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. Sub-objective 1E. Characterize genomic diversity among a broad sample of highly influential germplasm in the U.S. beef industry. 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, produced 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 individual marker associations were conducted and compared with analyses of similar populations in Canada and Australia for growth, feed efficiency, and carcass traits. Crossbred cows born in 1999 and 2000 have been individually fed to determine maintenance requirements and continue to be evaluated for longevity (productive herd life), reproductive and maternal traits. Grandprogeny of these cows are now being produced as terminal progeny; vaccination response, treatment and diagnostic records, lung lesions at slaughter, and blood counts on entering the feedlot are being collected to support research on genomics of disease resistance. An ongoing experiment evaluated F1 cows sired by breeds adapted to tropical or subtropical environments in both types of environments (subtropical, Louisiana; temperate, Nebraska). Data collection in Louisiana ended this year (after having produced six or seven calf crops), although cows in Nebraska will be bred for a few more years to estimate breed differences in longevity and associated traits. Fertility rates during spring breeding were measured on five types of mature F1 ewes produced by mating Dorset, Dorper, Katahdin, Rambouillet, and White Dorper rams to Romanov ewes. Reproductive traits were recorded on 3- and 4-year-old Rambouillet-Romanov reciprocal crossbred ewes mated to two terminal sire breeds. A total of 926 crossbred lambs (½ Romanov, ¼ White Dorper, and either ¼ Katahdin or ¼ USMARC Composite) were produced to facilitate creation of an easy-care maternal line of prolific hair sheep. Flocks of Katahdin and Polypay (easy-care maternal breeds of prolific hair and wool sheep, respectively) continued to expand. Cows were bred by artificial insemination to a new sample of 174 highly influential sires of 16 breeds (Angus, Hereford, Simmental, Charolais, Limousin, Red Angus, Gelbvieh, Shorthorn, Brangus, Beefmaster, Maine-Anjou, Brahman, Chiangus, Santa Gertrudis, Salers, and Braunvieh). Backcross calves of each of these breeds were born this year, contributing to the goal of grading up to essentially purebred herds of each of these breeds. The F1 progeny were evaluated for feed efficiency and many other traits. Whole genome simultaneous multiple QTL analysis was conducted with GenSel, a program developed at Iowa State. This program does not incorporate gene x gene interactions into the statistical model. We now know that incorporating epistatic models into whole genome analyses of cattle pedigrees is overly ambitious given the current state of the art (is not currently feasible).
1. Whole genome SNP associations with beef cattle growth. A recently developed tool for cattle genomics is a beadchip that can quickly assay 58,000 genetic markers. The associations of these markers with traits important to cattle production are mostly unknown. The associations potentially can be used to aid selection by cattle breeders or management decisions by ranchers and feeders. The beadchip was used on 2,600 crossbred cattle that had been weighed at birth, at weaning, and at one year of age. Hundreds of strong associations between markers and growth were estimated with a high degree of confidence. These markers were often located on chromosomes previously identified as affecting growth. Thousands of weaker associations between markers and growth were identified with less confidence. These markers were distributed across all chromosomes. Many markers were associated with all phases of growth but a few were associated with only one or two phases. These results will be useful in developing additional tools that can be widely used by the cattle industry.
2. Selection for easy calving in heifers affects other traits. Heifers are much more likely to experience difficulty at calving than older cows. It is possible to select for heifers and calves that calve more easily and maintain or increase growth, but this is likely to affect other traits also. Changes in other traits were evaluated in lines of cattle successfully selected for easier calving. Calving occurred 3 days earlier and gestation was 2 days shorter in the experimentally selected lines. Calves from these lines grew faster from birth to weaning, were shorter, and had similar pelvic measurements compared to control lines selected to have the same weight at 1 year of age. Calving assistance in older cows was not affected by the selection. Results for these traits do not show any detrimental changes from selection for easier calving heifers.
Bennett, G.L. 2008. Experimental selection for calving ease and postnatal growth in seven cattle populations. I. Changes in estimated breeding values. Journal of Animal Science. 86(9):2093-2102.