scholarly journals Development of genetic models for estimation of racing performances in German thoroughbreds

2004 ◽  
Vol 47 (6) ◽  
pp. 505-516
Author(s):  
A.-E. Bugislaus ◽  
R. Roehe ◽  
H. Uphaus ◽  
E. Kalm
Keyword(s):  
Animal Model ◽  
Linear Regression ◽  
Statistical Models ◽  
Regression Models ◽  
Genetic Variance ◽  
Fixed Effect ◽  
Random Regression ◽  
Square Root ◽  
Breeding Values ◽  
High Performing

Abstract. The objective of this study was to develop new statistical models for genetic estimation of racing performances in German thoroughbreds. Analysed performance traits were "square root of rank at finish", "square root of distance to first placed horse in a race" and "log of earnings". These traits were found to be influenced by the carried weight, which was determined by the horse's earlier performance. Therefore, new traits were developed based on random regression models, which were independent from the carried weights. Heritabilities were first estimated for these created traits "new rank at finish" (h2 = 0.101) and "new distance to first placed horse in a race" (h2 = 0.142) by using two univariate animal models. When considering a linear regression of carried weights as fixed effect in the statistical model, heritabilities for "square root of rank at finish" (h2 = 0.086) and "square root of distance to first placed horse in a race" (h2 = 0.124) decreased. Breeding values of “new rank at finish” and "new distance to first placed horse in a race" were compared with breeding values of "square root of rank at finish" and "square root of distance to first placed horse in a race", in which carried weight was considered as fixed regression in the model. These two different models were compared by two criteria. Breeding values were overestimated for low performing thoroughbreds and underestimated for high performing horses when considering a linear regression of carried weights as fixed effect in the model. Statistical models considering new created traits ("new rank at finish" and "new distance to first placed horse in a race") which were independent of carried weights, showed better suitability for genetic estimation. Due to high genetic correlation with other traits and showing highest genetic variance a univariate animal model for the trait “new distance to first placed horse in a race” was recommended for genetic estimation.

scholarly journals Genetic analysis of distance-dependent racing performances in German Thoroughbreds

2010 ◽  
Vol 53 (6) ◽  
pp. 629-638
Author(s):  
A.-E. Bugislaus
Keyword(s):  
Animal Model ◽  
Selection Index ◽  
Genetic Correlations ◽  
Selection Criterion ◽  
Distance Effect ◽  
Random Regression ◽  
Square Root ◽  
Multivariate Statistical ◽  
Breeding Values ◽  
Univariate Model

Abstract. The objective of this study was to develop a new multivariate statistical model for genetic estimation of distance-dependent racing performances in German Thoroughbreds. Analysed performance traits were »square root of distance to first placed horse in races over sprint distances (until 1 400 m)«, »square root of distance to first placed horse in races over mile distances (from 1 401 m to 1 900 m)« and »square root of distance to first placed horse in races over long distances (over 1 900 m)«. These traits were found to be influenced by the carried weight, which was determined by the horses’ earlier performance. Therefore, new traits were developed based on random regression models, which were independent from the carried weights. Genetic parameters were first evaluated for these new created traits »new distance to first placed horse in races over sprint distances« (h²=0.088), »new distance to first placed horse in races over mile distances« (h²=0.081) and »new distance to first placed horse in races over long distances« (h²=0.137) using a multivariate animal model. Genetic correlations between these traits were high, but differed from rg=1. A further heritability was estimated for the distance-independent trait »new distance to first placed horse in races over all distances« (h²=0.101) applying a univariate animal model with a fixed distance effect. These two different models were compared by two criteria. The ranking of breeding values for the distance-independent trait (estimated with the univariate model) was first correlated with each of the rankings of breeding values for the three distance-dependent traits (estimated with the multivariate model). Correlations varied from r=0.668 to r=0.813. The second criterion for comparison was the percentage of incorrectly selected raced stallions by breeding values estimated with the univariate model. Between 47.4 % and 69.7 % of stallions were incorrectly selected. The use of a total selection index including breeding values of the three distance-dependent traits with suitable weightings was recommended as a possible future selection criterion.

scholarly journals Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic

2011 ◽  
Vol 50 (No. 1) ◽  
pp. 7-13 ◽  
Author(s):  
L. Zavadilová ◽  
E. Němcová ◽  
J. Přibyl ◽  
J. Wolf
Keyword(s):  
Animal Model ◽  
Milk Production ◽  
Legendre Polynomials ◽  
Environmental Effect ◽  
Random Regression ◽  
Breeding Values ◽  
Regression Test ◽  
Days Open ◽  
Calving Season ◽  
Definition Of

The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

Statistical Models for the Twinning Rate

1987 ◽  
Vol 36 (3) ◽  
pp. 297-312 ◽  
Author(s):  
J.O. Fellman ◽  
A.W. Eriksson
Keyword(s):  
Linear Regression ◽  
Statistical Models ◽  
Maternal Age ◽  
Regression Models ◽  
Model Building ◽  
Data Sets ◽  
Linear Regression Models ◽  
Linear Regression Technique ◽  
Secular Decline ◽  
Disaggregated Data

AbstractLinear regression models are used to explain the variations in the twinning rates. Data sets from different countries are analysed and maternal age, parity and marital status are the main regressors. The model building technique is also used in order to study the secular decline in the twinning rate. Linear regression technique makes it possible to compare the effect of different factors but the method requires sufficiently disaggregated data.

scholarly journals Reliability of breeding values between random regression and 305-day lactation models

2016 ◽  
Vol 51 (11) ◽  
pp. 1848-1856
Author(s):  
Alessandro Haiduck Padilha ◽  
◽  
Jaime Araujo Cobuci ◽  
Darlene dos Santos Daltro ◽  
José Braccini Neto
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Protein Yield ◽  
Random Regression ◽  
Holstein Cattle ◽  
Average Increase ◽  
Data Set ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

scholarly journals Bias in genetic evaluation using random regression test-day model

2008 ◽  
Vol 16 (2) ◽  
pp. 103 ◽  
Author(s):  
M. LIDAUER ◽  
E.A. MÄNTYSAARI
Keyword(s):  
Animal Model ◽  
Evaluation Model ◽  
Protein Yield ◽  
Random Regression ◽  
Data Sets ◽  
Heterogeneous Variance ◽  
Breeding Values ◽  
Regression Test ◽  
Estimated Breeding Values ◽  
Reduced Data

The effect of an upgraded Finnish evaluation model on bias in estimated breeding values for protein yield was investigated. Evaluations based on repeatability animal model and on random regression test-day model without and with heterogeneous variance adjustment were compared. Comparisons were based on the average difference between pedigree indices and the future estimated breeding values, based on own or on daughter performance records. This was defined as empirical bias. The pedigree indices were computed from reduced data sets where four years of the most recent data were excluded. Results showed an upward bias in the protein yield pedigree indices for Ayrshire young sires of 2.2 kg, 2.5 kg and 1.8 kg from the repeatability animal model, random regression test-day model and random regression test-day model with heterogeneous variance adjustment, respectively. Pedigree indices for daughters of young sires were upward biased, whereas pedigree indices for daughters of proven sires were slightly underestimated when heterogeneous variance was not accounted. Inclusion of test-day yields from the fourth lactation onwards increased the bias. Moving from repeatability animal model to random regression test-day model did not reduce the bias, whereas adjustment of heterogeneous variance reduced bias.;

PSXV-1 Genetic evaluation of longevity of cows culled due to fertility-related problems using random regression models and censored data

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 261-261
Author(s):  
Hinayah R Oliveira ◽  
Stephen P Miller ◽  
Luiz F Brito ◽  
Flavio S Schenkel
Keyword(s):  
Censored Data ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Structural Problems ◽  
Time Period ◽  
And Performance ◽  
Genetic Evaluations ◽  
The Impact

Abstract A recent study showed that longevity based on different culling reasons should be considered as different traits in genetic evaluations. However, it is still necessary to create a pipeline that avoid including/excluding animals culled for different reasons in every genetic evaluation run. This study aimed to: 1) perform a genetic evaluation of the longevity of cows culled due to fertility-related problems including records of animals culled for other reasons (i.e., age, structural problems, disease, and performance) as censored records; and, 2) identify the impact of censored data in the genetic parameters and breeding values estimated. Two longevity indicators were evaluated: traditional (TL; time from first calving to culling) and functional (FL; time period in which the cow was alive and also calving after its first calving) longevity. Both TL and FL were evaluated from 2 to 15 years-old, and codified as binary traits for each age (0 = culled and 1 = alive/calved). Both trait definitions were analyzed using a Bayesian random regression linear model. Animals culled for reasons other than fertility were either excluded from the data (standard) or had their records censored after the culling date reported in the dataset (censored). After the quality control, 154,419 and 450,124 animals had uncensored and censored records, respectively. Heritabilities estimated for TL over the ages ranged from 0.02 to 0.13 for standard, and from 0.01 to 0.12 for censored datasets. Heritabilities estimated for FL ranged from 0.01 to 0.14 (standard), and from 0.01 to 0.13 (censored). Average (SD) correlation of breeding values predicted over all ages, using the standard and censored datasets, was 0.77 (0.16) for TL, and 0.83 (0.11) for FL. Our findings suggest that including censored data in the analyses might impact the genomic evaluations and further work is need to determine the optimal predictive approach.

Predicting breeding values for milk yield of Guzerá (Bos indicus) cows using random regression models

2014 ◽  
Vol 167 ◽  
pp. 41-50 ◽  
Author(s):  
D.J.A. Santos ◽  
M.G.C.D. Peixoto ◽  
R.R. Aspilcueta Borquis ◽  
J.C.C. Panetto ◽  
L. El Faro ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Bos Indicus ◽  
Random Regression ◽  
Breeding Values

Analysis of censored survival data using random regression models

2001 ◽  
Vol 72 (1) ◽  
pp. 1-10 ◽  
Author(s):  
R. F. Veerkamp ◽  
S. Brotherstone ◽  
B. Engel ◽  
T. H. E. Meuwissen
Keyword(s):  
Milk Yield ◽  
Survival Data ◽  
Variance Components ◽  
Regression Models ◽  
Proportional Hazards ◽  
Random Regression ◽  
Breeding Values ◽  
Censored Survival Data ◽  
Proportional Hazards Models ◽  
Randomly Censored Data

AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.

scholarly journals High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

2021 ◽  
Vol 12 ◽  
Author(s):  
Fabiana Freitas Moreira ◽  
Hinayah Rojas de Oliveira ◽  
Miguel Angel Lopez ◽  
Bilal Jamal Abughali ◽  
Guilherme Gomes ◽  
...  
Keyword(s):  
High Throughput ◽  
Regression Models ◽  
Genetic Architecture ◽  
Crop Improvement ◽  
Random Regression ◽  
Genome Wide Association Studies ◽  
Breeding Values ◽  
High Throughput Phenotyping ◽  
General Response ◽  
Over Time

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

scholarly journals Weight evaluation of Tabapuã cattle raised in northeastern Brazil using random-regression models

2017 ◽  
Vol 69 (2) ◽  
pp. 457-464 ◽  
Author(s):  
M.R. Oliveira ◽  
D.M. Azevêdo ◽  
C. Malhado ◽  
L. Pires ◽  
R. Martins Filho ◽  
...  
Keyword(s):  
Birth Weight ◽  
Regression Model ◽  
Growth Curve ◽  
Regression Models ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Northeastern Brazil ◽  
Random Regression ◽  
Phenotypic Correlation ◽  
Random Regression Model

ABSTRACT The objective of this study is to compare random-regression models used to describe changes in evaluation parameters for growth in Tabapuã bovine raised in the Northeast of Brazilian. The M4532-5 random-regression model was found to be best for estimating the variation and heritability of growth characteristics in the animals evaluated. Estimates of direct additive genetic variance increased with age, while the maternal additive genetic variance demonstrated growth from birth to up to nearly 420 days of age. The genetic correlations between the first four characteristics were positive with moderate to large ranges. The greatest genetic correlation was observed between birth weight and at 240 days of age (0.82). The phenotypic correlation between birth weight and other characteristics was low. The M4532-5 random-regression model with 39 parameters was found to be best for describing the growth curve of the animals evaluated providing improved selection for heavier animals when performed after weaning. The interpretation of genetic parameters to predict the growth curve of cattle may allow the selection of animals to accelerate slaughter procedures.

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