Genetic analysis of mastitis in dairy cattle with a Bayesian threshold model

2000 ◽  
Vol 2000 ◽  
pp. 16-16 ◽  
Author(s):  
H.N. Kadarmideen ◽  
R. Rekaya ◽  
D. Gianola
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Genetic Parameters ◽  
Linear Models ◽  
Early Stage ◽  
Threshold Model ◽  
Genetic Parameter ◽  
Genetic Selection ◽  
Stage Of Lactation ◽  
Selection For

Genetic parameters of mastitis are required in genetic selection for mastitis resistance. Excluding cows that are culled at an early stage of lactation due to mastitis from genetic parameter estimation may introduce culling bias. The use of linear models, suitable for continuous traits, is inappropriate for analysis of mastitis records because it is recorded as an all or none (binary) trait. Here, a Bayesian-threshold model with Markov chain Monte Carlo (MCMC) techniques was used to analyze mastitis data. The objective was to estimate genetic parameters of mastitis in first and all-lactation cows using complete or incomplete records on disease.

Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle

2000 ◽  
Vol 27 ◽  
pp. 83-84
Author(s):  
H. N. Kadarmideen ◽  
R. Thompson ◽  
G. Simm
Keyword(s):  
Animal Models ◽  
Dairy Cattle ◽  
Milk Production ◽  
Genetic Parameters ◽  
Linear Models ◽  
Threshold Model ◽  
Genetic Selection ◽  
Good Health ◽  
Economic Loss ◽  
Fertility Traits

A combination of better management and genetic selection for good health and fertility would provide a more effective long term solution for economic loss due to diseases and poor fertility. This would also help to address public concerns about the use of medical treatment in milk production. A balance in the genetic improvement of health and fertility together with milk production could be achieved through their inclusion in national genetic selection indices, for which genetic parameters are needed. One of the main objectives of this study was to estimate genetic parameters for various disease and fertility traits in the UK dairy cattle population, using records from a national recording scheme run by Livestock Services UK Ltd. Genetic analysis of traits recorded as present or absent (binary traits; e.g. diseases) requires the use of non-linear threshold models, because linear models require normality assumptions (e.g., Gianola 1982). The other objective of this study was to estimate genetic parameters for binary disease and fertility traits based on threshold animal models and to compare results with those from linear animal models.

Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models

2018 ◽  
Vol 22 (4) ◽  
Author(s):  
Shuxia Ni ◽  
Qiang Xia ◽  
Jinshan Liu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Subset Selection ◽  
Selection Method ◽  
Stochastic Search ◽  
Ar Model ◽  
Simulation Experiments ◽  
Selection For ◽  
Ar Models

Abstract In this paper, we propose and study an effective Bayesian subset selection method for two-threshold variable autoregressive (TTV-AR) models. The usual complexity of model selection is increased by capturing the uncertainty of the two unknown threshold levels and the two unknown delay lags. By using Markov chain Monte Carlo (MCMC) techniques with driven by a stochastic search, we can identify the best subset model from a large number of possible choices. Simulation experiments show that the proposed method works very well. As applied to the application to the Hang Seng index, we successfully distinguish the best subset TTV-AR model.

Can we breed Merino sheep with softer, whiter, more photostable wool?

2010 ◽  
Vol 50 (12) ◽  
pp. 1089 ◽  
Author(s):  
S. Hatcher ◽  
P. I. Hynd ◽  
K. J. Thornberry ◽  
S. Gabb
Keyword(s):  
Genetic Parameters ◽  
Selection Index ◽  
Genetic Correlations ◽  
Genetic Selection ◽  
Phenotypic Selection ◽  
Quality Traits ◽  
Current Generation ◽  
Merino Sheep ◽  
Initial Cohort ◽  
Selection For

Genetic parameters (heritability, phenotypic and genetic correlations) were estimated for a range of visual and measured wool traits recorded from the 2008 shearing of the initial cohort of Merino progeny born into the Sheep CRC’s Information Nucleus Flock. The aim of this initial analysis was to determine the feasibility of selectively breeding Merino sheep for softer, whiter, more photostable wool and to quantify the likely impact on other wool production and quality traits. The estimates of heritability were high for handle and clean colour (0.86 and 0.70, respectively) and moderate for photostability (0.18), with some evidence of maternal effects for both handle and photostability. The phenotypic correlations between handle and clean colour and between handle and photostability were close to zero, indicating that achieving the ‘triple’ objective of softer, whiter, more photostable wool in the current generation through phenotypic selection alone would be difficult. There was evidence of an antagonistic relationship between handle and photostability (–0.36), such that genetic selection for softer wool will produce less photostable wool that will yellow on exposure to UV irradiation. However genetic selection for whiter wool is complementary to photostability and will result in whiter wool that is less likely to yellow. Genetic selection to improve handle, colour and photostability can be achieved with few detrimental effects on other visual and measured wool traits, particularly if they are included in an appropriate selection index.

138 GENETIC PARAMETER ESTIMATION FOR EMBRYO TRANSFER TRAITS

2009 ◽  
Vol 21 (1) ◽  
pp. 169 ◽  
Author(s):  
F. A. Di Croce ◽  
A. M. Saxton ◽  
N. R. Rohrbach ◽  
F. N. Schrick
Keyword(s):  
Embryo Transfer ◽  
Fixed Effects ◽  
Large Scale ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Selection ◽  
Reproductive Traits ◽  
Residual Effects ◽  
Limiting Factors ◽  
Phenotypic Traits

Genetic selection has made tremendous progress on economically important traits in the beef industry. Most of the progress has been from quantitative genetics through use of expected progeny differences (EPD). These values allow prediction of differences in progeny of a sire compared to progeny of other sires. Development of EPD for male and female reproductive traits has largely been ignored because of low heritability of reproductive traits, even though reproduction plays a vital role in the economics of beef operations. Therefore, continued research in the area of genetic selection for fertility is becoming increasingly important. Critical limiting factors for animal breeding programs using MOET nucleus schemes include variability in superovulatory response of donor animals and resulting pregnancy of transferred embryos. Thus, the overall objective of this research was to develop genetic parameters associated with MOET to assist producers in identifying animals with greater genetic merit for these protocols. Records were examined from a large-scale MOET system in beef cattle that contained data only for cows in which at least one transferable embryo was obtained. Data on these animals were extracted and analyzed on 10 425 transferred embryos (2900 collections) from 611 donor animals (Angus, Brangus, and Charolais) utilizing semen from 215 bulls. Phenotypic traits examined included pregnancy status of the recipient following transfer (ET-preg; determined by rectal palpation at 60 days post-transfer and/or confirmed calving date of recipient), number of transferable embryos per collection (ET-trans), and number of unfertilized ova at collection (ET-UFO). Basic statistical analysis and pedigree/trait files were developed using procedures in SAS (SAS Institute, Cary, NC). Genetic parameters were estimated for a single-trait animal model using restricted maximum likelihood (REML) procedures in Wombat (Meyer K 2007 Zhejiang Uni. Science B 8, 815–821). Wombat also computed EPD and standard errors for each trait evaluated. The model included fixed effects of year as well as random animal and residual effects. The EPD for ET-preg ranged from –6.1 to 4.4% (SE = 2.2 to 4.2) for semen sires (sires of the transferred embryos) and –5.3 to 3.8% (SE = 3.2 to 4.2) for donor animals. Additionally, the heritability estimated for ET-preg was 0.03. Heritability estimated for ET-trans was 0.00, indicating minute genetic variation and thus, EPD were not presented. Heritability estimated for ET-UFO was 0.05 with EPD values (deviation of the number of UFO from the mean) ranging from –0.6 to 0.8 (SE = 0.3 to 0.6) for semen sires and –0.4 to 1.1 (SE = 0.5 to 0.6) for donor cows. As previously shown for reproductive traits, heritability of ET-preg, ET-trans, and ET-UFO was low. Genetic improvement in fertility by selection on embryo transfer traits is possible, but progress would be slow. Further studies are underway on a larger dataset to refine these estimates and to examine repeatability.

scholarly journals Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Heredity ◽  
2012 ◽  
Vol 109 (4) ◽  
pp. 235-245 ◽  
Author(s):  
B Mathew ◽  
A M Bauer ◽  
P Koistinen ◽  
T C Reetz ◽  
J Léon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Genetic Parameters ◽  
Monte Carlo Estimation ◽  
Estimation Of Genetic Parameters

scholarly journals Two-way selection for body weight in Tribolium on two levels of nutrition

1967 ◽  
Vol 9 (3) ◽  
pp. 309-330 ◽  
Author(s):  
R. T. Hardin ◽  
A. E. Bell
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Minimum Size ◽  
Direct Selection ◽  
Correlated Responses ◽  
Larval Weight ◽  
Large Size ◽  
Selection For ◽  
Selection Differentials

Parameters necessary for predicting direct and correlated responses for large and small 13-day larval weight in T. castaneum on two levels of nutrition were estimated in the base population. Larval weight in the GOOD environment was approximately twice that observed in POOR. Heritabilities (estimated from the ratio of sire component to total phenotype variance) of larval weight on the two rations were similar, 0·21 ± 0·06 and 0·19 ± 0·05 for GOOD and POOR, respectively. Heritabilities based on dam-offspring covariances were similar to these, but those obtained from full-sib covariances were much larger (0·97 ± 0·07 for GOOD and 0·69 ± 0·07 for POOR). This suggested that considerable dominance rather than maternal effects were present. The genetic correlation between growth on GOOD and growth on POOR was estimated as + 0·60 ± 0·21.The selection experiment was replicated four times with each replication extending over eight generations. Good agreement between predicted and observed values for direct selection was observed for large selection in both environments and small selection in POOR. However, response to small selection in GOOD was significantly greater than predicted in all four replications and was associated with increased selection differentials. Realized heritabilities were approximately the same for both directions in GOOD yet asymmetrical responses occurred for all replications due to unequal selection differentials. On the other hand, realized heritabilities were asymmetrical in POOR. Those observed for small selection were more than twice the size of those calculated for large lines. However, the responses in POOR were symmetrical since the selection differentials varied inversely with the realized heritabilities.Because of the asymmetry observed for heritabilities and selection differentials, correlated responses were poorly predicted. The average effective genetic correlation between growth in GOOD and growth in the POOR environment agreed remarkably well with the base estimate, yet asymmetry of the genetic correlation was a consistent phenomenon with values for the large lines being less than the base parameter while small lines were uniformly larger.Asymmetries of the various genetic parameters were not anticipated from base estimates. They were not caused by sampling or chance fluctuations since all four replications were remarkably consistent. Asymmetry for any one genetic parameter (e.g. heritability) was associated with a particular environment or direction of selection while other genetic parameters reacted asymmetrically in populations exposed to a different set of environmental treatments.For maximum performance in a single environment, these results show that selection should be practiced in that environment. With regard to mean performance in GOOD and POOR environments, selection for large size in POOR gave some 25% more gain than selection in GOOD. Selection for small size in either environment was equally effective in obtaining minimum size in both environments.

Some factors affecting the estimation of genetic parameters for cattle dystocia under a threshold model

1991 ◽  
Vol 53 (2) ◽  
pp. 151-156 ◽  
Author(s):  
E. J. Manfredi ◽  
M. San Cristobal ◽  
J. L. Foulley
Keyword(s):  
Maternal Effects ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Threshold Model ◽  
Genetic Parameter ◽  
Random Effect ◽  
Large Data ◽  
Marginal Maximum Likelihood ◽  
Factors Affecting ◽  
Maternal Grandsire

AbstractGenetic parameters for dystocia in the Main-Anjou breed were estimated. Data consisted of 28 178 birth records collected between 1978 and 1989 in 995 herds, with 161, 71 and 12 415 sires, maternal grandsires and dams, respectively, represented. Original scores (1 through 5) were collapsed in order to set two dystocia definitions: dystocia 1 (scores 1+2 v. 3+4+5) and dystocia 2 (scores 1 v. 2+3+4+5). Four models were proposed for genetic parameter estimation: (1) fixed effects plus sire effects; (2) model 1 plus maternal grandsire effect; (3) model 2 plus dam within maternal grandsire effects; (4) same as model 3 but a random effect ‘herds’ replaced a fixed effect ‘regions’. Two methods of fitting models were applied: marginal maximum likelihood and the ‘tilde-hat’ approach. Estimates of genetic parameters by the two methods were similar. Models ignoring maternal effects overestimated the heritability of direct effects especially in the case of dystocia 2. Dystocia definition was responsible for the greatest difference among estimated genetic parameters. Possible reasons for this are discussed. When analysing large data sets, it is recommended judiciously to collapse dystocia categories and to apply approximate statistical procedures to complete models including maternal effects.

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