Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

Abstract. Applying a multiple trait random regression (MT-RR) in national level and for whole test day records of a country is a great advance in animal breeding context. Having reliable (co) variance components is a critical step in applying multiple traits genetic evaluation especially in developing countries. Genetic parameters of milk, fat and protein yields were estimated for Iranian Holstein dairy cows. Data included 276 692 test day (TD) production traits records collected of 30 705 primiparous cows belonging to 619 sires. An animal multi-trait random regression model was employed in the analyses using the restricted maximum likelihood (REML) method. The model included herd-test-date, age-season of calving (by applying a fixed regression for each subclass of this effect) and year of calving as fixed effects and random regression (RR) coefficients for additive genetic (AG) and permanent environmental (PE) effects. Obtained results showed that daily heritabilities ranged from 0.10 to 0.21 for milk, from 0.05 to 0.08 for fat and from 0.08 to 0.18 for protein yield. Estimated heritability for 305-d milk, fat and protein yields were 0.25, 0.20 and 0.25, respectively. Correlations between individual test day records within traits were high for adjacent tests (nearly 1) and decreased as the interval between tests increased. Correlations between yields of milk, fat and protein on a given test day are also high and greater during late lactation than during early or mid-lactation. Genetic correlations between 305-d yield traits ranged from 0.75 to 0.92. The largest genetic correlation, as well as permanent environmental correlation, was observed between milk and protein yield.

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PSI-1 Evaluating genetic parameters and trends of weight traits for the Hays Converter beef breed by multiple-trait and random regression models.

Journal of Animal Science ◽

10.1093/jas/sky404.135 ◽

2018 ◽

Vol 96 (suppl_3) ◽

pp. 60-61

Author(s):

R Khorshidi ◽

M MacNeil ◽

D Hays ◽

M Abo-Ismail ◽

J Crowley ◽

...

Keyword(s):

Regression Models ◽

Genetic Parameters ◽

Random Regression ◽

Multiple Trait ◽

Beef Breed

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Estimation of genetic parameters for partial egg production periods by means of random regression models

Genetics and Molecular Research ◽

10.4238/2012.july.10.18 ◽

2012 ◽

Vol 11 (3) ◽

pp. 1819-1829 ◽

Cited By ~ 10

Author(s):

G.C. Venturini ◽

D.A. Grossi ◽

S.B. Ramos ◽

V.A.R. Cruz ◽

C.G. Souza ◽

...

Keyword(s):

Regression Models ◽

Egg Production ◽

Genetic Parameters ◽

Random Regression ◽

Estimation Of Genetic Parameters

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Estimation of genetic parameters for first-lactation test-day milk yield in Holstein Friesian cows fitting random regression models

International Journal of Genetics and Molecular Biology ◽

10.5897/ijgmb2015.0111 ◽

2015 ◽

Vol 7 (4) ◽

pp. 25-31

Author(s):

S Meseret ◽

B Tamir ◽

E Negussie

Keyword(s):

Milk Yield ◽

Regression Models ◽

Genetic Parameters ◽

Random Regression ◽

Holstein Friesian ◽

Estimation Of Genetic Parameters ◽

Friesian Cows

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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

Semina Ciências Agrárias ◽

10.5433/1679-0359.2021v42n3p1303 ◽

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.

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Estimation of Genetic Parameters for Daily Gains of Bulls with Multi-Trait and Random Regression Models

Archives Animal Breeding ◽

10.5194/aab-50-37-2007 ◽

2007 ◽

Vol 50 (1) ◽

pp. 37-46

Author(s):

H. Krejčová ◽

N. Mielenz ◽

J. Přibyl ◽

L. Schüler

Keyword(s):

Animal Model ◽

Animal Models ◽

Genetic Analysis ◽

Legendre Polynomials ◽

Regression Models ◽

Genetic Parameters ◽

Random Regression ◽

Dual Purpose ◽

Trait Model ◽

Estimation Of Genetic Parameters

Abstract. The average daily gains of 6,420 Czech Pied bulls (dual-purpose, Simmental type) from 7 breeding stations were analyzed using single-trait animal models, a multi-trait animal model and random regression models. The effects of station, year and season were taken into account by creating herd-year-season classes (HYS) with the season being defined as a 3-month class starting with December. Legendre polynomials of the 1st to the 4th degree were used to describe the daily gains within the HYS classes as well as to model bull-specific gain curves. The comparison of the h2-values estimated with single-trait models and those gained with a multi-trait model returned only insignificant differences. The comparison of genetic parameters based on the multi-trait model to those from different random regression models shows that polynomials of at least the 2nd degree are to be used for the genetic analysis of daily gains.

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