Genetic evaluation of growth in Nellore cattle by multiple-trait and random regression models

Nobre, P. R. C.; Misztal, I.; Tsuruta, S.; Bertrand, J. K.; Silva, L. O. C.; Lopes, Paulo Sávio

doi:10.2527/2003.814927x

Cited by 31 publications

(37 citation statements)

References 9 publications

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“…These polynomials should be of degree 4 or higher (Guo and Schaeffer, 2002). Nobre et al (2003b) considered the orthogonalisation and stricter convergence criterion (10 -12 rather than 10 -10 ) which is essential to obtain numerically accurate EPDs from RR by iteration. The dropping of effects with very small eigenvalues from models does not influence the results, but reduces memory and computing time requirements.…”

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confidence: 99%

Trajectory of body weight of performance tested dual-purpose bulls

Přibyl¹,

Krejcová²,

Přibylová³

et al. 2007

CZECH J ANIM SCI

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6 508 bulls of dual-purpose cattle at performance-test stations were weighed at intervals of 30 days from birth to 420 days. After all editing a total of 74 558 weight records were available. Live weight was evaluated by orthogonal Legendre Polynomial (LP) of degree 4 and by Linear Spline function (SP) with 5 knots. The fixed effects of test-day-year-station of weighing (TDS) and LP within station-year explain together 97% of variability. Variance components were estimated by REML (REMLF90 programme) taking into account heterogeneous variance during growth. The basic model included fixed effects: TDS, and fixed regression on age (LP F ), and random regression for additive genetic (SP G ) and permanent environmental of the animal (SP PE ) effects. Variability of all components increases with age. During the 50-400 day period the heritability is 0.28 on average. Heritability increases with the age of the animal; it is the highest at the end of the period. Correlations for body weights between different ages of the animal are high.

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mentioning

confidence: 99%

Trajectory of body weight of performance tested dual-purpose bulls

Přibyl¹,

Krejcová²,

Přibylová³

et al. 2007

CZECH J ANIM SCI

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“…For observation m containing trait t and with Legendre polynomials corresponding to age for trait t, the residual effect in MTM are approximately equivalent to the sum of residual, permanent environment and error effects in RRM (Nobre et al, 2003a).…”

Section: Datamentioning

confidence: 99%

“…In the RRM, the error was modeled as a fixed residual as described by Nobre et al (2003b). Then, when BLUP software supports weights, as in the case of this study, the effect r can be eliminated at a considerable saving in computations.…”

Section: Datamentioning

confidence: 99%

“…For better numerical properties of mixed model equations, the coefficients can be orthogonalized and their variances diagonalized as described by Nobre et al (2003b). In the transformation, some eigenvalues may be very close to zero.…”

Section: Datamentioning

confidence: 99%

“…Estimates of genetic RR coefficients provide a complete trajectory of genetic merit and expected progeny differences (EPDs) for any point on the longitudinal scale can be obtained by evaluating the regression equations at that point (Tier and Meyer, 2004). However, more memory requirement in RRM is demanded as the number of covariates in the model is increased (Nobre et al, 2003c).…”

Section: Introductionmentioning

confidence: 99%

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Genetic evaluation for large data sets by random regression models in Nellore cattle

Nobre¹,

Rosa²,

Silva³

2009

Arq. Bras. Med. Vet. Zootec.

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Expected progeny differences (EPD) of Nellore cattle estimated by random regression model (RRM) and multiple trait model (MTM) were compared. Genetic evaluation data included 3,819,895 records of up nine sequential weights of 963,227 animals measured at ages ranging from one day (birth weight) to 733 days. Traits considered were weights at birth, ten to 110-day old, 102 to 202-day old, 193 to 293-day old, 283 to 383-day old, 376 to 476-day old, 551 to 651-day old, and 633 to 733-day old. Seven data samples were created. Because the parameters estimates biologically were better, two of them were chosen: one with 84,426 records and another with 72,040. Records preadjusted to a fixed age were analyzed by a MTM, which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were carried out by REML, with five traits at a time. The RRM included the effects of age of animal, contemporary group, age of dam class, additive direct, permanent environment, additive maternal, and maternal permanent environment. Different degree of Legendre polynomials were used to describe random effects. MTM estimated covariance components and genetic parameters for weight at birth and sequential weights and RRM for all ages. Due to the fact that correlation among the estimates EPD from MTM and all the tested RM were not equal to 1.0, it is not possible to recommend RRM to genetic evaluation to large data sets.Keywords: beef cattle, multiple trait, random regression RESUMO Compararam-se as diferenças esperadas nas progênies (DEPs) de gado Nelore, estimadas por meio de um modelo de características múltiplas (MTM), com um modelo de regressão aleatória (RRM

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Genetic evaluation of growth of Kenya Boran cattle using random regression models

Wasike

Indetie

Pitchford

et al. 2007

Trop Anim Health Prod

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Data consisting of 18 884 weight records collected from 1273 Boran cattle from birth to 24 months of age were used to estimate covariance functions and genetic parameters for growth of Boran cattle using random regression (RR) models under a situation of small herd size and inconsistent recording. The RR model fitted quadratic Legendre polynomials of age at recording for additive genetic and permanent environmental effects. Genetic variance increased from birth, reaching an asymptotic value at 455 days and was maximum at 525 days of age after which it gradually dropped. Permanent environmental variance increased throughout the trajectory. Estimates of temporary environmental variance were heterogeneous across ages. Direct heritability and permanent environmental variance as a proportion of phenotypic variance fluctuated greatly during the early ages but later stabilized at intermediate to later ages; the estimates ranged from 0.11 to 0.33 and from 0.18 to 0.83, respectively. Genetic correlation estimates were positive, ranging from 0.10 to unity. The estimates declined with increasing in lag between the age points. Phenotypic correlation pattern was erratic between early ages, negatively low (-0.02) between the extreme data points and moderate to highly positive (>0.50) between intermediate and later points, with prominent spikes along the diagonal. It is concluded that RR models have potential for modelling growth of Boran cattle, notwithstanding conditions of small herd sizes and inconsistent recording.

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Genetic evaluation of growth in Nellore cattle by multiple-trait and random regression models

Cited by 31 publications

References 9 publications

Trajectory of body weight of performance tested dual-purpose bulls

Trajectory of body weight of performance tested dual-purpose bulls

Genetic evaluation for large data sets by random regression models in Nellore cattle

Genetic evaluation of growth of Kenya Boran cattle using random regression models

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