ESTIMATING THE COVARIANCE STRUCTURE OF TRAITS DURING GROWTH AND AGING, ILLUSTRATED WITH LACTATION IN DAIRY-CATTLE
ESTIMATING THE COVARIANCE STRUCTURE OF TRAITS DURING GROWTH AND AGING, ILLUSTRATED WITH LACTATION IN DAIRY-CATTLE
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DOI：
10.1017/s0016672300032559
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发表时间：
1994-08-01
期刊：
影响因子：
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通讯作者：
THOMPSON, R
中科院分区：
文献类型：
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作者：
KIRKPATRICK, M;HILL, WG;THOMPSON, R
随年龄变化的性状的数量变化对进化生物学家和育种家都很重要。我们提出了三种新的方法来估计的表型和添加剂的遗传协方差函数的性状，随着年龄的变化，并说明他们使用的数据，每天从英国荷斯坦奶牛泌乳记录。首先，一个新的技术开发，以适应一个连续的协方差函数的协方差矩阵。其次，该技术用于估计和校正使表型方差估计值膨胀的偏倚。第三，我们提供了一种估计协方差函数特征值和特征函数的数值方法。虽然算法是适度复杂，他们已经实现了一个软件包，是免费提供的。哺乳期的分析表明，新的方法比以前的优势。结果表明，表型方差膨胀高达39%以上的测量误差和短期环境影响的基础协方差结构。加性遗传变异的分析表明，约90%的加性遗传变异的哺乳期在第一个10个月与特征函数，对应于增加（或减少）生产在所有年龄。早期和晚期产奶量之间的遗传权衡被认为是在第二特征函数，但它占不到8%的加性方差。这表明，选择预计将增加整个泌乳期的产量。
Quantitative variation in traits that change with age is important to both evolutionary biologists and breeders. We present three new methods for estimating the phenotypic and additive genetic covariance functions of a trait that changes with age, and illustrate them using data on daily lactation records from British Holstein-Friesian dairy cattle. First, a new technique is developed to fit a continuous covariance function to a covariance matrix. Secondly, this technique is used to estimate and correct for a bias that inflates estimates of phenotypic variances. Thirdly, we offer a numerical method for estimating the eigenvalues and eigenfunctions of covariance functions. Although the algorithms are moderately complex, they have been implemented in a software package that is made freely available.Analysis of lactation shows the advantages of the new methods over earlier ones. Results suggest that phenotypic variances are inflated by as much as 39% above the underlying covariance structure by measurement error and short term environmental effects. Analysis of additive genetic variation indicates that about 90% of the additive genetic variation for lactation during the first 10 months is associated with an eigenfunction that corresponds to increased (or decreased) production at all ages. Genetic tradeoffs between early and late milk yield are seen in the second eigenfunction, but it accounts for less than 8% of the additive variance. This illustrates that selection is expected to increase production throughout lactation.