Application of random regression models to model growth curve in Maize using phenotypes derived from multi-spectral images

2021 ◽  
Author(s):  
Mahlet Anche ◽  
Kelly R Robbins ◽  
Michael A Gore ◽  
Nicolas Morales
Keyword(s):  
Growth Curve ◽  
Regression Models ◽  
Random Regression

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 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.

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

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