scholarly journals Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo

2016 ◽  
Vol 51 (7) ◽  
pp. 890-897 ◽  
Author(s):  
Mostafa Madad ◽  
Navid Ghavi Hossein-Zadeh ◽  
Abdol Ahad Shadparvar
Keyword(s):  
Genetic Correlation ◽  
Milk Yield ◽  
Environmental Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Information Criterion ◽  
Step Function ◽  
Random Regression ◽  
Residual Variance ◽  
Covariance Functions

Abstract: The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects, as well as to obtain genetic parameters for buffalo test-day milk yield using random regression models on Legendre polynomials (LPs). A total of 2,538 test-day milk yield (TDMY) records from 516 first lactation records of Khuzestan buffalo, calving from 1993 to 2009 and belonging to 150 herds located in the state of Khuzestan, Iran, were analyzed. The residual variances were modeled through a step function with 1, 5, 6, 9, and 19 classes. The additive genetic and permanent environmental random effects were modeled by LPs of days in milk using quadratic to septic polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by cubic and third order LP, respectively, and with the residual variance modeled through a step function with nine classes was the most adequate one to describe the covariance structure. The model with the highest significant log-likelihood ratio test (LRT) and with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) was considered to be the most appropriate one. Unexpected negative genetic correlation estimates were obtained between TDMY records of the twenty-fifth and thirty-seventh week (-0.03). Genetic correlation estimates were generally higher, close to unity, between adjacent weeks during the middle of lactation. Random regression models can be used for routine genetic evaluation of milk yield in Khuzestan buffalo.

scholarly journals Estimation of (co)variance components and genetic parameters for weights of red-winged tinamou using random regression models

2011 ◽  
Vol 40 (4) ◽  
pp. 781-787 ◽  
Author(s):  
P. Tholon ◽  
S.A. Queiroz
Keyword(s):  
Body Weight ◽  
Environmental Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Information Criterion ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
First Eigenvalue ◽  
Permanent Effect

The objective of this work was to determine genetic parameters for body weight of tinamou in captivity. It was used random regression models in analyses of data by considering the direct additive genetic (DA) and permanent environmental effects of the animal (PE) as random effects. Residual variances were modeled by using a fifth-order variance function. The mean population growth curve was fitted by sixth-order Legendre orthogonal polynomials. Direct additive genetic effects and animal environmental permanent effect were modeled by using Legendre polynomials of order two to nine. The best results were obtained by models with orders of fit of 6 for direct additive genetic effect and of order 3 for permanent effect by Akaike information criterion and of order 3 for both additive genetic effect and permanent effect by Schwarz Bayesian information criterion and likelihood ratio test. Heritability estimates ranged from 0.02 to 0.57. The first eigenvalue explained 94% and 90% of the variation from additive direct and permant environmental effects, respectively. Selection of tinamou for body weight is more effective after 112 days of age.

scholarly journals Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling

2011 ◽  
Vol 40 (3) ◽  
pp. 557-567 ◽  
Author(s):  
Jaime Araújo Cobuci ◽  
Claudio Napolis Costa ◽  
José Braccini Neto ◽  
Ary Ferreira de Freitas
Keyword(s):  
Multiple Regression ◽  
Milk Yield ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
Multiple Regression Model ◽  
Random Regression ◽  
Fixed Curve ◽  
Best Fit

Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.

scholarly journals Estimates of covariance functions for growth of Anglo-Nubian goats

2011 ◽  
Vol 40 (1) ◽  
pp. 106-114 ◽  
Author(s):  
José Ernandes Rufino de Sousa ◽  
Martinho de Almeida e Silva ◽  
José Lindenberg Rocha Sarmento ◽  
Wandrick Hauss de Sousa ◽  
Maria do Socorro Medeiros de Souza ◽  
...  
Keyword(s):  
Bayesian Information Criterion ◽  
Regression Models ◽  
Genetic Effect ◽  
Information Criterion ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
Ratio Test ◽  
Residual Variance ◽  
Covariance Functions ◽  
Heritability Estimates

It was used 4,313 weight records from birth to 196 days of age from 946 Anglo-nubiana breed goats, progenies from 43 sires and 279 dams, controlled in the period from 1980 to 2005, with the objective of estimating covariance functions and genetic parameters of animals by using random regression models. It was evaluated 12 random regression models, with degrees ranging from 1 to 7 for direct additive genetic and maternal and animal permanent environment effect and residual variance adjusted by using animal age ordinary polynomial of third order. Models were compared by using likelihood ratio test and by Bayesian information criterion of Schwarz and Akaike information criterion. The model selected based on Bayesian information criterion was the one that considered the maternal and direct additive genetic effect adjusted by a quadratic polynomial and the animal permanent environmental effect adjusted by a cubic polynomial (M334). Heritability estimates for direct effect were lower in the beginning and at the end of the studied period and maternal heritability estimates were higher at 196 days of age in comparison to the other growth phases. Genetic correlation ranged from moderate to high and they decreased as the distance between ages increased. Higher efficiency in selection for weight can be obtained by considering weights close to weaning, which is a period when the highest estimates of genetic variance and heritability are obtained.

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.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

2009 ◽  
Vol 123 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Annaiza Braga Bignardi ◽  
Lenira El Faro ◽  
Vera Lucia Cardoso ◽  
Paulo Fernando Machado ◽  
Lucia Galvão de Albuquerque
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Holstein Cows ◽  
Southeastern Brazil

Genetic evaluation using random regression models with different covariance functions for test-day milk yield in an admixture population of Thailand goats

2014 ◽  
Vol 86 (7) ◽  
pp. 655-660 ◽  
Author(s):  
Mongkol Thepparat ◽  
Wuttigrai Boonkum ◽  
Monchai Duangjinda ◽  
Sornthep Tumwasorn ◽  
Sansak Nakavisut ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Covariance Functions ◽  
Admixture Population

scholarly journals Schätzung genetischer Parameter für Testtagsergebnisse der Milchleistung bei ostfriesischen Milchschafen mit Bayes-Methoden für longitudinale Daten

2002 ◽  
Vol 45 (1) ◽  
pp. 61-68
Author(s):  
A. Horstick ◽  
O. Distl
Keyword(s):  
Protein Content ◽  
Milk Yield ◽  
Bayesian Methods ◽  
Regression Models ◽  
Genetic Parameters ◽  
Fat Content ◽  
Random Regression ◽  
Lower Saxony ◽  
Milk Performance ◽  
Heritability Estimates

Abstract. Title of the paper: Estimation of genetic parameters for test day results of milk performance in East Friesian milk sheep using Bayesian methods for longitudinal data The objectives of this study were to estimate genetic parameters of milk performance traits in East Friesian milk sheep by using test day models with random regresssion. The analysis was based on 7545 test day records of 918 East Friesian milk sheep with 1380 lactation records. The data were provided by the sheep breeding organizations of Lower-Saxony, Westphalia, and Bavaria. The milk recordings were collected in the years 1992 to 2000. The average values of the heritability estimates by using random regression models were for the milk yield h2 = 0.25 ± 0.03, for the fat content h2 = 0.46 ± 0.09, and for the protein content h2 = 0.63 ± 0.12. The range of heritability estimates in dependence of the days in milk was for milk yield h2 = 0.03 to 0.70, for fat content h2 = 0.30 to 0.70, and for protein content h2 = 0.44 to 0.92.

scholarly journals Random regression models for milk, fat and protein in Colombian Buffaloes

2015 ◽  
pp. 4415-4426
Author(s):  
Naudin Hurtado-Lugo ◽  
Humberto Tonhati ◽  
Raul Aspilcuelta-Borquis ◽  
Cruz Enríquez-Valencia ◽  
Mario Cerón-Muñoz
Keyword(s):  
Environmental Effects ◽  
Milk Fat ◽  
Fixed Effects ◽  
Regression Models ◽  
Random Regression ◽  
Residual Effects ◽  
Covariance Functions ◽  
Lp Residual ◽  
Quadratic Effects ◽  
Residual Variances

Objective. Covariance functions for additive genetic and permanent environmental effects and, subsequently, genetic parameters for test-day milk (MY), fat (FY) protein (PY) yields and mozzarella cheese (MP) in buffaloes from Colombia were estimate by using Random regression models (RRM) with Legendre polynomials (LP). Materials and Methods. Test-day records of MY, FY, PY and MP from 1884 first lactations of buffalo cows from 228 sires were analyzed. The animals belonged to 14 herds in Colombia between 1995 and 2011. Ten monthly classes of days in milk were considered for test-day yields. The contemporary groups were defined as herd-year-month of milk test-day. Random additive genetic, permanent environmental and residual effects were included in the model. Fixed effects included the contemporary group, linear and quadratic effects of age at calving, and the average lactation curve of the population, which was modeled by third-order LP. Random additive genetic and permanent environmental effects were estimated by RRM using third- to- sixth-order LP. Residual variances were modeled using homogeneous and heterogeneous structures. Results. The heritabilities for MY, FY, PY and MP ranged from 0.38 to 0.05, 0.67 to 0.11, 0.50 to 0.07 and 0.50 to 0.11, respectively. Conclusions. In general, the RRM are adequate to describe the genetic variation in test-day of MY, FY, PY and MP in Colombian buffaloes.Key words: Cattle, genetics, zootechnics (Source: EuroVoc).

Population parameter estimation of daily milk yield of the Chios sheep using test-day random regression models and Gibbs sampling

2005 ◽  
Vol 81 (2) ◽  
pp. 233-238 ◽  
Author(s):  
G. Banos ◽  
G. Arsenos ◽  
Z. Abas ◽  
Z. Basdagianni
Keyword(s):  
Gibbs Sampling ◽  
Milk Yield ◽  
Fourth Order ◽  
Regression Models ◽  
Post Partum ◽  
Random Regression ◽  
Population Parameter ◽  
Residual Variance ◽  
Daily Milk Yield ◽  
Daily Milk

AbstractParameters of daily milk yield during the first three lactations of Chios ewes were estimated with random regression models. Data consisted of 42 675 test-day records of 7121 ewes from 75 flocks that had lambed between 1998 and 2000. Models fitted fourth order fixed regressions on Legendre polynomials of the number of days post partum and fourth order random regressions on the individual animal. (Co)variance components were estimated with Gibbs sampling. Lactations were analysed separately. The four eigen values accounted for 0·80 to 0·84, 0·11 to 0·15, 0·04 to 0·05 and about 0·01 of the animal variance, respectively, depending on lactation number. Animal variance estimates, including genetic and, partly, permanent environment effects, were high at the beginning of each lactation and decreased as lactation progressed, suggesting that the animal effect is most important to early daily records. Residual variance was highest in the middle of lactation, suggesting that non-systematic environmental factors play a bigger at that time. Animal correlation estimates between daily yield records ranged from 0·26 to 0·99, were highest for adjacent days and decreased for days further apart. The decline had a different shape in the three lactations and was more evident in the first, suggesting that the three lactations may be biologically distinct traits. Animal correlation estimates between daily and total lactation milk yield ranged from 0·61 to 0·98 and were highest in the middle and lowest towards the end of lactation. Early lactation daily yield had an animal correlation of 0·70 to 0·80 with total lactation milk yield, in all three lactations. Results of this study suggest that daily milk yield records in the early stages of lactation may be useful for selection of ewes with high producing ability and accurate prediction of total lactation milk yield.

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