Mixed model procedures for the Australian beef industry. 1. Multiple-trait model for estimation of breeding values for 200-day and final weights of cattle

1985 ◽  
Vol 36 (3) ◽  
pp. 527 ◽  
Author(s):  
H-U Graser ◽  
K Hammond
Keyword(s):  
Maternal Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Growth Traits ◽  
Breeding Value ◽  
Relationship Matrix ◽  
Beef Industry ◽  
Multiple Trait ◽  
Breeding Values ◽  
Numerator Relationship Matrix

A multiple-trait mixed model is defined for regular use in the Australian beef industry for the estimation of breeding values for continuous traits of sires used non-randomly across a number of herds and/or years. Maternal grandsires, the numerator relationship matrix, appropriate fixed effects, and the capacity to partition direct and maternal effects are incorporated in this parent model. The model was fitted to the National Beef Recording Scheme's data bank for three growth traits of the Australian Simental breed, viz 200-, 365- and 550-day weights. Estimates are obtained for the effects of sex, dam age, grade of dam, age of calf and breed of base dam. The range in estimated breeding value is reported for each trait, with 200-day weight being partitioned into 'calves' and 'daughters' calves', for the Simmental sires commonly used in Australia. Estimates of the fixed effects were large, and dam age, grade of dam and breed of base dam had an important influence on growth to 365 days of age. The faster growth of higher percentage Simmental calves to 200 days continued to 550 days. Estimates of genetic variance for the traits were lower than reported for overseas populations of Simmental cattle, and the genetic covariance between direct and maternal effects for 200-day weight was slightly positive.

scholarly journals Reduced Animal Models Fitting Only Equations for Phenotyped Animals

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohammad Ali Nilforooshan ◽  
Dorian Garrick
Keyword(s):  
Animal Model ◽  
Animal Models ◽  
Mixed Model ◽  
Reduced Model ◽  
Relationship Matrix ◽  
Full Model ◽  
Breeding Values ◽  
Reduced Animal Model ◽  
Numerator Relationship Matrix ◽  
Model Equations

Reduced models are equivalent models to the full model that enable reduction in the computational demand for solving the problem, here, mixed model equations for estimating breeding values of selection candidates. Since phenotyped animals provide data to the model, the aim of this study was to reduce animal models to those equations corresponding to phenotyped animals. Non-phenotyped ancestral animals have normally been included in analyses as they facilitate formation of the inverse numerator relationship matrix. However, a reduced model can exclude those animals and obtain identical solutions for the breeding values of the animals of interest. Solutions corresponding to non-phenotyped animals can be back-solved from the solutions of phenotyped animals and specific blocks of the inverted relationship matrix. This idea was extended to other forms of animal model and the results from each reduced model (and back-solving) were identical to the results from the corresponding full model. Previous studies have been mainly focused on reduced animal models that absorb equations corresponding to non-parents and solve equations only for parents of phenotyped animals. These two types of reduced animal model can be combined to formulate only equations corresponding to phenotyped parents of phenotyped progeny.

Breeding Value Estimation for Pigs in Closed Nucleus Herds

1988 ◽  
Vol 1988 ◽  
pp. 12-12 ◽  
Author(s):  
Naomi R. Wray
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Simultaneous Estimation ◽  
Genetic Trend ◽  
Traditional Methods ◽  
Breeding Values ◽  
Value Estimation ◽  
The Uk

Best Linear Unbiased Prediction (BLUP) is now the method of choice for the estimation of breeding values in dairy and beef populations. The advantages of this mixed model methodology over traditional methods are well documented and include the simultaneous estimation of fixed effects and prediction of random effects and the utilization of records from all relatives to predict an individuals breeding value. In addition, account is taken of genetic trend and of reduction in genetic variance due to selection. In Canada, BLUP is now used for breeding value estimation of pigs but the structure of the Canadian pig industry is one of many herds practising selection with the herds linked by a widespread use of artificial insemination. The advantages of BLUP have not been investigated for the situation of the UK pig industry where most selection is performed within closed nucleus herds.The objectives of this study were to use computer simulation to determine rates of response, accuracy of prediction and accummulation of inbreeding for pigs in closed nucleus herds when selection decisions were based on estimated breeding values (EBVs) derived from BLUP compared to more traditional methods of phenotypic selection and index selection.

THE EFFECT OF DIFFERENT NUMERATOR RELATIONSHIP MATRICES ON BREEDING VALUES ESTIMATED FROM MULTIPLE TRAIT BLUP

1987 ◽  
Vol 67 (1) ◽  
pp. 201-204
Author(s):  
R. A. KEMP ◽  
J. W. WILTON
Keyword(s):  
Relationship Matrix ◽  
Genetic Progress ◽  
Multiple Trait ◽  
Breeding Values ◽  
Reduced Animal Model ◽  
Numerator Relationship Matrix ◽  
Best Linear Unbiased ◽  
The Mean ◽  
Estimated Breeding Values ◽  
Accuracy Of Prediction

A numerator relationship matrix (Ac) due to sires and dams was compared with a numerator relationship matrix (Ai) due to sires and maternal grandsires in a multiple-trait-reduced animal model (MT-RAM). Best linear unbiased predictors of estimated breeding values (EBV) for 200-d weight (WW) and postweaning gain (PG) (gain from 200 to 365 d of age) were estimated from data simulating a beef cattle population. As expected, mean EBV and bias (EBV-BV) for both traits were not significantly affected by different relationship matrices. The mean variances of EBV with Ac were larger than those with Ai for both traits. The mean EBV variances were closer to mean BV variances with Ac compared to Ai, which is consistent with increased precision of EBV. Product-moment correlations of EBV and BV (accuracy of prediction) were not equal (P < 0.01) for Ac compared to Ai with WW or PG. The EBV using Ac were more accurate than EBV using Ai. The increased precision and accuracy of EBV from a MT-RAM with Ac would result in greater genetic progress in the population. Key words: Relationship matrices, estimated breeding values, MT-RAM

scholarly journals Breeding Value and Variance Component Estimation From Data Containing Inbred Individuals: Application to Gynogenetic Families in Common Carp (Cyprinus carpio L.)

Genetics ◽  
1997 ◽  
Vol 145 (4) ◽  
pp. 1243-1249 ◽  
Author(s):  
Piter Bijma ◽  
Johan A M Van Arendonk ◽  
Henk Bovenhuis
Keyword(s):  
Variance Component ◽  
Mixed Model ◽  
Estimation Procedure ◽  
Breeding Value ◽  
Relationship Matrix ◽  
Variance Component Estimation ◽  
Numerator Relationship Matrix ◽  
Component Estimation ◽  
Small Advantage ◽  
General Method

Under gynogenetic reproduction, offspring receive genes only from their dams and completely homozygous offspring are produced within one generation. When gynogenetic reproduction is applied to fully inbred individuals, homozygous clone lines are produced. A mixed model method was developed for breeding value and variance component estimation in gynogenetic families, which requires the inverse of the numerator relationship matrix. A general method for creating the inverse for a population with unusual relationships between animals is presented, which reduces to simple rules as is illustrated for gynogenetic populations. The presence of clones in gynogenetic populations causes singularity of the numerator relationship matrix. However, clones can be regarded as repeated observations of the same genotype, which can be accommodated by modifying the incidence matrix, and by considering only unique genotypes in the estimation procedure. Optimum gynogenetic sib family sizes for estimating heritabilities and estimates of their accuracy were derived and compared to those for conventional full-sib designs. This was done by means of a deterministic derivation and by stochastic simulation using Gibbs sampling. Optimum family sizes were smallest for gynogenetic families. Only for low heritabilities, there was a small advantage in accuracy under the gynogenetic design.

scholarly journals An Algorithm for Genetic Analysis of Full-Sib Datasets with Mixed-Model Software Lacking a Numerator Relationship Matrix Function, and a Comparison with Results from a Dedicated Genetic Software Package

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1169
Author(s):  
Gary R. Hodge ◽  
Juan Jose Acosta
Keyword(s):  
Mixed Model ◽  
Genetic Parameter ◽  
Male Parent ◽  
Parameter Estimates ◽  
Relationship Matrix ◽  
Forest Tree Breeding ◽  
Tree Model ◽  
Individual Tree ◽  
Breeding Values ◽  
Numerator Relationship Matrix

Research Highlights: An algorithm is presented that allows for the analysis of full-sib genetic datasets using generalized mixed-model software programs. The algorithm produces variance component estimates, genetic parameter estimates, and Best Linear Unbiased Prediction (BLUP) solutions for genetic values that are, for all practical purposes, identical to those produced by dedicated genetic software packages. Background and Objectives: The objective of this manuscript is to demonstrate an approach with a simulated full-sib dataset representing a typical forest tree breeding population (40 parents, 80 full-sib crosses, 4 tests, and 6000 trees) using two widely available mixed-model packages. Materials and Methods: The algorithm involves artificially doubling the dataset, so that each observation is in the dataset twice, once with the original female and male parent identification, and once with the female and male parent identities switched. Five linear models were examined: two models using a dedicated genetic software program (ASREML) with the capacity to specify A or other pedigree-related functions, and three models with the doubled dataset and a parent (or sire) linear model (ASREML, SAS Proc Mixed, and R lme4). Results: The variance components, genetic parameters, and BLUPs of the parental breeding values, progeny breeding values, and full-sib family-specific combining abilities were compared. Genetic parameter estimates were essentially the same across all the analyses (e.g., the heritability ranged from h2 = 0.220 to 0.223, and the proportion of dominance variance ranged from d2 = 0.057 to 0.058). The correlations between the BLUPs from the baseline analysis (ASREML with an individual tree model) and the doubled-dataset/parent models using SAS Proc Mixed or R lme4 were never lower than R = 0.99997. Conclusions: The algorithm can be useful for analysts who need to analyze full-sib genetic datasets and who are familiar with general-purpose statistical packages, but less familiar with or lacking access to other software.

CONSIDERATIONS OF A METHOD OF ANALYZING DIALLEL CROSSES OF ATLANTIC SALMON

1979 ◽  
Vol 21 (1) ◽  
pp. 121-128 ◽  
Author(s):  
G. W. Friars ◽  
J. K. Bailey ◽  
R. L. Saunders
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Maternal Effects ◽  
Mixed Model ◽  
Growth Traits ◽  
Diallel Cross ◽  
Length Variation ◽  
Relative Importance ◽  
Growth Data ◽  
Mixed Model Analysis

Inferences derived from a proposed mixed model analysis of a diallel cross involving four stocks of Atlantic salmon (Salmo salar) are illustrated with growth data on weight and length. Variation between stocks was more apparent when samples represented dams as opposed to sires, thus stressing the relative importance of maternal effects. However, the ranking of stocks was not altered when either the means of sire sources or the means of dam sources were considered. No heterotic effects were found for the growth traits studied.

Estimates of genetic parameters for growth traits of Gobra cattle

1998 ◽  
Vol 66 (2) ◽  
pp. 349-355 ◽  
Author(s):  
M. Diop ◽  
L. D. Van Vleck
Keyword(s):  
Animal Models ◽  
Maternal Effects ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Random Effect ◽  
Random Effect Model ◽  
Genetic Effects ◽  
Breeding Value ◽  
Effect Model ◽  
Additive Genetic Effects

AbstractEstimates of (co)variance components and genetic parameters were obtained for birth (no. = 3909), weaning (no. = 3425), yearling (no. = 2763), and final weight (no. = 2142) for Gobra cattle at the Centre de Recherches Zootechniques de Dahra (Senegal), using single trait animal models. Data were analysed by restricted maximum likelihood. Four different animal models were fitted for each trait. Model 1 considered the animal as the only random effect. Model 2 included in addition to the additive direct effect of the animal, the environmental effect due to the dam. Model 3 added the maternal additive genetic effects and allowed a covariance between the direct and maternal genetic effects. Model 4 fitted both maternal genetic and permanent environmental effects. Inclusion of both types of maternal effects (genetic and environmental) provided a better fit for birth and weaning weights than models with one maternal effect only. For yearling and final weights, the improvement was not significant. Important maternal effects werefound for all traits. Estimates of direct heritabilities were substantially higher when maternal effects were ignored. Estimates of direct and maternal heritabilities with model 4 were 0·07 (s.e. 0·03) and 0·04 (s.e. 0·02), 0·20 (s.e. 0·05) and 0·21 (s.e. 0.05), 0·24 (s.e. 0·07) and 0·21 (s.e. 0·06), and 0·14 (s.e. 0·06) and 0.16 (s.e. 0·06) for birth, weaning, yearling and final weights, respectively. Correlations between direct and maternal genetic effects were negative for all traits, and large for weaning and yearling weights with estimates of -0·61 (s.e. 0·33) and -0·50 (s.e. 0·31), respectively. There was a significant positive linear phenotypic trend for weaning and yearling weights. Linear trends for additive direct and maternal breeding values were not significant for any trait except maternal breeding value for yearling weight.

scholarly journals Analysis of non-genetic and genetic influences underlying the growth of Kajli lambs

2020 ◽  
Vol 50 (4) ◽  
pp. 613-625
Author(s):  
A. Ali ◽  
K. Javed ◽  
I. Zahoor ◽  
K.M. Anjum
Keyword(s):  
Birth Weight ◽  
Genetic Correlation ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Breeding Value ◽  
Season Of Birth ◽  
Genetic Trend ◽  
Breeding Values ◽  
Heritability Estimates ◽  
Birth Type

Data on 2931 Kajli lambs, born from 2007 to 2018, were used to quantify environmental and genetic effects on growth performance of Kajli sheep. Traits considered for evaluation were birth weight (BWT), 120-day adjusted weight (120DWT), 180-day adjusted weight (180DWT), 270-day adjusted weight (270DWT), and 365-day adjusted weight (365DWT). Fixed effects of year of birth, season of birth, sex, birth type, and dam age on these traits were evaluated using linear procedures of SAS, 9.1. Similarly, BWT, 120DWT, 180DWT, and 270DWT were used as fixed effects mixed model analyses. Variance components, heritability and breeding values were estimated by restricted maximum likelihood. The genetic trend for each trait was obtained by regression of the estimated breeding values (EBV) on year of birth. Analyses revealed substantial influence of birth year on all traits. Sex and birth type were the significant sources of variation for BWT and 120DWT. Season of birth did not influence birth weight meaningfully, but had a significant role in the expression of 120DWT, 180DWT, and 270DWT. Heritability estimates were generally low (0.003 ± 0.018 to 0.099 ± 0.067) for all traits. With the exception of the genetic correlation of 180DWT and 365DWT, the genetic correlations between trait were strong and positive. Only 365DWT had a positive genetic trend. Although the heritability estimates for almost all weight traits were low, high and positive genetic correlations between BWT and other weight traits suggest that selection based on BWT would result in the improvement of other weight traits as a correlated response.Keywords: bodyweight, breeding value, genetic correlation, sheep

scholarly journals Short communication: Estimation of genetic trends for body weight traits in Markhoz goat at different ages

2019 ◽  
Vol 17 (1) ◽  
pp. e04SC01 ◽  
Author(s):  
Meysam Latifi ◽  
Mohammad Razmkabir
Keyword(s):  
Body Weight ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Selection ◽  
Information Criteria ◽  
Pedigree Information ◽  
Genetic Trend ◽  
Breeding Values ◽  
Different Ages ◽  
Genetic Trends

The objective of the present study was to estimate genetic trends for body weight at different ages in Markhoz goat, including birth weight (BW, n = 4758), weaning weight (WW, n= 3685), 6-month weight (6MW, n = 3420), 9-month weight (9MW, n = 3032) and 12-month weight (12MW, n = 2697). Data and pedigree information were collected from 1992 until 2014 at the Breeding Center of Markhoz goat, Sanandaj, Iran. The GLM procedure of SAS was used for selecting the variables and identifying significant fixed effects in the equation of model. Various animal models were applied for genetic analysis and the best model was determined based on Akaike information criteria (AIC). Breeding values of animals were predicted using Wombat program. Genetic trends were obtained by regressing the average predicted breeding values on birth year for each trait. Based on the best model, direct estimated genetic trends were positive and significance for WW, 6MW, 9MW and 12 MW were 15.51, 26.28, 58.36 and 76.70 g/year, respectively (p<0.001). Maternal genetic trend for BW and WW were 0.61 and 5.47 g/year, respectively (p<0.01). The low and moderate generic trends obtained in the present study, indicated the possibility of growth traits improvements through genetic selection at all ages in Markhoz goat.

scholarly journals Genetic consistency between gait analysis by accelerometry and evaluation scores at breeding shows for the selection of jumping competition horses

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244064
Author(s):  
Anne Ricard ◽  
Bernard Dumont Saint Priest ◽  
Marjorie Chassier ◽  
Margot Sabbagh ◽  
Sophie Danvy
Keyword(s):  
Principal Components ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Breeding Value ◽  
Stride Frequency ◽  
Relationship Matrix ◽  
Evaluation Scores ◽  
The One ◽  
The Relationship ◽  
Selection Of

The aim was to assess the efficiency of gaits characteristics in improving jumping performance of sport horses and confront accelerometers and judge scores for this purpose. A sample of 1,477 young jumping horses were measured using accelerometers for walk, trot, and canter. Of these, 702 were genotyped with 541,175 SNPs after quality control. Dataset of 26,914 horses scored by judges in breeding shows for gaits and dataset of 142,682 horses that performed in jumping competitions were used. Analysis of accelerometric data defined three principal components from 64% to 89% of variability explained for each gait. Animal mixed models were used to estimate genetic parameters with the inclusion to up 308,105 ancestors for the relationship matrix. Fixed effects for the accelerometric variables included velocity, gender, age, and event. A GWAS was performed on residuals with the fixed effect of each SNP. The GWAS did not reveal other QTLs for gait traits than the one related to the height at withers. The accelerometric principal components were highly heritable for the one linked to stride frequency and dorsoventral displacement at trot (0.53) and canter (0.41) and moderately for the one linked to longitudinal activities (0.33 for trot, 0.19 for canter). Low heritabilities were found for the walk traits. The genetic correlations of the accelerometric principal components with the jumping competition were essentially nil, except for a negative correlation with longitudinal activity at canter (-0.19). The genetic correlation between the judges’ scores and the jumping competition reached 0.45 for canter (0.31 for trot and 0.17 for walk). But these correlations turned negative when the scores were corrected for the known parental breeding value for competition at the time of the judging. In conclusion, gait traits were not helpful to select for jumping performances. Different gaits may be suitable for a good jumping horse.

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