scholarly journals Estimating genetic parameters in natural populations using the ‘animal model’

2004 ◽  
Vol 359 (1446) ◽  
pp. 873-890 ◽  
Author(s):  
Loeske E. B. Kruuk
Keyword(s):  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Basis ◽  
Genetic Parameters ◽  
Natural Populations ◽  
Environmental Variation ◽  
Natural Environments ◽  
Evolutionary Patterns ◽  
Powerful Means ◽  
Estimation Of Variance

Estimating the genetic basis of quantitative traits can be tricky for wild populations in natural environments, as environmental variation frequently obscures the underlying evolutionary patterns. I review the recent application of restricted maximum–likelihood ‘animal models’ to multigenerational data from natural populations, and show how the estimation of variance components and prediction of breeding values using these methods offer a powerful means of tackling the potentially confounding effects of environmental variation, as well as generating a wealth of new areas of investigation.

scholarly journals Estimation of variance components, genetic parameters and genetic trends for litter size of swines

2010 ◽  
Vol 39 (10) ◽  
pp. 2155-2159 ◽  
Author(s):  
Leandro Barbosa ◽  
Paulo Sávio Lopes ◽  
Adair José Regazzi ◽  
Robledo de Almeida Torres ◽  
Mário Luiz Santana Júnior ◽  
...  
Keyword(s):  
Litter Size ◽  
Variance Components ◽  
Genetic Parameters ◽  
Random Effect ◽  
Additive Model ◽  
Fixed Effect ◽  
Likelihood Method ◽  
Phenotypic Variance ◽  
Estimation Of Variance ◽  
Genetic Trends

Records of Large White breed animals were used to estimate variance components, genetic parameters and trends for the character total number of born piglets (TNBP) as measure of litter size. For obtaining variance components and genetic parameters, it was used the Restricted Maximum Likelihood Method using MTDFREML software. Two mixed models (additive and repeatability) were evaluated. The additive model contained fixed effect of the contemporary group and the following random effects: direct additive genetic and residual effect for the first parturition. Repeatability model had the same effects of the additive model plus parturition order fixed effect and non-correlated animal permanent environment random effect for the second, third and forth parturition. Direct additive heritability estimates for TNBP were 0.15 and 0.20 for the additive and repeatability models, respectively. The estimate of the ration among variance of the non-correlated effect of animal permanent environment effect and the phenotypic variance, expressed as total variance proportion (c2) was 0.09. The estimates of yearly genetic trends obtained in the additive and repeatability models have similar behaviors (0.02 piglets/sow/year).

scholarly journals Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

2016 ◽  
Vol 59 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Hedi Hammami ◽  
Mohamed Amine Ferchichi ◽  
Boulbaba Rekik
Keyword(s):  
Dairy Cattle ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Bayesian Procedure ◽  
Milk Traits ◽  
Residual Variances ◽  
Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

scholarly journals The effects of demography and genetics on the neutral distribution of quantitative traits

2018 ◽  
Author(s):  
Evan M. Koch
Keyword(s):  
Quantitative Trait ◽  
Quantitative Traits ◽  
Genetic Parameters ◽  
Natural Populations ◽  
Null Distribution ◽  
Practical Applications ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Molecular Phenotypes ◽  
Neutral Models

1AbstractNeutral models for quantitative trait evolution are useful for identifying phenotypes under selection in natural populations. Models of quantitative traits often assume phenotypes are normally distributed. This assumption may be violated when a trait is affected by relatively few genetic variants or when the effects of those variants arise from skewed or heavy-tailed distributions. Traits such as gene expression levels and other molecular phenotypes may have these properties. To accommodate deviations from normality, models making fewer assumptions about the underlying trait genetics and patterns of genetic variation are needed. Here, we develop a general neutral model for quantitative trait variation using a coalescent approach by extending the framework developed by Schraiber and Landis (2015). This model allows interpretation of trait distributions in terms of familiar population genetic parameters because it is based on the coalescent. We show how the normal distribution resulting from the infinitesimal limit, where the number of loci grows large as the effect size per mutation becomes small, depends only on expected pairwise coalescent times. We then demonstrate how deviations from normality depend on demography through the distribution of coalescence times as well as through genetic parameters. In particular, population growth events exacerbate deviations while bottlenecks reduce them. This model also has practical applications, which we demonstrate by designing an approach to simulate from the null distribution of QST, the ratio of the trait variance between subpopulations to that in the overall population. We further show that it is likely impossible to distinguish sparsity from skewed or heavy-tailed distributions of mutational effects using only trait values sampled from a population. The model analyzed here greatly expands the parameter space for which neutral trait models can be designed.

Estimation of variance components for body weight of grasscutters (Thryonomys swinderianus) using a repeatability model

2020 ◽  
Vol 47 (2) ◽  
pp. 33-36
Author(s):  
I. Udeh
Keyword(s):  
Animal Model ◽  
Body Weight ◽  
Variance Components ◽  
Genetic Parameters ◽  
Growth Potential ◽  
Likelihood Method ◽  
Mass Selection ◽  
Thryonomys Swinderianus ◽  
Estimate Variance ◽  
Estimation Of Variance

Genetic parameters for growth and other economically important traits of grasscutters are scant in literature. Therefore, the aim of this study was to estimate variance components,heritability and repeatability of body weight of grasscutters using restricted maxim um likelihood method in a repeatability animal model. Sixteen grasscutter families were used for the study. Each family was made up of one male and four females. Each grasscutter has four repeated records giving a total of 320. The pedigree consisted of 80 animals, progenies of 16 sires and 16 dams. Fixed factors included in the model were family and sex. The WOMBAT program was used for the analysis. The heritability of body weight of grasscutters ranged from 0.23±0.04 to 0.68±0.10, thus implying that mass selection will be appropriate for this population. The repeatability estimates ranged from 0.82±0.08 to 0.93±0.11. It can be concluded that the number of body weight records was a good indicator of the animal's growth potential and that mass selection will be reliable.

scholarly journals Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing among full-sibs

2021 ◽  
Author(s):  
Antoine Fraimout ◽  
Zitong Li ◽  
Mikko J. Sillanpää ◽  
Pasi Rastas ◽  
Juha Merilä
Keyword(s):  
Natural Selection ◽  
Variance Components ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Identity By Descent ◽  
Quantitative Genetic ◽  
Genome Wide ◽  
Quantitative Genetic Parameters

ABSTRACTAdditive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most wild animal populations. Using estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci, we estimated quantitative genetic parameters for traits known to be under directional natural selection in nine-spined sticklebacks (Pungitius pungitius) and compared these to traditional pedigree-based estimators. Using four different datasets, with varying sample sizes and pedigree complexity, we further assessed the performance of different Genomic Relationship Matrices (GRM) to estimate additive and dominance variance components. Large variance in IBD relationships allowed accurate estimation of genetic variance components, and revealed significant heritability for all measured traits, with negligible dominance contributions. Genome-partitioning analyses revealed that all traits have a polygenic basis and are controlled by genes at multiple chromosomes. The results demonstrate how large full-sib families of highly fecund vertebrates can be used to obtain accurate estimates quantitative genetic parameters to provide insights on genetic architecture of quantitative traits in non-model organisms from the wild. This approach should be particularly useful for studies requiring estimates of genetic variance components from multiple populations as for instance when aiming to infer the role of natural selection as a cause for population differentiation in quantitative traits.

scholarly journals Estimating Genetic Parameters using a Random Regression Model

2008 ◽  
pp. 53-55
Author(s):  
Szilárd Márkus ◽  
Eva Němcová ◽  
István Fazekas ◽  
István Komlósi
Keyword(s):  
Regression Model ◽  
Variance Components ◽  
Genetic Parameters ◽  
Random Regression ◽  
Environmental Variance ◽  
Data Set ◽  
Computational Costs ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Estimation Of Variance

One of the most important part of the genetic evaluation using a random regression model is the estimation of variance components. This is the topic of many papers because the large computational costs. We can use restricted maximum likelihood (REML), Gibbs sampling and ℜ method for the estimation of genetic parameters. The variance components are necessary to calculate the heritabilities and repeatabilities.The aim of our paper is to estimate the variance components using a random regression repeatability model from test day data set of Hungarian Holstein-Friesian dairy cows and to analyse the change of additive genetic and permanent environmental variance, heritability and repeatability over lactation.

scholarly journals Schätzung von Varianzkomponenten mit Gruppenmittelwerten am Beispiel von Legehennen, gehalten in Gruppenkäfigen

2003 ◽  
Vol 46 (5) ◽  
pp. 491-498
Author(s):  
N. Mielenz ◽  
V. Nurgiartiningsih ◽  
M. Schmutz ◽  
L. Schüler
Keyword(s):  
Animal Model ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Relationships ◽  
Information Matrix ◽  
Egg Weight ◽  
Complex Relationships ◽  
Estimate Variance ◽  
Additive Genetic Effects ◽  
Estimation Of Variance

Abstract. Title of the paper: Estimation of variance components from group mean records of laying hens housed in group cages Two models are presented to estimate variance components if only group mean records are available. The first model accounts for additive genetic relationships and full-sib group effects (SIMIANER and GJERDE, 1991) and the second model contains the additive genetic effects of all animals from one cage by using modified design matrices. Estimates of the genetic parameters were obtained by the MIVQUE-method (RAO, 1971; LAMOTTE, 1973). The variances of the estimated heritabilities were derived from the information matrix. Estimations from individual records and from average records (cage average) were compared in a small application on laying hen data. The analysed trait was single egg weight measured on hens housed in group cages. It could be shown: If cage variance is negligible, than for the estimation of the heritabilities full-sib data can be used successfully. The application of the modified animal model is suggested, because this model can take into account more complex relationships between the animals of one cage.

Sign in / Sign up

Export Citation Format

Share Document