• Asian-Australasian Journal of Animal Sciences
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• 제24권2호
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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.

• 농업과학연구
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• 제39권2호
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• pp.219-226
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• 2012

The acyl-CoA dehydrogenase, C-2 to C-3 short chain (ACADS) gene is known to be related with fat metabolism, especially coverts the fat to the energy sources in cattle. In human, the mutations in this gene cause SCAD deficiency, which is one of the fatty acid metabolism disorders. The ACADS gene is located on bovine chromosome 17. The objective of this study was to identify SNPs in Hanwoo ACADS gene and identify the relationships with economic traits. In this study, two SNPs, T1570G SNP in exon 2 and G13917A SNP in exon 4, were observed. Moreover, in the coding region, 2 missense mutations, T (Cys) ${\rightarrow}$ G (Trp) mutation at 1570 bp and G (Arg) ${\rightarrow}$ A (Gln) mutation at 13917 bp, were observed. These mutations were subjected to the PCR-RFLP for typing 198 Hanwoo animals. The observed genotype frequency for T1570G was 0.135 (TT), 0.860 (TG) and 0.005 (GG), respectively. Also, 0.900 (GG) and 0.100 (GA) were observed for the G13917A mutation. The association of these SNPs with four economic traits, CW (Carcass Weight), BF (Backfat Thickness), LMA (Longissimus Muscle Area), MS (Marbling Score), were also observed. The results indicated that no significant results were observed in all four traits (P>0.05). This might indicate that further studies are ultimately needed to use the SNPs in ACADS gene in lager populations for effectively used for the marker assisted selection.

• Asian-Australasian Journal of Animal Sciences
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• 제31권12호
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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.

• Genomics & Informatics
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• 제20권4호
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• Asian-Australasian Journal of Animal Sciences
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• 제14권4호
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• pp.587-596
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• 2001

In the last decade, rapid developments in molecular biotechnology and of genomic tools have enabled the creation of dense linkage maps across whole genomes of human, plant and animals. Successful development and implementation of interval mapping methodologies have allowed detection of the quantitative trait loci (QTL) responsible for economically important traits in experimental and commercial livestock populations. The candidate gene approach can be used in any general population with the availability of a large resource of candidate genes from the human or rodent genomes using comparative maps, and the validated candidate genes can be directly applied to commercial breeds. For the QTL detected from primary genome scans, two incipient fine mapping approaches are applied by generating new recombinants over several generations or utilizing historical recombinants with identity-by-descent (IBD) and linkage disequilibrium (LD) mapping. The high resolution definition of QTL position from fine mapping will allow the more efficient implementation of breeding programs such as marker-assisted selection (MAS) or marker-assisted introgression (MAI), and will provide a route toward cloning the QTL.

• Mycobiology
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• 제29권3호
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• pp.132-134
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• 2001

Since it is known that Agrobacterium tumefaciens, which has long been used to transform plants, can transfer the T-DNA to yeast Saccharomyces cerevisiae during tumourigenesis, a variety of fungi were subjected to transformation to improve their transformation frequency. In this study, I report the A. tumefaciens-mediated transformation of filamentous fungus Aspergillus niger. Transfer of the binary vector pBIN9-Hg, containing the bacterial hygromycin B phosphotransferase gene under the control of the Aspergillus nidulans trpC promoter and terminator as a selectable marker, led to the selection of $50{\sim}100$ hygromycin B-resistant transformants per $1{\times}10^7$ conidia of A. niger. This efficiency is improved $10{\sim}20$ fold more than reported elsewhere. In order to avoid the difficulties in selection transformant from the over-growing non-transformant, I used top agar containing 900 ${\mu}g/ml$ of hygromycin. Genomic PCR and Southern analysis showed that all transformants contained single T-DNA insert per fungal genome. This technique offers an easier and more efficient method than that of using protoplast.

• Asian-Australasian Journal of Animal Sciences
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• 제32권12호
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• pp.1836-1843
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• 2019

Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.14-14
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• 2017

The discipline of plant breeding is experiencing a renaissance impacting crop improvement as a result of new technologies, however fundamental questions remain for predicting the phenotype and how the environment and genetics shape it. Inexpensive DNA sequencing, genotyping, new statistical methods, high throughput phenotyping and gene-editing are revolutionizing breeding methods and strategies for improving both quantitative and qualitative traits. Genomic selection (GS) models use genome-wide markers to predict performance for both phenotyped and non-phenotyped individuals. Aerial and ground imaging systems generate data on correlated traits such as canopy temperature and normalized difference vegetative index that can be combined with genotypes in multivariate models to further increase prediction accuracy and reduce the cost of advanced trials with limited replication in time and space. Design of a GS training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for analyzing unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. Environmental covariates can be identified using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. These new tools and knowledge challenge the plant breeder to ask the right questions and choose the tools that are appropriate for their crop and target traits. Contemporary plant breeding requires teams of people with expertise in genetics, phenotyping and statistics to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.

• 식물조직배양학회지
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• 제25권4호
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• pp.267-271
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• 1998

상추의 자엽 조직을 GR 유전자가 도입된 A. tumefaciens LBA 4404와 2일간 공동배양후, carbenicillin 500mg/L, kanamycin 50mg/L NAA 0.1mg/L와 2iP 1.0 mg/L가 함유된 MS 재분화배지에 옳겨 약 4주후에 kanamycin 저항성 개체를 얻었다. 형질전환된 것으로 추정되는 식물체는 kanamycin 100mg/L가 함유된 MS선발배지에서 생존하였다. PCR 분석결과, GR 유전자가 형질전환체의 게놈상에 삽입되어 있음을 확인하였다. 형질전환체의 Southern blot 분석을 통하여 ECL-labelling된 GR 유전자와 동일한 것으로 판단되는 약 1.8 kb 위치에서 밴드를 확인할 수 있었다. RT-PCR 분석으로 GR 유전자가 전사됨을 확인할 수 있었다. 개화후 이들 개체의 종자를 받아 NPTII 유전자의 발현여부를 조사한 결과 R$_1$ 세대에서도 NPTII 유전자가 발현됨을 확인하였다.

• 농업과학연구
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• 제49권3호
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• pp.407-420
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• 2022

Chicken dominates meat consumption because it is low in fat and high in protein and has less or no religious and cultural barriers. Recently, meat quality traits have become the focus of the poultry industry more than ever. Currently, poultry farming is focusing on meat quality to satisfy meat consumer preferences, which are mostly based on high-quality proteins and a low proportion of saturated fatty acids. Meat quality traits are polygenic traits controlled by many genes. Thus, it is difficult to improve these traits using the conventional selection method because of their low to moderate heritability. These traits include pH, colour, drop loss, tenderness, intramuscular fat (IMF), water-holding capacity, flavour, and many others. Genome-wide association studies (GWAS) are an efficient genomic tool that identifies the genomic regions and potential candidate genes related to meat quality traits. Due to their impact on the economy, meat quality traits are used as selection criteria in breeding programs. Various genes and markers related to meat quality traits in chickens have been identified. In chickens, GWAS have been successfully done for intramuscular fat (IMF) content, ultimate pH (pHu) and meat and skin colour. Moreover, GWAS have identified 7, 4, 4 and 6 potential candidate genes for IMF, pHu, meat colour and skin colour, respectively. Therefore, the current review summarizes the significant genes identified by genome-wide association studies for meat quality traits in chickens.