Location:2011 Annual Report
1a. Objectives (from AD-416)
Objective 1: Develop methods and strategies for measuring feed intake and related phenotypes of steers, replacement heifers, and mature cows. Objective 2: Determine breed and within-breed genetic effects on feed efficiency, growth, and fertility of cattle. Sub-objective 2.A. Determine breed and within-breed genetic effects on nutrient utilization. Sub-objective 2.B. Determine breed and within-breed genetic effects on reproductive efficiency. Objective 3: Discover QTL and estimate genomic effects for traits contributing to differences in efficiency among cattle. Objective 4: Fine map identified QTL for reproductive rate in cattle. Objective 5: Enhance existing simulation models to investigate genetic-by-environmental interactions on beef life-cycle efficiency and integrate into decision support software. Objective 6: Identify nutritional effects on expression of genes and subsequent phenotypes, and integrate this information with our current understanding of physiology to enhance management decisions.
1b. Approach (from AD-416)
Major challenges of the beef cattle industry are to improve efficiency and reduce negative environmental impacts of animal production. Provision of nutrients (feed) constitutes about 65 to 75% of the cost of beef cattle production. Less than 20% of the nutrients consumed are converted to useful products. The incomplete and inefficient utilization of nutrients has an adverse effect on efficiency of production and a negative effect on the environment. Traditional approaches have resulted in successful alteration of production traits such as weight at slaughter, growth rate, mature weight, and body composition, but have not altered feed efficiency. Those approaches do not provide the ability to economically identify animals with high genetic merit for feed efficiency on a timely basis, because the required phenotypic data are impractical to obtain in normal genetic evaluation programs. Available evidence suggests feed consumption and related traits are likely to be moderately heritable. These traits are extremely important components in any strategy for the permanent, cumulative, and sustainable genetic improvement of biological and economic efficiencies of beef production. Application of quantitative trait loci (QTL) technology provides opportunities to improve feed efficiency in beef production. The identification of QTL would make it possible to utilize the relatively large amount of observed variation and moderate heritability in improving feed efficiency. However, currently there are no tools (EPD, QTL, or markers for QTL) that facilitate direct selection to modify feed consumption, feed efficiency, or nutrient requirements in the growing-finishing animal or productive female. Research in this project is being undertaken to study genetic and environmental factors that lead to variation in efficiency in beef production. This project addresses measures of efficiency at different phases of the production system to identify those factors that have additive merit and antagonistic relationships across the different phases of production. The initial component of the project is to develop facilities and methodologies to efficiently collect economically and biologically important phenotypic data relevant to efficiency. After developing capabilities to measure the phenotypes, the contribution of genetics and environment/management to variation in efficiency of production will be determined. Genetic variation will be evaluated using both quantitative genetics and QTL discovery. Information gained from both the genetic and environmental studies will be used to parameterize simulation models that provide decision support software to allow producers to simulate potential outcomes to optimize production efficiency when different combinations of animal genetics and management strategies are used.
3. Progress Report
Phenotypic data for individual feed intake and weight change were collected on steers (n = ~ 420), heifers (n = ~ 380), and mature cows (n = ~ 180). Six chromosomal regions have been fine mapped for feed efficiency. Two regions have been validated in an unrelated population of animals. These regions were also evaluated in a population of heifers. Phenotypic measures of milk production, mammary soundness, and feet and leg structure were collected on five-year-old cows (n = ~ 183). Reproductive success and calf performance were determined. Ovarian follicle counts of 14-month-old heifers (n = 450) were performed by ultrasonography, and subjected to association analysis using genotypes from the Bovine SNP50 Chip. Regions on chromosome 2 and 3 were identified to be associated with antral follicle count. SNP associating with antral follicle count, age at puberty, and heifer pregnancy rate were mapped to the Titin gene. There was no evidence of a functional role of Titin in the mammalian ovary, real-time RT-PCR was performed on pools of cDNA from ovarian cortex, luteal tissue, and granulosa cells. The highest levels of mRNA for Titin were identified in medium and large follicles. In situ hybridization did not demonstrate localization of Titin mRNA in granulosa cells from follicles = 5 mm. Levels of Titin in the ovary may be below the sensitivity of in situ hybridization or the results from real-time RT-PCR may be false. Titin’s functional role in follicular development may not be required until a follicle is greater than 5 mm in diameter. Heifers (n ~ = 500) have been phenotyped for antral follicle count and ovarian size for confirmation of the association identified on bovine chromosome 2. Nutritional treatments were imposed on 178 peripuberal heifers to determine the effects of over nutrition on subsequent feed and production efficiency. Reproductive and production data were collected on 517 females that had received nutritional treatments as heifers, and feed intake (n = 93) and nutrient balance trials were conducted on 2-year-old cows that had received these treatments. Nutritional treatments were imposed on 120 prepubertal heifers to determine the effects of under nutrition on postpubertal ovarian size and follicular development, and conception. Under nutrition during early vs. late gestation on fetal development and postnatal performance were evaluated in 80 primiparous heifers. Ovarian size and antral folicle counts were determined. Growth, puberty, and pregnancy data was collected on ~400 daughters that were the result of the previous years’ uterine nutrient environment study. To investigate possible transplacental sharing of DNA between mother and fetus during gestation or at parturition, blood samples for genotyping assays were taken from 27 multiparous cows having produced only male (n = 17) or female (n = 10) progeny for 5 or more parturitions, and from 35 first-parity heifers and all their progeny. Blood and tissue samples on twin born animals (n ~ = 200) were also collected for genotyping assays to assess the degree of allele sharing between twins due to anastomoses of placental vascularity.
1. Fitting nutrition to cattle genetics. Establishing the growth pattern and the proportion of mature weight at puberty of cows is important in developing management and nutrition programs targeted to increase the efficiency of beef production. Few data sets that track skeletal and body weight from birth through maturity are available. Even fewer data sets are available that compare diverse genetic types of cattle. ARS researchers in Clay Center, Nebraska, assembled the data sets that allowed comparisons of growth patterns across diverse genetic types of cows. This research described the proportion of mature weight and skeletal size cows reach at puberty, and that the proportion differs with genetic type. Knowing how growth rate changes as a cow ages, and the target weight required for successful reproductive performance allows for the development of nutrient management programs that fit the genetics of the cow.
Tovar-Luna, I., Puchala, R., Sahlu, T., Freetly, H.C., Goetsch, A.L. 2010. Effects of stage of lactation and level of feed intake on energy utilization by Alpine dairy goats. Journal of Dairy Science. 93(10):4829-4837.