• Journal of Animal Science and Technology
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• v.48 no.2
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• pp.161-168
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• 2006

Genetic parameters for linear type and composite traits were estimated by using Bayesian inference via Gibbs sampling with a multiple threshold animal model in Holstein cows. Fifteen linear type traits and 5 composite traits were included to estimate genetic variance and covariance components in the model. In this study, 30,204 records were obtained in the cows from 305 sires. Heritability estimates for linear type traits had the estimates as high as 0.28~0.64. Heritability estimates for composite traits were also high, when the traits were assumed to be categorical traits. Final score was more correlated with the composite traits than with the linear type traits.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.42 no.6
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• pp.591-599
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• 2009

Genetic parameters for growth-related traits were estimated in 9-month old of two Korean abalone subspecies, Haliotis discus hannai and H. discus discus, using multiple traits of animal model. The data were collected from the records of 3,504 individuals produced from 16 sires and 17 dams in H. discus hannai and 821 individuals produced from 3 sires and 4 dams in H. discus discus, which was evaluated at the Bukjeju branch, NFRDI, from May 20, 2004 to February 14, 2005. The heritability estimates obtained from restricted maximum likelihood (REML) method range from 0.29 to 0.31 for three growth traits (shell length, shell width and body weight) in H. discus hannai and from 0.22 to 0.28 in H. discus discus, respectively. The heritabilities for shell shape and condition factor were lower than others of growth traits such as ranging from 0.03 to 0.24 in H. discus hannai and from 0.06 to 0.11 in H. discus discus, respectively. Genetic and phenotypic correlations were >0.91 between shell parameters and weight in two abalone subspecies, respectively, indicating that breeding for weight gains could be successfully achieved by selecting for shell length.

• Animal Bioscience
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• v.34 no.2
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• pp.185-191
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• 2021

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses. Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model. Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model). Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.5
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• pp.549-556
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• 2022

The aim of this study was to evaluate the genetic parameters of growth traits for improvement breeding in olive flounder Paralichthys olivaceus at the 8^th generation of selective breeding in April 2021. Growth traits such as total length, body weight and condition factor at 11 months of age were measured for 7,508 individuals with confirmed paternity. Data were analyzed using the restricted maximum likelihood method applied to a multiple traits animal model. The effects of sex and family were significantly different across traits (P<0.05). The heritability values of total length, body weight and condition factor were estimated to be high as 0.479, 0.457, and 0.466, respectively. Correlation analysis between phenotypic and breeding values, indicated that the selection accuracy was 75.9-85.2% for all traits. To increase the selection accuracy for parent fish selection, the sex and pedigree characteristics that affect each trait should be considered. Moreover, further improvement of multiple traits can be achieved if the correlation between each trait is appropriately considered.

• Animal Bioscience
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• v.36 no.12
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• pp.1785-1795
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• 2023

Objective: This study was to estimate heritabilities, additive genetic correlations, and phenotypic correlations between number of piglets born alive (NBA), litter birth weight (LTBW), number of piglets weaned (NPW) and litter weaning weight (LTWW) in different parities of Landrace (L), Yorkshire (Y), Landrace×Yorkshire (LY), and Yorkshire×Landrace (YL) sows in a commercial swine operation in Northern Thailand. Methods: Two models were utilized, a single trait repeatability model (RM) and a multiple trait animal model (MTM). The RM assumed reproductive records from different parities to be repeated values of the same trait, whereas the MTM assumed these records to be different traits. The two models accounted for the fixed effects of farrowing year-season, genetic group of the sow, heterosis, and age at first farrowing, and the random effects of sow, boar, and residual. Results: Heritability estimates from RM were 0.02±0.01 for NBA, 0.10±0.01 for LTBW, 0.04±0.01 for NPW, and 0.11±0.01 for LTWW. Heritability estimates from MTM fluctuated across parities, ranging from 0.04±0.01 in parity 2 to 0.09±0.02 in parity 4 for NBA, 0.07±0.02 in parity 2 to 0.16±0.02 in parity 3 for LTBW, 0.04±0.02 in parity 4 to 0.08±0.01 in parity 1 for NPW, and 0.16±0.02 in parity 1 to 0.20±0.02 in parity 2 for LTWW. Additive genetic correlation estimates from MTM were also variable, ranging from 0.29±0.24 between NBA in parity 1 and NBA in parity 2 to 0.99±0.05 between LTWW in parity 3 and LTWW in parity 4. Conclusion: The findings of this study highlight the advantage of using MTM for the genetic improvement of reproductive traits in swine and contribute to the development of sustainable swine breeding programs in Thailand.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.575-579
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• 2000

First lactation records of 683 Murrah buffaloes maintained at NDRI, Karnal which were progeny of 84 sires used for comparing the heritability estimates of age at first calving, first lactation milk yield and first service period under single and multiple trait models using restricted maximum likelihood (REML) method of estimation under an individual animal model. The results indicated that the heritability estimates may vary under single and multiple trait models depending upon the magnitude of genetic and environmental correlation among the traits being considered. Therefore, a single or multiple trait model is recommended for estimation of variance components depending upon the goal of breeding programme. However, there may not be any advantage of considering a trait with zero or near zero heritability and having no or very low genetic correlation with other traits in the model. Lower heritability estimates of part lactation yield (120-day milk yield) implied that there may not be any advantage of considering this trait in place of actual 305-day milk yield, whereas, comparable heritability estimates of predicted 305-day milk yield suggested that it could be used for sire evaluation to reduce the cost of milk recording under field conditions.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.7
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• pp.848-854
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• 2010

The objectives of this study were to estimate genetic parameters for the carcass price and carcass traits contributing to carcass grading and to investigate the influence of each carcass trait on the carcass price using multiple regression and path analyses. Data for carcass traits and carcass prices were collected from March 2003 to January 2009 on steers of Korean cattle raised at private farms. The analytical mixed animal model, including slaughter house-year-month combination, linear and quadratic slaughter age as fixed effects and random animal and residual effects, was used to estimate genetic parameters. The effects of carcass traits on the carcass price were evaluated by applying multiple regression analyses. Heritability estimates of carcass traits were $0.20{\pm}0.08$ for carcass weight (CWT), $0.33{\pm}0.10$ for back fat thickness (BFT), $0.07{\pm}0.05$ for eye-muscle area (EMA) and $0.25{\pm}0.10$ for marbling score (MS), and those of carcass prices were $0.21{\pm}0.10$ for auction price per 1 kg of carcass weight (AP) and $0.13{\pm}0.07$ for total price (CP). Genetic correlation coefficients of AP with CWT and MS were $-0.35{\pm}0.29$ and $0.99{\pm}0.04$, respectively, and those of CP with CWT and MS were $0.59{\pm}0.22$ and $0.39{\pm}0.29$ respectively. If an appropriate adjustment for temporal economic value is available, the moderate heritability estimates of AP and CP might suggest their potential use as the breeding objectives for improving the gross incomes of beef cattle farms. The large genetic correlation estimates of carcass price variables with CWT and MS implied that simultaneous selection for both CWT and MS would be also useful in enhancing income.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.923-927
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• 2008

The objective of this study was to compare random regression model and multiple trait animal model estimates of the (co) variance of total sperm cells over the active lifetime of AI boars. Data were provided by Smithfield Premium Genetics (Rose Hill, NC). Total number of records and animals for the random regression model were 19,629 and 1,736, respectively. Data for multiple trait animal model analyses were edited to include only records produced at 9, 12, 15, 18, 21, 24, and 27 months of age. For the multiple trait method estimates of genetic and residual variance for total sperm cells were heterogeneous among age classifications. When comparing multiple trait method to random regression, heritability estimates were similar except for total sperm cells at 24 months of age. The multiple trait method also resulted in higher estimates of heritability of total sperm cells at every age when compared to random regression results. Random regression analysis provided more detail with regard to changes of variance components with age. Random regression methods are the most appropriate to analyze semen traits as they are longitudinal data measured over the lifetime of boars.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.328-333
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• 2015

A genome wide association study was conducted using estimated breeding value (EBV) for milk production traits from 1st to 4th lactation. Significant single nucleotide polymorphism (SNP) markers were selected for each trait and the differences were compared by lactation. DNA samples were taken from 456 animals with EBV which are Holstein proven bulls whose semen is being sold or the daughters of old proven bulls whose semen is no longer being sold in Korea. High density genome wide SNP genotype was investigated and the significance of markers associated with traits was tested using the breeding value estimated by a multiple lactation model as a dependent variant. As the result of significance comparisons by lactations, several differences were found between the first lactation and subsequent lactations (from second to 4th lactation). A similar trend was noted in mean deviation and correlation of the estimated effects by lactation. Since there was a difference in the genes associated with EBV for each trait between first and subsequent lactations, a multi-lactation model in which lactation is considered as a different trait is genetically useful. Also, significant markers in all lactations and common markers for different traits were detected, which can be used as markers for quantitative trait loci exploration and marker assisted selection in milk production traits.