Selection of dairy sheep in Greece for parasitic nematode resistance: defining the aggregate genotype and evaluating selection schemes

1999 ◽  
Vol 69 (3) ◽  
pp. 535-542 ◽  
Author(s):  
A. Kominakis ◽  
G. Theodoropoulos
Keyword(s):  
Genetic Gain ◽  
Milk Fat ◽  
Genetic Correlations ◽  
Selection Procedure ◽  
Nematode Resistance ◽  
Repeated Measurements ◽  
Production Traits ◽  
Dairy Sheep ◽  
Wide Range ◽  
Selection Of

AbstractThe effects on genetic and economic responses of adding faecal egg count (FEC) in the aggregate genotype of dairy sheep in Greece were investigated. The extra responses obtained in the full aggregate genotype were expressed as a percentage change of the responses in terms of genetic gain, profit and selection response of single traits before adding the trait. The initial aggregate genotype included the traits milk fat yield (FY) and number of lambs weaned per ewe per year (NLW). Inclusion of FEC in the aggregate genotype resulted always in increased genetic gain and profit. The extra responses from adding the FEC in the index selection were variable and often very large, depending on the parameters varied i.e. the economic weight of FEC and the genetic correlation of FEC with the other production traits. For a wide range in the size of the genetic correlations between FEC and other traits, gains of FEC and no appreciable losses of responses for FY and NLW were predicted when FEC accounted proportionately for 0·10 to 0·20 of the total monetary genetic deviation. While FEC showed a wide range of change, selection responses of FY and NLW remained remarkably insensitive under various weightings of FEC and different genetic correlations of FEC with the production traits. The genetic and economic efficiency of alternative selection schemes were also evaluated. A two-stage selection procedure involving preliminary selection of rams on dam’s FEC, FY, NLW and on their own FEC and final culling on progeny’s FEC, FY and NLW was predicted to be the most efficient in both genetic and economic terms. Female replacements should be selected on dam’s FEC, FY and NLW (first stage) and their own FEC, FY and NLW (second stage). When repeated measurements of FEC are taken, the recommended number of FEC measurements was found to be 4.

scholarly journals Estimation of genetic parameters and comparison of random regression animal and sire models of production traits in the first three lactations of Iranian Holsteins

2014 ◽  
Vol 30 (2) ◽  
pp. 261-279 ◽  
Author(s):  
A. Mohammadi ◽  
S. Alijani
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Random Regression ◽  
Lactation Period ◽  
Production Traits ◽  
Environmental Variance ◽  
In The Beginning ◽  
Residual Variances

This study was conducted to compare of random regression (RR) animal and sire models for estimation of the genetic parameters for production traits of Iranian Holstein dairy cows. For this purpose, the test day records were used belonged to first three lactations of cows and for, milk, fat and protein yields traits where, collected from 2003 to 2010, by the national breeding center of Iran. The genetic parameters were estimated using restricted maximum likelihood algorithm. To compare the model, different criterion -2logL value, AIC, BIC and RV were used for considered traits. Residual variances were considered homogeneous over the lactation period. Obtained results showed that additive genetic variance was highest in the beginning and end lactation and permanent environmental variance was highest in beginning of lactation than other lactation period. Heritabilities estimate for milk, fat and protein yields by RR animal and sire models were found to be lowest during early lactation (0.05, 0.04 and 0.07; 0.05, 0.19 and 0.13; 0.14, 0.19 and 0.15, for milk, fat and protein yields and in first, second and third lactation respectively). However, estimated heritabilities during lactation did not vary among different order Legendre polynomials, and also between RR animal and sire models. The variation in genetic correlations estimate in the RR animal and sire models was larger in the first lactation than in the second and third lactations. Thus, based on the results obtained, it can be inferred that the RR animal model is better for modeling yield traits in Iranian Holsteins.

Genetic parameters of milk traits in Latxa dairy sheep

2001 ◽  
Vol 73 (3) ◽  
pp. 407-412 ◽  
Author(s):  
A. Legarra ◽  
E. Ugarte
Keyword(s):  
Milk Fat ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Selection Criterion ◽  
Genetic Effect ◽  
Milk Composition ◽  
Additive Genetic Effect ◽  
Dairy Sheep ◽  
Milk Traits

AbstractA total of 7444 lactation records which include milk, fat and protein yields (MY, FY, PY) and fat and protein content (F%, P%) from 6429 Black-Faced Latxa ewes were employed to estimate genetic parameters for milk traits. Traits were standardized to 120 days of lactation. For the calculation of composition traits, not all test-days had their composition measured and therefore a correction taking this into account was included in the analysis. A first-derivative restricted maximum likelihood algorithm was used on an animal model with repeatability analysis, using models including fixed effects (flock-year-season of lambing, age-parity at lambing, number of lambs, interval between lambing and first milk recording and the combination of sampled test-days) and random effects (the additive genetic effect and the permanent environmental effect). The resulting heritabilities were 0·20, 0·16, 0·18, 0·14 and 0·38 for MY, FY, PY, F% and P% respectively. Heritability of F% was much lower than expected, probably due to problems derived from the recording method. Genetic correlations were high and positive between yields and moderately positive between F% and P%, and negative or null between yields and composition, as has been reported for other European dairy sheep breeds. As most of the milk produced by Latxa dairy sheep is processed into cheese, the inclusion of milk sampling in official milk recording and a change in the selection criterion are recommended to avoid a long-term worsening in milk composition.

scholarly journals Random regression models to estimate genetic parameters for test-day milk yield and composition in Iranian buffaloes

2013 ◽  
Vol 56 (1) ◽  
pp. 276-284 ◽  
Author(s):  
M. Madad ◽  
N. Ghavi Hossein-Zadeh ◽  
A. A. Shadparvar ◽  
D. Kianzad
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Milk Protein ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Production Traits ◽  
Environmental Correlations ◽  
Protein Percentage ◽  
Heritability Estimates

Abstract. The objective of this study was to estimate genetic parameters for milk yield and milk percentages of fat and protein in Iranian buffaloes. A total of 9,278 test-day production records obtained from 1,501 first lactation buffaloes on 414 herds in Iran between 1993 and 2009 were used for the analysis. Genetic parameters for productive traits were estimated using random regression test-day models. Regression curves were modeled using Legendre polynomials (LPs). Heritability estimates were low to moderate for milk production traits and ranged from 0.09 to 0.33 for milk yield, 0.01 to 0.27 for milk protein percentage and 0.03 to 0.24 for milk fat percentage, respectively. Genetic correlations ranged from −0.24 to 1 for milk yield between different days in milk over the lactation. Genetic correlations of milk yield at different days in milk were often higher than permanent environmental correlations. Genetic correlations for milk protein percentage ranged from −0.89 to 1 between different days in milk. Also, genetic correlations for milk percentage of fat ranged from −0.60 to 1 between different days in milk. The highest estimates of genetic and permanent environmental correlations for milk traits were observed at adjacent test-days. Ignoring heritability estimates for milk yield and milk protein percentage in the first and final days of lactation, these estimates were higher in the 120 days of lactation. Test-day milk yield heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in Iranian milking buffaloes.

scholarly journals The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

2020 ◽  
Vol 33 (3) ◽  
pp. 382-389 ◽  
Author(s):  
Yun-Mi Lee ◽  
Chang-Gwon Dang ◽  
Mohammad Z. Alam ◽  
You-Sam Kim ◽  
Kwang-Hyeon Cho ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Milk Production ◽  
Genetic Gain ◽  
Milk Fat ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Data Set ◽  
Selection For ◽  
Estimated Breeding Value

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction.Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L<sub>SB</sub>) and that for the production of cows (L<sub>SC</sub>) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L<sub>DB</sub>) and cows (L<sub>DC</sub>) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

Modeling Biomass Gasification: A New Approach to Utilize Renewable Sources of Energy

2008 ◽  
Author(s):  
M. Vaezi ◽  
M. Passandideh-Fard ◽  
M. Moghiman ◽  
M. Charmchi
Keyword(s):  
Selection Procedure ◽  
Calorific Value ◽  
Gas Temperature ◽  
New Approach ◽  
Wide Range ◽  
Specific Biomass ◽  
Thermochemical Equilibrium ◽  
Biomass Materials ◽  
Gasification Efficiency ◽  
Selection Of

Thermochemical equilibrium modeling is the basis of the numerical method implemented in this study to predict the performance of a biomass gasifier. To validate the model, a close agreement is shown between numerical and experimental results. The model is then used in order to optimize the selection procedure of a specific biomass for a certain application. For this purpose, the minimum and maximum amount of carbon, hydrogen, and oxygen for 55 different biomass materials are extracted to calculate the range of variation of oxygen content and carbon/hydrogen ratio. The influences of such variations on syngas characteristics are then studied. Syngas characteristics are comprised of syngas calorific value, outlet gas temperature, gasification efficiency, and the volume of syngas obtained. The results are plotted in a generalized format that may be used for a wide range of biomass materials. These plots can be used for the selection of a biomass material based on desired conditions. Therefore, the developed model in this study provides a tool for design optimization of a biomass downdraft gasifier.

Genetic parameters of milk cholesterol content in Holstein cattle

2018 ◽  
Vol 98 (4) ◽  
pp. 714-722 ◽  
Author(s):  
Duy N. Do ◽  
Allison Fleming ◽  
Flavio S. Schenkel ◽  
Filippo Miglior ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Cholesterol Content ◽  
Production Traits ◽  
Fat Percentage ◽  
Bivariate Models ◽  
Estimate Heritability ◽  
Fat Yield ◽  
Using Data

This study aimed to estimate heritability for milk cholesterol (CHL) and genetic correlations between milk CHL and other production traits (test-day milk, fat, and protein yields, fat and protein percentages, and somatic cell score). Milk CHL content was determined by gas chromatography and expressed as mg of CHL in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk). Univariate models were used to estimate variances and heritability, whereas bivariate models were used to compute correlations using data from 1793 cows. The average concentrations (standard deviation) of CHL_fat and CHL_milk were 275.63 (75) mg and 11.16 (3.63) mg, respectively. Milk CHL content was significantly affected by days in milk and herd (P < 0.05), but not by parity, regardless of the scale of expression. Heritability estimates for CHL_fat and CHL_milk were 0.06 ± 0.04 and 0.17 ± 0.06, respectively. Phenotypic and genetic correlations between CHL_fat and CHL_milk were 0.82 and 0.44 ± 0.24, respectively. CHL_fat had nonsignificant genetic correlations with all production traits, whereas CHL_milk had significant (P < 0.05) genetic correlations with milk yield (−0.47), fat yield (0.51), protein percentage (0.56), and fat percentage (0.88). This is the first study to estimate genetic parameters for milk CHL content. Further studies are required to assess the possibility of genetically selecting cows with lower milk CHL content.

Genetic and phenotypic correlations between linear type traits and production traits in Holstein-Friesian dairy cattle

1994 ◽  
Vol 59 (2) ◽  
pp. 183-187 ◽  
Author(s):  
S. Brotherstone
Keyword(s):  
Milk Fat ◽  
Genetic Correlations ◽  
Protein Yield ◽  
Yield Traits ◽  
Production Traits ◽  
Linear Type ◽  
Phenotypic Correlations ◽  
Holstein Friesian ◽  
Type Traits ◽  
Genetic And Phenotypic Correlations

AbstractFirst lactation production and linear type records of 72 559Holstein-Friesian cows, calving from 1982 to 1989, were analysed by multivariate restricted maximum likelihood, using a sire model. The data comprised offspring of 1066 randomly used sires, and 91 proven i.e. widely used bulls. All phenotypic correlations between the type traits and the yield traits were small, but moderate genetic correlations were obtained between milk, fat and protein yield and angularity (~—0·43) and between the yield traits and udder depth (~0·44), indicating that higher yielding heifers are more angular and have deeper udders. The heritabilities of the type traits were in line with previous analyses, but those for milk, fat and protein yield were rather high at 0·47, 0·52 and 0·45 respectively

scholarly journals New residual feed intake criterion for longitudinal data

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Ingrid David ◽  
Van-Hung Huynh Tran ◽  
Hélène Gilbert
Keyword(s):  
Regression Model ◽  
Feed Intake ◽  
Genetic Correlations ◽  
Residual Feed Intake ◽  
Repeated Measurements ◽  
Regression Coefficients ◽  
Production Traits ◽  
Breeding Values ◽  
Time Points ◽  
Heritability Estimates

Abstract Background Residual feed intake (RFI) is one measure of feed efficiency, which is usually obtained by multiple regression of feed intake (FI) on measures of production, body weight gain and tissue composition. If phenotypic regression is used, the resulting RFI is generally not genetically independent of production traits, whereas if RFI is computed using genetic regression coefficients, RFI and production traits are independent at the genetic level. The corresponding regression coefficients can be easily derived from the result of a multiple trait model that includes FI and production traits. However, this approach is difficult to apply in the case of multiple repeated measurements of FI and production traits. To overcome this difficulty, we used a structured antedependence approach to account for the longitudinality of the data with a phenotypic regression model or with different genetic and environmental regression coefficients [multi- structured antedependence model (SAD) regression model]. Results After demonstrating the properties of RFI obtained by the multi-SAD regression model, we applied the two models to FI and production traits that were recorded for 2435 French Large White pigs over a 10-week period. Heritability estimates were moderate with both models. With the multi-SAD regression model, heritability estimates were quite stable over time, ranging from 0.14 ± 0.04 to 0.16 ± 0.05, while heritability estimates showed a U-shaped profile with the phenotypic regression model (ranging from 0.19 ± 0.06 to 0.28 ± 0.06). Estimates of genetic correlations between RFI at different time points followed the same pattern for the two models but higher estimates were obtained with the phenotypic regression model. Estimates of breeding values that can be used for selection were obtained by eigen-decomposition of the genetic covariance matrix. Correlations between these estimated breeding values obtained with the two models ranged from 0.66 to 0.83. Conclusions The multi-SAD model is preferred for the genetic analysis of longitudinal RFI because, compared to the phenotypic regression model, it provides RFI that are genetically independent of production traits at all time points. Furthermore, it can be applied even when production records are missing at certain time points.

scholarly journals Genetic parameters for egg production traits in crosses between local and exotic chickens estimated by Bayesian inference

2021 ◽  
Vol 44 (3) ◽  
pp. 1-8
Author(s):  
I. Udeh ◽  
S. I. Omeje
Keyword(s):  
Egg Production ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Breeding Value ◽  
Appreciable Amount ◽  
Production Traits ◽  
Base Population ◽  
Egg Weight ◽  
Highest Posterior Density ◽  
Selection Of

Estimates of genetic parameters for economic traits are important to enable the breeder estimate the breeding value of individuals available for selection. Thus, this study was carried out to estimate genetic parameters namely heritability and genetic correlations among egg production traits in two strain crosses using bivariate animal model in Bayesian (MCMC) method. The egg production data were obtained from four generations which comprised the base population of two commercial egg strains and the local chicken, the inbred population derived from the base population, the F1 crossbred population obtained from the crossing of the local and exotic inbred chickens and the backcross population obtained from the backcrossing of the crossbred to their parents. A total number of 1,138 daughters of 62 sires and 620 dams were used for the study. The autocorrelations among samples in the MCMC chain were less than 0.1 for all lag values indicating that all samples of the posterior distribution were independent. The estimated heritability for age at first egg, body weight at first egg, hen day egg number, weight of first egg, egg weight at thirty week and egg weight at forty week were 0.62, 0.48, 0.47, 0.53, 0.54 and 0.56 for strain 1 crosses and 0.43, 0.48, 0.49, 0.52, 0.52 and 0.53 for strain 2 crosses. The corresponding highest posterior density interval ranged from 0.22 to 0.91 for strain 1 crosses and 0.07 to 0.83 for strain 2 crosses. The genetic correlations among egg production traits ranged from 0.06 to 1.97 in strain 1 and 0.06 to 2.59 in strain 2 crosses. The estimates were within the range reported in literature for egg production traits in chicken and imply that appreciable amount of additive genes exist in the strain crosses which could be used for the selection of superior birds. The magnitude of genetic correlations implies that selection of one trait could lead to correlated response to the other traits.

scholarly journals Genetic parameters for milk production and persistency in the Iranian Holstein population by the multitrait random regression model

2014 ◽  
Vol 57 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Khabat Kheirabadi ◽  
Sadegh Alijani
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Genetic Correlations ◽  
Random Regression ◽  
Genetic Dissection ◽  
Production Traits ◽  
Data Set ◽  
Random Regression Model ◽  
Regression Test ◽  
Fat Yield

Abstract. For genetic dissection of milk, fat, and protein production traits in the Iranian primiparous Holstein dairy cattle, records of these traits were analysed using a multitrait random regression test-day model. Data set included 763 505 test-day records from 88 204 cows calving since 1993. The (co)variance components were estimated by Bayesian method. The obtained results indicated that as in case of genetic correlations within traits, genetic correlations between traits decrease as days in milk (DIM) got further apart. The strength of the correlations decreased with increasing DIM, especially between milk and fat. Heritability estimates for 305-d milk, fat, and protein yields were 0.31, 0.29, and 0.29, respectively. Heritabilities of test-day milk, fat, and protein yields for selected DIM were higher in the end than at the beginning or the middle of lactation. Heritabilities for persistency ranged from 0.02 to 0.24 and were generally highest for protein yield (0.05 to 0.24) and lowest for fat yield (0.02 to 0.17), with milk yield having intermediate values (0.06 to 0.22). Genetic correlations between persistency measures and 305-d production were higher for protein and milk yield than for fat yield. The genetic correlation of the same persistency measures between milk and fat yields averaged 0.76, and between milk and protein yields averaged 0.82.

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