|Liu, Ge - George|
|XU, LINGYANG - University Of Maryland|
|Van Tassell, Curtis - Curt|
|Schroeder, Steven - Steve|
|GARCIA, JOSE FERNANDO - Universidade Estadual Paulista (UNESP)|
|TAYLOR, JEREMY - University Of Missouri|
|SCHNABEL, ROBERT - University Of Missouri|
|LEWIN, HARRIS - University Of California|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/4/2013
Publication Date: 2/12/2014
Citation: Liu, G., Bickhart, D.M., Xu, L., Hutchison, J.L., Sonstegard, T.S., Van Tassell, C.P., Schroeder, S.G., Garcia, J., Taylor, J.F., Schnabel, R.D., Lewin, H.A. 2014. Population sequencing reveals breed and sub-species specific CNVs in cattle. Meeting Abstract. Advances in Genome Biology & Technology Conference Meeting Abstract. p.58.
Technical Abstract: Individualized copy number variation (CNV) maps have highlighted the need for population surveys of cattle to detect the rare and common variants. While SNP and comparative genomic hybridization (CGH) arrays have provided preliminary data, next-generation sequence (NGS) data analysis offers an increased resolution and sensitivity for CNV detection. This study analyzed NGS sequence data derived from 67 taurine (consisting of the Angus, Holstein, Jersey, Limousin and Romagnola breeds) and 20 indicine cattle (consisting of the Brahman, Gir and Nelore breeds). Individual genome sequence coverage ranged from 4X to 30X, with an average coverage of 11.8X across animals. To identify CNVs, a customized read-depth CNV was used, calling algorithm that utilizes population-scale data to derive a smoothing coefficient for lowess normalization. We identified 2,947 unique CNV regions that account for approximately 4% (117 Mbp) of the cattle genome. Several dairy-breeds and beef-breeds specific variants were found, including a duplication of the cell growth-related RICTOR gene in dairy breeds and a duplication of the Patatin-like phospholipase 3 (PNPLA3) gene in beef breeds. Identification of low frequency and breed-specific CNVs within cattle will enable a better understanding of functional variants influenced by domestication and selection for performance traits.