Unknown parent and contemporary groups for genetic evaluation of Brazilian Holstein using autoregressive test-day models

Silva, Delvan Alves da; Costa, C. N.; Silva, Alessandra Alves da; Silva, Fabyano Fonseca e; Lopes, Paulo Sávio; Santos, G.G.; Thompson, Gertrude; Carvalheira, J.

doi:10.1016/j.livsci.2018.11.022

Cited by 5 publications

(10 citation statements)

References 22 publications

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“…In the present study, the HYS was the most auto-correlated effect (ranging from 0.7 to 0.9) probably because of the shorter time lag between events (between seasons) compared with the PE effect where the events are much more distant (between calvings). A similar pattern was reported in autoregressive analysis of production traits (e.g., milk yield) where the autocorrelations between lactation orders (long term effect) were negligible compared with the short term autocorrelations between test-days within lactations (Carvalheira et al ., 2002; Costa et al ., 2009; Silva et al ., 2019 b ).…”

Section: Resultsmentioning

confidence: 99%

“…A suitable approach to overcome this problem may be fitting an autoregressive covariance structure to describe the time decaying correlation between consecutive events over time. This methodology has been successfully applied on genetic evaluations of milk yield and its components, where its efficiency in disentangling the genetic components resulted in higher heritabilities, higher reliabilities for estimated breeding values (EBV) and lower residual variances (Carvalheira et al, 1998(Carvalheira et al, , 2002Silva et al, 2019b). Despite these positive results obtained in longitudinal production traits, to the best of our knowledge there are no reports of its application on genetic modeling of longitudinal reproductive traits.…”

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confidence: 99%

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Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

Silva

Lopes

Costa

et al. 2020

Journal of Dairy Research

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We investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

Silva

Lopes

Costa

et al. 2020

Journal of Dairy Research

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“…On the other hand, studies (Carvalheira et al, ; Silva, Costa, et al, ; Vasconcelos, Santos, Bagnato, & Carvalheira, ) have shown that AR model could be fitted using a minimum of two TD records per cow, per lactation. This can be an advantage in situations involving small herds with relative high frequency of cows having few records (such as in Brazil), since avoids loss of information from data editing.…”

Section: Discussionmentioning

confidence: 99%

“…This can be an advantage in situations involving small herds with relative high frequency of cows having few records (such as in Brazil), since avoids loss of information from data editing. However, it is relevant to make clear that recommendations for improvement of data recording have been extensively reported for Brazilian dairy industry (Costa et al, , ; Silva, Costa, et al, ). The genetic correlation between TD may differ from unit, meaning that the expression at each DIM may have different additive genetic variance.…”

Section: Discussionmentioning

confidence: 99%

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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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Genotype by environment interaction for Holstein cattle populations using autoregressive and within- and across-country multi-trait reaction norms test-day models

Silva

Lopes

Costa³

et al. 2021

animal

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Unknown parent and contemporary groups for genetic evaluation of Brazilian Holstein using autoregressive test-day models

Cited by 5 publications

References 22 publications

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

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

Genotype by environment interaction for Holstein cattle populations using autoregressive and within- and across-country multi-trait reaction norms test-day models

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