Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle

Oliveira, Hinayah Rojas de; Silva, Fabyano Fonseca e; Silva, Marcos Vinícius Gualberto Barbosa da; Siqueira, Otávio Henrique Gomes Barbosa Dias de; Machado, M. A.; Panetto, J. C. do C.; Glória, Leonardo Siqueira; Brito, Luiz F.

doi:10.1016/j.livsci.2017.05.007

Cited by 14 publications

(16 citation statements)

References 21 publications

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“…One option to reduce the number of residual variance classes is to group similar classes together. Several methods can be used to group different classes of residual variance, such as self-organizing maps [ 50 ], change point [ 51 ], and visual inspection [ 52 ]. Testing different numbers of classes for the residual variance should be considered in subsequent studies in order to simplify the RRM.…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, the sudden increase in the heritability estimates observed at high ages specifically for the group of disease under the FLa definition ( Figure 3 b), and the groups of structural problems and performance under the FLb definition ( Figure 3 c) are likely due to a poor fit of a high order polynomial at these points when using heterogeneous residual variance. Moreover, various studies currently available in the literature have reported unstable heritability estimates in the extremes of the curve for several traits when using RRM based on high-order Legendre polynomials [ 50 , 58 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

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Using Random Regression Models to Genetically Evaluate Functional Longevity Traits in North American Angus Cattle

Oliveira

Brito

Miller³

et al. 2020

Animals

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This study aimed to propose novel longevity indicators by comparing genetic parameters for traditional (TL; i.e., the cow’s lifespan after the first calving) and functional (FL; i.e., how long the cow stayed in the herd while also calving; assuming no missing (FLa) or missing (FLb) records for unknown calving) longevity, considering different culling reasons (natural death, structural problems, disease, fertility, performance, and miscellaneous). Longevity definitions were evaluated from 2 to 15 years of age, using single- and multiple-trait Bayesian random regression models (RRM). The RRM fitting heterogenous residual variance and fourth order Legendre polynomials were considered as the optimal models for the majority of longevity indicators. The average heritability estimates over ages for FLb (from 0.08 to 0.25) were always higher than those for FLa (from 0.07 to 0.19), and higher or equal to the ones estimated for TL (from 0.07 to 0.23), considering the different culling reasons. The average genetic correlations estimated between ages were low to moderate (~0.40), for all longevity definitions and culling reasons. However, removing the extreme ages (i.e., 2 and >12 years) increased the average correlation between ages (from ~0.40 to >0.70). The genetic correlations estimated between culling reasons were low (0.12 and 0.20 on average, considering all ages and ages between 3 and 12 years old, respectively), indicating that longevity based on different culling reasons should be considered as different traits in the genetic evaluations. Higher average genetic correlations (estimated from 3 to 12 years old) were observed between TL and FLb (0.73) in comparison to TL and FLa (0.64), or FLa and FLb (0.65). Consequently, a higher average proportion of commonly-selected sires, for the top 1% sires, was also observed between TL and FLb (91.74%), compared to TL and FLa (59.68%), or FLa and FLb (61.01%). Higher prediction accuracies for the expected daughter performances (calculated based on the pedigree information) were obtained for FLb in comparison to TL and FLa. Our findings indicate that FLb is preferred for the genetic evaluation of longevity. In addition, it is recommended including multiple longevity traits based on different groups of culling reasons in a selection sub-index, as they are genetically-different traits. Genetic selection based on breeding values at the age of four years is expected to result in greater selection responses for increased longevity in North American Angus cattle.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using Random Regression Models to Genetically Evaluate Functional Longevity Traits in North American Angus Cattle

Oliveira

Brito

Miller³

et al. 2020

Animals

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“…As previously mentioned, lactations with less than five TD records were eliminated, and all remaining cows presented records in the first lactation. Several studies have used these criteria (Konstantinov et al, 2015;Oliveira et al, 2017) to avoid convergence failures in RR models due to small number of TD per cow and per lactation (Misztal, 2006;Robbins, Misztal, & Bertrand, 2005). In addition, RR models based on LP are sensitive to few records per cow, mainly for estimations at the extremes of the lactation curve (Meyer, 2005;Misztal, 2006).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, when few records per animal are available, even in populations with a high frequency of small herds, parameter estimation and artefacts issues may appear (Meyer, ; Misztal, ). To minimize these effects, a minimum of five TD records per animal has been adopted in several studies (Konstantinov, Nieuwhof, & Hancock, ; Oliveira et al, ), thus implying in loss information for genetic evaluations.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, genetic evaluations using alternate TD models in Brazilian Holstein cattle are required to evaluate their efficiency in this population. Both RR and AR models present remarkable advantages and have already been successfully applied to genetic evaluations in Holstein cattle (Carvalheira et al, 2002;Costa, Carvalheira, Cobuci, Freitas, & Thompson, 2009;Costa et al, 2008;Muir et al, 2007;Oliveira et al, 2017;Vasconcelos et al, 2004). Despite the existing difference between RR and AR models, mainly in terms of covariance structures, Meuwissen and Pool (2001) reported very similar performance for these models using only the first lactation cows.…”

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Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Silva

Costa

Silva

et al. 2019

J Animal Breeding Genetics

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Autoregressive (AR) and random regression (RR) models were fitted to test‐day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

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Updated knowledge in the estimation of genetics parameters: a Bayesian approach in white oat (Avena sativa L.)

et al. 2022

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Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle

Cited by 14 publications

References 21 publications

Using Random Regression Models to Genetically Evaluate Functional Longevity Traits in North American Angus Cattle

Using Random Regression Models to Genetically Evaluate Functional Longevity Traits in North American Angus Cattle

Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Updated knowledge in the estimation of genetics parameters: a Bayesian approach in white oat (Avena sativa L.)

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