2012 Annual Report
The increasing use of high-density genomic arrays is generating tremendous amounts of data on individual animals, allowing clearer delineation of heritable differences in production efficiencies and product quality. Yet, such volumes of data pose technical challenges. Only by placing these data in a quantitative framework can the information be usefully and seamlessly incorporated into farming and food systems. Genomic and quantitative data at the U.S. Meat Animal Research Center (USMARC) will be used to estimate genetic effects on traits affecting life-cycle production of beef cattle and sheep. This program will consider the integration of quantitative genetic and genomic information into genetic evaluation systems.
One phase of the program will consider genetic improvement of beef cattle through selection on phenotypic and genetic marker data. Genetic markers, such as single nucleotide polymorphisms (SNP), help to determine the genetic merit of candidate animals without individual or progeny performance data. This proxy is particularly useful for traits that are prohibitive to measure due to time (e.g., longevity) or expense (e.g., carcass tenderness). Scientists at USMARC have been instrumental in building an extensively phenotyped beef cattle population. This resource, known as the Germplasm Evaluation population, is designed to determine associations between genomic variation and economically important traits. Along with graduate students, collaborators will work on topics such as whole genome selection to improve feed efficiency, the construction of genome maps, and the analysis of markers to determine their usefulness as indicators of genetic merit for multiple traits.
The other phase of the program will focus on research involving the development and evaluation of an easy-care maternal line of hair sheep that can raise triplet lambs on pasture without labor or supplemental feeding. Genetic resistance to scrapie and ovine progressive pneumonia and tolerance to parasites are key attributes of the easy-care label. Genetic markers are available to help delineate differences among some of these traits. However, the ‘best’ approach for combining genotype and performance information in a selection regime is unclear. Collaborators will address this conundrum, as well as the design of mating schemes for low-input production systems generally.