Estimation of genetic variability and correlations between production traits (milk yield, fat content, protein content) selected type (angularity, fore udder height, rear front teat placement, length, depth) was done using data sets 10,860 first-calving Holstein-Friesian cows raised in the territory Republic Serbia. Genetic variance covariance were obtained Restricted Maximum Likelihood (REML) ...

The study of evolutionary constraint is an active and important area, and genetic correlations and quantitative genetic techniques more generally have been the dominant approach to constraint. Here, I argue that genetic correlations are not very useful for studying constraint, review recent alternative approaches, and briefly discuss the state of our knowledge of the evolutionary importance of ...

INTRODUCTION There is a plethora of statistical literature on modeling and analysis of repeated records, often referred to as longitudinal or growth curve data. These can be analysed fitting a linear model using (restricted) maximum likelihood (Ware 1985). This framework accommodates a wide range of assumptions about the structural form of covariance matrices (e.g. Jennrich and Schluchter 1986)...

Partitioning of the phenotypic variation into its causal components to determine genetic 2 parameters is one of the basic tasks in quantitative genetics. For multiple characteristics of 3 interest, this involves the estimation of covariance matrices due to genetic and other effects. 4 It is well known that such estimates can be subject to substantial sampling variation, as the 5 number of param...

In this paper we derive sharp lower and upper bounds for the covariance of two bounded random variables which are applicable when knowledge about their expected values, variances or both is available. When only values known, our result can be viewed as an extension Bhatia–Davis inequality variances. We also provide a number different ways to standardize covariance. For binary pair variables, on...

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covar...

Variance components of the covariance function coefficients in a random regression test-day model were estimated by Legendre polynomials up to a fifth order for first-parity records of Dutch dairy cows using Gibbs sampling. Two Legendre polynomials of equal order were used to model the random part of the lactation curve, one for the genetic component and one for permanent environment. Test-day ...