2026-04-13T13:55:04Zhttps://recercat.cat/oai/requestoai:recercat.cat:20.500.12327/29212025-10-22T11:17:17Zcom_2072_4428com_2072_4427col_2072_487898
RECERCAT
author
Rezende, F.M.
author
Ferraz, J.B.S.
author
Eler, J.P.
author
Silva, R.C.G.
author
Mattos, E.C.
author
Ibanez-Escriche, Noelia
other
Producció Animal
group
Genètica i Millora Animal
2025-10-22T11:17:17Z
2025-10-22T11:17:17Z
2012-03-31
De Rezende, Fernanda Marcondes, José Bento Sterman Ferraz, Joanir Pereira Eler, Roulber Carvalho Gomes Da Silva, E. C. Mattos, and Noelia Ibáñez‐Escriche. 2012. “Study of Using Marker Assisted Selection on a Beef Cattle Breeding Program by Model Comparison.” Livestock Science 147 (1–3): 40–48. doi:10.1016/j.livsci.2012.03.017.
1871-1413
http://hdl.handle.net/20.500.12327/2921
https://doi.org/10.1016/j.livsci.2012.03.017
A data set of a commercial Nellore beef cattle selection program was used to compare
breeding models that assumed or not markers effects to estimate the breeding values,
when a reduced number of animals have phenotypic, genotypic and pedigree information
available. This herd complete data set was composed of 83,404 animals measured for
weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle
score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait
analyses were performed by MTDFREML software to estimate fixed and random effects
solutions using this complete data. The additive effects estimated were assumed as the
reference breeding values for those animals. The individual observed phenotype of each
trait was adjusted for fixed and random effects solutions, except for direct additive
effects. The adjusted phenotype composed of the additive and residual parts of observed
phenotype was used as dependent variable for models’ comparison. Among all measured
animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three
models were compared in terms of changes on animals’ rank, global fit and predictive
ability. Model 1 included only polygenic effects, model 2 included only markers effects
and model 3 included both polygenic and markers effects. Bayesian inference via Markov
chain Monte Carlo methods performed by TM software was used to analyze the data for
model comparison. Two different priors were adopted for markers effects in models 2 and
3, the first prior assumed was a uniform distribution (U) and, as a second prior, was
assumed that markers effects were distributed as normal (N). Higher rank correlation
coefficients were observed for models 3_U and 3_N, indicating a greater similarity of
these models animals’ rank and the rank based on the reference breeding values. Model
3_N presented a better global fit, as demonstrated by its low DIC. The best models in
terms of predictive ability were models 1 and 3_N. Differences due prior assumed
to markers effects in models 2 and 3 could be attributed to the better ability of normal
prior in handle with collinear effects. The models 2_U and 2_N presented the worst
performance, indicating that this small set of markers should not be used to genetically
evaluate animals with no data, since its predictive ability is restricted. In conclusion,
model 3_N presented a slight superiority when a reduce number of animals have
phenotypic, genotypic and pedigree information. It could be attributed to the variation
retained by markers and polygenic effects assumed together and the normal prior
assumed to markers effects, that deals better with the collinearity between markers.
eng
Attribution-NonCommercial-NoDerivatives 4.0 International
Study of using marker assisted selection on a beef cattle breeding program by model comparison
info:eu-repo/semantics/article