scholarly journals Estimation of genetic parameters for test-day milk production at different stages of lactation of Finnish Ayrshire heifers

1996 ◽  
Vol 5 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Anne Kettunen ◽  
Esa A. Mäntysaari
Keyword(s):  
Milk Production ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Regression Coefficients ◽  
Random Regression ◽  
Standard Errors ◽  
Repeated Observations ◽  
Lactation Curve ◽  
Geographical Regions ◽  
Estimation Of Genetic Parameters

Genetic parameters for test-day milk production at different stages of lactation of Finnish Ayrshire heifers were estimated with the REML method using the AI algorithm and animal model. The data consisted of 38 679 first lactation test-day milk yields of 4205 cows from 231 herds in three geographical regions (North Savo, Central Ostrobothnia and Lapland). To identify different test days, records were numbered according to the days in milk after calving, and were further categorized into three part-lactations according to the test-day classification. Expressions in the three part-lactations were considered as separate traits, and tests were treated as repeated observations within the trait. Heritability estimates for test-day milk yield varied between 0.11 and 0.17, being lowest at the beginning of lactation. Genetic correlations between test-day milk yields at different trimesters ranged from 0.64 to 0.91, being highest between consecutive trimesters. Standard errors of the estimates of genetic parameters varied between 0.02 and 0.08. Genetic interrelationships differed from 1.0, supporting the assumption that genetic variation exists in the shape of the lactation curve. The necessity of considering deviations from the general lactation curve in the test-day model, e.g. fitting random regression coefficients, is discussed.

scholarly journals Estimation of Genetic Parameters for Peak Yield, Yield and Persistency Traits in Murciano-Granadina Goats Using Multi-Traits Models

Animals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 411
Author(s):  
Judith C. Miranda ◽  
José M. León ◽  
Camillo Pieramati ◽  
Mayra M. Gómez ◽  
Jesús Valdés ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Selection Criteria ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Multiple Trait ◽  
Lactation Curve ◽  
Derivative Free ◽  
Selection For ◽  
Estimated Breeding Values ◽  
Estimation Of Genetic Parameters

This paper studies parameters of a lactation curve such as peak yield (PY) and persistency (P), which do not conform to the usual selection criteria in the Murciano-Granadina (MG) breed, but are considered to be an alternative to benefit animal welfare without reducing production. Using 315,663 production records (of 122,883 animals) over a period of 24 years (1990–2014), genetic parameters were estimated with uni-, bi- and multivariate analysis using multiple trait derivative free restricted maximum likelihood (MTDFREML). The heritability (h2)/repeatability (re) of PY, yield (Y) and P was estimated as 0.13/0.19, 0.16/0.25 and 0.08/0.09 with the uni-trait and h2 of bi- and multi-traits analysis ranging from 0.16 to 0.17 of Y, while that of PY and Y remained constant. Genetic correlations were high between PY–Y (0.94 ± 0.011) but low between PY–P (–0.16 ± 0.054 to –0.17 ± 0.054) and between Y–P (–0.06 ± 0.058 to –0.05 ± 0.058). Estimates of h2/re were low to intermediate. The selection for Y–PY or both can be implemented given the genetic correlation between these traits. PY–P and Y–P showed low to negligible correlation values indicating that if these traits are implemented in the early stages of evaluation, they would not be to the detriment of PY–Y. The combination of estimated breeding values (EBVs) for all traits would be a good criterion for selection.

scholarly journals Comparison of random regression models, traditional animal model and with the inclusion of molecular markers in the estimation of genetic parameters in Colombian Holstein cattle

2021 ◽  
Vol 42 (3) ◽  
pp. 1303-1322
Author(s):  
Daniel Cardona-Cifuentes ◽  
◽  
Albeiro López-Herrera ◽  
Luis Gabriel González-Herrera ◽  
Mario Fernando Cerón-Muñoz ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Somatic Cell ◽  
Milk Production ◽  
Regression Models ◽  
Genetic Parameters ◽  
Evaluation Model ◽  
Random Regression ◽  
Holstein Cattle ◽  
Protein Percentage ◽  
Estimation Of Genetic Parameters

The use of molecular markers to identify desirable genes in animal production is known as marker-assisted selection. The traditional genetic evaluation model uses the BLUP methodology; when genetic markers are included in the evaluation model, the methodology is known as M-BLUP. In contrast, random regression models (RRM), unlike the models based on production at 305 days, consider factors that change for each animal from one test to another. The objective of this study was to compare variance components, genetic parameters and breeding values for milk production, protein percentage and somatic cell score in Colombian Holstein cattle using BLUP, M-BLUP and RRM. For the estimation of genetic parameters and values, 2003 lactations corresponding to 1417 cows in 55 herds were used, and effects of the order of delivery, herd, and contemporary group were included. The three traits presented greater heritability under the MBLUP model: 0.44 for protein percentage, 0.27 for milk production and 0.28 for somatic cell score. This was because the genetic variance was greater when M-BLUP was used, which allowed a greater accuracy of the breeding value estimation in the three traits. Therefore, the model that includes information on molecular markers is more suitable for genetic evaluation in Colombian Holstein cattle.

scholarly journals Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

2013 ◽  
Vol 12 (1) ◽  
pp. 143-153 ◽  
Author(s):  
D.J.A. Santos ◽  
M.G.C.D. Peixoto ◽  
R.R. Aspilcueta Borquis ◽  
R.S. Verneque ◽  
J.C.C. Panetto ◽  
...  
Keyword(s):  
Milk Production ◽  
Regression Models ◽  
Genetic Parameters ◽  
Bos Indicus ◽  
Random Regression

scholarly journals Estimation of genetic parameters using a random regression monthly test-day model in an Ethiopian dairy cattle population

2016 ◽  
Vol 50 (1) ◽  
pp. 64-70 ◽  
Author(s):  
Gebregziabher Gebreyohannes ◽  
Skorn Koonawootrittriron ◽  
Mauricio A. Elzo ◽  
Thanathip Suwanasopee
Keyword(s):  
Dairy Cattle ◽  
Genetic Parameters ◽  
Random Regression ◽  
Cattle Population ◽  
Estimation Of Genetic Parameters

scholarly journals Estimation of Genetic Parameters for Body Weight and Some Colour Traits in the Seventh-generation Index Selected Nigeria Heavy Local Chicken Ecotype

2021 ◽  
Author(s):  
I.J. Ohagenyi ◽  
F.C. Iregbu ◽  
V.C. Udeh
Keyword(s):  
Body Weight ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
The Body ◽  
Pedigree Selection ◽  
Egg Colour ◽  
Local Chicken ◽  
Range Test ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Background: This study was conducted to estimate the genetic parameters of body weight and some colour traits in seventh generation (G7) index selected Nigerian Heavy Local Chicken Ecotype (NHLCE) progenies at point of lay to 12 weeks. Methods: 5 sires and 12 hens were used to generate the progenies used for the experiment. Traits measured included weekly body weight, egg colour, beak colour and feather colour. Data collected were subjected to one way analysis of variance in a Paternal half sib analysis using Animal model of SAS (2003). Four weeks body weight measurements, egg colour, beak colour and feather colour for 5 sires ranged from 1.29±0.05 1.54±0.07; 2.55±0.02 to 4.00±0.02; 2.45±0.02 to 4.83±0.02 and 1.73±0.02 to 4.58±0.04 respectively. Result: The new Duncan’s multiple range test shows that sire families are similar (p greater than 0.05) in the body weight and beak colour, but significantly differed (p greater than 0.05) in the egg colour and feather colour. The heritability estimates of mature body weight for week 3 was medium, while estimates of heritability for weekly mature body weight for weeks 1, 2 and 4, egg colour, beak colour and feather colour of NHLCE were low heritability. Low h2 of traits suggest that progeny and pedigree selection could be employed for improvement of the egg colour, beak colour and feather colour of NHLCE. The study showed positive genetic correlations between beak colour and egg colour, negative genetic correlations between beak and feather colour. This means that no decision can be taken in isolation as the selection of one trait will have consequences on other traits.

scholarly journals Estimation of genetic parameters and comparison of random regression animal and sire models of production traits in the first three lactations of Iranian Holsteins

2014 ◽  
Vol 30 (2) ◽  
pp. 261-279 ◽  
Author(s):  
A. Mohammadi ◽  
S. Alijani
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Random Regression ◽  
Lactation Period ◽  
Production Traits ◽  
Environmental Variance ◽  
In The Beginning ◽  
Residual Variances

This study was conducted to compare of random regression (RR) animal and sire models for estimation of the genetic parameters for production traits of Iranian Holstein dairy cows. For this purpose, the test day records were used belonged to first three lactations of cows and for, milk, fat and protein yields traits where, collected from 2003 to 2010, by the national breeding center of Iran. The genetic parameters were estimated using restricted maximum likelihood algorithm. To compare the model, different criterion -2logL value, AIC, BIC and RV were used for considered traits. Residual variances were considered homogeneous over the lactation period. Obtained results showed that additive genetic variance was highest in the beginning and end lactation and permanent environmental variance was highest in beginning of lactation than other lactation period. Heritabilities estimate for milk, fat and protein yields by RR animal and sire models were found to be lowest during early lactation (0.05, 0.04 and 0.07; 0.05, 0.19 and 0.13; 0.14, 0.19 and 0.15, for milk, fat and protein yields and in first, second and third lactation respectively). However, estimated heritabilities during lactation did not vary among different order Legendre polynomials, and also between RR animal and sire models. The variation in genetic correlations estimate in the RR animal and sire models was larger in the first lactation than in the second and third lactations. Thus, based on the results obtained, it can be inferred that the RR animal model is better for modeling yield traits in Iranian Holsteins.

scholarly journals Random regression models using different functions to estimate genetic parameters for milk production in Holstein Friesians

2016 ◽  
Vol 46 (9) ◽  
pp. 1649-1655
Author(s):  
Mariana de Almeida Dornelles ◽  
Paulo Roberto Nogara Rorato ◽  
Luis Telo Lavadinho da Gama ◽  
Fernanda Cristina Breda ◽  
Carlos Bondan ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomials ◽  
Fixed Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Residual Variance ◽  
Lower Estimation ◽  
The One ◽  
Residual Variances

ABSTRACT: The objective of this study was to compare the functions of Wilmink and Ali and Schaeffer with Legendre polynomials in random regression models using heterogeneous residual variances for modeling genetic parameters during the first lactation in the Holstein Friesian breed. Five thousand eight hundred and eighty biweekly records of test-day milk production were used. The models included the fixed effects of group of contemporaries and cow age at calving as covariable. Statistical criteria indicated that the WF.33_HE2, LEG.33_HE2, and LEG.55_HE4 functions best described the changes in the variances that occur throughout lactation. Heritability estimates using WF.33_HE2 and LEG.33_HE2 models were similar, ranging from 0.31 to 0.50. The LEG.55_HE4 model diverged from these models, with higher estimates at the beginning of lactation and lower estimates after the 16th fortnight. The LEG55_HE4, among the three better models indicated by the index, is the one with highest number of parameters (14 vs 34) and resulted in lower estimation of residual variance at the beginning and at the end of lactation, but overestimated heritability in the first fortnight and presented a greater difficulty to model genetic and permanent environment correlations among controls. Random regression models that used the Wilmink and Legendre polynomials functions with two residual variance classes appropriately described the genetic variation during lactation of Holstein Friesians reared in Rio Grande do Sul.

scholarly journals Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

2014 ◽  
Vol 49 (5) ◽  
pp. 372-383 ◽  
Author(s):  
Maria Gabriela Campolina Diniz Peixoto ◽  
Daniel Jordan de Abreu Santos ◽  
Rusbel Raul Aspilcueta Borquis ◽  
Frank Ângelo Tomita Bruneli ◽  
João Cláudio do Carmo Panetto ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomial ◽  
Legendre Polynomials ◽  
Regression Models ◽  
Environmental Effect ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
Fifth Order

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

scholarly journals Model comparison and estimation of genetic parameters for body weight in commercial broilers

2013 ◽  
Vol 93 (1) ◽  
pp. 67-77 ◽  
Author(s):  
G. Maniatis ◽  
N. Demiris ◽  
A. Kranis ◽  
G. Banos ◽  
A. Kominakis
Keyword(s):  
Body Weight ◽  
Akaike Information Criterion ◽  
Model Evaluation ◽  
Model Comparison ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
Biological Properties ◽  
Genetic Covariance ◽  
Estimation Of Genetic Parameters

Maniatis, G., Demiris, N., Kranis, A., Banos, G. and Kominakis, A. 2013. Model comparison and estimation of genetic parameters for body weight in commercial broilers. Can. J. Anim. Sci. 93: 67–77. The availability of powerful computing and advances in algorithmic efficiency allow for the consideration of increasingly complex models. Consequently, the development and application of appropriate statistical procedures for model evaluation is becoming increasingly important. This paper is concerned with the application of an alternative model determination criterion (conditional Akaike Information Criterion, cAIC) in a large dataset comprising 203 323 body weights of broilers, pertaining to 7 (BW7) and 35 (BW35) days of age. Seven univariate and seven bivariate models were applied. Direct genetic, maternal genetic and maternal environmental (c2) effects were estimated via REML. The model evaluation criteria included conditional Akaike Information Criterion (cAIC), Bayesian Information Criterion (BIC) and the standard Akaike Information Criterion (henceforth marginal; mAIC). According to cAIC the best-fitting model included direct genetic, maternal genetic and c2 effects. Maternal heritabilities were low (0.10 and 0.03) compared to the direct heritabilities (0.17 and 0.21), while c2 was 0.05 and 0.04 for BW7 and BW35, respectively. BIC and mAIC favoured a model that additionally included a direct-maternal genetic covariance, resulting in highly negative direct-maternal genetic correlations (−0.47 and −0.64 for BW7 and BW35, respectively) and higher direct heritabilities (0.25 and 0.28 for BW7 and BW35, respectively). Results suggest that cAIC can select different animal models than mAIC and BIC with different biological properties.

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