Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

Scalez, Daiane Cristina Becker; Fragomeni, B. O.; Santos, D. C.C.; Passafaro, Tiago Luciano; Alencar, M. M. de; Toral, Fábio Luiz Buranelo

doi:10.1590/rbz4720150300

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…They reported increasing heritability estimates from 0.33 ± 0.01 at day 0 to 0.40 ± 0.01 at day 140, with lower but increasing repeatability estimates from 0.36 ± 0.01 to 0.41 ± 0.01. Although they studied weight gain using heterogeneous residual variance, other authors, in agreement with the present study, concluded that homogeneous residual variances describe the growth data appropriately (Hassen, Wilson, & Rouse, ; Scalez et al, ).…”

Section: Discussionsupporting

confidence: 87%

“…However, by the way of recording feed efficiency component traits (as BW and FI), these can be analysed repeatedly or longitudinally through repeatability (REPMs) or random regression models (RRMs). Nevertheless, studies analysing these records repeatedly or longitudinally during the animal's life in beef cattle are scarce (Durunna et al, ; Scalez et al, ; Selapa, Nephawe, Maiwashe, Norrie, & Ngambi, ). While REPMs assume no change in the genetic parameters over time, the RRMs have the flexibility to model changes over the test period (Van der Werf, ).…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle

Torres‐Vázquez

Duijvesteijn

Werf

et al. 2019

J Animal Breeding Genetics

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The objectives of this study were to compare different models for analysing body weight (BW) and average daily feed intake (ADFI) data collected during a 70-day feedlot test period and to explore whether genetic parameters change over time to evaluate the implications of selection response. (Co)variance components were estimated using repeatability and random regression models in 2,071 Angus steers.Models included fixed effects of contemporary group, defined as herd-year-obser-vation_date-age, with additive genetic and permanent environmental components as random effects. Models were assessed based on the log likelihood, Akaike's information criterion and the Bayesian information criterion. For both traits, random regression models (RRMs) presented a better fit, indicating that genetic parameters change over the test period. Using a two-trait RRM, the heritability from day 1 up to day 70 for BW increased from 0.40 to 0.50, while for ADFI, it decreased from 0.44 to 0.33. The genetic correlation increased from 0.53 at day 1 up to 0.79 at day 70.Selection based on an index assuming no change in genetic parameters would yield a 2.78%-3.13% lower selection response compared to an index using parameters estimated with RRMs and assuming these genetic parameters are correct. Results imply that it may be beneficial to implement RRMs to account for the change of parameters across the feedlot period in feed efficiency traits. K E Y W O R D S beef cattle, body weight, feed efficiency, feed intake, model comparison, random regression How to cite this article: Torres-Vázquez JA, Duijvesteijn N, van der Werf JHJ, Clark SA. Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle. J Anim Breed Genet. 2020;137:281-291.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle

Torres‐Vázquez

Duijvesteijn

Werf

et al. 2019

J Animal Breeding Genetics

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“…again at the end of the growth curve. Most studies that include maternal effects in growth analyses have generally shown reductions after weaning, indicating their low importance (Scalez et al, 2018;Cavalcante et al, 2019;Portes et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Beef cattle breeding programs are often based on body weight and weight gains at different ages (Portes et al, 2020). In this scenario, random regression models have been extensively used to assess genetic effects on these traits, as they more accurately describe phenotypic and genetic changes over time (Scalez et al, 2018). To fit random regression models, a continuous function that allows describing genetic and environmental changes over time should be implicitly fitted.…”

Section: Introductionmentioning

confidence: 99%

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

Cavalcante¹,

Luz²,

Oliveira³

et al. 2021

Semina: Ciênc. Agrár.

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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.

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“…The regression on orthogonal polynomials do not require prior assumptions about the shape of curves to be modelled and can be recommended as general purpose functions, especially if higher orders of fit are feasible. Other functions for modeling included the spline function (Iwaisaki et al 2005, Zamani et al 2016, Nemutandani et al 2018, Scalez et al 2018.…”

Section: Methodology Analyses and Software Usedmentioning

confidence: 99%

Genetic evaluation of growth using random regression models

Venkataramanan

2021

Indian J of Anim Sci

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The variability in growth traits provides enormous scope for improvement through selection and breeding. However, growth is a longitudinal trait measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling the trait as a growth curve. RRM accommodate repeated records for traits which change gradually and continually, over time, and do not require stringent assumptions about constancy of variances and correlations. RRM has the advantage that, variance components can be estimated for any point in the trajectory of the growth curve and genetic parameters could be estimated for any age class within the range of ages included in the study. RRM is suitable for group breeding schemes and field performance recording systems where the growth data will be uneven and for varying age points. Worldwide, several studies on use of the tool, RRM in growth of various livestock species are available, but literature on such studies is scanty from India. The methodology used, data requirement, assumptions, validity, software available and application of RRM in the field are discussed based on the earlier reports.

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Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

Cited by 5 publications

References 23 publications

Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle

Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle

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

Genetic evaluation of growth using random regression models

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