Estimation of Genetic Parameters for Daily Gains of Bulls with Multi-Trait and Random Regression Models

Krejcová, H; Mielenz, N.; Přibyl, J.; Schüler, L.

doi:10.5194/aab-50-37-2007

Cited by 15 publications

(19 citation statements)

References 14 publications

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“…Similar results have been reported by Albuquerque and Meyer (2001b) for weights at standard ages using random regression models and by Yokoo et al (2007) between measures of scrotal circumference at 365, 450 and 550 days of age. The genetic correlations obtained for weight gains showed a magnitude similar to that reported by Krejcová et al (2007), except for some negative correlations (-0.131 to 0.958).…”

Section: Genetic Parameter Estimates and Response To Selectionsupporting

confidence: 66%

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Genetic parameter estimates and response to selection for weight and testicular traits in Nelore cattle

Neto¹,

Lôbo²,

Mota³

et al. 2011

Genet. Mol. Res.

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ABSTRACT. We estimated genetic parameters for various phases of body and testicular growth until 550 days of age in Nelore cattle, using Bayesian inference, including correlation values and error estimates. days of age; scrotal circumference at 365 (SC365), 450 (SC450) and 550 (SC550) days of age, and testicular growth between 365/450 (TG1), 450/550 (TG2) and 365/550 (TG3) days of age. The model included contemporary group (current farm, year and two-month period of birth, sex, and management group) and age of dam at calving, divided into classes as fixed effects. The model also included random effects for direct additive, maternal additive and maternal permanent environmental, and residual effects. The direct heritability estimates ranged from 0.23 to 0.39, 0.13 to 0.39 and 0.32 to 0.56 for weights at standard ages, weight gains and testicular measures, respectively. The genetic correlations between weights (0.69 to 0.94) and scrotal circumferences (0.91 to 0.97) measured at standard ages were higher than those between weight gain and testicular growth (0.18 to 0.97 and 0.36 to 0.77, respectively). The weights at standard ages responded more effectively to selection, and also gave strong correlations with the other traits.

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Section: Genetic Parameter Estimates and Response To Selectionsupporting

confidence: 66%

“…Paneto et al (2002) obtained estimates ranging from 0.16 to 0.32 for total gains between standard ages. Krejcová et al (2007), studying daily weight gains over nine periods by multi-trait and random regression analysis, reported heritabilities ranging from 0.045 to 0.29.…”

Section: Genetic Parameter Estimates and Response To Selectionmentioning

confidence: 99%

Genetic parameter estimates and response to selection for weight and testicular traits in Nelore cattle

Neto¹,

Lôbo²,

Mota³

et al. 2011

Genet. Mol. Res.

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“…New algorithms such as derivative-free restricted maximum likelihood (DFREML) and the relevant computer programs have been developed (GRASER et al, 1987;MEYER, 1998) with the aim of more precise and unbiased evaluations. Improvement of the computational power of computers has made it possible to perform more complicated animal models, and as well as single-trait animal models, multi-trait and random regression animal models are in use in various researches KREJČOVÁ et al, 2007a;KREJČOVÁ et al, 2007b). CANTET et al (2000) using a simulated data, studied the different situations of: (a) complete pedigree; (b) 50% of phenotypes with sire missing; (c) 50% of phenotypes with dam missing (d) and 50% of phenotypes with sire and dam missing (the paternities of 12.5%, the maternities of 12.5%, and the paternities and maternities of 12.5% of the records were lost at random).…”

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confidence: 99%

Effects of missing pedigree information on dairy cattle genetic evaluations (short communication)

2008

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Estimating genetic merit of livestock closest to their true genetic merit is a preliminary goal in animal breeding. The accuracy of genetic evaluations depends on the recording system and the method of evaluation. Whereas applying more complicated models may improve the accuracy of evaluations inconsiderably, improving data quality is more effective. The data were on pedigree and milk performances (milk yield, fat yield and fat percentage) of 9834 dairy cows in Isfahan, Iran, with both known parents. Genetic parameters were estimated by derivative-free restricted maximum likelihood method, applying an animal model (full relationships), sire model (dam missed), dam model (sire missed) and a half-missed model (half sire / half dam). All the models were compared to animal model. Sire model had the smallest size of pedigree structure, while dam model had an inexistency of between herd relationships. The results showed underestimating additive genetic variance by sire and half-missed models and overestimating it by dam model. An important finding of this study was that there is an unfavorable interaction between missing sire and dam information that caused the lowest goodness of fit for half-missed model. Also, usually, sire missing makes more important problems to the pedigree structure and genetic evaluations than dam missing. The research revealed that, even using an animal model, there are some delicacies in introducing the relationship matrix for sex-limited traits, which requires special attention to the pedigree of sires. (WILCOX et al., 1992). Because daughter-dam regression method tends to be biased by maternal effects, it has not been widely used. Nowadays, using REML procedures, evaluations are free of this bias (NDLOVU, 1993). In 1962, the method changed to daughter-herdmate comparisons which was further improved in 1965 by taking some additional parameters into account. The MCC (Modified Contemporary Comparison) method was implemented in October 1974 in attempting to take into consideration some items including genetic differences in herdmates, pedigree information, daughter distribution over herds, length of lactation, number of herdmates, and the number and average repeatability of herdmate sires (WILCOX et al., 1992). By the invention of Henderson's methodology of best linear unbiased prediction (BLUP), sire, sire-maternal grandsire, and animal models were invented and investigated. The techniques are well accepted and BLUP nowadays is the preferred method of evaluation (HERRENDÖRFER et al., 1999; WILCOX et al., 1992). Initially, evaluations were taking from a best linear unbiased prediction (BLUP) sire model. Then, it was improved by adding maternal grandsires to the pedigree structure (sire-MGS model) in order to improve the accuracy by considering more genetic relationships, direct effect as sire and maternal effect as sire of dam, partial accounting of merit of mates, and differences in maternal ability of dams. In February and August 1989, animal model proofs became available for type a...

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“…The animals are usually grouped according to the expectation of a similar curve with respect to the systematic effects. However, Krejčová et al (2007) constructed a fixed regression curve within test-days classes of observations of different animals.…”

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confidence: 99%

Models for evaluation of growth of performance tested bulls

Přibyl¹,

Krejcová²,

Přibylová³

2008

CZECH J ANIM SCI

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Before being used for insemination, young bulls of Czech Fleckvieh (CF) are tested for growth at performance-test stations. While at stations, the bulls are weighed monthly. Evaluation included 7 448 bulls with 82 676 records of weight measured from 6 to 520 days of life. In the station-year-period (HYS), which can be prolonged up to 3 months, different groups were tested according to the beginning of growth curve and according to test-days of weighing. Weight analyses were used to handle heterogeneous variability based on age. Legendre Polynomials (LP) with 5 parameters described the average growth curve for HYS classes. Deviations from average curves were decomposed into genetic (G), animal's permanent environment (PE) and residual (RES) components. Functions of (G) and (PE) were tested using LP random regression (RR) methodology with 5 or 3 parameters and Linear Spline (SP) function with 5 knots. Variance increases with the age of the animals. From 100 to 400 days, heritability was nearly the same with a mild depression in the middle of the period. The average was h 2 = 0.31 and ended with h 2 = 0.36. Results were similar for variance components, heritability, genetic, environmental and phenotype correlations from different models with different LP and SP functions. Higher RES variability occurred only for LP with 3 parameters. For traits like live weight, the RR should have at least 3 parameters and SP function should be used.

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Estimation of Genetic Parameters for Daily Gains of Bulls with Multi-Trait and Random Regression Models

Cited by 15 publications

References 14 publications

Genetic parameter estimates and response to selection for weight and testicular traits in Nelore cattle

Genetic parameter estimates and response to selection for weight and testicular traits in Nelore cattle

Effects of missing pedigree information on dairy cattle genetic evaluations (short communication)

Models for evaluation of growth of performance tested bulls

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