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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #300235

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: The use of population-scale sequencing to identify CNVs impacting productive traits in different cattle breeds

item Bickhart, Derek
item Xu, Lingyang
item Hutchison, Jana - Edwards
item Sonstegard, Tad
item Van Tassell, Curtis - Curt
item Schroeder, Steven - Steve
item Garcia, Jose
item Taylor, Jeremy
item Schnabel, Robert
item Lewin, Harris
item Liu, Ge - George

Submitted to: Plant and Animal Genome Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 1/10/2014
Publication Date: 1/10/2014
Citation: Bickhart, D.M., Xu, L., Hutchison, J.L., Sonstegard, T.S., Van Tassell, C.P., Schroeder, S.G., Garcia, J.F., Taylor, J.F., Schnabel, R.D., Lewin, H., Liu, G. 2014. The use of population-scale sequencing to identify CNVs impacting productive traits in different cattle breeds. Plant and Animal Genome Conference Proceedings. Plant Animal Genome XXII, San Diego, CA, Jan. 11–15, P078.

Interpretive Summary:

Technical Abstract: Individualized copy number variation (CNV) maps have highlighted the need for population surveys of cattle to detect 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. In this study, we 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, we used a customized read-depth CNV 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- and beef-breed 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.