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United States Department of Agriculture

Agricultural Research Service


Location: Genetics, Breeding, and Animal Health Research

2008 Annual Report

1a. Objectives (from AD-416)
OBJECTIVE 1. Characterize quantitative and molecular genetic variation between and within breeds for traits that affect life-cycle efficiency of beef cattle. Sub-objective 1A. Characterize direct and maternal breed and heterosis effects among diverse breeds of cattle, and genetic variances and covariances within breeds for calving ease, survival, rate and efficiency of growth, carcass composition, meat quality, age and weight at puberty, reproduction, maternal performance, cow size, and herd life. Sub-objective 1B. Identify and fine map QTL regions that affect quality of beef and efficiency of production. Sub-objective 1C. Determine the efficiency of feed use among mature cows during the production interval from parturition until weaning. Sub-objective 1D. Identify genetic components associated with bovine disease resistance. OBJECTIVE 2. Examine potential interactions of genetically diverse breeds of cattle with climatic or nutritional environments. OBJECTIVE 3. Evaluate breeds to create an easy-care maternal line of hair sheep for use in low-input production systems. Sub-objective 3A. Evaluate wool and hair breeds in intensive and low-input production systems during traditional fall breeding and for fertility during challenging spring breeding. Sub-objective 3B. Evaluate life-cycle productivity of reciprocal crosses between the Romanov and Rambouillet breeds. Sub-objective 3C. Create an easy-care maternal line of hair sheep. OBJECTIVE 4. Evaluate power of experimental designs to estimate quantitative and molecular genetic parameters. OBJECTIVE 5. Develop statistical theory and computational algorithms to incorporate DNA information and multi-breed comparisons into genetic evaluations of beef cattle.

1b. Approach (from AD-416)
Genetic variation among and within breeds, including allelic variation, provides a foundation for genetic improvement through selection and for management of genetic effects through crossbreeding (or mating) systems. Improvement of production efficiency and sustainability of beef cattle and sheep production systems are dependent on greater knowledge of genetic effects on fundamental traits affecting life-cycle efficiency such as fertility, prolificacy, maternal ability, offspring survival, health, longevity, and adaptation to production environments. Two broad approaches will be pursued: 1) large-scale animal experimentation and 2) development and application of statistical theory and software to support discovery and estimation of genetic effects. The first three objectives use experimental populations to provide genotypic and phenotypic data for traits known to affect life-cycle efficiency and for matching genetic resources with specific marketing and production situations. Large-scale beef cattle and sheep experiments using both quantitative and molecular approaches are planned to provide genotypic and phenotypic data for estimation of genetic effects on fundamental traits. Cattle research will emphasize multi-breed genetic evaluation, estimation of genetic parameters within breed, and structuring of populations to facilitate genomic research leading to development of DNA tests for economically important traits. A population of cattle is being developed for QTL identification that has recent ties to industry genetics, several half-sib families large enough to contribute to identifying QTL through linkage, and many smaller families and several potential origins of QTL allowing fine mapping, association analyses, and marker validation. Potential interactions of temperate and tropically-adapted cattle breeds with temperate and subtropical environments will be investigated through evaluation of F1 cows consistent with commercial production systems in the subtropical environment of Louisiana and the temperate environment of Nebraska. Sheep experimentation will focus on breed evaluation, leading to creation and development of an easy-care maternal line of hair sheep. The animal experiments will be complemented by research to develop and apply statistical technologies required for discovery, estimation, and use of genetic effects, including incorporation of genetic markers into multibreed genetic evaluations for beef cattle. The fourth objective addresses designs of experimental populations for estimation of genetic effects. Various mating plans will be simulated and evaluated for their power to detect QTL effects of various sizes, power of detecting breed-specific heterosis, and for the standard errors of other genetic effects. The final objective focuses on the development and application of statistical theory required for analysis of data and exploitation of genetic effects by livestock industries. Whole genome selection will be investigated as a method to reduce bias and improve accuracy of genetic prediction.

3. Progress Report
Crossbred bulls and cows, the progeny of sires from the seven beef breeds with the most registrations, were mated to produce their final calf crop in 2007. Progeny of these crossbred parents have been produced since 2003 (~500 per year) and have undergone extensive performance recording for feed intake, carcass and tenderness traits in steers, and reproductive traits in females. Cattle with feed intake records (1,311 steers and 707 heifers) were genotyped this year for approximately 52,000 markers. In addition, their 73 sires and 515 steers in the F1 generation were genotyped for this same set of markers. Analyses of marker associations, leading to whole genome selection tools, are underway for feed intake, disease incidence, and a host of growth and carcass traits. Crossbred cows born in 1999 and 2000 continue to be evaluated for longevity (productive herd life), maintenance requirements (feed intake), and reproductive traits. An ongoing experiment evaluated F1 cows sired by breeds adapted to tropical or subtropical environments in both types of environments (subtropical, Louisiana; temperate, Nebraska). The F1 cows were managed consistent with commercial production systems in each environment. Comprehensive data were recorded on F1 cows and their calves as usual to evaluate maternal and reproductive traits of the cows. Reproductive traits were measured on five types of mature Romanov crossbred ewes bred in May. The crossbred ewes were sired by Dorset, Dorper, Katahdin, Rambouillet, and White Dorper rams. Reproductive traits were recorded on 2- and 3-year-old Rambouillet-Romanov reciprocal crossbred ewes. Romanov x White Dorper, Romanov x Katahdin, and Romanov x USMARC Composite crossbred lambs were produced to provide the genetic foundation for an easy-care maternal line of shedding sheep. Calves were produced in the fall of 2007 by a new sample of highly influential Shorthorn, Maine-Anjou, Brahman, Chiangus, Santa Gertrudis, Salers, and Braunvieh sires. Bulls from these same breeds also sired calves in the spring of 2008, along with a new sample of highly influential sires representing the seven breeds that register the most beef cattle in the United States. The fall-born 2007 calves have been weaned and moved to a facility for the collection of individual feed intake data. Whole genome genetic evaluations were estimated based on analyses of growth, carcass and feed efficiency data recorded on 3,600 cattle that were also genotyped for 52,000 genetic markers. The accuracy of whole genome selection was assessed using computer simulation and actual data. Co-segregating markers spanning 7 megabase regions of the genome were constructed to facilitate fine mapping. The genetic marker data were used to determine paternity, identify incorrect cow-calf pairings, and detect misclassified types of birth (twin or single). National Program 101 Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1D: Develop and implement genome-enabled genetic improvement programs.

4. Accomplishments
1. Across-Breed EPD Adjustment Factors. Beef cattle breed associations produce expected progeny differences (EPD) as a measure of the genetic merit of individual bulls and cows for several economically relevant traits. However, these EPD are not directly comparable among different breeds. To address this problem, across-breed EPD adjustment factors were calculated on 16 breeds for growth traits (birth, weaning, and yearling weights and maternal milk) and on 8 breeds for carcass traits (ribeye area, backfat depth, and marbling) using data from the USMARC Germplasm Evaluation program and EPD from breed associations. These factors were released at the Beef Improvement Federation meeting to a North American audience, followed by publication on various websites and in the popular press. This research allows producers to more effectively use available genetic resources and thereby more rapidly improve production of lean beef and meat quality. National Program Component 1: Understanding, improving, and effectively using animal genetic and genomic resources. Problem Statement 1D: Develop and implement genome-enabled genetic improvement programs.

2. Easy-Care Sheep Genetics. Sheep producers are reluctant to use prolific breeds in low-input, pasture-lambing production systems because of the perception that such breeds require more labor during lambing and that the increased prolificacy will be entirely offset by lower lamb survival. Easy-care sheep that achieve high prolificacy and high lamb survival while lambing on pasture are needed to improve efficiency of production, increase profitability, and address the issue of animal well-being. Research established that Romanov crossbred ewes can successfully raise triplets on pasture without labor or supplemental feed. Therefore, progressive sheep producers can use appropriate breed resources to increase profitability by marketing more lambs per ewe and reducing use of labor and harvested feed. National Program Component 1: Understanding, improving and effectively using animal genetic and genomic resources. Problem Statement 1D: Develop and implement genome-enabled genetic improvement programs.

5. Significant Activities that Support Special Target Populations

Review Publications
Jenkins, T.G., Ferrell, C.L. 2007. Daily dry matter intake to sustain body weight of mature, nonlactating, nonpregnant cows. Journal of Animal Science. 85(7):1787-1792.

Cushman, R.A., Allan, M.F., Thallman, R.M., Cundiff, L.V. 2007. Characterization of biological types of cattle (Cycle VII): Influence of postpartum interval and estrous cycle length on fertility. Journal of Animal Science. 85(9):2156-2162.

Echternkamp, S.E., Cushman, R.A., Allan, M.F., Thallman, R.M., Gregory, K.E. 2007. Effects of ovulation rate and fetal number on fertility in twin-producing cattle. Journal of Animal Science. 85(12):3228-3238.

Echternkamp, S.E., Thallman, R.M., Cushman, R.A., Allan, M.F., Gregory, K.E. 2007. Increased calf production in cattle selected for twin ovulations. Journal of Animal Science. 85(12):3239-3248.

Kachman, S.D., Van Vleck, L.D. 2007. Technical Note: Calculation of standard errors of estimates of genetic parameters with the multiple-trait derivative-free restricted maximal likelihood programs. Journal of Animal Science. 85:2375-2381.

Thallman, R.M., Kuehn, L.A., Allan, M.F., Bennett, G.L., Koohmaraie, M. 2008. Opportunities for collaborative phenotyping for disease resistance traits in a large beef cattle resource population. Developments in Biologicals. 132:327-330.

Freking, B.A., Leymaster, K.A., Vallet, J.L., Christenson, R. 2007. Number of fetuses and conceptus growth throughout gestation in lines of pigs selected for either ovulation rate or uterine capacity. Journal of Animal Science. 85(9):2093-2103.

Arnaud, F., Caporale, M., Varela, M., Biek, R., Chessa, B., Alberti, A., Golder, M., Mura, M., Zhang, Y., Yu, L., Pereira, F., De Martini, J.C., Leymaster, K.A., Spencer, T.E., Palmarini, M. 2007. A paradigm for virus-host coevolution: Sequential counter-adaptations between endogenous and exogenous retroviruses. PLoS Pathogens Vol. 3(11):e170 doi:10.1371/journal.ppat.0030170.

Van Vleck, L.D. 2007. Technical Note: Computing numerator relationships between any pair of animals. Genetics and Molecular Research. 6(3):685-690.

Last Modified: 06/23/2017
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