Random regression

Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models

N. W. Selapa, Nephawe, K. A., Maiwashe, A., and Norris, D., “Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models”, vol. 11, pp. 271-276, 2012.

The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects.

Estimation of genetic parameters for partial egg production periods by means of random regression models

G. C. Venturini, Grossi, D. A., Ramos, S. B., Cruz, V. A. R., Souza, C. G., Ledur, M. C., L. Faro, E., Schmidt, G. S., and Munari, D. P., “Estimation of genetic parameters for partial egg production periods by means of random regression models”, vol. 11, pp. 1819-1829, 2012.

We estimated genetic parameters for egg production in different periods by means of random regression models, aiming at selection based on partial egg production from a generation of layers. The production was evaluated for each individual by recording the number of eggs produced from 20 to 70 weeks of age, with partial records taken every three weeks for a total of 17 periods. The covariance functions were estimated with a random regression model by the restricted maximum likelihood method.

Genetic analysis of longitudinal data in beef cattle: a review

S. E. Speidel, Enns, R. M., and Crews, Jr., D. H., “Genetic analysis of longitudinal data in beef cattle: a review”, vol. 9. pp. 19-33, 2010.

Currently, many different data types are collected by beef cattle breed associations for the purpose of genetic evaluation. These data points are all biological characteristics of individual animals that can be measured multiple times over an animal´s lifetime. Some traits can only be measured once on an individual animal, whereas others, such as the body weight of an animal as it grows, can be measured many times.

Joint analysis of beef growth and carcass quality traits through calculation of co-variance components and correlations

H. R. Mirzaei, Verbyla, A. P., and Pitchford, W. S., “Joint analysis of beef growth and carcass quality traits through calculation of co-variance components and correlations”, vol. 10, pp. 433-447, 2011.

A joint growth-carcass model using random regression was used to estimate the (co)variance components of beef cattle body weights and carcass quality traits and correlations between them. During a four-year period (1994-1997) of the Australian “southern crossbreeding project”, mature Hereford cows (N = 581) were mated to 97 sires of Jersey, Wagyu, Angus, Hereford, South Devon, Limousin, and Belgian Blue breeds, resulting in 1141 calves.

Modeling of crossbred cattle growth, comparison between cubic and piecewise random regression models

H. R. Mirzaei, Pitchford, W. S., and Verbyla, A. P., “Modeling of crossbred cattle growth, comparison between cubic and piecewise random regression models”, vol. 10, pp. 2230-2244, 2011.

Two analyses, cubic and piecewise random regression, were conducted to model growth of crossbred cattle from birth to about two years of age, investigating the ability of a piecewise procedure to fit growth traits without the complications of the cubic model. During a four-year period (1994-1997) of the Australian “Southern Crossbreeding Project”, mature Hereford cows (N = 581) were mated to 97 sires of Angus, Belgian Blue, Hereford, Jersey, Limousin, South Devon, and Wagyu breeds, resulting in 1141 steers and heifers born over four years.

Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model

B. J. DeGroot, Keown, J. F., Van Vleck, L. D., and Kachman, S. D., “Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model”, vol. 6, pp. 434-444, 2007.

Genetic parameters were estimated with restricted maximum likelihood for individual test-day milk, fat, and protein yields and somatic cell scores with a random regression cubic spline model. Test-day records of Holstein cows that calved from 1994 through early 1999 were obtained from Dairy Records Management Systems in Raleigh, North Carolina, for the analysis. Estimates of heritability for individual test-days and estimates of genetic and phenotypic correlations between test-days were obtained from estimates of variances and covariances from the cubic spline analysis.

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