Abstract
Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used in an effort to achieve normality before analysis. However, the transformed data are often still not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box–Cox transformation to approach normality and to estimate (co)variance components. We also proposed using random regression models (RRM) for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box–Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated.
Highlights
Gastrointestinal nematode infection causes significant losses to livestock industries worldwide
OPTIMUM BOX–COX TRANSFORMATION Descriptive statistics of fecal egg count (FEC) for each week and overall data are presented in Table 1 and Figure 1
Estimates of λ obtained by ML were: 0.139, 0.149, and 0.132, indicating that the logarithmic transformation typically used for FEC data is not optimal, as λ was greater than 0 for all variables
Summary
Gastrointestinal nematode infection causes significant losses to livestock industries worldwide It reduces meat and milk production, increases mortality, requires anthelmintic use, and often results in changes in herd management. In New Zealand anthelminthic expenses are about $27.9 million/year (Bisset, 1994), and in the U.S.A. these parasites cost to the American livestock industry, approximately, $2 billion/year in lost productivity and increased operating expenses (Sonstegard and Gasbarre, 2001). These authors noted that anthelmintics are frequently used to prevent potential economic losses, resulting in an increased anthelmintic resistance in cattle and increased consumer concern about drug residues in animal products. A genetic component to host resistance in cattle has been reported by Gasbarre et al (1990), where heritability of parasite resistance was estimated to be approximately 0.30, allowing for moderate genetic progress. Gasbarre et al (2002) believe that QTL mapping and marker-assisted selection (MAS) could be used to accelerate genetic improvement
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