scholarly journals Heritability and repeatability of the number of lambs born and reared estimated using linear and threshold models

2011 ◽  
Vol 54 (3) ◽  
pp. 271-279
Author(s):  
D. Piwczyński ◽  
B. Kowaliszyn ◽  
S. Mroczkowski
Keyword(s):  
Linear Model ◽  
Gibbs Sampling ◽  
Variance Components ◽  
Genetic Parameters ◽  
Linear Models ◽  
Threshold Model ◽  
Standard Errors ◽  
Threshold Models ◽  
Similar Tendency ◽  
Average Information

Abstract. The research was conducted on 3 844 Polish Merino lamb dams born in 1991‑2001, used in 15 flocks from the Pomerania and Kujawy region in Poland. The assessed parameters were the number of lambs born from a dam after lambing (LSB) (1, 2, 3) and the number of lambs reared (LSW) (0, 1, 2, 3). The genetic parameters LSB and LSW were estimated with the use of two methods: Average Information – REML (AI-REML) and Gibbs sampling (GS). For estimation of components by means of the AI-REML method the animal’s linear model was used, and in the case of the GS method a threshold model was also used alongside the linear one. The LSB heritability estimated using the AI-REML and GS methods in combination with a linear model were similar and their values were respectively 0.025 and 0.029, with similar standard errors for variance components. Applying the GS method combined with a threshold model resulted in a two times higher heritability (0.054) compared to when linear models were used. A similar tendency was found to exist in respect of estimated repeatability. When using linear models, the obtained values were closely matched: 0.064 (AI-REML) and 0.065 (GS). The highest repeatability occurred when a threshold model was used (0.118). The LSW heritability was low and, depending on the model and method (0.016-0.020). Similar values LSW repeatability were obtained with the use of linear models (0.048 – REML and 0.049 – GS), and when a threshold model was used the result was higher – 0.070.

scholarly journals Genetic parameters for longevity measures in Brazilian Holstein cattle using linear and threshold models

2014 ◽  
Vol 57 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Elisandra Lurdes Kern ◽  
Jaime Araujo Cobuci ◽  
Cláudio Napolis Costa ◽  
José Braccini Neto ◽  
Gabriel Soares Campos ◽  
...  
Keyword(s):  
Variance Components ◽  
Genetic Parameters ◽  
Threshold Model ◽  
Holstein Cows ◽  
Holstein Cattle ◽  
Threshold Models ◽  
Productive Life ◽  
Selection For ◽  
Estimate Variance ◽  
Genetic Evaluations

Abstract. The aim in this study was to estimate variance components and heritability of different longevity measures related to productive life and survival at a specified age, using linear and threshold models, to specify the more appropriate measure to express longevity in Brazilian Holstein cows. Production and reproduction records of Holstein cows were collected from 1991 to 2010, for cows born between 1987 and 2006. Variance components were obtained by restricted maximum likelihood (REML) for measures of productive life and a Bayesian analysis for survival measures. The heritability estimates for longevity measures ranged from 0.06 to 0.09, using the linear model and from 0.05 to 0.18 for traits using the threshold model. This suggests an inexpressive genetic gain using selection for these traits, whereas improvements in environmental factors which affect these animals may lead to greater phenotypic gains. Survival up to 48 months from first calving was the measureing point defined as the most appropriate to be included in future official genetic evaluations of Holstein cattle in Brazil.

Threshold and linear models for genetic evaluation of visual scores in Hereford and Braford cattle

2019 ◽  
Vol 59 (4) ◽  
pp. 619 ◽  
Author(s):  
G. S. Campos ◽  
F. A. Reimann ◽  
P. I. Schimdt ◽  
L. L. Cardoso ◽  
B. P. Sollero ◽  
...  
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Multinomial Logistic Regression ◽  
Threshold Model ◽  
Predictive Ability ◽  
Genetic Evaluation ◽  
Breeding Value ◽  
Second Step ◽  
Threshold Models ◽  
Genetic Merit

Data from 127539 Hereford and Braford cattle were used to compare estimates of genetic parameters for navel, conformation, precocity, muscling and size visual scores at yearling, using linear and threshold animal models. In a second step, these models were cross-validated using a multinomial logistic regression in order to quantify the association between phenotype and genetic merit for each trait. For navel score, higher heritability was obtained with the threshold model (0.42 ± 0.02) in relation to the linear model (0.22 ± 0.02). However, similar heritability was estimated in both models for conformation, precocity, muscling and size, with values of 0.18 ± 0.01, 0.19 ± 0.01, 0.19 ± 0.01 and 0.26 ± 0.01, respectively, using linear model, and of 0.19 ± 0.01, 0.19 ± 0.01, 0.20 ± 0.01, and 0.29 ± 0.01, respectively, using threshold model. For navel score, Spearman correlations between sires’ breeding values predicted using linear and threshold models ranged from 0.60 (1% of the best sires are selected) to 0.96 (all sires are selected). For conformation, precocity, muscling and size scores, low changes in sires’ rank are expected using these models (Spearman correlations >0.86), regardless of the proportion of sires selected. Except for navel with the linear model, the direction of the associations between phenotype and genetic merit were in accordance with its expectation, as there were increases in the phenotype per unit of change in the breeding value. Thus, the threshold model would be recommended to perform genetic evaluation of navel score in this population. However, linear and threshold models showed similar predictive ability for conformation, precocity, muscling and size scores.

Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle

2000 ◽  
Vol 27 ◽  
pp. 83-84
Author(s):  
H. N. Kadarmideen ◽  
R. Thompson ◽  
G. Simm
Keyword(s):  
Animal Models ◽  
Dairy Cattle ◽  
Milk Production ◽  
Genetic Parameters ◽  
Linear Models ◽  
Threshold Model ◽  
Genetic Selection ◽  
Good Health ◽  
Economic Loss ◽  
Fertility Traits

A combination of better management and genetic selection for good health and fertility would provide a more effective long term solution for economic loss due to diseases and poor fertility. This would also help to address public concerns about the use of medical treatment in milk production. A balance in the genetic improvement of health and fertility together with milk production could be achieved through their inclusion in national genetic selection indices, for which genetic parameters are needed. One of the main objectives of this study was to estimate genetic parameters for various disease and fertility traits in the UK dairy cattle population, using records from a national recording scheme run by Livestock Services UK Ltd. Genetic analysis of traits recorded as present or absent (binary traits; e.g. diseases) requires the use of non-linear threshold models, because linear models require normality assumptions (e.g., Gianola 1982). The other objective of this study was to estimate genetic parameters for binary disease and fertility traits based on threshold animal models and to compare results with those from linear animal models.

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 Optimal definition of contemporary groups for crossbred pigs in a joint purebred and crossbred genetic evaluation

2020 ◽  
Author(s):  
Yvette Steyn ◽  
Daniela A Lourenco ◽  
Ching-Yi Chen ◽  
Bruno D Valente ◽  
Justin Holl ◽  
...  
Keyword(s):  
Variance Components ◽  
Visual Inspection ◽  
Genetic Parameters ◽  
Performance Test ◽  
Predictive Ability ◽  
Definition Of ◽  
Relative Differences ◽  
The Impact ◽  
Crossbred Pigs ◽  
Average Information

Abstract In the pig industry, purebred animals are raised in nucleus herds and selected to produce crossbred progeny to perform in commercial environments. Crossbred and purebred performances are different, correlated traits. All purebreds in a pen have their performance assessed together at the end of a performance test. However, only selected crossbreds are removed (based on visual inspection) and measured at different times, creating many small contemporary groups (CG). This may reduce EBV prediction accuracies. Considering this sequential recording of crossbreds, the objective was to investigate the impact of different CG definitions on genetic parameters and EBV prediction accuracy for crossbred traits. Growth rate (GP) and ultrasound backfat (BFP) records were available for purebreds. Lifetime growth (GX) and backfat (BFX) were recorded on crossbreds. Different CG were tested: CG_all included farm, sex, birth year and birth week, CG_week added slaughter week, and CG_day used slaughter day instead of week. Data of 124,709 crossbreds were used. The purebred phenotypes (62,274 animals) included 3 generations of purebred ancestors of these crossbreds and their CG mates. Variance components for 4-trait models with different CG definitions were estimated with average information REML. Purebred traits’ variance components remained stable across CG definitions and varied slightly for BFX. Additive genetic variances (and heritabilities) for GX fluctuated more: 812 ± 36 (0.28 ± 0.01), 257 ± 15 (0.17 ± 0.01) and 204 ± 13 (0.15 ± 0.01) for CG_all, CG_week, and CG_day, respectively. Age at slaughter (AAS) and HCW adjusted for age were investigated as alternatives for GX. Both have potential for selection but lower heritabilities compared to GX: 0.21 ± 0.01 (0.18 ± 0.01), 0.16 ± 0.02 (0.16 + 0.01), and 0.10 ± 0.01 (0.14 ± 0.01) for AAS (HCW) using CG_all, CG_week, and CG_day, respectively. The predictive ability, linear regression (LR) accuracy, bias, and dispersion of crossbred traits in crossbreds favored CG_day, but correlations with unadjusted phenotypes favored CG_all. In purebreds, CG_all showed the best LR accuracy, while showing small relative differences in bias and dispersion. Different GC scenarios showed no relevant impact on BFX EBV. This study shows that different CG definitions may affect evaluation stability and animal ranking. Results suggest that ignoring slaughter dates in CG is more appropriate for estimating crossbred trait EBV for purebred animals

scholarly journals Bivariate analysis for the improvement of genetic evaluations with incomplete records in Charolais cattle

2021 ◽  
Vol 26 (2) ◽  
pp. e2128
Author(s):  
Jessica Beatriz Herrera-Ojeda ◽  
Gaspar Manuel Parra-Bracamonte ◽  
Nicolás López-Villalobos ◽  
José Herrera-Camacho ◽  
Karlos Edmundo Orozco-Durán
Keyword(s):  
Incomplete Data ◽  
Variance Components ◽  
Genetic Parameters ◽  
Live Weight ◽  
Joint Analysis ◽  
Standard Errors ◽  
Bivariate Analysis ◽  
Bivariate Models ◽  
Charolais Cattle ◽  
Genetic Evaluations

Objective: Estimate (co)variance components and genetic parameters of live weight traits and examine the effect of selection culling when using bivariate analysis in registered Charolais beef cattle. Materials and methods: The effect of incomplete data over accuracies was compared, expected progeny differences (EPD) and standard errors of prediction (SEP) were obtained and evaluated by comparing univariate and bivariate models for birth (BW), weaning (WW) and yearling (YW) weights. Results: Bivariate models for WW and YW, improved accuracies of EPDs and reduced the SEPs. Joint analysis for BW and WW increased in a 38% the accuracies and reduced SEP estimators for YW (p<0.001). Accuracies of EPD for BW obtained from univariate models were improved when BW was included in bivariate models. Conclusions: The results support the use of bivariate genetic analysis in limited or incomplete live weight indicators databases that were registered after birth, such as weaning and yearling weight.

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