• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.489-494
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• 1998

This paper describes a design of graphical user interface for a simulated breeding tool with multifield. The term field is used here as a population of visualized individuals that are candidates of selection. Multi-field interface enables the user to breed his/her favorite phenotypes by selection independently in each field, and he/she can copy arbitrary individual into another field. As known on genetic algorithms, a small population likely leads to premature convergence trapped by a local optimum, and migration among plural populations is useful to escape from local optimum. The multi-field user interface provides easy implementation of migration and wider diversity. We show the usefulness of multi-field user interface through an example of a breeding system of 2D CG images.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1483-1503
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• 2011

To advance the effectiveness of the current Hanwoo improvement system, we developed a general simulation that compared a series of breeding schemes under realistic user circumstances. We call this system the Integrated General Model (IGM) and it allows users to control the breeding schemes and selection methods by manipulating the input parameters. The Current Hanwoo Performance and Progeny Test (CHPPT) scheme was simulated with a Modified Hanwoo Performance and Progeny Test (MHPPT) scheme using a Hanwoo Breeding Farm cow population of the Livestock Improvement Main Center (LOMC) of the National Agricultural Cooperatives Federation (NACF). To compare the two schemes, a new method, the Simple Hanwoo Performance Test (SHPT), which uses ultrasound technology for measuring the carcass traits of live animals, was developed. These three models, including the CHPPT, incorporated three types of selection criteria: phenotype (PH), true breeding value (TBV), and estimated breeding value (EBV). The simulation was scheduled to mimic an actual Hanwoo breeding program; thus, the simulation was run to include the years 1983-2020 for each breeding method and was replicated 10 times. The parameters for simulation were derived from the literature. Approximately 642,000 animals were simulated per replication for the CHPPT scheme; 129,000 animals were simulated for the MHPPT scheme and 112,000 animals for the SHPT scheme. Throughout the 38-year simulation, all estimated parameters of each simulated population, regardless of population size, showed results similar to the input parameters. The deviations between input and output values for the parameters in the large populations were statistically acceptable. In this study, we integrated three simulated models, including the CHPPT, in an attempt to achieve the greatest genetic gains within major economic traits including body weight at 12 months of age (BW12), body weight at 24 months of age (BW24), average daily gain from 6 to 12 months (ADG), carcass weight (CWT), carcass longissimus muscle area (CLMA), carcass marbling score (CMS), ultrasound scanned longissimus muscle area (ULMA), and ultrasound scanned marbling score (UMS).

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.2
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• pp.162-172
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• 2011

In this paper, simulation was used to determine accuracies of genomic breeding values for polygenic traits associated with many thousands of markers obtained from high density genome scans. The statistical approach was based upon stochastically simulating a pedigree with a specified base population and a specified set of population parameters including the effective and noneffective marker distances and generation time. For this population, marker and quantitative trait locus (QTL) genotypes were generated using either a single linkage group or multiple linkage group model. Single nucleotide polymorphism (SNP) was simulated for an entire bovine genome (except for the sex chromosome, n = 29) including linkage and recombination. Individuals drawn from the simulated population with specified marker and QTL genotypes were randomly mated to establish appropriate levels of linkage disequilibrium for ten generations. Phenotype and genomic SNP data sets were obtained from individuals starting after two generations. Genetic prediction was accomplished by statistically modeling the genomic relationship matrix and standard BLUP methods. The effect of the number of linkage groups was also investigated to determine its influence on the accuracy of breeding values for genomic selection. When using high density scan data (0.08 cM marker distance), accuracies of breeding values on juveniles were obtained of 0.60 and 0.82, for a low heritable trait (0.10) and high heritable trait (0.50), respectively, in the single linkage group model. Estimates of 0.38 and 0.60 were obtained for the same cases in the multiple linkage group models. Unexpectedly, use of BLUP regression methods across many chromosomes was found to give rise to reduced accuracy in breeding value determination. The reasons for this remain a target for further research, but the role of Mendelian sampling may play a fundamental role in producing this effect.

• Journal of Animal Science and Technology
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• v.44 no.5
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• pp.507-518
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• 2002

A multiple-trait stochastic computer simulation model was constructed to predict the breeding schemes and selection methods on Hanwoo(Korean cattle). The model could be used four kinds of selection criteria (random, phenotype and true or estimated breeding values). At the test run in various population size for 20 years, all estimated parameters of the each simulated populations were resulted similar to input parameters. The deviations between input and output values of parameter in the large population were smaller than in the small population. The simulated results obtained from ten small populations consisted with one sire and ten dams in each population for 500 years were as follows; Inbreeding coefficients of population were similar to theoretical estimating function. Mean values of each traits selected were randomly drifted by generation, but they were converged into a value when inbreeding coefficients came close to one. Additive genetic variances within each population were reduced by generation, and they were converged into zero when inbreeding coefficients came close to one. These results indicated that the simulated populations hold to statistical properties of input parameters.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.6
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• pp.772-784
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• 2012

Marker-assisted gene pyramiding aims to produce individuals with superior economic traits according to the optimal breeding scheme which involves selecting a series of favorite target alleles after cross of base populations and pyramiding them into a single genotype. Inspired by the science of evolutionary computation, we used the metaphor of hill-climbing to model the dynamic behavior of gene pyramiding. In consideration of the traditional cross program of animals along with the features of animal segregating populations, four types of cross programs and two types of selection strategies for gene pyramiding are performed from a practical perspective. Two population cross for pyramiding two genes (denoted II), three population cascading cross for pyramiding three genes(denoted III), four population symmetry (denoted IIII-S) and cascading cross for pyramiding four genes (denoted IIII-C), and various schemes (denoted cross program-A-E) are designed for each cross program given different levels of initial favorite allele frequencies, base population sizes and trait heritabilities. The process of gene pyramiding breeding for various schemes are simulated and compared based on the population hamming distance, average superior genotype frequencies and average phenotypic values. By simulation, the results show that the larger base population size and the higher the initial favorite allele frequency the higher the efficiency of gene pyramiding. Parents cross order is shown to be the most important factor in a cascading cross, but has no significant influence on the symmetric cross. The results also show that genotypic selection strategy is superior to phenotypic selection in accelerating gene pyramiding. Moreover, the method and corresponding software was used to compare different cross schemes and selection strategies.

• Journal of Animal Science and Technology
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• v.53 no.1
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• pp.1-6
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• 2011

This simulation study was performed to investigate the accuracy of the estimated breeding value by using genomic information (GEBV) by way of Bayesian framework. Genomic information by way of single nucleotide polymorphism (SNP) from a chromosome with length of 100cM were simulated with different marker distance (0.1cM, 0.5cM), heritabilities (0.1, 0.5) and half sibs families (20 heads, 4 heads). For generating the simulated population in which animals were inferred to genomic polymorphism, we assumed that the number of quantitative trait loci (QTL) were equal with the number of no effect markers. The positions of markers and QTLs were located with even and scatter distances, respectively. The accuracies of estimated breeding values by way of indicating correlations between true and estimated breeding values were compared on several cases of marker distances, heritabilities and family sizes. The accuracies of breeding values on animals only having genomic information were 0.87 and 0.81 in marker distances of 0.1cM and 0.5cM, respectively. These accuracies were shown to be influenced by heritabilities (0.87 at $h^2$ =0.10, 0.94 at $h^2$ =0.50). According to half sibs' family size, these accuracies were 0.87 and 0.84 in family size of 20 and 4, respectively. As half sibs family size is high, accuracy of breeding appeared high. Based on the results of this study it is concluded that the amount of marker information, heritability and family size would influence the accuracy of the estimated breeding values in genomic selection methodology for animal breeding.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.5
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• pp.597-602
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• 2001

This study was conducted to examine the relationships between the methods used to determine the number of transferable embryos collected per flush and the estimated cumulative genetic improvements in the Japanese Holstein MOET breeding population. Cumulative genetic improvements were predicted by Monte Carlo simulation using three different determination methods (MODEL 1, MODEL 2, and MODEL 3), for calculating the number of embryos collected per flush. Moreover EBVs were estimated including or ignoring coefficients of inbreeding in MME. Inbreeding coefficients were also predicted. The number of transferable embryos was determined using normal, gamma, and Poisson distributions in MODEL 1, gamma and Poisson distributions in MODEL 2, and only the Poisson distribution in MODEL 3. The fitness of MODEL 2 in relation to field data from Hokkaido Japan was the best, and the results for MODEL3 indicated that this model is unsuitable for determining the number of transferable embryos. The largest cumulative genetic improvement (3.11) in the 10th generation was predicted by MODEL 3 and the smallest (2.83) by MODEL 2. Mean coefficients of correlation between the true and estimated breeding values were 0.738, 0.729, and 0.773 in MODELS 1, 2, and 3, respectively. It is suggested that the smallest genetic improvement in MODEL 2 resulted from the smallest correlation coefficient between the true and estimated breeding values. The differences in milk, fat, and protein yields between MODELS 2 and 3 were 182.0, 7.0, and 5.6 kg, respectively, in real units when each trait was independently selected. The inbreeding coefficient was the highest (0.374) in MODEL 2 and the lowest (0.357) in MODEL 3. The effects of different methods for determining the number of transferable embryos per flush on genetic improvements and inbreeding coefficients of the simulated populations were remarkable. The effects of including coefficients of inbreeding in MME, however, were unclear.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.3
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• pp.299-303
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• 2012

Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.

• Journal of Animal Science and Technology
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• v.64 no.1
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• pp.183-186
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• 2022

Lactiplantibacillus plantarum (L. plantarum) ST was isolated from De'ang pickled tea in Yunnan Province, China. The genomes of strain ST were fully sequenced and analyzed using the PacBio RS II sequencing system. Our previous study has shown that L. plantarum ST is a potential probiotic strain. It had strong tolerance in the simulated artificial gastrointestinal tract, and in the antagonism tests, this strain showed strong antibacterial activity. Therefore, as a probiotic, it may be used in animal breeding. L. plantarum ST genome was composed of 1 circular chromosome and 7 plasmids. The length of the whole genome was 3320817 bp, and the annular chromosome size was 3058984 bp, guanine + cytosine (G ± C) content (%) was 44.76%, which contained 2945 protein-coding sequences (CDS). This study will contribute to a further comprehensive understanding of L. Plantarum ST at the genomic level and provide a theoretical basis for its future application in animal breeding.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.137-144
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• 2004

Algorithms for estimating breeding values on several categorical data by using latent variables with threshold conception were developed and showed. Thresholds on each categorical trait were estimated by Newton’s method via gradients and Hessian matrix. This algorithm was developed by way of expansion of bivariate analysis provided by Quaas(2001). Breeding values on latent variables of categorical traits and observations on linear traits were estimated by preconditioned conjugate gradient(PCG) method, which was known having a property of fast convergence. Example was shown by simulated data with two linear traits and a categorical trait with four categories(CE=calving ease) and a dichotomous trait(SB=Still Birth) in threshold animal mixed model(TAMM). Breeding value estimates in TAMM were compared to those in linear animal mixed model (LAMM). As results, correlation estimates of breeding values to parameters were 0.91${\sim}$0.92 on CE and 0.87${\sim}$0.89 on SB in TAMM and 0.72~0.84 on CE and 0.59~0.70 on SB in LAMM. As conclusion, PCG method for estimating breeding values on several categorical traits with linear traits were feasible in TAMM.