Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 29, 2011
Publication Date: January 24, 2012
Citation: Silva, M.V., Van Tassell, C.P., Sonstegard, T.S., Cobuci, J.A., Gasbarre, L.C. 2012. Box-Cox Transformation and Random Regression Models for Fecal Egg Count Data. Frontiers in Genetics. 2:112.
Interpretive Summary: Fecal egg count (FEC) is commonly used to measure resistance to nematodes in ruminants. However, measurements of FEC at any single time point over the course of a parasite infection are not always indicative of the phenotype of the animal in response to an infection. Lonngitudinal data like the FEC phenotypes measured several times over the life of the animal are needed to generate appropriate modeling of phenotypic measurements to produce accurate genetic evaluation of livestock. In this manuscript, we report for the first time the application of random regression models to Fecal egg count (FEC) collected as longitudinal data. infection curves were calculated by mathematical functions for each animal using weekly FEC measurements similar to how lactation curves are generated for dairy cattle. Consequently, the efficiency of random regression models in analyzing FEC data and determining FEC heritability were analyzed. Results indicated FEC infection curves to be a moderately heritable characteristic.
Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. Sometimes these phenotypes are measured several times over the life of the animal, so called longitudinal data. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6,375 FEC measures were determined for 409 animals between 1992 and 2003 from Beltsville Agricultural Research Center Wye Angus herd. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to estimate (co)variance components. The Random Regression Models (RRM) may be used as a new tool for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM, by the Restricted Maximum Likelihood method. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated FEC to be a moderately heritable characteristic and indicated that measurements of FEC obtained in the period between 12th and 26th weeks are genetically correlated.