• 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.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.33 no.7
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• pp.1057-1067
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• 2020

Objective: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle. Methods: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals' information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h²) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson's method. Results: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 ㎠, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h² of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs. Conclusion: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.10
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• pp.1261-1267
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• 2010

Milk production traits are important economic traits for dairy cattle. The aim of the present study was to refine the position of previously detected quantitative trait loci (QTL) on bovine chromosome 6 affecting milk production traits in Chinese Holstein dairy cattle. A daughter design with 918 daughters from 8 elite sire families and 14 markers spanning the previously identified QTL region were used in the analysis. We employed a combined linkage and linkage disequilibrium analysis (LDLA) approach with two options for calculating the IBD probabilities, one was based on haplotypes of all 14 markers (named Method 1) and the other based on haplotypes with sliding windows of 5 markers (named Method 2). For milk fat yield, the two methods revealed a highly significant QTL located within a 6.5 cM interval (Method 1) and a 4.0 cM interval (Method 2), respectively. For milk protein yield, a highly significant QTL was detected within a 3.0 cM interval (Method 1) or a 2.5 cM interval (Method 2). These results confirmed the findings of our previous study and other studies, and greatly narrowed down the QTL positions.

• Journal of Animal Science and Technology
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• v.44 no.2
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• pp.151-164
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• 2002

Genetic variance and covariance components of the linear traits and the ordered categorical traits, that are usually observed as dichotomous or polychotomous outcomes, were simultaneously estimated in a multivariate threshold animal model with concepts of arbitrary underlying liability scales with Bayesian inference via Gibbs sampling algorithms. A multivariate threshold animal model in this study can be allowed in any combination of missing traits with assuming correlation among the traits considered. Gibbs sampling algorithms as a hierarchical Bayesian inference were used to get reliable point estimates to which marginal posterior means of parameters were assumed. Main point of this study is that the underlying values for the observations on the categorical traits sampled at previous round of iteration and the observations on the continuous traits can be considered to sample the underlying values for categorical data and continuous data with missing at current cycle (see appendix). This study also showed that the underlying variables for missing categorical data should be generated with taking into account for the correlated traits to satisfy the fully conditional posterior distributions of parameters although some of papers (Wang et al., 1997; VanTassell et al., 1998) presented that only the residual effects of missing traits were generated in same situation. In present study, Gibbs samplers for making the fully Bayesian inferences for unknown parameters of interests are played rolls with methodologies to enable the any combinations of the linear and categorical traits with missing observations. Moreover, two kinds of constraints to guarantee identifiability for the arbitrary underlying variables are shown with keeping the fully conditional posterior distributions of those parameters. Numerical example for a threshold animal model included the maternal and permanent environmental effects on a multiple ordered categorical trait as calving ease, a binary trait as non-return rate, and the other normally distributed trait, birth weight, is provided with simulation study.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.8
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• pp.1073-1079
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• 2008

The objectives of this study were to identify polymorphisms of insulin-like growth factor I (IGF-I) gene and to investigate their association with growth traits in Nanjiang Huang goats. Five hundred and ninety-two animals were used to detect the polymorphisms in the complete coding sequence, part of introns and the 5'-regulatory region of the IGF-I gene by means of PCR-SSCP. A new single nucleotide polymorphism (G to C transversion) was identified at intron 4 of the IGF-I gene in the goats. Two alleles and three genotypes were observed in this group. The frequency of G and C alleles was 54.6 and 45.4%, respectively. The statistical analysis showed that polymorphism of the IGF-I gene had a significant association (p<0.05) with birth weight (BW), body weight at 6 months (W6) and at 12 months (W12), heart girth at 2 months (G2), body length at 6 months (L6), wither height at 6 months (H6) and at 12 months (H12) and heart girth at 12 months (G12). The goats with genotype CC had significantly higher BW, W6, W12, G2, L6, H6, H12 and G12 than those with genotype GC and had significantly higher W12, H6, H12 and G12 than those with genotype GG. Therefore, genotype CC may be the most advantageous for growth traits in the Nanjiang Huang goat. However, no significant association between SNP genotypes and other growth traits was observed. These results indicated that the SNP marker of the IGF-I gene may be a potential molecular marker for growth traits in Nanjiang Huang goats.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.4
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• pp.458-462
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• 2005

The study was aimed at detecting polymorphism of the fifth intron in lipoprotein lipase (LPL) gene and analyzing association between the polymorphism and productive traits. A pair of primers was designed for amplifying the fifth intron. Sequence analysis indicated that a G1171C substitution existed in Large White breed. The mutation was detected by PCR-AfaI-RFLP. Polymorphism analysis in a pig resource family showed that there existed significant effects on carcass and meat quality traits. Thoraxwaist fat thickness of BB genotype was significantly higher (14.2%, p<0.05) than that of AA on carcass traits, while BB genotype was significantly lower (3.6% p<0.01, 4.1% p<0.01; 2.3% p<0.01, 1.9% p<0.01; 1.8% p<0.01, 1.4% p<0.05) than AA and AB genotype in pH of m. Longissimus Dorsi (LD), m. Biceps Femoris (BF), m. Semipinali Capitis (SC). The allelic frequencies were also significantly different between indigenous Chinese breeds and exotic breeds. Data analyzed revealed that the mutation locus affected production traits mostly by additive effects. Based on these results, it is necessary to do more studies on LPL gene before making the LPL locus into the application of marker-assisted selection (MAS) programs.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.5
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• pp.643-649
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• 2018

Objective: POU class 1 homeobox 1 (POU1F1) mediates growth hormone expression and activity by altering transcription, eventually resulting in growth rate variations. Therefore, we aimed to identify chicken POU1F1 polymorphisms and evaluate the association between single nucleotide polymorphisms (SNPs) and growth-related traits, and logistic growth curve parameter traits (${\alpha}$, ${\beta}$, and ${\gamma}$). Methods: Three SNPs (M_1 to M_3) were used to genotype 585 $F_1$ and 88 $F_0$ birds from five Korean native chicken lines using a polymerase chain reaction-restriction fragment length polymorphism method. Results: Single marker analyses and traits association analyses showed that M_2 was significantly associated with body weight at two weeks, weight gain from hatch to 2 weeks, and weight gain from 16 to 18 weeks (p<0.05). M_3 was significantly associated with weight gain from 14 to 16 weeks and from 16 to 18 weeks, and asymptotic body weight (${\alpha}$) (p<0.05). No traits were associated with M_1. The POU1F1 haplogroups were significantly associated with weight gain from 14 to 16 weeks (p = 0.020). Linkage disequilibrium test and Haploview analysis shown one main haploblock between M_2 and M_3 SNP. Conclusion: Thus, POU1F1 significantly affects the growth of Korean native chickens and their growth curve traits.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.5
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• pp.627-632
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• 2002

Genetic parameters for dairy performance traits were estimated, breeding values for the traits of all breeding sires and cows were predicted and the genetic trends were estimated using the breeding values in the Central Cattle Breeding Station (CCBS). A total of 3,801 records for Bangladeshi Local, 756 records for Red Sindhi and 959 records for Sahiwal covering the period from 1961 to 1997 were used in this analysis. Traits considered were total milk production per lactation (TLP), lactation length (LL) and daily milk yield (DMY). The genetic parameters were estimated by the REML using MTDFREML program. The breeding values were predicted by a best linear unbiased prediction (BLUP). In all sets of data, the genetic trends for the dairy performance traits were computed as averages of breeding values for cows born in the particular year. The estimates of heritability for TLP (0.26 and 0.27) and DMY (0.28 and 0.27) were moderate in Bangladeshi local and Red Sindhi breed, respectively. Furthermore, the heritability estimate for LL (0.24) was moderate in Red Sindhi. The estimates of heritabilities for all traits were low in Sahiwal. The repeatability estimate was high for TLP, moderate for LL and moderate to high for DMY. All variances estimated in Bangladeshi Local were low, comparing the respective values estimated in both Red Sindhi and Sahiwal. On the other hand, additive genetic variances for the three traits were estimated very low in Sahiwal. The genetic trends for the three dairy production traits have not been positive except for the recent trend in Bangladeshi Local.

• Journal of Animal Science and Technology
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• v.52 no.6
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• pp.467-473
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• 2010

A total of 4,323,781 records for informal 16 primary linear descriptive traits of dairy cows in Holstein breed from 1988 to 2007 in USA were analyzed to estimate adjustment factors for lactation number and stage. While all factors in the model were highly significant (P < 0.01), major influences on linear type traits were due to lactation number and stage. The frequencies of lactation number 1 through 6 were 58.6, 22.0, 11.8, 4.8, 2.1, and 0.8%, respectively. Further, the frequencies of lactation stage were 0.7, 76.9, 15.3, 4.9, and 2.1%, respectively, for springing, early, medium, late, and dry. To adjust 16 linear traits (stature, dairy form, strength, body depth, rump width, rump angle, legs rear view, leg set, foot angle, fore udder, rear udder height, rear udder width, udder support, udder depth, and front teat placement), additive and multiplicative adjustment factors of lactation number (lactations 2 to 4) and stage (springing, medium, late and dry) were estimated with the solutions in the generalized linear model, assigning lactation 1 and stage early as base class. Additive adjustment factors of lactation number ranged from -1.23 to 2.908, while multiplicative factors ranged from 0.853 to 2.207. Further, additive and multiplicative adjustment factors for lactation stage ranged from -0.668 to 0.785, and from 0.891 to 1.154. Application of adjustment factors to 20 randomly sampled sub-data sets produced the results that additive adjustment factors for both lactation number and stage reduced more mean square of lactation number and stage over 16 linear traits than any combination of adjustments, and leaded additive adjustment factors for both lactation number and stage as a choice of methods for adjustment of informal 16 primary linear type traits collected by classifiers of AI studs.