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United States Department of Agriculture

Agricultural Research Service

Research Project: RESOURCE DEVELOPMENT FACILITATING BOVINE GENOME SEQUENCE USE TO IMPROVE CATTLE PRODUCTION EFFICIENCY, PRODUCT QUALITY & ENVIRONMENTAL IMPACT

Location: Genetics, Breeding, & Animal Health

2010 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
There was substantial progress on the overall aims of the project to enhance the utility of the bovine genome sequence, apply these advances to development of DNA marker-based systems to improve cattle genetics for target traits, and to evaluate the effects of using DNA marker-based selection on a wide range of traits including those not intentionally targeted by the selection process.

Major efforts include continued annotation of the genome sequence expanding knowledge of gene content and boundaries and identify genomic regions encoding for RNA with important direct regulatory functions. A manuscript detailing the Bovine Gene Atlas (a description of the array of genes expressed in over 90 normal, apparently healthy cattle tissues from fetal, juvenile, and adult animals) was prepared to complement the web-based browser for viewing the data, and describes several unique discoveries regarding gene expression in cattle. Among other uses, this data is useful for determining the likelihood a given gene might be responsible for differences in animal characteristics. A parallel effort involved the application of the latest sequencing technology to examine the complete repertoire of the protein-coding class of RNA in muscle and fat tissues, the most comprehensive look at the “transcriptome” yet available. This data provided a large amount of new information on the boundaries of genes expressed in these tissues, and the different subtypes of RNA molecules that are produced from a single gene. A complementary effort examined microRNA abundance in four different bovine skeletal muscles and three adipose (fat) tissues. MicroRNA are a class of small non-protein coding RNAs that regulate gene translation and subsequent developmental decisions, and the data help to evaluate the role microRNA may play in development and growth of skeletal muscle and adipose tissue in cattle. A collaborative effort extended this study by generating microRNA profiles of over 80 normal cattle tissues to create a “microRNA Atlas” of cattle.

Another major effort was devoted to exploring application of high density genotyping arrays, which were developed in part through efforts of the first two years of the project, to identify genomic regions harboring DNA sequence variation affecting production traits. Numerous such regions were identified for a wide range of traits using an array with 50,000 markers. For example, candidate genes were identified in a genomic region of chromosome 6 that shows consistent association with incidence of pinkeye and respiratory disease, allowing these genes to be targeted for further marker development to provide assays with predictive merit to the industry. In addition, genomic regions associated with feed intake, gain on feed, and feed efficiency measures were identified. Finally, the effects of selection using markers in two genes associated with muscling and meat tenderness were evaluated. Results support that selection permits gains in muscle area, retail product yield, and fatness, with no detectable detrimental effects on other off-target traits.


4.Accomplishments
1. Identification of DNA markers associated with respiratory disease in cattle. Previously, scientists at the U.S. Meat Animal Research Center (USMARC) in Clay Center, NE had established that bovine chromosome 20 is the site of genetic variation affecting a range of infectious disease-related phenotypes, using new high-density genotyping arrays. They have now examined the annotated genome of cattle in the area of this variation and identified two functional candidate genes with known roles in immune function, with gene symbols ANKRA2 and RP105. Previously discovered DNA sequence markers not present in the initial arrays, but predicted to lie in this area of the genome, were tested for association with disease traits in order to provide supporting data for the original study, and identify markers with better predictive merit for disease resistance. Seven markers in the two genes displayed association, providing potential markers for selecting animals with superior response to disease challenge.

2. Reducing the cost of genome wide association using sample pooling. Whole genome association (WGA) studies based on high-density genotyping arrays are a powerful but expensive tool for dissecting genetic variation underlying a trait. For traits with low heritability such as host resistance to infectious disease, the large sample sizes required to succeed lead to costs in the millions of dollars per trait. A strategy to reduce these costs would be to pool the DNA of many animals and genotype the pools, but the power of this approach has not been tested using arrays available for cattle genotyping. ARS researchers at Clay Center, Nebraska performed a pilot study demonstrating that DNA pooling can work with a 50,000 marker commercial cattle genotyping array, and extended it to show that with some tissues such as lung, the tissue can even be pooled before DNA extraction to further reduce cost. A tissue pooling approach reduces the cost of WGA by as much as three orders of magnitude, so a single study costs in the thousands of dollars rather than millions, making it possible to address more traits with higher accuracy with the limited resources available.

3. DNA markers associated with cattle birth weight and growth to yearling age. Undesirable increases in birth weight and calving difficulty can occur as a correlated response to selection for desirable increases in weaning weight and postweaning growth of beef calves. ARS researchers in Clay Center, NE examined BovineSNP50 genotypes and phenotypes of over 2500 crossbred cattle, and determined that several single nucleotide polymorphisms (SNP), located throughout the genome, were associated with postnatal growth but not birth weight. These SNP may now be tested to determine their utility for selection to increase growth without affecting birth weight. A few other SNP, associated with large effects on birth weight and postnatal growth, may be considered in selection and mating decisions to control birth weight and growth.

4. New procedures for partial-genome evaluation to partition individual additive genetic effects into polygenic and genotypic components. Numerous progeny are needed to accurately predict an individual's breeding value in traditional evaluation of polygenic effects informed by phenotype and pedigree records. Accuracy of non-parent breeding value predictions can be increased by including genotypes of informative single nucleotide polymorphisms (SNP) in the genetic evaluation. ARS researchers in Clay Center, NE applied partial-genome evaluation procedures to feedlot intake and gain records of 1100 crossbred steers, and determined that including genotypes of the 96 most informative SNP for each trait could increase non-parent accuracy by the equivalent of 5 to 20 progeny for intake, gain and efficiency traits. More accurate breeding value predictions enabled by partial-genome evaluations could accelerate genetic improvement for these economically important traits.


Review Publications
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.

Frylinck, L., Van Wyk, G.L., Smith, T.P., Strydom, P.E., van Marle-Koster, E., Webb, E.C., Koohmaraie, M., Smith, M.F. 2009. Evaluation of Biochemical Parameters and Genetic Markers for Association with Meat Tenderness in South African Feedlot Cattle. Meat Science. 83:657-665.

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.

Sanchez Castano, C., Smith, T.P., Wiedmann, R.T., Vallejo, R.L., Salem, M., Yao, J., Rexroad Iii, C.E. 2009. Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library. Biomed Central (BMC) Genomics. 10:559.

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.

Durso, L.M., Harhay, G.P., Smith, T.P.L., Bono, J.L., Desantis, T.Z., Harhay, D.M., Andersen, G.L., Keen, J.E., Laegreid, W.W., Clawson, M.L. 2010. Animal-to-Animal Variation in Fecal Microbial Diversity among Beef Cattle. Applied and Environmental Microbiology. 76(14):4858-4862.

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.

Last Modified: 9/29/2014
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