• Asian-Australasian Journal of Animal Sciences
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• v.33 no.2
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• pp.197-202
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• 2020

Objective: The objective of this study was to identify environmental factors strongly associated with and to estimate genetic parameters of reproductive traits in Japanese Black heifers. Methods: Data included reproduction records of Japanese Black heifers born between 2004 and 2014. First service non-return rate (NRR) to 56 days from first to successful insemination (FS), number of services per conception (IN), age at first calving (AFC) and gestation length were analyzed with the use of the general linear model. Genetic parameters were estimated with the use of the univariate animal model of the residual maximum likelihood. Results: Averages of reproductive traits over eleven years were assessed, and the effects of farm, year, month, artificial insemination technician and interaction of farm×year on the traits were determined. Estimated heritability of FS was very low and that of AFC was higher than that of the other traits. A close genetic relation was observed among NRR, IN, and FS; however, their heritabilities were very low. AFC shows favorable genetic correlation with IN and FS. Conclusion: Low heritabilities of most reproductive traits in Japanese Black heifers are strongly influenced by farm management practices, and that large residual variances make genetic evaluation difficult. Among the reproductive traits, AFC is potentially more useful for genetic improvement of heifer reproductive traits because it has high heritability and favorable genetic correlations with IN and FS.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.1
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• pp.7-12
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• 2006

The objective of this study was to estimate genetic parameters between first and later parities as different traits in reproductive traits of pigs using multiple trait animal model procedures. Data related to reproductive traits from a total of 2,371 individuals maintained at a farm were taken from the pedigree file. Sires and dams were consisted of Duroc, Landrace, and Yorkshire breeds, respectively. The first and later parity records were considered as different traits. Traits included in analyses were total pigs born (TB1), number of pigs born alive (NBA1), number of pigs weaned (NW1), and litter weaning weight (LWT1) in the first parity, and total pigs born (TB2), number of pigs born alive (NBA2), number of pigs weaned (NW2), litter weaning weight (LWT2) and interval between farrowing events (FTF) in later parities. Heritability estimates of TB1, NBA1, NW1 and LWT1 in the first parity were 0.27, 0.25, 0.16 and 0.20, respectively. For TB2, NBA2, NW2, LWT2 and FTF in later parities, heritabilities were estimated as 0.15, 0.15, 0.08, 0.11 and 0.07, respectively. Genetic correlations between sow reproductive traits in the first parity and in the second and later parity were estimated to be 0.89, 0.77, 0.58 and 0.66, respectively, between TB1 and TB2, NBA1 and NBA2, NW1 and NW2, and LWT1 and LWT2. While phenotypic correlations between TB1 and TB2, NBA1 and NBA2, NW1 and NW2, and LWT1 and LWT2 were estimated as 0.18, 0.15, 0.06 and 0.10, respectively. Genetic correlations between reproductive traits of first and later parities were not high indicating that reproductive traits for sows should be analyzed while considering the parities as different traits.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.1
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• pp.1-6
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• 2001

Additive and dominance genetic variances were estimated for purebred Landrace selected with line breeding from 1989 to 1995 at Miyazaki Livestock Experiment Station, Kawaminami Branch. Ten body measurements, two reproductive traits and fifteen carcass traits were analyzed with single-trait mixed model analysis. The estimates of narrow-sense heritabilities by additive model were in the range of 0.07 to 0.46 for body measurements, 0.05 to 0.14 for reproductive traits, and 0.05 to 0.68 for carcass traits. The additive model tended to slightly overestimate the narrow-sense heritabilities as compared to the additive and dominance model. The proportion of the dominance variance to total genetic variance ranged from 0.11 to 0.91 for body measurements, 0.00 to 0.65 for reproductive traits, and 0.00 to 0.86 for carcass traits. Large differences among traits were found in the ratio of dominance to total genetic variance. These results suggested that dominance effect would affect the expression of all ten body measurements, one reproductive trait, and nine carcass traits. It is justified to consider the dominance effects in genetic evaluation of the selected lines for those traits.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1550-1556
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• 2017

Objective: This study estimated the genetic parameters for productive and reproductive traits. Methods: The data included production and reproduction records of animals that have calved between 1979 and 2013. The genetic parameters were estimated using multivariate mixed models (DMU) package, fitting univariate and multivariate mixed models with average information restricted maximum likelihood algorithm. Results: The estimates of heritability for milk production traits from the first three lactation records were $0.03{\pm}0.03$ for lactation length (LL), $0.17{\pm}0.04$ for lactation milk yield (LMY), and $0.15{\pm}0.04$ for 305 days milk yield (305-d MY). For reproductive traits the heritability estimates were, $0.09{\pm}0.03$ for days open (DO), $0.11{\pm}0.04$ for calving interval (CI), and $0.47{\pm}0.06$ for age at first calving (AFC). The repeatability estimates for production traits were $0.12{\pm}0.02$, for LL, $0.39{\pm}0.02$ for LMY, and $0.25{\pm}0.02$ for 305-d MY. For reproductive traits the estimates of repeatability were $0.19{\pm}0.02$ for DO, and to $0.23{\pm}0.02$ for CI. The phenotypic correlations between production and reproduction traits ranged from $0.08{\pm}0.04$ for LL and AFC to $0.42{\pm}0.02$ for LL and DO. The genetic correlation among production traits were generally high (>0.7) and between reproductive traits the estimates ranged from $0.06{\pm}0.13$ for AFC and DO to $0.99{\pm}0.01$ between CI and DO. Genetic correlations of productive traits with reproductive traits were ranged from -0.02 to 0.99. Conclusion: The high heritability estimates observed for AFC indicated that reasonable genetic improvement for this trait might be possible through selection. The $h^2$ and r estimates for reproductive traits were slightly different from single versus multi-trait analyses of reproductive traits with production traits. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.11
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• pp.1657-1663
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• 2019

Objective: A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms. Methods: The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs. Results: The accuracies under the GBLUP model for BFT and CWT ranged from 0.33-0.72 and 0.33-0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies. Conclusion: The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.

• Journal of Aquaculture
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• v.13 no.1
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• pp.13-19
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• 2000

A genetic analysis of reproductive characters of masu salmon at three year classes was described. the reproductive performance of masu salmon spawning at 2 years of age was analyzed using data number fertility hatchability and growth traits to the juvenile stage. The phenotypic correlations among the traits were also estimated. it was determined that egg volume was the principle deter-minant of egg number and that the relationship of number to size was negative. It is recommended that selection for egg size be included in all selection programs and egg number be ignored in any welection program designed to increase body size. Phenotypic correlations between body size of parents and egg traits as well as between body size of offspring and egg traits were not significantly positive or negative magnitude at three year classes.

• Korean Journal of Animal Reproduction
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• v.23 no.4
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• pp.371-380
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• 1999

Animal reproductive performance could be affected by several regulatory factors, including nutritional, environmental, and genetic factors. Particularly, during the last half of this century, animal reproductive performance has been remarkably successful in improving the efficiency of livestock production. For some traits efficiency gains have been achieved with little or no knowledge of the genes underlying the traits. And, they have depended upon the phenotypic selection by statistical methods to estimate the genetic parameters of some reproductive traits. In spite of these successes, it is clear that recent advances in both developmental biology and molecular biology are set to revolutionize he practice of animal reproductive performance n the 21th century. (omitted)

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1852-1862
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• 2018

Objective: The purpose of this study was to investigate a single step genome-wide association study (ssGWAS) for identifying genomic regions affecting reproductive traits in Landrace and Large White pigs. Methods: The traits included the number of pigs weaned per sow per year (PWSY), the number of litters per sow per year (LSY), pigs weaned per litters (PWL), born alive per litters (BAL), non-productive day (NPD) and wean to conception interval per litters (W2CL). A total of 321 animals (140 Landrace and 181 Large White pigs) were genotyped with the Illumina Porcine SNP 60k BeadChip, containing 61,177 single nucleotide polymorphisms (SNPs), while multiple traits single-step genomic BLUP method was used to calculate variances of 5 SNP windows for 11,048 Landrace and 13,985 Large White data records. Results: The outcome of ssGWAS on the reproductive traits identified twenty-five and twenty-two SNPs associated with reproductive traits in Landrace and Large White, respectively. Three known genes were identified to be candidate genes in Landrace pigs including retinol binding protein 7, and ubiquitination factor E4B genes for PWL, BAL, W2CL, and PWSY and one gene, solute carrier organic anion transporter family member 6A1, for LSY and NPD. Meanwhile, five genes were identified to be candidate genes in Large White, two of which, aldehyde dehydrogenase 1 family member A3 and leucine rich repeat kinase 1, associated with all of six reproduction traits and three genes; retrotransposon Gag like 4, transient receptor potential cation channel subfamily C member 5, and LHFPL tetraspan subfamily member 1 for five traits except W2CL. Conclusion: The genomic regions identified in this study provided a start-up point for marker assisted selection and estimating genomic breeding values for improving reproductive traits in commercial pig populations.

• Animal Bioscience
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• v.35 no.11
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• pp.1649-1655
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• 2022

Objective: The primary objective of this study was to determine the genetic parameters for reproductive traits among Large White pigs, including the following traits: total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), gestation length (GL), age at first service (AFS) and age at first farrowing (AFF). Methods: The dataset consisted of 19,036 reproductive records from 4,986 sows, and a multi-trait animal model was used to estimate genetic variance components of seven reproductive traits. Results: The heritability estimates for these reproductive traits ranged from 0.09 to 0.26, with the highest heritability for GL and AFF, and the lowest heritability for NBA. The repeatabilities for TNB, NBA, LWB, ABW, and GL were ranged from 0.16 to 0.34. Genetic and phenotypic correlations ranged from -0.41 to 0.99, and -0.34 to 0.98, respectively. In particular, the correlations between TNB, NBA and LBW, between AFS and AFF, exhibited a strong positive correlation. Furthermore, for TNB, NBA, LBW, ABW, and GL, genetic correlations of the same trait between different parities were moderately to strongly correlated (0.32 to 0.97), and the correlations of adjacent parities were higher than those of nonadjacent parities. Conclusion: All the results in the present study can be used as a basis for the genetic assessment of the target population. In the formulation of dam line selection index, AFS or AFF can be considered to combine with TNB in a multiple trait swine breeding value estimation system. Moreover, breeders are encouraged to increase the proportion of sows at parity 3-5 and reinforce the management of sows at parity 1 and parity ≥8.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.8
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• pp.1224-1232
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• 2020

Objective: The QingYu pig is well known for its excellent meat quality attributes in Sichuan province, China. In order to improve its production efficiency, the determination of genetic factors contributing to quantifiable economic traits of livestock is important. Moreover, the cross-breeding of QingYu pigs with western breeds possessing strong growth attributes is an efficient way to improve the performance of this breed. Methods: Here, the genetic parameters of several important reproductive traits of QingYu pigs were estimated, include total number born (TNB), number born alive, litter birth weight, individual birth weight, number of piglets weaned, litter weaning weight, and individual weaning weight. The data was analyzed using the ASReml 3.0 software (NSW Inc., Sydney, Australia). Furthermore, the effects of crossing Berkshire with QingYu (BQ) pigs on carcass and meat quality traits, as well as the effects of slaughter weight on carcass and meat quality of BQ were characterized. Results: QingYu pigs exhibited superior reproductive traits. The TNB available to QingYu pigs was more than 8 per parity. The observed repeatability of the reproductive traits of the QingYu pigs was between 0.10 and 0.23. The significantly correlated genetic and phenotypic of reproduction traits were consistent. Interestingly, the BQ pigs exhibited improved carcass quality, with a significant increase in loin muscle area, lean percentage and reduction in sebum percentage. As a result, BQ had higher L_45min, lower cooking scores, and lower drip loss. In addition, the loin muscle area, body length, and sebum percentage were significantly higher in 90 and 100 kg animals. Cooking loss showed a significant increase at 80 kg, and marbling increased significantly from 90 kg. Conclusion: The results of this study suggest that QingYu pigs exhibit excellent reproductive properties and heritability of these traits. Crossing with Berkshire is an efficient strategy to improve the carcass and meat quality of QingYu pigs for commercial operations. Furthermore, it appears as though the optimal slaughter weight of BQ pigs is at approximately 90 kg.