Accounting for heterogeneity of phenotypic variance in Iranian Holstein test-day milk yield records

Aliloo, Hassan; Miraie-Ashtiani, Seyed Reza; Shahrbabak, M. Moradi; Urioste, J. I.; Sadeghi, M. H.

doi:10.1016/j.livsci.2014.05.012

Cited by 8 publications

(9 citation statements)

References 37 publications

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“…A reduction of the values 2LogL, AIC, and BIC, as Legendre polynomial order decreases with a significant change in the log likelihood, indicated random regression models of the fourth-order Legendre polynomials as the best fit, but residual values indicated the fifth-order as the model. In some studies, AIC, BIC, and RV indicated models with larger number of parameters in the literature (Biassus et al, 2011;Aliloo et al, 2014). The present results may be a consequence of hyper parameterization of the models.…”

Section: Resultssupporting

confidence: 48%

“…The highest values of permanent environmental eigenvalues suggested that the permanent environmental effect could be modeled with a fifth-order Legendre polynomial, in comparison to the genetic effect. Although these results suggested different Legendre polynomial orders for additive genetic and permanent environmental effects, there is not a consensus in the literature (Araújo et al, 2006;Costa et al, 2008;Biassus et al, 2011;Aliloo et al, 2014). Aliloo et al (2014) reporting that the (Table 3), the heritabilities obtained from 305-day lactation models were 0.24 (LMF) and 0.17 (LMP); and random regression models showed heritability estimates of 0.21 (RRF4 and RRF5) and 0.20 (RRP4 and RRP5).…”

Section: Resultsmentioning

confidence: 91%

“…Although AIC, BIC, 2LogL, and residual variances showed conflicting results in the present study, the use of different approaches may help to explain the differences between random regression models. The analysis of the eigenvalues indicates the decreasing importance of adding more parameters (Aliloo et al, 2014). The first three additive eigenvalues explained a sufficiently large proportion of the variances for the models RRF4 and RRP4.…”

Section: Resultsmentioning

confidence: 93%

“…For permanent environmental effects, the first eigenvalue accounted for about 70% of the total permanent environmental variance in all models, and the first four eigenvalues explained it by 98%. According to Aliloo et al (2014), the choice of the best model is not an easy task because the use of different tests may indicate different models. Although AIC, BIC, 2LogL, and residual variances showed conflicting results in the present study, the use of different approaches may help to explain the differences between random regression models.…”

Section: Resultsmentioning

confidence: 99%

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Reliability of breeding values between random regression and 305-day lactation models

Padilha

Cobuci

Daltro

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.Index terms: breeding value, correlation, Legendre polynomials, reliability. Confiabilidade de valores genéticos entre modelos de regressão aleatória e de lactação aos 305 diasResumo -O objetivo deste trabalho foi verificar o ganho em confiabilidade dos valores genéticos estimados (VGE), quando os modelos de regressão aleatória são aplicados em lugar dos modelos convencionais de lactação até os 305 dias, tendo-se utilizado registros de produção de gordura e proteína de bovinos da raça Holandesa no Brasil, para futuras avaliações genéticas. O conjunto de dados continha 262.426 registros de produção de gordura e proteína no dia do controle e 30.228 registros de produção acumulada de gordura e proteína até os 305 dias de lactação. Aplicaram-se modelos de regressão aleatória unicaracterística, com os polinômios de Legendre, e modelos unicaracterística de lactação aos 305 dias. A herdabilidade estimada para as produções até os 305 dias pelos modelos de lactação foi 0,24 (gordura) e 0,17 (proteína), e, pelos modelos de regressão aleatória, foi 0,20 (gordura) e 0,21 (proteína). Correlações de Spearman dos VGEs entre os modelos de lactação e de regressão aleatória, para a produção aos 305 dias, variaram de 0,86 a 0,97 e 0,86 a 0,98 (touros) e de 0,80 a 0,89 e 0,81 a 0,86 (vacas), quanto à gordura e à proteína, respectivamente. O aumento médio em confiabilidade dos VGEs para a produção aos 305 dias variou de 2 a 16% (gordura) e de 4 a 26% (proteína), em touros, e variou de 24 a 38% (gordura e proteína) em vacas, o que é superior àquela obtida pelos modelos de lactação. Os modelos de regressão aleatória com uso dos polinômios de Legendre melhorarão a av...

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Section: Resultssupporting

confidence: 48%

Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Reliability of breeding values between random regression and 305-day lactation models

Padilha

Cobuci

Daltro

et al. 2016

Pesq. agropec. bras.

View full text Add to dashboard Cite

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“…Lower values by the Akaike information criterion (AIC) and Bayesian information criterion (BIC) denote better fitting of the model for genetic evaluations (Aliloo et al, 2014). Thus, the best models were MP10%, MP5%, MP270, MPCO, MP305 and OMP (Table 1).…”

Section: Resultsmentioning

confidence: 99%

Editing and modeling of milk production data for genetic evaluation of Murrah buffaloes

Souza

Rezende

Carneiro

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to assess the effect of editing and modeling of milk production data for genetic evaluation of Murrah buffaloes. Six strategies for evaluating milk production were analyzed: observed milk production (OMP); adjustment of milk production data to 305 (MP305) and 270 (MP270) days of lactation; removal of the 5 (MP5%) and 10% (MP10%) shortest lactation periods; and milk production along the lactation period as linear covariate (MPCO). Genetic parameters were estimated using the Bayesian inference, with heritability estimates of 0.19 to 0.23 and repeatability estimates of 0.35 to 0.36. Sires classified by OMP were high correlated to those classified by the other models, however, correlations to MP270, MP305 and MPCO decreased when considering only the best 20% sires. OMP showed greater differences in absolute mean deviations when compared with MPCO, MP270 and MP305. The strategies of analysis had similar heritabilities and stabilities. However, changes in the ranking of sires with better classifications, due to overestimation of genetic values, as occurred in the models MP305, MP270 and MPCO, may lead to a decrease in the genetic progress of the herd.Index terms: Bubalus bubalis, lactation period, genetic parameters, bull summary. Edição e modelagem de dados de produção de leite para avaliação genética de buffalos de MurrahResumo -O objetivo deste trabalho foi verificar as consequências da edição e modelagem da produção de leite para avaliação genética de bubalinos da raça Murrah. Foram analisadas seis estratégias para avaliação da produção de leite: produção de leite observada (PL_OBS); ajuste para a produção de leite aos 305 dias (PL_305) e aos 270 dias (PL_270); exclusões dos 5 e 10% menores períodos de lactação (PL_5% e PL_10%) e duração da lactação como covariável linear (PL_CO). Os parâmetros genéticos foram estimados por meio de inferência Bayesiana, com estimativas de herdabilidades e repetibilidades de 0,19 a 0,23 e 0,35 a 0,36, respectivamente. Touros classificados pelo PL_OBS apresentaram altas correlações com os que foram classificados pelos outros modelos. Contudo, ao considerar apenas os 20% melhores touros, verificou-se diminuição das correlações de PL_270, PL_305 e PL_CO com PL_OBS. Maiores diferenças dos desvios médios absolutos do PL_OBS foram com PL_CO, PL_270 e PL_305. As estratégias de análise apresentaram herdabilidades e estabilidade semelhantes. Contudo, as inversões de posições dos touros com melhores classificações, como consequencia das superestimativas dos valores genéticos nos modelos PL305, PL270 e PL_CO podem acarretar em diminuição do progresso genético nos rebanhos.Termos para indexação: Bubalus bubalis, duração da lactação, parâmetros genéticos, sumário de touros.

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Homogeneity and heterogeneity of variance components for milk and protein yield at different cluster sizes in Iranian Holsteins

2016

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Accounting for heterogeneity of phenotypic variance in Iranian Holstein test-day milk yield records

Cited by 8 publications

References 37 publications

Reliability of breeding values between random regression and 305-day lactation models

Reliability of breeding values between random regression and 305-day lactation models

Editing and modeling of milk production data for genetic evaluation of Murrah buffaloes

Homogeneity and heterogeneity of variance components for milk and protein yield at different cluster sizes in Iranian Holsteins

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