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United States Department of Agriculture

Agricultural Research Service


Location: Genetics, Breeding, & Animal Health

2008 Annual Report

1a.Objectives (from AD-416)
Objective 1. Investigate the basic biology of the bovine genome and support annotation of the genome sequence. Functional annotation of the genome will assist in understanding the relationships between the genetics of individual animals and interaction of genetics with the production environment, and will leverage the investment in collection of the complete cattle genome sequence.

Objective 2. Develop approaches, methods, and software to support efficient conversion of QTL data into DNA markers with predictive merit. It has been established that DNA sequence variation among cattle contributes to differences in economically important traits between individual animals. Our overall hypothesis is that we can successfully identify specific DNA markers that track functional variation, and develop new methods to make the process of marker identification and testing more efficient.

Objective 3. Develop DNA marker systems with predictive merit for phenotypes important to cattle production in the United States, and characterize their effects on a wide range of economically important traits. The hypothesis is that single nucleotide polymorphisms (SNP) can be identified that are in linkage disequilibrium (LD) with functional DNA sequence variation causing effects on production traits. The corollary hypothesis is that an SNP or set of SNP can have sufficient predictive merit in a wide range of production settings and breed backgrounds, permitting Marker Assisted Selection (MAS) to improve herd genetics and Marker Assisted Management (MAM) to classify animals by genetic potential. The starting point for marker development is initial discovery of QTL positions in the genome. For the current CRIS cycle, three sources of QTL will be utilized. First, published bovine QTL developed at USMARC or at other institutions provide QTL targets. More than 15 QTL for just the two traits of meat tenderness and marbling have been reported. Second, development of comparative QTL database in Objective 2a is expected to identify additional target regions. Finally, we propose to use WGA approach to identify QTL using the SNP developed in Objective 2b. Whichever method is used to provide initial focus on genomic segments, we will develop an additional set of targeted SNP markers for testing to identify marker systems with the most consistent and widely applicable predictive merit. Below we describe the approaches that will be used, the details of proposed WGA studies, and marker system testing. As in the expiring CRIS projects, our focus will be on phenotypes of meat quality (tenderness and marbling), yield, and feed efficiency, however in this CRIS cycle we propose to incorporate the phenotypes available on cows to examine reproductive longevity and success.

Objective 4. Evaluate DNA marker systems in use by industry for their effectiveness in a variety of genetic backgrounds and potential impact on a range of production traits. The hypothesis is that utility of marker systems may depend on genetic background, or have unintentional effects on other traits than that for which the marker was developed. In many cases, data supporting the assertion of association.

1b.Approach (from AD-416)
The primary objective of the genome project at USMARC, since its inception in 1992, has been development of genetic markers that the cattle industry can use to identify animals with higher genetic merit for production traits not amenable to traditional phenotypic selection. While the original effort to create genome maps was successful, modern industry practices would benefit most from markers that can be cheaply genotyped and possess reliable predictive merit. Two complementary initiatives will be used to identify markers with predictive merit. A major component will be creation of an annotated genome sequence to put functional context to genomic positions discovered in QTL experiments both within cattle, and through application of comparative approaches exploiting QTL information from model species, including humans (described in Objective 1). The second initiative strives to detect existing, naturally occurring variation in DNA sequence affecting performance via genome scans or whole genome association experiments (described in Objectives 2 and 3). Once markers with predictive merit for a given trait have been identified and tested, they will be examined for potential effects on a wide range of cattle production traits to infer the likely effects of marker-assisted selection (described in Objective 4).

3.Progress Report
Construction of a web interface for a Bovine Gene Atlas Database providing details on the content of protein-coding genes collected using “next generation” sequencing technology for 95 bovine tissues. The data assist in determining gene boundaries and provide a snapshot of genes expressed in a normal, healthy animal. The searchable database reveals differences between cattle gene expression compared to other mammals and potential cattle-specific genes. Profiled five of these tissues’ small RNA fraction to identify microRNAs regulating gene expression. Characterized microRNAs at key stages of muscle development in swine. Several novel microRNAs not reported in other species were identified. Identified variation in the copy number of chromosomal segments in the cattle genome among 64 animals, revealing large-scale deletions and duplications of DNA segments. This data will be used to determine if CNV affects feed efficiency or carcass traits. Developed a highly parallel, high-density platform (BovSNP50) for collecting intensive genotypic information about animal populations, similar to approaches used in other species. In collaboration with researchers at BARC, University of Missouri, and Illumina we accomplished milestones for FY2008 and FY2009 with public release of the product in December 2007. The novel process we used was featured on the cover of Nature Methods in April 2008. The BovSNP50 is now the worldwide standard and being employed across the globe. Completed simulated dataset analysis to identify suitable approaches for whole genome analysis of BovSNP50 data. Determined that marker-based relationships between animals can replace pedigree information in mixed models to predict individual breeding values, that exceed the accuracy of traditional methods that use pedigree relationships. Genotyped a population of over 2500 animals with the BovSNP50 and analyzed birth and weaning weights, postweaning growth, feed intake and carcass traits. This research provides direct evidence that data from the BovSNP50 chip will be able to predict genetic merit for beef cattle traits. Evaluated markers in genes found on bovine chromosome 5 and identified SNP with predictive merit for fat content and total retail product yield of the carcass. Identified other markers associated with meat tenderness, twinning rate and ovulation rate. A marker for growth rate in the gene SPP1 was evaluated for potential effects on twinning rate, ovulation rate, and gestation length. National Program 101 Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1A: Develop and implement genome-enabling tools and reagents. Problem Statement 1B: Identify functional genes and their interactions. National Program 101 Component 2: Enhancing animal adaptation, well-being and efficiency in diverse production systems. Problem Statement 2B: Reducing reproductive losses.

1. Release of the BovSNP50 genotyping chip. The technology to genotype a large number of markers (SNP) has recently matured and provided an opportunity to make a giant leap forward in application of genome technology in livestock production. The design of these highly parallel assays depends on prior knowledge of a very large number of SNP (ideally, millions of SNP, but some hundreds of thousands can suffice). Using a novel approach we devised in collaboration with researchers at BARC and University of Missouri, and personnel from Illumina, a commercial genotyping platform service provider, we identified large numbers of SNP in cattle and produced a genotyping platform for research and marker-guided selection. This platform was released in December 2007 and is now the global standard for performing genomic research in cattle. It has been used to provide “genome enabled” predictions of genetic merit in dairy cattle by BARC and we are in the process of doing the same type of work in beef cattle. National Program 101 Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1A: Develop and implement genome-enabling tools and reagents.

2. Construction of a Gene Atlas for cattle. Genome research in cattle was hampered by the lack of data on the expression of genes in normal tissues. Generally expression data from other organisms such as mice or humans was substituted with the assumption that expression patterns would be similar across species, despite definitive evidence that this is not commonly the case. We collected the most in-depth data presently available on gene expression of 95 normal cattle tissues using “next generation” sequencing technology, and created an interactive database permitting a genome-level view of expression across tissues, tissue types, and tissue functions (e.g., immune tissues, neurological tissues, etc). The bovine Gene Atlas will have substantial impact in research of cattle physiology, genetics, and genomics by providing a control for gene expression patterns to contrast against expression in various physiological states or genetic backgrounds, after it is made available on the web (in progress), and facilitates annotation of the unfinished bovine genome sequence. National Program 101 Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1A: Develop and implement genome-enabling tools and reagents.

3. Identification of a chromosomal region associated with incidence of treatment for three pathogenic diseases on bovine chromosome 20. Incidence of bovine respiratory disease (BRD), infectious keratoconjunctivitis (pinkeye), and infectious pododermatitis (footrot) were used to generate the incidence of treatment of one or more pathogenic diseases. Four half-sib families were used in this study. There was evidence of a quantitative trait locus (QTL) on bovine chromosome 20 in two families. This QTL is in a similar location where QTL for Johne’s disease and mastitis have been detected. It is probable that a gene in this region partially regulates immunological defense mechanisms. This study shows that this region of the genome harbors a gene that is expressed in the presence of pathogens. This is the initial phase of a study to develop markers associated with incidence of diseases. Incidence of diseases is a major concern in the beef industry. National Program 101 Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1B: Identify functional genes and their interactions.

4. Fine mapping of QTL for twinning and ovulation. To enhance production efficiency in cattle for lowly heritable traits, DNA diagnostic tools will be required. This study through the development of diagnostic DNA markers confirmed a QTL previously discovered at USMARC for twinning and ovulation rate on bovine chromosome 5. This project was done using a low density single nucleotide polymorphism (SNP) map with previously genotyped markers in a large extended pedigree of 16,035 USMARC twinning animals. This study is the first in cattle to show significant SNP marker associations with reproductive traits using imprinting in the genetic model. Genetic markers from this study with significant associations for ovulation and twinning rate may be useful to increase or decrease the incidence of twinning in industry populations. National Program Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1B: Identify functional genes and their interactions. National Program 101 Component 2: Enhancing animal adaptation, well-being and efficiency in diverse production systems. Problem Statement 2B: Reducing reproductive losses.

6.Technology Transfer

Number of Non-Peer Reviewed Presentations and Proceedings3
Number of Newspaper Articles and Other Presentations for Non-Science Audiences5
Number of Other Technology Transfer2

Review Publications
Clawson, M.L., Richt, J., Baron, T., Biacabe, A., Czub, S., Heaton, M.P., Smith, T.P., Laegreid, W.W. 2008. Association of a bovine prion gene haplotype with atypical BSE. PLoS One [serial online]. 3(3):e1830. Available:

Miller, L.C., Harhay, G.P., Lager, K.M., Smith, T.P., Neill, J.D. 2008. Effect of porcine reproductive and respiratory syndrome virus on porcine alveolar macrophage function as determined using serial analysis of gene expression (SAGE). Developments in Biologicals. 132:169-174.

Van Tassell, C.P., Smith, T.P., Matukumalli, L.K., Taylor, J.F., Schnabel, R.D., Lawley, C.T., Haudenschild, C., Moore, S.S., Warren, W.C., Sonstegard, T.S. 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature Methods. 5:247-252.

Heaton, M.P., Keele, J.W., Harhay, G.P., Richt, J., Koohmaraie, M., Wheeler, T.L., Shackelford, S.D., Casas, E., King, D.A., Sonstegard, T.S., Van Tassell, C.P., Neibergs, H.L., Chase, C.C., Kalbfleisch, T.S., Smith, T.P., Clawson, M.L., Laegreid, W.W. 2008. Prevalence of the prion gene E211K variant in U.S. cattle. BioMed Central (BMC) Veterinary Research [journal online]. 4:25. Available: (

Snelling, W.M., Chiu, R., Schein, J.E., Hobbs, M., Abbey, C.A., Adelson, D.L., Aerts, J., Bennett, G.L., Bosdet, I.E., Boussaha, M., Brauning, R., Caetano, A.R., Costa, M.M., Crawford, A.M., Dalrymple, B.P., Eggen, A., Everts-Van Der Wind, A., Floriot, S., Gautier, M., Gill, C.A., Green, R.D., Holt, R., Jann, O., Jones, S.J., Kappes, S.M., Keele, J.W., De Jong, P.J., Larkin, D.M., Lewin, H.A., Mcewan, J.C., Mckay, S., Marra, M.A., Mathewson, C.A., Matukumalli, L.K., Moore, S.S., Murdoch, B., Nicholas, F.W., Osoegawa, K., Roy, A., Salih, H., Schibler, L., Schnabel, R.D., Silveri, L., Skow, L.C., Smith, T.P., Sonstegard, T.S., Taylor, J.F., Tellam, R., Van Tassell, C.P., Williams, J.L., Womack, J.E., Wye, N.H., Yang, G., Zhao, S. 2007. A physical map of the bovine genome. Genome Biology. 8:R165 (17 pp).

Last Modified: 4/19/2014
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