Analysis of copy number variations in Holstein cows identify potential mechanisms contributing to differences in residual feed intake
- 689 Downloads
Genomic structural variation is an important and abundant source of genetic and phenotypic variation. In this study, we performed an initial analysis of copy number variations (CNVs) using BovineHD SNP genotyping data from 147 Holstein cows identified as having high or low feed efficiency as estimated by residual feed intake (RFI). We detected 443 candidate CNV regions (CNVRs) that represent 18.4 Mb (0.6 %) of the genome. To investigate the functional impacts of CNVs, we created two groups of 30 individual animals with extremely low or high estimated breeding values (EBVs) for RFI, and referred to these groups as low intake (LI; more efficient) or high intake (HI; less efficient), respectively. We identified 240 (~9.0 Mb) and 274 (~10.2 Mb) CNVRs from LI and HI groups, respectively. Approximately 30–40 % of the CNVRs were specific to the LI group or HI group of animals. The 240 LI CNVRs overlapped with 137 Ensembl genes. Network analyses indicated that the LI-specific genes were predominantly enriched for those functioning in the inflammatory response and immunity. By contrast, the 274 HI CNVRs contained 177 Ensembl genes. Network analyses indicated that the HI-specific genes were particularly involved in the cell cycle, and organ and bone development. These results relate CNVs to two key variables, namely immune response and organ and bone development. The data indicate that greater feed efficiency relates more closely to immune response, whereas cattle with reduced feed efficiency may have a greater capacity for organ and bone development.
KeywordsCattle genome Copy number variation (CNV) Feed efficiency Residual feed intake (RFI) Single nucleotide polymorphism (SNP)
GEL and EEC conceived and designed the experiments. EEC collected samples, RFI data, and generated the SNP genotyping data. HDN and JLH performed statistical analyses to estimate RFI. YH and GEL performed in silico prediction and computational analyses. DMB and HC conducted qPCR validations. GEL, EEC, and DMB wrote the manuscript. GEL was supported by NRI/AFRI grants no. 2007-35205-17869 and 2011-67015-30183 from the USDA CSREES (now NIFA) and Projects 1265-31000-098 and 1265-31000-097 from USDA-ARS. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The USDA is an equal opportunity provider and employer.
- Bickhart DM, Hou Y, Schroeder SG, Alkan C, Cardone MF, Matukumalli LK, Song J, Schnabel RD, Ventura M, Taylor JF, Garcia JF, Van Tassell CP, Sonstegard TS, Eichler EE, Liu GE (2012) Copy number variation of individual cattle genomes using next-generation sequencing. Genome Res 22:778–790PubMedCrossRefGoogle Scholar
- Hou Y, Liu GE, Bickhart DM, Matukumalli LK, Li C, Song J, Gasberre LC, Van Tassell CP, Sonstegard TS (2011a) Genomic regions showing copy number variations associate with resistance or susceptibility to gastrointestinal nematodes in Angus cattle. Funct Integr Genomics 12:81–92PubMedCrossRefGoogle Scholar
- Hou Y, Bickhart DM, Hvinden ML, Li C, Song J, Boichard DA, Fritz S, Eggen A, Denise S, Wiggans GR, Sonstegard TS, Van Tassell CP, Liu GE (2012) Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array. BMC Genomics 13:376, Epub ahead of printPubMedCrossRefGoogle Scholar
- Koch RM, Swiger LA, Chambers D, Gregory KE (1963) Efficiency of food use in beef cattle. J Anim Sci 22:486–494Google Scholar
- Liu GE, Hou Y, Zhu B, Cardone MF, Jiang L, Cellamare A, Mitra A, Alexander LJ, Coutinho LL, Dell’Aquila ME, Gasbarre LC, Lacalandra G, Li RW, Matukumalli LK, Nonneman D, Regitano LCD, Smith TPL, Song J, Sonstegard TS, Van Tassell CP, Ventura M, Eichler EE, McDaneld TG, Keele JW (2010) Analysis of copy number variations among diverse cattle breeds. Genome Res 20:693–703PubMedCrossRefGoogle Scholar
- Lopez-Villalobos N, Berry DP, Horan B, Buckley F, Kennedy E, O’Donovan M, Shalloo L, Dillon P (2008) Genetics of residual energy intake in Irish grazing dairy cows. Proc NZ Soc Anim Prod 78:97–100Google Scholar
- Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedCrossRefGoogle Scholar
- National Research Council (2001) Nutrient requirements of dairy cattle, 7th rev edn. National Academy Press, Washington, DCGoogle Scholar
- Orth R (1992) Sample day and lactation report. DHIA 200 Fact Sheet A-2. Mid-states DRPC, Ames, IAGoogle Scholar
- Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, Tyler-Smith C, Carter N, Scherer SW, Tavare S, Deloukas P, Hurles ME, Dermitzakis ET (2007) Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315:848–853PubMedCrossRefGoogle Scholar