scholarly journals AFEchidna is a R package for genetic evaluation of plant and animal breeding datasets

2021 ◽  
Author(s):  
Weihua Zhang ◽  
Ruiyan Wei ◽  
Yan Liu ◽  
Yuanzhen Lin
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Linear Models ◽  
R Package ◽  
Animal Breeding ◽  
Mixed Linear Models ◽  
Best Linear Unbiased ◽  
Genetic Assessment

Progeny tests play important roles in plant and animal breeding programs, and mixed linear models are usually performed to estimate variance components of random effects, estimate the fixed effects (Best Linear Unbiased Estimates, BLUEs) and predict the random effects (Best Linear Unbiased Predictions, BLUPs) via restricted maximum likehood (REML) methods in progeny test datasets. The current pioneer software for genetic assessment is ASReml, but it is commercial and expensive. Although there is free software such as Echidna or the R package sommer, the Echidna syntax is complex and the R package functionality is limited. Therefore, this study aims to develop a R package named AFEchidna based on Echidna software. The mixed linear models are conveniently implemented for users through the AFEchidna package to solve variance components, genetic parameters and the BLUP values of random effects, and the batch analysis of multiple traits, multiple variance structures and multiple genetic parameters can be also performed, as well as comparison between different models and genomic BLUP analysis. The AFEchidna package is free, please email us ([email protected]) to get a copy if one is interested for it. The AFEchidna package is developed to expand free genetic assessment software with the expectation that its efficiency could be close to the commercial software.

scholarly journals Selection in several environments by BLP as an alternative to pooled anova in crop breeding

2009 ◽  
Vol 33 (5) ◽  
pp. 1342-1350 ◽  
Author(s):  
Júlio Sílvio de Sousa Bueno Filho ◽  
Roland Vencovsky
Keyword(s):  
Plant Breeding ◽  
Random Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Models ◽  
Minimum Variance ◽  
Pooled Analysis ◽  
Balanced Designs ◽  
Best Linear Unbiased ◽  
Combine Information

Plant breeders often carry out genetic trials in balanced designs. That is not always the case with animal genetic trials. In plant breeding is usual to select progenies tested in several environments by pooled analysis of variance (ANOVA). This procedure is based on the global averages for each family, although genetic values of progenies are better viewed as random effects. Thus, the appropriate form of analysis is more likely to follow the mixed models approach to progeny tests, which became a common practice in animal breeding. Best Linear Unbiased Prediction (BLUP) is not a "method" but a feature of mixed model estimators (predictors) of random effects and may be derived in so many ways that it has the potential of unifying the statistical theory of linear models (Robinson, 1991). When estimates of fixed effects are present is possible to combine information from several different tests by simplifying BLUP, in these situations BLP also has unbiased properties and this lead to BLUP from straightforward heuristics. In this paper some advantages of BLP applied to plant breeding are discussed. Our focus is on how to deal with estimates of progeny means and variances from many environments to work out predictions that have "best" properties (minimum variance linear combinations of progenies' averages). A practical rule for relative weighting is worked out.

scholarly journals Evaluación fenotípica y genética para características de crecimiento en la raza criolla colombiana Costeño con Cuernos

2007 ◽  
Vol 7 (2) ◽  
pp. 12 ◽  
Author(s):  
Rodrigo Alfredo Martínez ◽  
Juan Esteban Pérez ◽  
Teófilo Herazo
Keyword(s):  
Birth Weight ◽  
Variance Components ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Phenotypic Correlation ◽  
Phenotypic Variance ◽  
Direct Effects ◽  
Weaning Weight ◽  
Number Of Births

<p>Se establecieron componentes de varianza, así como parámetros fenotípicos y genéticos, respecto de las variables ‘peso al nacimiento’, ‘peso al destete’ (ajustado a los 270 días) y ‘peso a los 480 días’ en un hato del ganado criollo colombiano Costeño con Cuernos. Se analizaron 2.281 registros de pesos al nacer, 1.722 de pesos al destete  y 1.086 de pesos ajustados a los 480 días utilizando la metodología de máxima verosimilitud restringida (DFREML). También se ajustó un modelo animal que incluyó efectos genéticos directos, maternos y de ambiente permanente, asumiendo como efectos fijos el año de nacimiento, el sexo del ternero y el número de partos de la madre; finalmente, se estimaron los parámetros genéticos ‘heredabilidad’, ‘repetibilildad’ y se establecieron correlaciones genéticas y fenotípicas. Se reportan bajas estimaciones de heredabilidad de los efectos directos, que varían entre 0,17 ± 0,001 y 0,21 ± 0,074 para los pesos al nacer y al destete, respectivamente; así mismo, fue baja la heredabilidad de los efectos genéticos maternos con relación al peso al nacimiento, aunque estos estimados aumentaron respecto de los pesos al nacer y al destete. Las correlaciones entre efectos directos y maternos fueron negativas, pero el mayor valor se encontró para el peso al nacimiento (-0,89). La contribución del ambiente permanente como proporción de la varianza fenotípica total fue baja y disminuyó a medida que aumentó la edad del animal.</p><p> </p><p><strong>Genetic and phenotypic evaluation to characterize growth traits of the native Colombian breed Costeño con Cuernos</strong></p><p>For a herd of native Colombian breed of cattle -Costeño con Cuernos (CCC)- estimates of variance components for phenotypic and genetic parameters were obtained for birth weight, weight at weaning (adjusted to 270 days) and weight at 480 days. Using the restricted maximum likelihood (REMI) methodology, 2281 birth weight records (PN), 1722 weaning weight records and 1086 weight records adjusted to 480 days were analyze by fitting a model which included direct and maternal genetics effects as well as permanent environmental effects, assuming that fixed effects were year of birth weight, calf gender and the mother number of births. The genetic parameters for heritability, repeatability, genetic and phenotypic correlation were estimated and genotypic and phenotypic correlation was established. Heritability estimates for direct effects are low and range from 0.17 ± 0.001 and 0.21 ± 0.074 for birth and weaning weight respectively; while estimates for maternal genetics effects were also low for PN, they were higher for weaning weight and weight at 480 days. There was a negative correlation between direct and maternal effects, and the higher value was for PN (-0.89). The contribution of the variable permanent environment measured as the contribution of the phenotypic variance was low and diminished as animal age increased.</p>

scholarly journals Selection of a suitable data set and model for the genetic evaluation of the linear description of conformation and type description in Old Kladruber horses

2012 ◽  
Vol 55 (2) ◽  
pp. 105-112
Author(s):  
L. Vostrý ◽  
K. Mach ◽  
J. Přibyl
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Suitable Model ◽  
Residual Variance ◽  
Linear Type ◽  
Colour Variant ◽  
Repeated Evaluation ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for 36 traits of the linear type in 977 Old Kladruber horses. Two subsets were tested to identify a suitable subset for analysis. One subset included repeated evaluation of certain individuals, whereas the other did not. The most suitable subset included repeated evaluation (n=1 390). The selection of a suitable model was made from 4 candidate models. These models comprised a number of random effects (direct individual effect and animal permanent environmental effect of the animal) and a number of fixed effects (colour variant, stud, colour variant × stud interaction, sex, age at description, year of birth, year of description). The model was selected based on the Akaike information criterion (AIC, Akaike 1974), residual variance and heritability coefficient. The model that included colour variant, stud, colour variant × stud interaction, sex, age at description, and year of description as fixed effects and direct individual and animal permanent environment as random effects was the most suitable model for the estimation of genetic parameters and for the subsequent estimation of breeding values.

Selection on lean growth in a nucleus of Landrace pigs: an analysis using Gibbs sampling

1996 ◽  
Vol 63 (2) ◽  
pp. 243-253 ◽  
Author(s):  
M. C. Rodriguez ◽  
M. Toro ◽  
L. Silió
Keyword(s):  
Gibbs Sampling ◽  
Variance Components ◽  
Fixed Effects ◽  
Prediction Method ◽  
Environmental Parameters ◽  
Linear Unbiased Prediction ◽  
Normal Distributions ◽  
Lean Growth ◽  
Best Linear Unbiased ◽  
Selection For

AbstractData from 4150 Landrace pigs tested during the period 1989-94 for backfat thickness and age at 100 kg in an open selection nucleus were analysed with the standard restricted maximum likelihood/best linear unbiased prediction method and with a Bayesian approach based on the marginal posterior distributions of parameters of interest achieved via Gibbs sampling. Breeding values and fixed effects were sampled from normal distributions and (co)variance components from inverted Wishart distributions. The Bayesian analysis indicated that the selection was effective for both traits. Assuming flat priors for the (co)variance components, the posterior means of the annual rates of response to selection for both traits were −0·473 days and −0·212 mm. The influence of informative priors constructed from (co)variances estimated in the French Landrace breed on inferences about genetic and common environmental parameters, genetic group effects and total and annual responses was also examined.

scholarly journals On non-negative estimation of variance components in mixed linear models

2016 ◽  
Vol 7 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Heba A. El Leithy ◽  
Zakaria A. Abdel Wahed ◽  
Mohamed S. Abdallah
Keyword(s):  
Variance Components ◽  
Linear Models ◽  
Mixed Linear Models ◽  
Estimation Of Variance

A Note on Exact Tests for Variance Components in Unbalanced Random and Mixed Linear Models

Biometrics ◽  
1996 ◽  
Vol 52 (1) ◽  
pp. 306 ◽  
Author(s):  
Rana Fayyad ◽  
Franklin A. Graybill ◽  
Richard K. Burdick
Keyword(s):  
Variance Components ◽  
Linear Models ◽  
Exact Tests ◽  
Mixed Linear Models

scholarly journals The Impact of Different Competitive Environments on Pacing and Performance

2018 ◽  
Vol 13 (6) ◽  
pp. 701-708 ◽  
Author(s):  
Marco J. Konings ◽  
Florentina J. Hettinga
Keyword(s):  
Fixed Effects ◽  
Linear Models ◽  
Real Life ◽  
Energy Resources ◽  
Mixed Linear Models ◽  
Start Position ◽  
Available Energy ◽  
Competitive Environments ◽  
And Performance ◽  
The Impact

Purpose: In real-life competitive situations, athletes are required to continuously make decisions about how and when to invest their available energy resources. This study attempted to identify how different competitive environments invite elite short-track speed skaters to modify their pacing behavior during head-to-head competition. Methods: Lap times of elite 500-, 1000- and 1500-m short-track speed skating competitions between 2011 and 2016 (N = 34,095 races) were collected. Log-transformed lap and finishing times were analyzed with mixed linear models. The fixed effects in the model were sex, season, stage of competition, start position, competition importance, event number per tournament, number of competitors per race, altitude, and time qualification. The random effects of the model were athlete identity and the residual (within-athlete race-to-race variation). Separate analyses were performed for each event. Results: Several competitive environments, such as the number of competitors in a race (a higher number of competitors evoked most likely a faster initial pace; coefficient of variation [CV] = 1.9–9.3%), the stage of competition (likely to most likely, a slower initial pace was demonstrated in finals; CV = −1.4% to 2.0%), the possibility of time qualification (most likely a faster initial pace; CV = 2.6–5.0%), and competition importance (most likely faster races at the Olympics; CV = 1.3–3.5%), altered the pacing decisions of elite skaters in 1000- and 1500-m events. Stage of competition and start position affected 500-m pacing behavior. Conclusions: As demonstrated in this study, different competitive environments evoked modifications in pacing behavior, in particular in the initial phase of the race, emphasizing the importance of athlete–environment interactions, especially during head-to-head competitions.

Sign in / Sign up

Export Citation Format

Share Document