more and 20% longer CNVs on UMD3.1 than on Btau_4.0, suggesting UMD3.1 is a better representation of the reference cattle genome. Farmers, scientist, and policy planners who need to improve animal health and production based on genome-enabled animal selection will benefit from this research.
Technical Abstract: Btau_4.0 and UMD3.1 are two distinct cattle reference genome assemblies. In our previous study using the low density BovineSNP50 array, we reported a copy number variation (CNV) analysis on Btau_4.0 with 521 animals of 21 cattle breeds, yielding 682 CNV regions with a total length of 139.8 megabases. Results: In this study using the high density BovineHD SNP array, we performed high resolution CNV analyses on both Btau_4.0 and UMD3.1 with 674 animals of 27 cattle breeds. We first compared CNV results derived from these two different SNP array platforms on Btau_4.0. With two thirds of the animals shared between studies, on Btau_4.0 we identified 3,346 candidate CNV regions representing 142.7 megabases (~4.70%) of the genome. With a similar total length but 5 times more event counts, the average CNVR length of current Btau_4.0 dataset is significantly shorter than the previous one (42.7kb vs. 205 kb). Although subsets of these two results overlapped, 64% (91.6 megabases) of current dataset was not present in the previous study. We also performed similar analyses on UMD3.1 using these BovineHD SNP array results. Approximately 50% more and 20% longer CNVs were called on UMD3.1 as compared to those on Btau_4.0. However, a comparable result of CNVRs (3,438 regions with a total length 146.9 megabases) was obtained. We suspect that these results are due to that UMD3.1’s efforts of placing unplaced contigs and removing unmerged alleles. Selected CNVs were further experimentally validated, achieving a 73% PCR validation rate, which is considerably higher than the previous validation rate. About 20-45% of CNV regions overlapped with cattle RefSeq genes and Ensembl genes. Panther and IPA analyses indicated that these genes provide a wide spectrum of biological processes involving immune system, lipid metabolism, cell, organism and system development. Conclusion: We present a comprehensive result of cattle CNVs at a higher resolution and sensitivity. We identified over 3,000 candidate CNV regions on both Btau_4.0 and UMD3.1, further compared current datasets with previous results, and examined the impacts of genome assemblies on CNV calling.