Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

Peixoto, Maria Gabriela Campolina Diniz; Santos, Daniel Jordan de Abreu; Aspilcueta-Borquis, Rusbel Raúl; Bruneli, F. A. T.; Panetto, J. C. do C.; Tonhati, Humberto

doi:10.1590/s0100-204x2014000500007

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…Similar to our study, for milk yield, Peixoto et al. (2014) and Prakash et al. (2017) reported the highest order for the permanent environment and third‐order for additive genetic effects in Sahiwal and Guzerá cattle, respectively.…”

Section: Discussionsupporting

confidence: 90%

“…Thus, the use of a higher-order for permanent environmental effects could result in a more flexible shape and should be able to model the individual deviations from the average lactation curve more precisely. Similar to our study, for milk yield, Peixoto et al (2014) and Prakash et al (2017) reported the highest order for the permanent environment and third-order for additive genetic effects in Sahiwal and Guzerá cattle, respectively.…”

Section: Discussionsupporting

confidence: 90%

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Random regression test‐day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows

Paiva

Mota

Lopes

et al. 2022

J Animal Breeding Genetics

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We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second-to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35 days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 90%

Random regression test‐day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows

Paiva

Mota

Lopes

et al. 2022

J Animal Breeding Genetics

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“…While, LP function of 5 th order and B-spline cubic function of 6 th order were found as second and third best fitted models, respectively. Peixoto et al (2014) earlier reported that the LP model of 6 th order attained the best values of Log L and AIC, while of 3 rd order for BIC criteria. d Biassus et al (2010) Behzadi and Mehrpoor (2018) for TDMY.…”

Section: Datamentioning

confidence: 89%

Comparison of daughter's performance of progeny tested sires with pedigree selected sires in Holstein Friesian crossbred cattle

Patel

Nayee

Saha

et al. 2020

Indian J of Anim Sci

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The present study was conducted to compare the estimated breeding values of progeny tested sires and pedigree selected sires for test day milk yield of crossbred Holstein Friesian cattle. First lactation milk yield records (1,20,198) of 12,971 daughters sired by 267 sires were collected from INAPH database maintained by NDDB. Variance and covariance components for test-day milk yield (TDMY) were estimated by different random regression test day models (RRTDM), viz. Cubic B- Spline function, Quadratic B-Spline function, Legendre polynomial (LP) function and Wilmink function using Average Information Restricted Maximum Likelihood (AIREML). Considering various criteria for comparison of different orders of TDMs, LP of 6th order for TDMY was the best fitted model for further estimation of breeding values. The heritability estimates ranged from 0.15 to 0.39 for TDMY using Leg_2 model. The additive genetic correlations were higher than the phenotypic correlations among different TDs. The additive genetic correlations between test day yields varied from 0.73 to 0.99. The expected progeny difference (EPD) for TDMYcalculated based on dam’s yield for the top ten and bottom ten PS bulls was 2,662.5 kg; whereas, the actual progeny difference (APD) for these bulls was -28.47 kg. While, EPD for top 10 and bottom 10 PT bulls selected based on EBVs was 2,820.52 kg whereas the APD for PT bulls was 890.48 kg. The difference in expected and actual MY of progeny was higher in PS bulls as compared to PT bulls indicating that the pedigree information for EPDs had a poor association with APDs and sire superiority is not reflected in progeny’s actual performance. The rank correlation between ranks of all PS and PT bulls were very poor and non-significant. The bulls selected based on estimated breeding values will give faster genetic progress and decision to select replacement bulls based on breeding values instead of dam’s yield will have positive effect on genetic progress.

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“…Assim, os modelos estatísticos utilizados para identificar os clones mais eficientes devem contemplar a natureza longitudinal dos dados e sua relação com fatores genéticos e ambientais. Os modelos de regressão aleatória (MRA) satisfazem tais exigências, e são amplamente utilizados em melhoramento animal (PEIXOTO et al, 2014;PINHEIRO et al, 2013), porém ainda pouco explorados em melhoramento vegetal (MARIGUELE et al, 2011). Diferentemente dos modelos multicaracterísticos que fornecem predições de valores genéticos apenas nas idades observadas, os MRA permitem tal predição em qualquer ponto da trajetória longitudinal observada (RESENDE; REZENDE; FERNANDES, 2001).…”

Section: Introductionunclassified

Bayesian random regression for genetic evaluation of South American leaf blight in rubber trees

Sandoval

Silva

Resende

et al. 2017

Revista Ciência Agronômica

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RESUMO -O mal das folhas causado pelo fungoMicrocyclus ulei é a doença mais séria dos seringais da América Latina. Com o objetivo de identificar clones de seringueira mais resistentes em diferentes ambientes ao longo do tempo, compararam-se diferentes modelos de regressão aleatória (MRA) ajustados via abordagem Bayesiana. Oito clones foram testados em campos clonais no delineamento em blocos completos casualizados com quatro repetições, utilizando-se 80 árvores por parcela. As duas fileiras centrais foram avaliadas a cada dois meses em relação às variáveis severidade (SEV) e índice de estroma em folha adulta (EFA). Foram incluídas como covariáveis nos modelos a circunferência do tronco e as variáveis climáticas de cada campo clonal. Os MRA foram comparados por meio do critério DIC (Deviance Information Criterion). O modelo M2 (que assumiu os efeitos aleatórios quadrático para clone e linear para planta) foi o melhor (menor valor de DIC) para descrever SEV e EFA em todas as localidades consideradas. Tal modelo permitiu inferir que os clones FDR 5788, CDC312 e CDC56 apresentaram maior resistência, enquanto o clone FX 3864 foi o que apresentou a maior suscetibilidade, em todas as localidades consideradas. Palavras-chave: Hevea brasiliensis. Microcyclus ulei. Dados longitudinais. Inferência estatística. Simulação estocástica.ABSTRACT -This model enabled also to infer that the clones FDR 5788, CDC312 and CDC56 presented high resitance, while the colne FX 3864 was the most susceptible in all studied locals. South American leaf blight caused by the fungus Microcyclus ulei is the most serious disease of rubber plantations in Latin America. With the aim of identifying more resistant rubber-tree clones in various environments over time, different random regression models (MRA) adjusted via a Bayesian approach were compared. Eight clones were tested in clonal fields in a randomised complete block design with four replications, using 80 trees per lot. The two central rows were evaluated every two months for the variables severity (SEV) and stroma index of the adult leaf (EFA). Trunk circumference and the climatic variables of each clonal field were included in the models as covariates. The MRA were compared by the deviance information criterion (DIC). The M2 model (which assumed quadratic random effects for clones and linear effects for plants) was the best (lower values for DIC) in describing the SEV and EFA in all the locations being considered. Such a model suggests that the FDR 5788, CDC312 and CDC56 clones displayed greater resistance, while the FX 3864 clone displayed the highest susceptibility in all considered locations.

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Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

Cited by 9 publications

References 34 publications

Random regression test‐day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows

Random regression test‐day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows

Comparison of daughter's performance of progeny tested sires with pedigree selected sires in Holstein Friesian crossbred cattle

Bayesian random regression for genetic evaluation of South American leaf blight in rubber trees

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