scholarly journals Estimation of genetic parameters for melanoma in the Old Kladruber horse

2012 ◽  
Vol 57 (No. 2) ◽  
pp. 75-82 ◽  
Author(s):  
L. Vostrý ◽  
B. Hofmanová ◽  
H. Vostrá Vydrová ◽  
J. Přibyl ◽  
I. Majzlík
Keyword(s):  
Animal Model ◽  
Genetic Variation ◽  
Correlation Coefficient ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Rank Correlation ◽  
Genetic Effect ◽  
Pedigree Information ◽  
Direct Genetic Effect ◽  
Estimation Of Genetic Parameters

The aim of this study was to assess the prevalence of melanoma to investigate a possible genetic variation of this trait in the Old Kladruber horse. A total of 564 grey varieties of the Old Kladruber horse, 238 males and 326 females, with five generations of ancestors (n = 1245 animals) were analysed. Melanoma status was recorded for different stages. Three different analyses were conducted: a linear animal model (LM) with melanoma classified into five categories, threshold animal model (TM) with melanoma classified into five categories and threshold animal model (TMb) with melanoma classified into two categories (0 = absence, 1 = presence). All models included the fixed effects of year of evaluation, age, line, sex, greying level, random direct genetic effect, and the effect of animal’s permanent environment. Heritability for melanoma occurrence was estimated for LM – 0.09, for TM – 0.27, and for TMb – 0.11. The coefficient of repeatability was estimated for LM – 0.77, for TM – 0.90, and for TMb – 0.99. The values of the Pearson’s correlation coefficient and Spearmen’s rank correlation coefficient among breeding values estimated by LM, TM, and TMb models were from 0.82 to 0.88 and from 0.83 to 0.90, respectively, for data with pedigree information and from 0.77 to 0.84 and 0.77 to 0.88, respectively, for a subset of animals with measurements. Results suggest that additive genetic variation of melanoma occurrence in the Old Kladruber horse seems large enough to be exploited in a specific breeding programme.  

scholarly journals Use of individual race results in the estimation of genetic parameters of trotting performance for Finnhorse and Standardbred trotters

1999 ◽  
Vol 8 (4-5) ◽  
pp. 353-363 ◽  
Author(s):  
T. THUNEBERG-SELONEN ◽  
J. PÖSÖ ◽  
E. MÄNTYSAARI
Keyword(s):  
Animal Model ◽  
Maximum Likelihood ◽  
Variance Components ◽  
Fixed Effects ◽  
Environmental Effect ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Performance Traits ◽  
Estimation Of Genetic Parameters

The heritability and repeatability for trotting performance traits were estimated from individual race results. Data comprised of records from 1991 to 1995 for 4808 Finnhorses and from 1993 to 1995 for 5869 Standardbred trotters. The statistical model included the additive genetic effect of an animal and two permanent environmental effects, and the fixed effects of sex, age, starting method*starting lane combination, driver and race. The first permanent environmental effect described repeatability over a horse’s career while the second one characterized repeatability within a racing year. Variance components for three trotting performance traits were estimated by the animal model and the method of restricted maximum likelihood (REML). Heritability and repeatability estimates were moderately high for time at finish (h 2 =0.23–0.28 and r=0.50–0.57), moderate for ranking within a race (h 2 =0.12 and r=0.25) and low for earnings (h 2 =0.05–0.09 and r=0.15–0.18). Time at finish seemed to be the most usable measure of trotting performance because of its wide information substance. However, time at finish does not take into account records of disqualified horses or of those which did not finish, but use of earnings, either from individual race results or preferably from annual records, is one possible way to consider records of such horses.;

scholarly journals Animal Model Versus Polynomial Legendre Test-Day In Dairy Cattle

2020 ◽  
Vol 23 (1) ◽  
pp. 5-12
Author(s):  
Mircea Cătălin Rotar ◽  
Horia Grosu ◽  
Mihail Alexandru Gras ◽  
Rodica Ştefania Pelmuş ◽  
Cristina Lazăr ◽  
...  
Keyword(s):  
Animal Model ◽  
Dairy Cattle ◽  
Fixed Effects ◽  
Rank Correlation ◽  
Pedigree Information ◽  
Lactation Period ◽  
Data Set ◽  
Breeding Values ◽  
Normal Limits ◽  
Model Versus

AbstractThe aim of the study was to compare the classical animal model (based on total milk for 305 days) with the Test-Day model (using monthly records of milk yield from Official Records of Performances). The data set derived from a total 175 animals (cows with records, parents of these animals and the descendants) from two Romanian breeds (Romanian Black Spotted and Montbeliarde), the phenotypic and the pedigree information arisen from National Research Development Institute for Animal Biology and Nutrition (IBNA-Balotesti). The selection criteria to be included in the analysis for each cow was to have at least 3 test-days and the days in milk between 200 and 330 for the Test-Day model and the total amount of the 305- day lactation yield for classical Animal Model respectively. Both models use B.L.U.P methodology and for that reason all the estimates were adjusted for fixed effects and all the breeding values and the solution for fixed effects were estimated simultaneous. For the animal model the fixed effects used was the breed and the year of performing and for the Test-Day model was an extra one, the test day effect. The correlation calculated between test days was very high (over 90%) for consecutive tests, and was getting lower when the days between tests was higher (under 40%). Also, in terms of heritability the values were in normal limits throughout lactation, except at the beginning and end of lactation period where these values were a little bit higher. The comparison of the ranking of breeding values with Spearman rank correlation shows that in 80% of the cases the ranking was similar for both models. As the ranking correlations shows, it is certain that the two models are very similar when they are used for genetic evaluation. But, in conclusion, we can say that for a better lactation curve estimation it is recommending to use test-day model for dairy cattle.

A note on heritability estimates for growth traits in male and female Romanov sheep

1993 ◽  
Vol 57 (2) ◽  
pp. 326-328 ◽  
Author(s):  
G. A. María ◽  
K. G. Boldman ◽  
L. D. van Vleck
Keyword(s):  
Animal Model ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Random Effects ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Effect ◽  
Male And Female ◽  
Males And Females ◽  
Direct Genetic Effect

A total of 1855 records were analysed using restricted maximum likelihood (REML) techniques to estimate heritabilities separately for males and females lambs on birth weight (BW), weaning weight (WW), 90-day weight (W90) and average daily gains birth to weaning (Cl) and weaning to 90 days (C2). An animal model including fixed effects of year × season, parity, litter size and rearing type; and random effects of direct genetic effect (h2D) and residual was applied. Estimates ofh2Dfor BWwere 048 (males) and 0·50 (females); for WW 0·35 (males) and 0·22 (females); for W90 0·21 (males) and 0·31 (females); for Cl 0·20 (males) and 0·25 (females); and for C2 0·18 (males) and 0·29 (females).

scholarly journals Selection of a suitable data set and model for the estimation of genetic parameters of the weaning weight in beef cattle

2007 ◽  
Vol 50 (6) ◽  
pp. 562-574
Author(s):  
L. Vostrý ◽  
J. Přibyl ◽  
Z. Veselá ◽  
V. Jakubec
Keyword(s):  
Beef Cattle ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Suitable Model ◽  
Data Set ◽  
Weaning Weight ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for weaning weight of beef cattle in the Czech Republic. Nine subsets were tested for the selection of a suitable subset. The subsets differed from each other in the limit of sampling criteria. The most suitable subset satisfied these conditions: at least 5 individuals per each sire, 5 individuals per HYS (herd, year, season), 2 sires per HYS, and individuals per dams that have at least one half-sister and two offspring (n = 4 806). The selection of a suitable model was carried out from 10 models. These models comprised some of the random effects: direct genetic effect, maternal genetic effect, permanent maternal environment effect, HYS, sire × herd or sire × year interaction, and some of the fixed effects: dam’s age, sex (young bull, heifer × single, twin born), HYS, year, herd. The direct heritability (h2a) ranged from 0.06 to 0.17, of maternal heritability (h2m) from 0.03 to 0.06. The genetic correlations between the direct and maternal effect (ram) were in the range of –0.15 –0.42.

scholarly journals Selection of a suitable data set and model for the genetic evaluation of the linear description of conformation and type description in Old Kladruber horses

2012 ◽  
Vol 55 (2) ◽  
pp. 105-112
Author(s):  
L. Vostrý ◽  
K. Mach ◽  
J. Přibyl
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Suitable Model ◽  
Residual Variance ◽  
Linear Type ◽  
Colour Variant ◽  
Repeated Evaluation ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for 36 traits of the linear type in 977 Old Kladruber horses. Two subsets were tested to identify a suitable subset for analysis. One subset included repeated evaluation of certain individuals, whereas the other did not. The most suitable subset included repeated evaluation (n=1 390). The selection of a suitable model was made from 4 candidate models. These models comprised a number of random effects (direct individual effect and animal permanent environmental effect of the animal) and a number of fixed effects (colour variant, stud, colour variant × stud interaction, sex, age at description, year of birth, year of description). The model was selected based on the Akaike information criterion (AIC, Akaike 1974), residual variance and heritability coefficient. The model that included colour variant, stud, colour variant × stud interaction, sex, age at description, and year of description as fixed effects and direct individual and animal permanent environment as random effects was the most suitable model for the estimation of genetic parameters and for the subsequent estimation of breeding values.

Estimation of genetic parameters for growth traits of Japanese flounder Paralichthys olivaceus using an animal model

2010 ◽  
Vol 77 (1) ◽  
pp. 87-93 ◽  
Author(s):  
Yong-Xin Liu ◽  
Gui-Xing Wang ◽  
Yu-Fen Wang ◽  
Fei Si ◽  
Zhao-Hui Sun ◽  
...  
Keyword(s):  
Animal Model ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Paralichthys Olivaceus ◽  
Japanese Flounder ◽  
Estimation Of Genetic Parameters

scholarly journals Comparison of models for estimating genetic parameters and predicting breeding values for birth weight and calving ease in Czech Charolais cattle 

2014 ◽  
Vol 59 (No. 7) ◽  
pp. 302-309 ◽  
Author(s):  
L. Vostrý ◽  
Z. Veselá ◽  
A. Svitáková ◽  
H. Vostrá Vydrová
Keyword(s):  
Animal Model ◽  
Birth Weight ◽  
Beef Cattle ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Parameters Estimation ◽  
Calving Ease ◽  
Heritability Estimates

The most appropriate model for genetic parameters estimation for calving ease and birth weight in beef cattle was selected. A total of 27 402 field records were available from the Czech Charolais breed. For estimation of genetic parameters for calving ease and body weight, three bivariate models were tested: a linear-linear animal model (L-LM) with calving ease classified into four categories (1 – easy; 2–4 – most difficult), a linear-linear animal model (SC-LM) in which calving ease scores were transformed into Snell scores (Snell 1964) and expressed as percentage of assisted calving (ranging 0–100%), and a bivariate threshold-linear animal model (T-LM) with calving ease classified into four categories (1 – easy, 2–4 – most difficult). All tested models included fixed effects for contemporary group (herd × year × season), age of dam, sex and breed of a calf. Random effects included direct and maternal genetic effects, maternal permanent environmental effect, and residual error. Direct heritability estimates for calving ease and birth weight were, with the use of L-LM, SC-LM, and T-LM, from 0.096 ± 0.013 to 0.226 ± 0.024 and from 0.210 ± 0.024 to 0.225 ± 0.026, respectively. Maternal heritability estimates for calving ease and birth weight were, with the use of L-LM, SC-LM, and T-LM, from 0.060 ± 0.031 to 0.104 ± 0.125 and from 0.074 ± 0.041 to 0.075 ± 0.040, respectively. Genetic correlations of direct calving ease with direct birth weight ranged from 0.46 ± 0.06 to 0.50 ± 0.06 for all tested models; whereas maternal genetic correlations between these two traits ranged from 0.24 ± 0.17 to 0.25 ± 0.53. Correlations between direct and maternal genetic effects within-trait were negative and substantial for all tested models (ranging from –0.574 ± 0.125 to –0.680 ± 0.141 for calving ease and from –0.553 ± 0.122 to –0.558 ± 0.118 for birth weight, respectively), illustrating the importance of including this parameter in calving ease evaluations. Results indicate that any of the tested models could be used to reliably estimate genetic parameters for calving ease for beef cattle in the Czech Republic. However, because of advantages in computation time and practical considerations, genetic analysis using SC-LM (transformed data) is recommended.

scholarly journals Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Bjarke G. Poulsen ◽  
Birgitte Ask ◽  
Hanne M. Nielsen ◽  
Tage Ostersen ◽  
Ole F. Christensen
Keyword(s):  
Growth Rate ◽  
Animal Model ◽  
Genetic Effect ◽  
Predictive Performance ◽  
Genetic Effects ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Indirect Genetic Effects ◽  
Group Members ◽  
Direct Genetic Effect

Abstract Background Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ($${\mathbf{A}}$$ A ) and a combined pedigree and genomic relationship matrix ($${\mathbf{H}}$$ H ); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). Results The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when $${\mathbf{H}}$$ H was used rather than $${\mathbf{A}}$$ A . Conclusions This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with $${\mathbf{H}}$$ H than $${\mathbf{A}}$$ A ; (3) using $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A primarily improves the predictive performance of direct genetic effects.

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