RESUMO
Currently, many different data types are collected by beef cattle breed associations for the purpose of genetic evaluation. These data points are all biological characteristics of individual animals that can be measured multiple times over an animal's lifetime. Some traits can only be measured once on an individual animal, whereas others, such as the body weight of an animal as it grows, can be measured many times. Data such as growth has been often referred to as "longitudinal" or "infinite-dimensional" since it is theoretically possible to observe the trait an infinite number of times over the life span of a given individual. Analysis of such data is not without its challenges, and as a result many different methods have been or are beginning to be implemented in the genetic analysis of beef cattle data, each an improvement over its predecessor. These methods of analysis range from the classic repeated measures to the more contemporary suite of random regressions that use covariance functions or even splines as their base function. Each of the approaches has both strengths and weaknesses in the analysis of longitudinal data. Here we summarize past and current genetic evaluation technology for analyzing this type of data and review some emerging technologies beginning to be implemented in national cattle evaluation schemes, along with their potential implications for the beef industry.
Assuntos
Bovinos/genética , Herança Multifatorial , Animais , Peso Corporal/genética , Cruzamento , Indústria Alimentícia , Variação Genética , Modelos Lineares , Estudos Longitudinais , Modelos EstatísticosRESUMO
Selection for the wide range of traits for which most beef breed associations calculate expected progeny differences focus on increasing the outputs of the production system, thereby increasing the genetic potential of cattle for reproductive rates, weights, growth rates, and end-product yield. Feed costs, however, represent a large proportion of the variable cost of beef production and genetic improvement programs for reducing input costs should include traits related to feed utilization. Feed conversion ratio, defined as feed inputs per unit output, is a traditional measure of efficiency that has significant phenotypic and genetic correlations with feed intake, growth rate, and mature size. One limitation is that favorable decreases in feed to gain either directly or due to correlated response to increasing growth rate do not necessarily relate to improvement in efficiency of feed utilization. Residual feed intake is defined as the difference between actual feed intake and that predicted on the basis of requirements for maintenance of body weight and production. Phenotypic independence of residual feed intake with growth rate, body weight, and other energy depots can be forced. However, genetic associations may remain when a phenotypic prediction approach is used. Heritability estimates for phenotypic residual feed intake have been moderate, ranging from 0.26 to 0.43. Genetic correlations of phenotypic residual feed intake with feed intake have been large and positive, suggesting that improvement would produce a correlated response of decreased feed intake. Residual feed intake estimated by genetic regression results in a zero genetic correlation with its predictors, which reduces concerns over long-term antagonistic responses such as increased mature size and maintenance requirements. The genetic regression approach requires knowledge of genetic covariances of feed intake with weight and production traits. Cost of individual feed intake measurements on potential replacements must be considered in implementation of national cattle evaluations for efficiency of feed utilization. These costs need to be compared to expected, and, if possible, realized rates of genetic change and the associated reduction in feed input requirements.