Combination of B-Spline and Legendre functions in random regression models to fit growth curve of Moghani sheep

2016 ◽  
Vol 145 ◽  
pp. 115-122 ◽  
Author(s):  
P. Zamani ◽  
M.R. Moradi ◽  
D. Alipour ◽  
Farhad Ghafouri-Kesbi
Keyword(s):  
Growth Curve ◽  
Regression Models ◽  
Random Regression ◽  
Legendre Functions ◽  
B Spline

scholarly journals Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials

animal ◽  
2018 ◽  
Vol 12 (4) ◽  
pp. 667-674 ◽  
Author(s):  
L.F.M. Mota ◽  
P.G.M.A. Martins ◽  
T.O. Littiere ◽  
L.R.A. Abreu ◽  
M.A. Silva ◽  
...  
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Selection Response ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Spline Functions ◽  
B Spline ◽  
Meat Type

scholarly journals Konstruktion von Indizes mit Restriktionen in Random Regression Modellen zur Veränderung der Wachstumskurve

2007 ◽  
Vol 50 (6) ◽  
pp. 619-627
Author(s):  
N. Mielenz ◽  
L. Schüler
Keyword(s):  
Body Weight ◽  
Growth Curve ◽  
Regression Models ◽  
Test Period ◽  
Regression Coefficients ◽  
Random Regression ◽  
Breeding Value ◽  
Time Interval ◽  
Final Body Weight ◽  
Index Construction

Abstract. Title of the paper: Index construction with restrictions in random regression models to change the pattern of the growth curve Random regression models provide estimated breeding values (EBV) for the complete growth curve for any target age. The animal-specific curves can be described as the weighted sum of continuous covariates with random regression coefficients. By using the covariance matrix K of the additive genetic regression coefficients the response to index selection can be calculated for any age or time of the test period. In this study selection indexes with equality restrictions based on the eigenvectors of matrix K were used to modify the growth curve of the population. In order to demonstrate the index construction a matrix K was used, estimated from repeated measurements for body weight of bulls by using Legendre polynomials as covariates. Indexes for high and low growth rate until age at the reflection point were derived subject to the restriction of zero gain for initial and final body weight. Selection strategies for improving body weight at the end of the test period while holding the daily gain in a certain time interval on a desired level were compared. By using so-called "restrictive economic values", an aggregate breeding value for body weight was derived from EBV for individual growth curve.

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.

scholarly journals Modeling of average growth curve in Santa Ines sheep using random regression models

2011 ◽  
Vol 40 (2) ◽  
pp. 314-322 ◽  
Author(s):  
José Lindenberg Rocha Sarmento ◽  
Robledo de Almeida Torres ◽  
Wandrick Hauss de Sousa ◽  
Lucia Galvão de Albuquerque ◽  
Raimundo Nonato Braga Lôbo ◽  
...  
Keyword(s):  
Growth Curve ◽  
Regression Models ◽  
Random Regression ◽  
Average Growth ◽  
Breeding Values ◽  
Cubic Polynomial ◽  
Linear Fit ◽  
Santa Inês ◽  
The Difference ◽  
Cubic Function

Polynomial functions of age of different orders were evaluated in the modeling of the average growth trajectory in Santa Ines sheep in random regression models. Initially, the analyses were performed not considering the animal effect. Subsequently, the random regression analyses were performed including the random effects of the animal and its mother (genetic and permanent environment). The linear fit was lower, and the other orders were similar until near 100 days of age. The cubic function provided the closest fit of the observed averages, mainly at the end of the curve. Orders superior to this one tended to present incoherent behavior with the observed weights. The estimated direct heritabilities, considering the linear fit, were higher to those estimated by considering other functions. The changes in animal ranking based on predicted breeding values using linear fit and superior orders were small; however, the difference in magnitude of the predicted breeding values was higher, reaching values 77% higher than those obtained with the cubic function. The cubic polynomial function is efficient in describing the average growth curve.

scholarly journals Covariance functions under B-spline polynomials to model Polled Nellore cattle growth

2021 ◽  
Vol 42 (6supl2) ◽  
pp. 3977-3990
Author(s):  
Diego Helcias Cavalcante ◽  
◽  
Carlos Syllas Monteiro Luz ◽  
Marcelo Richelly Alves de Oliveira ◽  
Wéverton José Lima Fonseca ◽  
...  
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Fixed Effects ◽  
Random Regression ◽  
Spline Functions ◽  
Average Growth ◽  
B Spline ◽  
Covariance Functions ◽  
Model Animal ◽  
Nellore Cattle

B-spline functions have been used in random regression models (RRM) to model animal weight from birth to adulthood because they are less vulnerable to common difficulties of other methods. However, its application to model growth traits of Polled Nellore cattle has been little studied. Therefore, this study aimed to evaluate polynomial functions of different orders and segment numbers to model effects associated with the Polled Nellore cattle growth curve. For this purpose, we used 15,148 weight records of 3,115 animals aged between 1 and 660 days and reared in northern Brazil and born between 1995 and 2010. Random effects were modeled using B-spline polynomials. As random effects, we considered the direct and maternal genetic additives, as well as direct and maternal permanent environments. As fixed effects were included contemporary group, cow age at calving (linear and quadratic) and fourth-order Legendre polynomials to represent average growth curve. The residue was modeled by considering seven age classes. The bestfitted model was the one that considered cubic B-spline functions with four knots for direct additive genetic effects and three knots for maternal genetic, animal permanent environment, and maternal permanent environment effects (C6555). Therefore, covariance functions under B-spline polynomials are efficient and can be used to model the growth curve of Polled Nellore cattle from birth to 660 days of age.

scholarly journals Random regression models in the evaluation of the growth curve of Simbrasil beef cattle

2013 ◽  
Vol 12 (1) ◽  
pp. 528-536 ◽  
Author(s):  
R.R. Mota ◽  
L.F.A. Marques ◽  
P.S. Lopes ◽  
L.P. da Silva ◽  
A.M. Hidalgo ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Growth Curve ◽  
Regression Models ◽  
Random Regression

scholarly journals MicroRNA profiles of dry secretions through the first three weeks of the dry period from Holstein cows

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ellie J. Putz ◽  
Austin M. Putz ◽  
Hyeongseon Jeon ◽  
John D. Lippolis ◽  
Hao Ma ◽  
...  
Keyword(s):  
Regression Models ◽  
Transition Period ◽  
Mirna Profile ◽  
Random Regression ◽  
Q Value ◽  
Dry Period ◽  
Regulate Gene Expression ◽  
Intramammary Infections ◽  
Non Coding Rnas ◽  
Insight Into

AbstractIn dairy cows, the period from the end of lactation through the dry period and into the transition period, requires vast physiological and immunological changes critical to mammary health. The dry period is important to the success of the next lactation and intramammary infections during the dry period will adversely alter mammary function, health and milk production for the subsequent lactation. MicroRNAs (miRNAs) are small non-coding RNAs that can post transcriptionally regulate gene expression. We sought to characterize the miRNA profile in dry secretions from the last day of lactation to 3, 10, and 21 days post dry-off. We identified 816 known and 80 novel miRNAs. We found 46 miRNAs whose expression significantly changed (q-value < 0.05) over the first three weeks of dry-off. Additionally, we examined the slopes of random regression models of log transformed normalized counts and cross analyzed the 46 significantly upregulated and downregulated miRNAs. These miRNAs were found to be associated with important components of pregnancy, lactation, as well as inflammation and disease. Detailing the miRNA profile of dry secretions through the dry-off period provides insight into the biology at work, possible means of regulation, components of resistance and/or susceptibility, and outlets for targeted therapy development.

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