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United States Department of Agriculture

Agricultural Research Service

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2010 Annual Report

1a. Objectives (from AD-416)
Objective 1: Develop biological resources and computational tools to enhance characterization of the bovine genome sequence. Objective 2: Use genotypic data and resulting bovine haplotype map to enhance genetic improvement in dairy cattle through development and implementation of whole genome selection and enhanced parentage verification approaches. Objective 3: Characterize conserved genome elements and identify functional genetic variation.

1b. Approach (from AD-416)
Completion of our objectives is expected, in the short term, to result in development and implementation of genome-wide selection. Ultimately the longer term objective of QTN discovery to better understand livestock biology will require a combination of quantitative genetics, LD-MAS, genome annotation, and gene expression analyses, all of which are components of this proposal and areas of expertise in our group. Efforts to characterize genome activity and structure conservation and variation are an extension of our current research program in QTL mapping and bioinformatics. This proposal completely leverages the resources derived from the Bovine Genome and HapMap projects, for which the authors of this proposal have played prominent roles. As more of the genetic variation for a specific trait is explained, a better understanding of pleiotropic and epistatic gene action will be needed. This knowledge will be developed through characterizing changes at a very fine level combined with studies of animals with known genotype associated with phenotypes resulting from selection programs. Tools used in this characterization are likely to include, but not be limited to, gene expression patterns, protein expression or structural changes, or regulatory changes.

3. Progress Report
ARS scientists developed the infrastructure to test implementation of less expensive, low-density single nucleotide polymorphism (SNP) beadchip assays for parentage discovery and genome-enhanced prediction of Net Merit in dairy cattle. This included SNP marker selection for assay design and pre-release testing of a commercial assay containing approximately 3,000 markers. Industry cooperators aligned with commercial DNA service providers and ear tag manufacturers are set to validate application of this 3K genotyping product by examining over 10,000 DNA samples for parentage and genetic prediction. More details can be found in report for project 1265-3100-081-29N. ARS scientists along with consortium members generated the world’s largest resource of DNA sequence information for cattle. This resource contains more than 550 billion bases of genome sequence derived from 19 breeds of cattle. ARS directly constructed 120 genomic DNA libraries for sequence analysis, and contributed more than one-fifth of the data from in-house DNA sequence instrumentation. Analyses of the entire data set has identified >50 million high-quality SNP, and some of this data was used to design ultra-high density SNP assays at Illumina and Affymetrix. ARS scientists led the development of the Illumina commercial product through agreement 1265-3100-081-26N. The utility of this Illumina assay (777K) was tested by mapping two monogenic traits to 1 million base pair intervals. This included a gene locus for tropical thermo-tolerance and one for a late onset neuro-muscular degenerative genetic disease in dairy cattle. Analyzed genetic variation from various cattle herds in western Kenya to determine effective population structure of native breeds by determining the percentage of taurine and indicine DNA in 400 animals. There is genetic variation in indicine derived DNA that is correlated strongly with resistance to deadly cattle diseases endemic to East Africa. Completed sequencing of more than 1 billion bases of genome sequence from ancient cattle DNA (Aurochs) extracted from a 6,500 year-old bone discovered in England. This material pre-dates domestication in this region, and the sequence information will provide a foundation for understanding diversity before artificial selection of cattle and determine those genes important for disease resistance, production, and cryopreservation. A systematic, genome-wide analysis of copy number variations (CNVs) was conducted in modern cattle (90 from 17 beef and dairy breeds) using array comparative genomic hybridization (array CGH), quantitative PCR, and fluorescent in situ hybridization. Over 50% of the CNV regions were common across array CGH experiments and were performed to study genomic integrity in transgenic cattle cell lines. No significant differences in CNVregion abundance were found from comparative analyses of “good” and "bad" cell lines.

4. Accomplishments
1. The results of our development of the commercial genotyping tool (BovineSNP50, Illumina) continues to have a major impact on livestock research and the dairy AI industry. We received the USDA Secretary’s Honor Award for Excellence by implementing genome selection in dairy cattle. Awareness of the success in the development and application of beadchips to genomic research in cattle fueled development of additional SNP beadchips (Illumina and Affymetrix) for cattle. We led the development of two of these lower (Illumina 3K) and higher density (Illumina 777K) products, and their impact on the industry is anticipated to be high as testing of different genetic improvement paradigms based on cheaper SNP chips begins. The BovineSNP50 assay still is the global de facto standard for cattle genomics research and genetic prediction use with sales having surpassed 500,000 samples.

2. Significantly improved the genome map of cattle relative to copy number variation and segmental duplication regions. Our continued leadership in this research area has identified over 200 candidate CNV regions (CNVRs) in total and 177 within known chromosomes, which harbor or are adjacent to gains or losses. These 177 high-confidence CNVRs cover 28.1 mega bases or ~1.07% of the genome. Multiple gene families, including ULBP, have gone through ruminant lineage-specific gene amplification. We detected and confirmed marked differences in their CNV frequencies across diverse breeds, indicating that some cattle CNVs are likely to arise independently in breeds and contribute to breed differences. These results provide a valuable resource beyond microsatellites and single nucleotide polymorphisms to explore the full dimension of genetic variability for future cattle genomic research.

3. Supported genomics research at Beltsville in a multitude of species and applications. Our unit provides scientific, computing, labor, and bioinformatic support for projects at Beltsville that want to incorporate next-generation sequencing applications into their investigations. Over the past year, our efforts have been highlighted by other researchers through the discovery of differential gene expression studies from corn smut, SNP discovery in catfish, more metagenomic studies of the bovine rumen, expression studies for cattle, and genome sequence for water buffalo, cattle, and varroa mites.

4. Generated and analyzed the world’s largest resource of DNA sequence information for cattle (>550 billion bases) representing more than 19 breeds and containing the locations of more than 48 million high-quality SNP that form the basis for the vast diversity present in cattle.

Review Publications
Hayes, B.J., Bowman, A.J., Chamberlain, A.J., Savin, K., Van Tassell, C.P., Sonstegard, T.S., Goddard, M.E. 2009. A validated genome wide association study to breed cattle adapted to an environment altered by climate change. PLoS One. Aug 18:4(8)e:6676.

Liu, G., Ventura, M., Cellamare, A., Chen, L., Cheng, Z., Zhu, B., Song, J., Eichler, E.E. 2009. Analysis of recent segmental duplications in the bovine genome. BMC Genomics. 10:571.

Zhu, B., Jiang, L., Liu, G. 2010. A dynamic neighboring extension search algorithm for genome coordinate conversion in the presence of short sequence duplications. Gene Expression to Genetical Genomics. 2:29-36.

Wiggans, G.R., Van Raden, P.M., Bacheller, L.R., Tooker, M.E., Hutchison, J.L., Cooper, T.A., Sonstegard, T.S. 2010. Selection and management of DNA markers for use in genomic evaluation. Journal of Dairy Science. 93(5):2287-2292.

Meyers, S.N., McDaneld, T.G., Swist, S.L., Marron, B.M., Steffen, D.J., O'Toole, D., O'Connell, J.R., Beever, J.E., Sonstegard, T.S., Smith, T.P. 2010. A Deletion Mutation in Bovine SLC4A2 is Associated with Osteopetrosis in Red Angus Cattle. Biomed Central (BMC) Genomics. 11:337.

Liu, G., Hou, Y., Zhu, B., Cardone, M.F., Jiang, L., Cellamare, A., Mitra, A., Alexander, L.J., Coutinho, L.L., Gasbarre, L.C., Heaton, M.P., Li, R.W., Matukumalli, L.K., Nonneman, D.J., De A Regitano, L.C., Smith, T.P., Song, J., Sonstegard, T.S., Van Tassell, C.P., Eichler, E.E., Mcdaneld, T.G., Keele, J.W. 2010. Analysis of copy number variations among cattle breeds. Genome Research. 20:693-703.

Mitra, A., Liu, G., Song, J. A genome-wide analysis of array-based comparative genomic hybridization (CGH)data to detect intra-species variations and evolutionary relationships. PLoS One. Nov. 24; 4(11):e7978.

Ramos, A.M., Crooijmans, R.P.M.A., Affara, N.A., Amaral, A.J., Archibald, A.L., Beever, J.E., Bendixen, C., Churcher, C., Clark, R., Dehais, P., Hansen, M.S., Hedegaard, J., Hu, Z.L., Kerstens, H.H., Law, A.S., Megens, H.J., Milan, D., Nonneman, D.J., Rohrer, G.A., Rothschild, M.F., Smith, T.P.L., Schnabel, R.D., Van Tassell, C.P., Taylor, J.F., Wiedmann, R.T., Schook, L.B., Groenen, M.A.M. 2009. Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology. PLoS One 4(8):E6524. p. 1-13.

Snelling, W.M., Allan, M.F., Keele, J.W., Kuehn, L.A., Mcdaneld, T.G., Smith, T.P., Sonstegard, T.S., Thallman, R.M., Bennett, G.L. 2010. Genome-Wide Association Study of Growth in Crossbred Beef Cattle. Journal of Animal Science. 88(3):837-848.

Decker, J.E., Pires, J.C., Conant, G.C., McKay, S.D., Heaton, M.P., Chen, K., Cooper, A., Vilkki, J., Seabury, C.M., Caetano, A.R., Johnson, G.S., Brenneman, R.A., Hanotte, O., Eggert, L.S., Wiener, P., Kim, J.J., Kim, K.S., Sonstegard, T.S., Van Tassell, C.P., Neibergs, H.L., McEwan, J.C., Brauning, R., Coutinho, L.L., Babar, M.E., Wilson, G.A., McClure, M.C., Rolf, M.M., Kim, J., Schnabel, R.D., Taylor, J.F. 2009. Resolving the Evolution of Extant and Extinct Ruminants With High-Throughput Phylogenomics. Proceedings of the National Academy of Sciences. 106(44):18644-18649.

Weigel, K.A., Van Tassell, C.P., O'Connell, J.R., Van Raden, P.M., Wiggans, G.R. 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science. 93(5):2229-2238.

Last Modified: 05/26/2017
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