ESTIMATION OF ADDITIVE AND NONADDITIVE GENETIC VARIANCES IN NONINBRED POPULATIONS UNDER SIRE OR FULLSIB MODEL

1989 ◽  
Vol 69 (1) ◽  
pp. 61-68
Author(s):  
C. Y. LIN ◽  
A. J. LEE

The separation of additive and nonadditive genetic variances has been a problem for animal breeding researchers because conventional methods of statistical analyses (least squares or ANOVA type) were not able to accomplish this task. Henderson presented computing algorithms for restricted maximum likelihood (REML) estimation of additive and nonadditive genetic variances from an animal model for noninbred populations. Unfortunately, application of this algorithm in practice requires extensive computing. This study extends Henderson's methodology to estimate additive genetic variance independently of nonadditive genetic variances under halfsib (sire), fullsib nested and fullsib cross-classified models. A numerical example illustrates the REML estimation of additive [Formula: see text] and additive by additive [Formula: see text] genetic variances using a sire model. Key words: Genetic variance, additive, nonadditive, dairy

2018 ◽  
Author(s):  
Stefanie Muff ◽  
Alina K. Niskanen ◽  
Dilan Saatoglu ◽  
Lukas F. Keller ◽  
Henrik Jensen

Abstract1. The animal model is a key tool in quantitative genetics and has been used extensively to estimate fundamental parameters, such as additive genetic variance, heritability, or inbreeding effects. An implicit assumption of animal models is that all founder individuals derive from a single population. This assumption is commonly violated, for instance in cross-bred livestock breeds, when an observed population receive immigrants, or when a meta-population is split into genetically differentiated subpopulations. Ignoring genetic differences among different source populations of founders may lead to biased parameter estimates, in particular for the additive genetic variance.2. To avoid such biases, genetic group models, extensions to the animal model that account for the presence of more than one genetic group, have been proposed. As a key limitation, the method to date only allows that the breeding values differ in their means, but not in their variances among the groups. Methodology previously proposed to account for group-specific variances included terms for segregation variance, which rendered the models infeasibly complex for application to most real study systems.3. Here we explain why segregation variances are often negligible when analyzing the complex polygenic traits that are frequently the focus of evolutionary ecologists and animal breeders. Based on this we suggest an extension of the animal model that permits estimation of group-specific additive genetic variances. This is achieved by employing group-specific relatedness matrices for the breeding value components attributable to different genetic groups. We derive these matrices by decomposing the full relatedness matrix via the generalized Cholesky decomposition, and by scaling the respective matrix components for each group. To this end, we propose a computationally convenient approximation for the matrix component that encodes for the Mendelian sampling variance. Although convenient, this approximation is not critical.4. Simulations and an example from an insular meta-population of house sparrows in Norway with three genetic groups illustrate that the method is successful in estimating group-specific additive genetic variances and that segregation variances are indeed negligible in the empirical example.5. Quantifying differences in additive genetic variance within and among populations is of major biological interest in ecology, evolution, and animal and plant breeding. The proposed method allows to estimate such differences for subpopulations that form a connected meta-population, which may also be useful to study temporal or spatial variation of additive genetic variance.


1995 ◽  
Vol 65 (2) ◽  
pp. 145-149 ◽  
Author(s):  
Armando Caballero ◽  
Peter D. Keightley ◽  
William G. Hill

SummaryThe variation from spontaneous mutations for 6-week body weight in the mouse was estimated by selection from a cross of two inbred sublines, C57BL/6 and C57BL/10, separated about 50 years previously from the same inbred line. Selection was practised high and low for 12 generations from theF2, followed by one generation of relaxation. The lines diverged by approximately 1·7 g or 0·7 sd. The additive genetic variance was estimated in theF2by restricted maximum likelihood and from the selection response, and from this variance the mutational heritabilityhM2was estimated using the number of generations since divergence. Estimates ofhM2range from 0·08 to 0·10% depending on the method of analysis. These estimates are similar to those found for other species, but lower than other estimates for the mouse. It is concluded that substantial natural and, perhaps, artificial selection operated during the maintenance of the sublines.


1998 ◽  
Vol 49 (4) ◽  
pp. 607 ◽  
Author(s):  
S. J. Schoeman ◽  
G. G. Jordaan

Postweaning liveweight gain records of 1610 young bulls obtained both in feedlot and under pasture were used to estimate (co)variance components using a multivariate restricted maximum likelihood analysis. The pedigree file included 3477 animals. Heritability estimates for liveweights and gain in both environments correspond to most previously reported estimates. The genetic correlation of gain between the 2 environments was -0·12, suggesting a large genotype testing environment interaction and re-ranking of animal breeding values across environments. Results of this analysis suggest the need for environment-specific breeding values for postweaning gain.


1990 ◽  
Vol 70 (1) ◽  
pp. 67-71 ◽  
Author(s):  
R. I. CUE

Estimates of genetic parameters of calving ease were obtained in Ayrshires. A restricted maximum likelihood model was used with the fixed effects of herd, month-season of calving, sex of calf and dam weight, and the random effect of sire (of calf). The heritability of the direct effect in heifers and in adult cows was approximately 2%, with a genetic correlation between the direct effect in heifers and in adult cows of close to 70%. Key words: Variance, heritability, calving ease, Ayrshire


2018 ◽  
Author(s):  
Caroline E. Thomson ◽  
Isabel S. Winney ◽  
Oceane C. Salles ◽  
Benoit Pujol

AbstractNon-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the “animal model” of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance.


2020 ◽  
Vol 44 (5) ◽  
pp. 5-8
Author(s):  
I. Udeh

The objective of this study was to estimate the variance components and heritability of bodyweight of grasscutters at 4, 6 and 8 months of age using EM algorithm of REML procedures. The data used for the study were obtained from the bodyweight records of 20 grasscutters from four families at 4, 6 and 8 months of age. The heritability of bodyweight of grasscutters at 4, 6 and 8 months of age were 0.14, 0.10 and 0.12 respectively. This implies that about 10 – 14 % of the phenotypic variability of body weight in this grasscutter population was accounted by additive genetic variance while environmental and gene combination variance made a larger contribution. The implication is that selection of grasscutters in this population should not be based on the information on the animals alone but also information fromits relatives.


Sign in / Sign up

Export Citation Format

Share Document