Genetic parameters for clinical mastitis in Holstein-Friesians in the United Kingdom: a Bayesian analysis

2001 ◽  
Vol 73 (2) ◽  
pp. 229-240 ◽  
Author(s):  
H. N. Kadarmideen ◽  
R. Rekaya ◽  
D. Gianola
Keyword(s):  
Genetic Parameters ◽  
Genetic Parameter ◽  
Mixed Linear Model ◽  
Clinical Mastitis ◽  
Parameter Estimates ◽  
Data Sets ◽  
Data Sampling ◽  
Environmental Variance ◽  
Data Set ◽  
Monte Carlo Techniques

AbstractA Bayesian threshold-liability model with Markov chain Monte Carlo techniques was used to infer genetic parameters for clinical mastitis records collected on Holstein-Friesian cows by one of the United Kingdom’s national recording schemes. Four data sets were created to investigate the effect of data sampling methods on genetic parameter estimates for first and multi-lactation cows, separately. The data sets were: (1) cows with complete first lactations only (8671 cows); (2) all cows, with first lactations whether complete or incomplete (10 967 cows); (3) cows with complete multi-lactations (32 948 records); and (4) all cows with multiple lactations whether complete or incomplete (44 268 records). A Gaussian mixed linear model with sire effects was adopted for liability. Explanatory variables included in the model varied for each data set. Analyses were conducted using Gibbs sampling and estimates were on the liability scale. Posterior means of heritability for clinical mastitis were higher for first lactations (0·11 and 0·10 for data sets 1 and 2, respectively) than for multiple lactations (0·09 and 0·07, for data sets 3 and 4, respectively). For multiple lactations, estimates of permanent environmental variance were higher for complete than incomplete lactations. Repeatability was 0·21 and 0·17 for data sets 3 and 4, respectively. This suggests the existence of effects, other than additive genetic effects, on susceptibility to mastitis that are common to all lactations. In first or multi-lactation data sets, heritability was proportionately 0·10 to 0·19 lower for data sets with all records (in which case the models had days in milk as a covariate) than for data with only complete lactation records (models without days in milk as a covariate). This suggests an effect of data sampling on genetic parameter estimates. The regression of liability on days in milk differed from zero, indicating that the probability of mastitis is higher for longer lactations, as expected. Results also indicated that a regression on days in milk should be included in a model for genetic evaluation of sires for mastitis resistance based on records in progress.

Genetic and economic relationships between somatic cell count and clinical mastitis and their use in selection for mastitis resistance in dairy cattle

2001 ◽  
Vol 73 (1) ◽  
pp. 19-28 ◽  
Author(s):  
H. N. Kadarmideen ◽  
J. E. Pryce
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Genetic Parameters ◽  
Clinical Mastitis ◽  
Parameter Estimates ◽  
Data Set ◽  
Selection For ◽  
Genetic Regression ◽  
Genetic Responses

AbstractClinical mastitis (CM) and monthly test-day somatic cell count (SCC) records on Holstein cows were used to investigate the genetic and economic relationship of lactation average (of natural logarithms of) monthly test-day SCC (LSCC) with CM. After editing, there were 23663 lactation records on 17937 cows from 257 herds. Three groups of herds were first identified as having low (L), medium (M) and high (H) incidences of CM from the original or pooled (P) data set. Genetic parameters were estimated for the original and three data sub-sets (derived from the three herd groups). Expected genetic responses to selection against CM were calculated using genetic parameters of each data set separately, with an adapted version of the UK national index (£PLI-profitable lifetime index). Indirect economic values of SCC (EVSCC) were calculated as the direct cost of CM per cow per lactation weighted by the genetic regression coefficient of CM lactation records on their sires’ predicted transmitting ability for SCC (PTASCC). All genetic regression analyses were based on linear and threshold-liability models. Heritabilities and repeatabilities, respectively, were 0034 and 0·111 for CM and 0120 and 0·347 for LSCC in the original data set. Genetic, permanent environmental, residual and phenotypic correlations between CM and LSCC for the original (pooled) data set were 0·70, 0·44, 013 and 0·20, respectively. Parameter estimates for the three herd groups differed, with magnitude of the estimates increasing with increase in incidence from L to H herd groups. The EVSCCper unit of PTASCCfor L, M, H and P herd groups, respectively, were £004, £0·15, £0·33 and £018 on the observed and £0·86, £0·96, £1·22 and £110 on the underlying-liability scales. Selection for mastitis resistance, using SCC as an indicator trait in an extended version of £PLI, resulted in a selection response of 0·9, 21, 1·7 and 1·9 more cases per 100 cows after 10 years of selection in L, M, H and P herd groups, respectively. These results suggest that genetic responses to selection for CM resistance as well as the EVSCCare specific to herd incidence and hence would be appropriate for customized selection indexes. The increase in CM cases was greater when CM was excluded from the £PLI (2·8v1·9), hence it is recommended that CM should be included in the breeding goal in order to arrest further decline or to make improvement in genetic resistance to clinical mastitis.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

Genetic parameter estimates for feeding pattern and production traits in pigs

1997 ◽  
Vol 1997 ◽  
pp. 31-31
Author(s):  
A.D. Hall ◽  
W.G. Hill ◽  
P.R. Bampton ◽  
A.J. Webb
Keyword(s):  
Feed Intake ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Feeding Pattern ◽  
Parameter Estimates ◽  
Group Housing ◽  
Production Traits ◽  
Production Environment ◽  
Feeding Patterns ◽  
Potential Benefits

Until recently, to enable accurate recording of feed intake, pigs were kept in individual pens. The advent of electronic feeders has allowed accurate records of feed intake and feeding patterns in group housing which is more similar to that found in the production environment. The objectives of this study were to estimate genetic parameters for these feeding pattern traits and their correlations with production traits to show potential benefits in selection.

scholarly journals A New Robust Diagnostic Plot for Classifying Good and Bad High Leverage Points in a Multiple Linear Regression Model

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Mohammed Alguraibawi ◽  
Habshah Midi ◽  
A. H. M. Rahmatullah Imon
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Multiple Linear Regression Model ◽  
Parameter Estimates ◽  
Data Sets ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Leverage Points ◽  
Diagnostic Plot ◽  
Leverage Point

Identification of high leverage point is crucial because it is responsible for inaccurate prediction and invalid inferential statement as it has a larger impact on the computed values of various estimates. It is essential to classify the high leverage points into good and bad leverage points because only the bad leverage points have an undue effect on the parameter estimates. It is now evident that when a group of high leverage points is present in a data set, the existing robust diagnostic plot fails to classify them correctly. This problem is due to the masking and swamping effects. In this paper, we propose a new robust diagnostic plot to correctly classify the good and bad leverage points by reducing both masking and swamping effects. The formulation of the proposed plot is based on the Modified Generalized Studentized Residuals. We investigate the performance of our proposed method by employing a Monte Carlo simulation study and some well-known data sets. The results indicate that the proposed method is able to improve the rate of detection of bad leverage points and also to reduce swamping and masking effects.

Genetic parameter estimates for early growth traits in Naeini goat

2012 ◽  
Vol 52 (11) ◽  
pp. 1046 ◽  
Author(s):  
Hasan Baneh ◽  
Mojtaba Najafi ◽  
Ghodrat Rahimi
Keyword(s):  
Environmental Effects ◽  
Variance Components ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Parameter ◽  
Average Daily Gain ◽  
Parameter Estimates ◽  
Estimate Variance ◽  
Daily Gain ◽  
Birth Type

The present study was carried out to estimate variance components for growth traits in Naeini goats. Bodyweight records were collected for two flocks under supervision of the Agriculture Organisation of the Esfahan province between 2000 and 2007. Investigated traits were birthweight (BW; n = 2483), weaning weight (WW; n = 1211) and average daily gain from birth to weaning (ADG; n = 1211). Environmental effects were investigated using fixed-effect models, while (co)variance components and genetic parameters were estimated with single- and three-trait analyses using REML methods and WOMBAT software. Six different animal models were fitted to the traits, with the best model for each trait determined by log-likelihood ratio tests (LRT). All traits were significantly influenced by herd, birth year, sex of the kid, birth type and dam age (P < 0.01). On the basis of LRT, maternal permanent environmental effects (c2) were significant for WW and ADG, while BW was affected only by direct genetic effects. Direct heritability estimates for BW, WW and ADG were 0.25 ± 0.05, 0.07 ± 0.06 and 0.21 ± 0.11, respectively. The estimate of c2 was 0.16 ± 0.06 for both WW and ADG. Estimates of genetic correlation for BW–ADG, BW–WW and ADG–WW were 0.49, 0.61 and 0.94, respectively. The estimated phenotypic correlations were positive and were between 0.03 (BW–ADG) and 0.95 (ADG–WW). These results indicate that selection can be used to improve growth traits in this goat breed.

Multitrait and repeatability estimates of random effects on litter size in sheep

2002 ◽  
Vol 74 (2) ◽  
pp. 209-216 ◽  
Author(s):  
C. Hagger
Keyword(s):  
Litter Size ◽  
Random Effects ◽  
Genetic Gain ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Parameter Estimates ◽  
Data Sets ◽  
Phenotypic Variance ◽  
The Third ◽  
Meat Sheep

AbstractFive data sets with records of first, second and third lambings of the White Alpine sheep (WAS1, WAS2), the Brown-Headed Meat sheep (BFS), the Black-Brown Mountain sheep (SBS) and the Valais Black-Nose sheep (SNS) of Switzerland were used to estimate phenotypic and genetic parameters for litter size using a multitrait and a repeatability model by the REML method. The sets contained litter information from 26 274, 25 165, 18 913, 14 953 and 21 726 ewes, respectively. Average numbers of litters per ewe were between 2·09 and 2·31. Average litter sizes at birth were between 1·36 and 1·57 lambs in first, between 1·52 and 1·75 in second and, between 1·56 and 1·86 in third parities. Multitrait estimates of heritability for size of first litters were 0·164, 0·157, 0·117, 0·223 and 0·116 for the WAS1, WAS2, BFS, SBS and SNS data, respectively. The corresponding estimates were 0·176, 0·165, 0·140, 0·208 and 0·134 for second and, 0·141, 0·155, 0·121, 0·145 and 0·107 for third litters. The systematic increase in phenotypic variances from first to third litter within data sets favoured the multivariate over the repeatability approach. Genetic correlations between size of the first three litters were, with one exception, above 0·927. Random flock ✕ year and sire of litter effects contributed between 2·2% and 13·2% and between 0·7% and 4·7% to the phenotypic variance of the traits, respectively. Residuals contributed between 70·6% and 84·2% to this parameter, estimates for the third litter were always highest. Heritability estimates from the repeatability model were smaller than the smallest multivariate estimates. Expected genetic gain in litter size from selection on the multitrait model was equal to the achieved response from the repeatability approach.

scholarly journals Genetic parameter estimates for important traits on the fifth generation in red tilapia in Vietnam

2021 ◽  
Vol 20 (1) ◽  
pp. 49-57
Author(s):  
Sang V. Nguyen
Keyword(s):  
Body Weight ◽  
Survival Rate ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Body Colour ◽  
Red Tilapia ◽  
Gxe Interaction

Genetic parameters comprising heritability, genetic correlation and genotype by environment interaction (GxE) for growth survival rate and body colour at harvest were estimated on the 5th selective generation of red tilapia grown in two environments, freshwater and brackishwater ponds. A total of 116 full-half-sib families was produced as well as 4,432 and 3,811 tagged individuals were tested in freshwater and brackishwater ponds, respectively. Genetic parameters were estimated by ASReml 4.1 software. The heritability for body weight and survival rate was high while medium heritability for body colour in freshwater was observed. The heritability for those traits of red tilapia in brackishwater. Together with the figures in earlier publication on previous generations (G1 to G4) in the same selective population, the expected medium to high response acquires if selection is done for each trait. Genetic correlations among harvest body weight, survival rate and body colour are insignificantly different and ranging from -0.25 to 0.37 (P > 0.05). These results implied that selection on one trait do not influence on responses of the other traits. GxE interaction for body weight and body colour between two tested environments is mostly negligible with genetic correlations ranging from 0.63 - 0.80 while it is important for survival trait (rg = -0.17 ± 0.40).

scholarly journals Coheritability and Coenvironmentability as Concepts for Partitioning the Phenotypic Correlation

2019 ◽  
Author(s):  
Jorge Vasquez-Kool
Keyword(s):  
Genetic Parameter ◽  
Three Dimensional ◽  
Large Data ◽  
Phenotypic Correlation ◽  
Shared Environment ◽  
Parameter Estimates ◽  
Extensive Literature ◽  
Data Set ◽  
Phenotypic Characters ◽  
Relative Contribution

AbstractCentral to the study of joint inheritance of quantitative traits is the determination of the degree of association between two phenotypic characters, and to quantify the relative contribution of shared genetic and environmental components influencing such relationship. One way to approach this problem builds on classical quantitative genetics theory, where the phenotypic correlation between two traits is modelled as the sum of a genetic component called the coheritability (hx,y), which reflects the degree of shared genetics influencing the phenotypic correlation, and an environmental component, namely the coenvironmentability (ex,y) that accounts for all other factors that exert influence on the observed trait-trait association. Here a mathematical and statistical framework is presented on the partition of the phenotypic correlation into these components. I describe visualization tools to analyze and ex,y concurrently, in the form of a three-dimensional (3DHER-plane) and a two-dimensional (2DHER-field) plots. A large data set of genetic parameter estimates (heritabilities, genetic and phenotypic correlations) was compiled from an extensive literature review, from which coheritability and coenvironmentability were derived, with the object to observe patterns of distribution, and tendency. Illustrative examples from a diverse set of published studies show the value of applying this partition to generate hypotheses proposing the differential contribution of shared genetics and shared environment to an observed phenotypic relationship between traits.

scholarly journals Genetic parameters and performance of papaya genotypes to black spot resistance (Asperisporium caricae)

2019 ◽  
pp. 649-655
Author(s):  
Ramon Moraes ◽  
Marcelo Vivas ◽  
Janieli Maganha Silva Vivas ◽  
Rogério Figueiredo Daher ◽  
Geraldo Amaral Gravina ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Resistance ◽  
Black Spot ◽  
Parameter Estimate ◽  
Open Flower ◽  
Monthly Basis ◽  
Data Set ◽  
Superior Genotype ◽  
And Performance

Estimation of genetic parameters such as genetic variability of germplasm allows inferring genotype-enviromental interaction for a given variable. The information is important for the process of choosing the variables to be applied to the superior genotype selection. This study aimed at evaluating characteristics related to genetic resistance of papaya to black spot during time testing, as well as estimating genetic parameters associated with some characteristics. The experiment was carried out in RCBD design at Agua Limpa farm, Espirito Santo state, Brazil, using six genotypes: ‘STZ-03’, ‘SS-PT’, ‘Golden’ (‘Solo’ group) ‘Maradol’ (‘Formosa’ group) ‘STA-04’ ‘STA-10’ (landraces), and four repetitions. The 6 treatments were arranged in single row, spacing 2 m between rows and 1.5 m within plants. Nine evaluations were performed during 9 months. We quantified plants on a monthly basis for the characters such as symptom appearance of black spot (FS) on leaves; the incidence of leaves with black spot symptoms (IBS); the severity of black spot on the fifth leaf (SBS5F) and on the leaf with axil attached to the first open flower (SBSFO). By means of the evaluation values, we built a Boxplot graphic to characterize the magnitude of the variables and to describe the dispersion of the data set throughout the evaluations. Analysis of variance, genetic parameter estimate and comparative test of mean were also conducted. The Boxplot graphic allowed classification and magnitude of the variables and described the dispersion of the data set during evaluations. The results showed that SBS5F and the SBSFO were the characteristics that generated reliable results to select genotypes in all evaluations. They showed high H² (Coefficient of genotypic determination), CVg (Coefficient of genotypic variance), CVr (Coefficient of relative variance) and AS (Selective accuracy). The months July, August, September and October showed higher representativeness to evaluate attributes related to resistance to black spot in papaya leaves.

Estimating Quantitative Genetic Parameters Using Sibships Reconstructed From Marker Data

Genetics ◽  
2000 ◽  
Vol 155 (4) ◽  
pp. 1961-1972 ◽  
Author(s):  
Stuart C Thomas ◽  
William G Hill
Keyword(s):  
Prior Knowledge ◽  
Genetic Parameters ◽  
Allele Frequencies ◽  
Parameter Estimates ◽  
Monte Carlo Techniques ◽  
Quantitative Genetic ◽  
Downward Bias ◽  
Mean Square Errors ◽  
Sibship Reconstruction ◽  
Quantitative Genetic Parameters

Abstract Previous techniques for estimating quantitative genetic parameters, such as heritability in populations where exact relationships are unknown but are instead inferred from marker genotypes, have used data from individuals on a pairwise level only. At this level, families are weighted according to the number of pairs within which each family appears, hence by size rather than information content, and information from multiple relationships is lost. Estimates of parameters are therefore not the most efficient achievable. Here, Markov chain Monte Carlo techniques have been used to partition the population into complete sibships, including, if known, prior knowledge of the distribution of family sizes. These pedigrees have then been used with restricted maximum likelihood under an animal model to estimate quantitative genetic parameters. Simulations to compare the properties of parameter estimates with those of existing techniques indicate that the use of sibship reconstruction is superior to earlier methods, having lower mean square errors and showing nonsignificant downward bias. In addition, sibship reconstruction allows the estimation of population allele frequencies that account for the relationships within the sample, so prior knowledge of allele frequencies need not be assumed. Extensions to these techniques allow reconstruction of half sibships when some or all of the maternal genotypes are known.

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