Additive Genetic Groups Research Articles

Genetic parameters for milk, growth, and reproductive traits in Guzerá cattle under tropical conditions.

This study aimed to estimate the genetic parameters of milk (305-day milk yield (MY305)), growth (weaning weight (WW), yearling weight (YW), and weight at 550days (W550)), and reproductive (age at first calving (AFC)) traits in Guzerá cattle by using Bayesian multiple-trait models. Systematic effects included sex and age at calving for the growth and milk traits, respectively. The additive genetic and contemporary groups (herd and year and season of birth) were included as random effects. Additionally, maternal genetic and permanent effects were also included as random effects for the WW. The heritability estimates were 0.29 (MY305), 0.42 (WW), 0.49 (YW), 0.56 (W550), and 0.25 (AFC). The genetic correlations among the growth traits were higher than 0.83; between the MY305 and WW, MY305 and YW, and MY305 and W550, the genetic correlations were 0.25, 0.32, and 0.36, respectively. The AFC was negatively correlated with the milk and growth traits. These results suggest the viability and potential of the joint selection for milk, beef, and reproductive traits in Guzerá cattle.

Genetic correlations between first parity and accumulated second to last parity reproduction traits as selection aids to improve sow lifetime productivity.

ObjectiveThe objective of this research was to estimate genetic correlations between number of piglets born alive in the first parity (NBA1), litter birth weight in the first parity (LTBW1), number of piglets weaned in the first parity (NPW1), litter weaning weight in the first parity (LTWW1), number of piglets born alive from second to last parity (NBA2+), litter birth weight from second to last parity (LTBW2+), number of piglets weaned from second to last parity (NPW2+) and litter weaning weight from second to last parity (LTWW2+), and to identify the percentages of animals (the top 10%, 25%, and 50%) for first parity and sums of second and later parity traits.MethodsThe 9,830 records consisted of 2,124 Landrace (L), 724 Yorkshire (Y), 2,650 LY, and 4,332 YL that had their first farrowing between July 1989 and December 2013. The 8-trait animal model included the fixed effects of first farrowing year-season, additive genetic group, heterosis of the sow and the litter, age at first farrowing, and days to weaning (NPW1, LTWW1, NPW2+, and LTWW2+). Random effects were animal and residual.ResultsHeritability estimates ranged from 0.08±0.02 (NBA1 and NPW1) to 0.29±0.02 (NPW2+). Genetic correlations between reproduction traits in the first parity and from second to last parity ranged from 0.17±0.08 (LTBW1 and LTBW2+) to 0.67±0.06 (LTWW1 and LTWW2+). Phenotypic correlations between reproduction traits in the first parity and from second to last parity were close to zero. Rank correlations between LTWW1 and LTWW2+ estimated breeding value tended to be higher than for other pairs of traits across all replacement percentages.ConclusionThese rank correlations indicated that selecting boars and sows using genetic predictions for first parity reproduction traits would help improve reproduction traits in the second and later parities as well as lifetime productivity in this swine population.

Thesis Abstract Study of different structures of additive genetic groups to increase the efficiency of genetic selection in beef cattle

The structure of genetic groups is a technique that allows animals with unknown paternity to be included in genetic evaluation programs. The ways these groups are formed are still arbitrary, making it important to study different formation strategies for genetic groups, aiming for a suitable framework for the genetic evaluation of seedstock in animal breeding programs. Therefore, the aim of this study was to define the structure of genetic groups suited for genetic evaluations, comparing them in regard to changes in the effective efficiency of the processing of animals with unknown parentage. Data from 116,368 Nellore cattle were analyzed focusing on the traits weaning weight, post-weaning weight, post-weaning weight gain, scrotal circumference at 18 months of age, and visual muscularity score at 18 months of age. Three scenarios were simulated from a database consisting only of complete pedigree animals (control group). The first scenario cut 30% of sires, the second cut 50%, and the third 70%. The training strategies for additive genetic groups were: the birth farm of the animal with unknown parentage; birth year (CROP) and the concatenation of year of birth and birth farm (CROPFARM). The variance components were calculated for the data bank control program by VCE, and breeding values were predicted using the PEST software, with two structures of models that differ by the inclusion or not of the fixed effect of additive genetic groups. The definition of the structure of genetic group suitable for genetic evaluation of animals was based on the efficiency of selection and comparison of estimated breeding values to the “control” breeding values and the rank of animals, where it was assumed that control animalswould have the maximum response to selection and any other group of animals would result in a reduction thereof. The results demonstrated that the additive genetic groups provide an improvement in the genetic prediction of animals with unknown paternity. Strategies that provided values closer to those of the control group were CROP and CROPFARM, and although showing similar values, the CROP strategy was superior in selecting the best animals.

Definição de grupos genéticos aditivos visando melhor predição de valores genéticos em bovinos de corte

O objetivo do estudo foi avaliar a estrutura de grupos genéticos aditivos como uma alternativa para melhor ajuste de animais com paternidade desconhecida nas avaliações genéticas. As características estudadas foram: peso a desmama; peso ao sobreano; ganho de peso pós-desmama em 345 dias; perímetro escrotal ao sobreano e o escore visual de musculosidade ao sobreano. Um banco em que todos os animais tinham paternidade conhecida foi utilizado como grupo controle. A partir deste, três cenários foram criados em que 30; 50 ou 70% dos indivíduos foram aleatoriamente assumidos como animais com paternidade desconhecida. As estratégias consideradas de grupos genéticos aditivos foram: ano de nascimento do animal com paternidade desconhecida; fazenda de nascimento do animal com paternidade desconhecida; ano de nascimento e fazenda de nascimento do animal e um grupo controle negativo que manteve os animais com pais desconhecidos. A estratégia adequada de grupo genético aditivo foi escolhida como sendo aquela que resultou em um maior coeficiente de regressão e com valor genético aditivo mais próximos ao predito para os animais do grupo controle. Os resultados que incluíram a estratégia de grupo genético aditivo nos modelos de predição dos valores genéticos mostraram maior coerência com o grupo controle, frente a não inclusão destes nas análises. Dentre as estratégias propostas, ano de nascimento e fazenda de nascimento do animal e apenas ano de nascimento do animal com paternidade desconhecida obtiveram os melhores resultados.

Somatic Cells Count and Its Genetic Association with Milk Yield in Dairy Cattle Raised under Thai Tropical Environmental Conditions.

Somatic cells count (SCC), milk yield (MY) and pedigree information of 2,791 first lactation cows that calved between 1990 and 2010 on 259 Thai farms were used to estimate genetic parameters and trends for SCC and its genetic association with MY. The SCC were log-transformed (lnSCC) to make them normally distributed. An average information-restricted maximum likelihood procedure was used to estimate variance components. A bivariate animal model that considered herd-yr-season, calving age, and regression additive genetic group as fixed effects, and animal and residual as random effects was used for genetic evaluation. Heritability estimates were 0.12 (SE = 0.19) for lnSCC, and 0.31 (SE = 0.06) for MY. The genetic correlation estimate between lnSCC and MY was 0.26 (SE = 0.59). Mean yearly estimated breeding values during the last 20 years increased for SCC (49.02 cells/ml/yr, SE = 26.81 cells/ml/yr; p = 0.08), but not for MY (0.37 kg/yr, SE = 0.87 kg/yr; p = 0.68). Sire average breeding values for SCC and MY were higher than those of cows and dams (p<0.01). Heritability estimates for lnSCC and MY and their low but positive genetic correlation suggested that selection for low SCC may be feasible in this population as it is in other populations of dairy cows. Thus, selection for high MY and low SCC should be encouraged in Thai dairy improvement programs to increase profitability by improving both cow health and milk yield.

Factors Influencing Genetic Change for Milk Yield within Farms in Central Thailand

The objective of this study was to characterize factors influencing genetic improvement of dairy cattle for milk production at farm level. Data were accumulated from 305-day milk yields and pedigree information from 1,921 first-lactation dairy cows that calved from 1990 to 2007 on 161 farms in Central Thailand. Variance components were estimated using average information restricted maximum likelihood procedures. Animal breeding values were predicted by an animal model that contained herd-year-season, calving age, and regression additive genetic group as fixed effects, and cow and residual as random effects. Estimated breeding values from cows that calved in a particular month were used to estimate genetic trends for each individual farm. Within-farm genetic trends (b, regression coefficient of farm milk production per month) were used to classify farms into 3 groups: i) farms with negative genetic trend (b kg/mo), and iii) farms with positive genetic trend (b>0.5 kg/mo). Questionnaires were used to gather information from individual farmers on educational background, herd characteristics, farm management, decision making practices, and opinion on dairy farming. Farmer's responses to the questionnaire were used to test the association between these factors and farm groups using Fisher's exact test. Estimated genetic trend for the complete population was kg/year for cows. At farm level, most farms (40%) had positive genetic trend ( to kg/mo) followed by farms with negative genetic trend (35%; to kg/mo) and those with no genetic trend (25%; to kg/mo). Except for educational background (p

Genetic trends in a Holstein × other breeds multibreed dairy population in Central Thailand

Genetic variability and genetic trends for 305-day milk yield (MY), 305-day fat yield (FY), and average 305-day fat percent (FP) were evaluated using monthly test-day records from first-lactation cows collected from 1991 to 2005 in 92 farms located in Central Thailand. Estimates of variance and covariance components and breeding values (EBV) were obtained using a multiple-trait animal model. Fixed effects were contemporary group (herd–year–season), calving age, additive genetic group as a function of Holstein fraction, and non-additive genetic group as function of heterosis effect. Random effects were animal and residual. Program ASREML was used to perform computations. Estimates of heritabilities were 0.38 ± 0.10 for MY, 0.25 ± 0.11 for FY, and 0.22 ± 0.11 for FP. Although the difference between the mean MY for cows in 1991 and 2005 was 324.1 kg, the regression of mean cow EBV for MY on year was 6.5 kg/year. Differences between mean cow EBV for FY and FP in 1991 and 2005 and their corresponding regressions of mean FY and FP on year were all near zero. Similarly, mean EBV for sires and dams of cows also showed near zero trends during these years. A factor contributing to the near complete absence of genetic trends was likely the variety of criteria used by producers to choose sires and to keep dams in addition to EBV (e.g., availability of semen, reproductive ability, adaptation to hot and humid conditions). It also appears that high percent Holstein cows failed to reach their production potential under the management, nutrition, and hot and humid climatic conditions in this tropical region. Changes in nutrition and management would be needed for high percent Holstein cows to show an upward trend in Central Thailand.

Schätzung von Varianzkomponenten mit Gruppenmittelwerten am Beispiel von Legehennen, gehalten in Gruppenkäfigen

Abstract. Title of the paper: Estimation of variance components from group mean records of laying hens housed in group cages Two models are presented to estimate variance components if only group mean records are available. The first model accounts for additive genetic relationships and full-sib group effects (SIMIANER and GJERDE, 1991) and the second model contains the additive genetic effects of all animals from one cage by using modified design matrices. Estimates of the genetic parameters were obtained by the MIVQUE-method (RAO, 1971; LAMOTTE, 1973). The variances of the estimated heritabilities were derived from the information matrix. Estimations from individual records and from average records (cage average) were compared in a small application on laying hen data. The analysed trait was single egg weight measured on hens housed in group cages. It could be shown: If cage variance is negligible, than for the estimation of the heritabilities full-sib data can be used successfully. The application of the modified animal model is suggested, because this model can take into account more complex relationships between the animals of one cage.

Additive genetic parameter estimates for heifer pregnancy and subsequent reproduction in Angus females.

A primary objective of this study was to determine whether the binary traits heifer pregnancy (HP) and subsequent rebreeding (SR) were heritable in an experimental population of Angus cattle. A second objective was to determine the nature of the additive genetic relationships among HP, SR, and stayability (S(5/1)) in the same population. Heifer pregnancy was defined as the observation of a heifer conceiving and remaining pregnant to palpation at 120 d, given exposure during the breeding season. Subsequent rebreeding was defined as the observation of a 2-yr-old conceiving and remaining pregnant to palpation at 105 d, given pregnancy as a yearling and exposure during the breeding season. Stayability was defined as the probability of a female having at least five calves, given she becomes a dam as a 2 yr old. Data were analyzed using a maximum a posteriori probit threshold model to predict breeding values on the liability scale and Method R procedures to estimate variance components in the determination of heritability (h2). Additive genetic groups were used in determining the additive genetic relationships among these fertility traits. Additive genetic groups were formed on one trait's breeding values and used in the prediction of another trait's breeding values. Analyses yielded h2 estimates that were out of the parameter space 8.5 and 46.3% for HP and SR, respectively, and 5.9% for the reestimation of S(5/1). The majority of point estimates outside the parameter space for SR converged toward 0, whereas those for HP and S(5/1) primarily converged toward 1. From the subsamples producing h2 estimates within the parameter space, average h2 for HP, SR, and S(5/1) were .21, .19, and .15, with standard deviations of .12, .14, and .08, respectively. The estimates of h2 indicate that HP and S(5/1) were heritable and should respond favorably to selection; however, SR did not appear heritable due to the large number of subsamples producing h2 estimates out of the parameter space. Fixed effect estimates for age of dam were significant for HP. From the analyses using additive genetic groups, the relationship among HP and S(5/1) appeared to be nonlinear. This potential nonlinear relationship seen between HP and S(5/1) indicates that selection for improved female fertility would be most effective by having predictions on both traits.

Additive genetic relationships between heifer pregnancy and scrotal circumference in Hereford cattle.

The objective of this study was to determine an appropriate method for using yearling scrotal circumference observations and heifer pregnancy observations to produce EPD for heifer pregnancy. We determined the additive genetic effects of and relationship between scrotal circumference and heifer pregnancy for a herd of Hereford cattle in Solano, New Mexico. The binary trait of heifer pregnancy was defined as the probability of a heifer conceiving and remaining pregnant to 120 d, given that she was exposed at breeding. Estimates of heritability for heifer pregnancy and scrotal circumference were .138+/-.08 and .714+/-.132, respectively. Estimates of fixed effects for age of dam and age were significant for heifer pregnancy and bull scrotal circumference. The estimate of the additive genetic correlation between yearling heifer pregnancy and yearling bull scrotal circumference was .002+/-.45. Additional analyses included models with additive genetic groups for scrotal circumference EPD for heifer pregnancy or heifer pregnancy EPD for scrotal circumference to account for a potential nonlinear relationship between scrotal circumference and heifer pregnancy. Results support the development of a heifer pregnancy EPD because of a higher estimated heritability than previously reported. The development of a heifer pregnancy EPD would be an additional method for improving genetic merit for heifer fertility.

Additive genetic groups for animals evaluated in more than one breed association national cattle evaluation.

Additive genetic groups were included in the 1993 Red Angus Association of America national cattle evaluation for phantom parents of individuals who were registered with the American Angus Association (AAA). Genetic groups were formed for each component in two multiple-trait evaluations in which all animal effects were fit. Additive direct effects were included for birth weight, weaning weight (WW), and milk (MILK). In a second analysis the additive direct effect of 160-d postweaning gain was analyzed with WW and MILK. Of the 387,665 animals, 50,838 had at least one phantom parent assigned to one of five genetic groups fit as fixed effects for each additive component. Of these 50,838 animals, 1,324 were identified as registered with the AAA. An average of 906 animals per component had an AAA EPD available. Animals with a known AAA EPD were designated into one of three groups of equal numbers based on AAA EPD for each component (1 = low, 2 = medium, 3 = high). Animals in the fourth genetic group were those registered with the AAA but with no EPD available for the component. The fifth genetic group included all other animals with phantom parents. Grouping on AAA EPD allowed for EPD on animals out of parent(s) registered with the AAA to be more closely aligned to the AAA EPD because they were regressed from the group solution instead of zero. Grouping based on EPD from another NCE should be considered in the production of multibreed EPD.

Influence of Nonadditive Effects on Estimation of Genetic Parameters in Dairy Cattle

A population was simulated having progeny that descended from sires and dams with various fractions of genes from two breeds. Additive breed effects and nonadditive effects from breed crosses were simulated.Data on performance were analyzed using mixed models that accounted for fixed additive genetic group effects and random sire effects. Three additive models, with genetic groups defined according to 1) breed composition of the progeny, 2) breed composition of the sire and the dam, or 3) linear regression on breed fraction of the progeny, were compared with a nonadditive model with a linear regression on breed fraction, heterozygosity and recombination in the genome of the progeny. Variance components were estimated using restricted maximum likelihood.Additive genetic variance and heritability were overestimated for an additive model with progeny groups. Additive models gave estimates for breed difference, group effects, and breeding values that were not equal to true additive genetic values. Breed differences were overestimated when sire groups were used. Estimators for each parameter were unbiased with the nonadditive model.

Inverse of Single Trait Additive Genetic Covariance Matrix with Unequal Variances Across Additive Genetic Groups

Recursive procedures are presented to compute the additive genetic covariance matrix and its inverse for a single trait when additive genetic variances differ across genetic groups. They are extensions of well-known recursive methods that assume equal additive variances for all genetic groups in a population. These procedures facilitate animal evaluation with mixed model analysis when animals being evaluated belong to different subpopulations within a breed or to different breeds and crossbred groups, especially when animals belonging to different genetic groups are related. Models should include additive and nonadditive fixed (group) and random effects depending on the trait being evaluated.