Comparison of an Animal Model and an Equivalent Reduced Animal Model for Computational Efficiency Using Mixed Model Methodology

1984 ◽  
Vol 58 (5) ◽  
pp. 1090-1096 ◽  
Author(s):  
H. T. Blair ◽  
E. J. Pollak
Keyword(s):  
Animal Model ◽  
Computational Efficiency ◽  
Mixed Model ◽  
Reduced Animal Model ◽  
Mixed Model Methodology

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.

New approaches to genetic evaluation of beef cattle

1988 ◽  
Vol 12 ◽  
pp. 99-110
Author(s):  
E. John Pollak
Keyword(s):  
Animal Model ◽  
Beef Cattle ◽  
Mixed Model ◽  
The United States ◽  
Genetic Evaluation ◽  
Beef Industry ◽  
Widespread Application ◽  
Cattle Industry ◽  
Reduced Animal Model ◽  
Genetic Evaluations

The beef cattle industry in the United States has undergone dramatic changes over the past decade with the adoption of genetic evaluation programs. The method of choice has been Henderson's mixed model methodology for best linear unbiased prediction (BLUP). The most prevalently used model is the animal model (Henderson and Quaas, 1976) computed by the equivalent reduced animal model (Quaas and Pollak, 1980).Neither the methodology or the models being used are particularly new. What is new in this industry is the widespread application of these techniques to the analysis of the data banks maintained by the breed organizations. Today many breed associations publish a national sire evaluation, and most of these have published their first in the last three years. This rapid proliferation of published evaluations has coincided with an attitude in the industry of promoting specification beef and predictable performance. Genetic evaluations provide information not only to achieve goals in selection but as well for merchandizing cattle based on quantifiable potential. The enthusiasm for genetic evaluations right now in the U.S. beef industry is high.

scholarly journals Selection of coffee progenies for resistance to nematode Meloidogyne paranaensis in infested area

2014 ◽  
Vol 14 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Sonia Maria Lima Salgado ◽  
Juliana Costa de Rezende ◽  
José Airton Rodrigues Nunes
Keyword(s):  
Genetic Variability ◽  
Coffea Arabica ◽  
Mixed Model ◽  
High Rate ◽  
Germplasm Bank ◽  
Genotypic Association ◽  
Coffee Plants ◽  
Plagiotropic Branches ◽  
Mixed Model Methodology ◽  
Selection Of

The purpose of this study was to select Coffea arabica progenies for resistance to M. paranaensis in an infested coffee growing area using Henderson's mixed model methodology. Forty-one genotypes were selected at the Coffee Active Germplasm Bank of Minas Gerais, and evaluated in regard to stem diameter, number of plagiotropic branches, reaction to the nematode, and yield per plant. There was genetic variability among the genotypes studied for all the traits evaluated, and among the populations studied for yield and reaction to the nematode, indicating possibilities for obtaining genetic gains through selection in this population. There was high rate of genotypic association between all the traits studied. Coffee plants of Timor Hybrid UFV408-01 population, and F3 progenies derived from crossing Catuaí Vermelho and Amphillo MR 2161 were the most promising in the area infested by M. paranaensis.

scholarly journals Plant selection in F2 segregating populations of papaya from commercial hybrids

2012 ◽  
Vol 12 (3) ◽  
pp. 191-198 ◽  
Author(s):  
Eder Jorge Oliveira ◽  
Gilberto de Andrade Fraife Filho ◽  
Juan Paulo Xavier de Freitas ◽  
Jorge Luiz Loyola Dantas ◽  
Marcos Deon Vilela de Resende
Keyword(s):  
Genetic Parameters ◽  
Mixed Model ◽  
Soluble Solids ◽  
Fruit Firmness ◽  
Plant Selection ◽  
F2 Population ◽  
The Common ◽  
Mixed Model Methodology ◽  
Fruit Diameter ◽  
Selection Of

The objective of this paper was to estimate the genetic parameters and additive genetic values in segregating populations of papaya using the mixed model methodology. Two F2 populations from Tainung and Calimosa hybrids were evaluated. The experimental design was an augmented block with four replicates, and Golden and Calimosa cultivars were the common treatment. Estimates of individual heritability were high for fruit length (FL) and weight (FW), moderate for fruit diameter (FD), and low for total soluble solids (TSS) and fruit firmness (FF). Considering FF and TSS as main traits for selection, genotypes of Calimosa-F2 population showed better performance to FF, but worse concerning TSS. It was selected 18.3% and 24.6% of plants from Tainung-F2 and Calimosa-F2 populations, respectively. Negative correlation between TSS and FF was not able to reduce the genetic gains. The segregating populations from Calimosa hybrid are more promising for the selection of papaya lines.

scholarly journals Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

2020 ◽  
Author(s):  
Dan Wang ◽  
Hui Tang ◽  
Jian-Feng Liu ◽  
Shizhong Xu ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Population Size ◽  
Computational Efficiency ◽  
Mixed Model ◽  
Association Studies ◽  
Approximate Algorithm ◽  
Source Codes ◽  
Model Based ◽  
Pairwise Interactions ◽  
Genome Wide ◽  
Model Algorithm

SummaryWe have developed a rapid mixed model algorithm for exhaustive genome-wide epistatic association analysis by controlling multiple polygenic effects. Our model can simultaneously handle additive by additive epistasis, dominance by dominance epistasis and additive by dominance epistasis, and account for intrasubject fluctuations due to individuals with repeated records. Furthermore, we suggest a simple but efficient approximate algorithm, which allows examination of all pairwise interactions in a remarkably fast manner of linear with population size. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model-based Plink on computational efficiency. It took less than 40 hours for the pairwise analysis of 5,000 individuals genotyped with roughly 350,000 SNPs with five threads on Intel Xeon E5 2.6GHz CPU.Availability and implementationSource codes are freely available at https://github.com/chaoning/GMAT.

scholarly journals Comparison of growth curves of two genotypes of dairy goats using nonlinear mixed models

2013 ◽  
Vol 152 (5) ◽  
pp. 829-842 ◽  
Author(s):  
J. G. L. REGADAS FILHO ◽  
L. O. TEDESCHI ◽  
M. T. RODRIGUES ◽  
L. F. BRITO ◽  
T. S. OLIVEIRA
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Mixed Model ◽  
Growth Curves ◽  
Growth Patterns ◽  
Dairy Goat ◽  
Residual Variance ◽  
Nonlinear Mixed Model ◽  
Error Structure ◽  
Mixed Model Methodology

SUMMARYThe objective of the current study was to assess the use of nonlinear mixed model methodology to fit the growth curves (weightv.time) of two dairy goat genotypes (Alpine, +A and Saanen, +S). The nonlinear functions evaluated included Brody, Von Bertalanffy, Richards, Logistic and Gompertz. The growth curve adjustment was performed using two steps. First, random effectsu1,u2andu3were linked to the asymptotic body weight (β1), constant of integration (β2) and rate constant of growth (β3) parameters, respectively. In addition to a traditional fixed-effects model, four combinations of models were evaluated using random variables: all parameters associated with random effects (u1,u2andu3), onlyβ1andβ2(u1andu2), onlyβ1andβ3(u1andu3) and onlyβ1(u1). Second, the fit of the best adjusted model was refined by using the power variance and modelling the error structure. Residual variance ($\sigma _e^2 $) and the Akaike information criterion were used to evaluate the models. After the best fitting model was chosen, the genotype curve parameters were compared. The residual variance was reduced in all scenarios for which random effects were considered. The Richards (u1andu3) function had the best fit to the data. This model was reparameterized using two isotropic error structures for unequally spaced data, and the structure known in the literature as SP(MATERN) proved to be a better fit. The growth curve parameters differed between the two genotypes, with the exception of the constant that determines the proportion of the final size at which the inflection point occurs (β4). The nonlinear mixed model methodology is an efficient tool for evaluating growth curve features, and it is advisable to assign biologically significant parameters with random effects. Moreover, evaluating error structure modelling is recommended to account for possible correlated errors that may be present even when using random effects. Different Richard growth curve parameters should be used for the predominantly Alpine and Saanen genotypes because there are differences in their growth patterns.

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