2009 Annual Report
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.
The BovSNP50 chip, created in collaboration with BARC, U. of Missouri, and Illumina, Inc., was used to analyze growth and carcass traits of a large research herd and identify numerous regions of the genome affecting growth including birth weight, weaning weight, and yearling weight. Relative differences between weights at different ages were analyzed along with feed intake and efficiency of growing steers and heifers. This data was used to identify potential genetic markers associated with animals of ideal production characteristics: low birth weight to improve calving ease and reduce calf loss, but high postnatal growth at minimal feed intake to achieve desired final weight with reduced cost of feed. Initial results from this study will be used for further analysis to validate effects and to identify a reduced set of markers for routine use. Markers that have progressed further down the development pipeline include some on BTA20 associated with health parameters including Johne’s disease, being validated in wider population samples. Additionally, copy number variation (CNV) where different animals have different number of copies of DNA segments, were examined as markers associated with bloat.
Obtained NRI grant support for Bioinformatics to implement genomic selection (BIGS) project to develop a genome browser linking whole-genome results and genome annotation, such as those developed as described above, and also to link to QTL information. Also, selection for genetic markers in use in cattle is moving frequencies of the markers towards 50% in four populations. Three-year evaluations of carcass characteristics and heifer reproduction are under way in two populations that are close to their desired marker frequency goals. When completed, this information will better define the role of genetic markers in genetic improvement programs. Finally, 205 markers in twenty genes were identified on chromosomes 3, 4, 5, 10, 14 and 17 in regions associated with meat tenderness and marbling. A variety of traits were analyzed and marker associations were identified. This research will be extended in ensuing years of the CRIS project to determine the best combination of markers to take advantage of this naturally occurring genetic variation influencing valuable production traits in beef cattle.
Larue, R.S., Jonsson, S.R., Silverstein, K.A., Lajoie, M., Bertrand, D., El-Mabrouk, N., Hotzel, I., Andresdottir, V., Smith, T.P., Harris, R. 2008. The artiodactyl APOBEC3 innate immune repertoire shows evidence for a multi-functional domain organization that existed in the ancestor of placental mammals. BioMed Central (BMC) Molecular Biology. 9:104.
Ratnakumar, A., Barris, W., McWilliam, S., Brauning, R., McEwan, J.C., Snelling, W.M., Dalrymple, B.P. 2009. A Multiway Analysis for Identifying High Integrity Bovine BACs. Biomed Central (BMC) Genomics. 10:46 (13 pp).
Casas, E., Snowder, G.D. 2008. A putative quantitative trait locus on chromosome 20 associated with bovine pathogenic disease incidence. Journal of Animal Science. 86:2455-2460.
McDaneld, T.G. 2009. MicroRNA: Mechanism of Gene Regulation and Application to Livestock. Journal of Animal Science. 87(E. Suppl.):E21-E28.
McDaneld, T.G., Smith, T.P., Doumit, M.E., Miles, J.R., Coutinho, L.L., Sonstegard, T.S., Matukumalli, L.K., Nonneman, D.J., Wiedmann, R.T. 2009. MicroRNA Transcriptome Profiles During Swine Skeletal Muscle Development. Biomed Central (BMC) Genomics. 10:77.
Clawson, M.L., Heaton, M.P., Keele, J.W., Smith, T.P., Harhay, G.P., Richt, J., Laegreid, W.W. 2008. A sequencing strategy for identifying variation throughout the prion gene of BSE-affected cattle. BMC Research Notes [journal online]. 1:32. Available: http://www.biomedcentral.com/1756-0500/1/32.
Wiedmann, R.T., Smith, T.P., Nonneman, D.J. 2008. SNP discovery in swine by reduced representation and high throughput pyrosequencing. BioMed Central (BMC) Genetics. 9:81.
Clawson, M.L., Keen, J.E., Smith, T.P., Durso, L.M., Mcdaneld, T.G., Mandrell, R.E., Davis, M.A., Bono, J.L. 2009. Phylogenetic Classification of Escherichia coli O157:H7 Strains of Human and Bovine Origin Using a Novel Set of Nucleotide Polymorphisms. Genome Biology [serial online]. 10:R56. Available: http://genomebiology.com/2009/10/5/R56.
Elsik, C.G., Gibbs, R., Skow, L., Tellam, R., Weinstock, G., Worley, K., Kappes, S.M., Green, R.D., Alexander, L.J., Bennett, G.L., Carroll, J.A., Chitko Mckown, C.G., Hamernik, D.L., Harhay, G.P., Keele, J.W., Liu, G., Macneil, M.D., Matukumalli, L.K., Rijnkels, M., Roberts, A.J., Smith, T.P., Snelling, W.M., Stone, R.T., Waterman, R.C., White, S.N. 2009. The Genome Sequence of Taurine Cattle: A Window to Ruminant Biology and Evolution. Science. 324:522-528.
Gibbs, R., Van Tassell, C.P., Weinstock, G.M., Green, R.D., Hamernik, D.L., Kappes, S.M., Liu, G., Matukumalli, L.K., Matukumalli, A., Sonstegard, T.S., Silva, M.V. 2009. Genome-Wide Survey of SNP Variation Uncovers the Genetic Structure of Cattle Breeds. Science. 24:528-532.
Matukumalli, L.K., Lawley, C.T., Schnabel, R.D., Taylor, J.F., Allan, M.F., Heaton, M.P., O'Connell, J., Moore, S.S., Smith, T.P., Sonstegard, T.S., Van Tassell, C.P. 2009. Development and Characterization of a High Density SNP Genotyping Assay for Cattle. PLoS One. 4(4):e5350. Available: http://dx.doi.org/10.1371/journal.pone.0005350.