Speidel S E, Enns R M, Crews D H
Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA.
Genet Mol Res. 2010 Jan 5;9(1):19-33. doi: 10.4238/vol9-1gmr675.
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.
目前，肉牛品种协会为了进行遗传评估收集了许多不同类型的数据。这些数据点都是个体动物的生物学特征，可以在动物的一生中进行多次测量。有些性状只能在个体动物上测量一次，而其他性状，如动物生长过程中的体重，则可以测量多次。像生长这样的数据通常被称为“纵向”或“无限维”数据，因为从理论上讲，在给定个体的寿命期间可以无限次观察该性状。对这类数据的分析并非没有挑战，因此在肉牛数据的遗传分析中已经或正在开始实施许多不同的方法，每种方法都比其前身有所改进。这些分析方法从经典的重复测量到更现代的随机回归方法，后者使用协方差函数甚至样条作为其基函数。在纵向数据分析中，每种方法都有其优缺点。在此，我们总结过去和当前用于分析此类数据的遗传评估技术，并回顾一些开始在国家牛评估计划中实施的新兴技术，以及它们对牛肉行业的潜在影响。
