• Asian-Australasian Journal of Animal Sciences
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• v.21 no.5
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• pp.677-684
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• 2008

Accurate estimation of dry matter intake (DMI) is a prerequisite to meet animal performance targets without penalizing animal health and the environment. The objective of the current study was to evaluate some of the existing models in order to predict DMI when lactating dairy cows were offered a total mixed ration containing a high level of concentrates and locally produced agricultural by-products. Six popular models were chosen for DMI prediction (Brown et al., 1977; Rayburn and Fox, 1993; Agriculture Forestry and Fisheries Research Council Secretariat, 1999; National Research Council (NRC), 2001; Cornell Net Carbohydrate and Protein System (CNCPS), Fox et al., 2003; Fuentes-Pila et al., 2003). Databases for DMI comparison were constructed from two different sources: i) 12 commercial farm investigations and ii) a controlled dairy cow experiment. The model evaluation was performed using two different methods: i) linear regression analysis and ii) mean square error prediction analysis. In the commercial farm investigation, DMI predicted by Fuentes-Pila et al. (2003) was the most accurate when compared with the actual mean DMI, whilst the CNCPS prediction showed larger mean bias (difference between mean predicted and mean observed values). Similar results were observed in the controlled dairy cow experiment where the mean bias by Fuentes-Pila et al. (2003) was the smallest of all six chosen models. The more accurate prediction by Fuentes-Pila et al. (2003) could be attributed to the inclusion of dietary factors, particularly fiber as these factors were not considered in some models (i.e. NRC, 2001; CNCPS (Fox et al., 2003)). Linear regression analysis had little meaningful biological significance when evaluating models for prediction of DMI in this study. Further research is required to improve the accuracy of the models, and may recommend more mechanistic approaches to investigate feedstuffs (common to the Asian region), animal genotype, environmental conditions and their interaction, as the majority of the models employed are based on empirical approaches.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.6
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• pp.765-772
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• 2017

Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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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.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.861-869
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• 2011

One of the main objects of recent genetic studies is to understand genetic factors that induce complex diseases. If there are interactions between loci, it is difficult to find such associations through a single-locus analysis strategy. Thus we need to consider the gene-gene interactions and/or gene-environment interactions. The MDR(multifactor dimensionality reduction) method is being used frequently; however, it is not appropriate to detect interactions caused by a small fraction of the possible genotype pairs. In this study, we propose a relative risk interaction explorer that detects interactions through the calculation of the relative risks between the control and disease groups from each genetic combinations. For illustration, we apply this method to MDR open source data. We also compare the MDR and the proposed method using the simulated data eight genetic models.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2745-2749
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• 2015

Objective: To study the PLCE1 gene rs2274223 polymorphism with regard to esophageal cancer and its interaction with diet, lifestyle, psychological and environmental factors in Southwest Shandong province. Materials and Methods: A case series study (case-case) was conducted. Questionnaire data were collected and 3 ml-5ml venous blood was drawn for DNA extraction among the qualified research subjects. PLCE1 gene polymorphism was detected after PCR amplification of DNA. SPSS 13.0 software was used for statistical analysis of the data. Results: The three genotypes A/A, A/G and G/G PLCE1 gene rs2274223 was 31, 16 and 4 cases, accounting for 60.8%, 31.4%, 0.08% respectively. The difference of three genotypes (AA/GA/GG) proportion between negative and positive family history of patients was statistically significant, ${\chi}^2=6.213$, p=0.045. There was no statistically significant relationship between PLCE1 gene rs2274223 polymorphism and smoking, drinking, ${\chi}^2=0.119$, p=0.998, and ${\chi}^2=1.727$, p=0.786. There was no linkage of the three rs2274223 PLCE1 gene genotypes (AA/GA/GG) proportion with eating fried, pickled, hot, mildew, overnight, smoked, excitant food, eat speed, salt taste or not (p>0.05). or with living environment pollution and nine risk factors of occupational exposure (p>0.05). There was no statistically significant difference in TS scores between different genotype of rs2274223 PLCE1 gene. Conclusions: The PLCE1 rs2274223 polymorphism has a relationship with family history of esophageal cancer, but does not have any significant association with age, gender, smoking, alcohol drinking, food hygiene, eating habits, living around the environment and occupation in cases.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.4
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• pp.335-339
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• 2006

The level of ${\beta}-glucan$ which is a major soluble dietary fiber found in the grain endosperm cell wall was highly variable among 25 barley genotypes grown at four locations including Suwon, Naju, Jinju, and Jeju. Statistically significant genotypic effects were observed for ${\beta}-glucan$ content at each or across growing sites (P<0.001). On average, 'Chalssalbori' showed the lowest percentage ${\beta}-glucan$ (4.04%) among genotypes in the grain, whereas 'Yonezawa Mochi' was highest in percentage ${\beta}-glucan$ (6.46%) compared to other genotypes. The significant difference between genotypes was approximately 1-2% across environments. The effects of location or interaction between locations and genotypes were not significant on the variation of ${\beta}-glucan$ contents. High ${\beta}-glucan$ content seemed to be greatly associated with such grain traits as waxiness and presence of husk except for 'Chalssalbori'. The waxy genotypes had a mean of 5.37% and values ranging from 5.28 to 5.47%, but normal genotypes had a mean of 4.78% and values ranging from 4.69 to 4.88% over environments. Hulless barley genotypes were also higher than hulled barley genotypes for the average ${\beta}-glucan$ content in both individual and over all environments. The difference between the hulled and hulless gene pools was on average of 0.37% with ranges from 0.19% to 0.56% at four environments. ${\beta}-glucan$ content measured from a mapping population of $F_5$-derived 107 lines derived from the cross between 'Yonezawa Mochi' and 'Neulssalbori' was not significantly associated with other agronomic traits except for 1,000-kernel weight at the '01 Suwon environment. Not too much information on the relationship of ${\beta}-glucan$ content to agronomic traits was available.

• Korean Journal of Biological Psychiatry
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• v.15 no.1
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• pp.14-22
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• 2008

Objectives : The authors purposed to present data for explaining gene-environmental interaction causing depressive disorder by examining the effects of genetic factors related to the serotonin system and environmental factors such as stressful life events in early adulthood. Methods : The subjects were 150 young adults(mean age 25.0${\pm}$0.54), a part of 534 freshmen who had completed the previous study of genotyping of TPH1 gene. We assessed characteristics of life events, depression and anxiety scale and checked if they had a depressive disorder with DSM-IV SCID interview. Along with TPH1 A218C genotype confirmed in previous study, TPH2 -1463G/A and 5HTR2A -1438A/G genes were genotyped using the SNaPshot$^{TM}$ method. Results : In comparison with the group without C allele of TPH1 gene, the number of life events had a significant effect on the probability of depressive disorder in the group with C allele. Other alleles or genotypes did not have a significant effect on the causality of life events and depressive disorder. Conclusion : The results of this study suggest that TPH1 C allele is a significant predictor of onset of depressive disorder following environmental stress. It means that the TPH1 gene may affect the gene-environmental interaction of depressive disorder.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.130-137
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• 2021

This study evaluated newly introduced, commercial super sweet corn hybrids (Zea mays L.) for their potential as breeding materials. Agronomic traits were measured and variance components were obtained using a linear mixed model to estimate the heritability. The trials were carried out in 2018 at two locations (Haenam and Oksan in South Korea). All traits had low heritability, except for mid tasseling and silking days. These traits with low heritability mostly had low genetic variance component estimate. In case of ear height ratio, significant genotype by location appeared to be responsible for low genetic variance, which in turn led to low heritability. Low heritability estimates from the trials with commercial hybrids were perhaps because those hybrids were highly improved for commercial success. Hence, this does not necessarily point to them having poor potential as breeding materials. To overcome low heritability, significant genotype by environment interaction, and achieve high selection efficiency, intermating among hybrids is recommended to create new recombinants before inbred line development.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.2
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• pp.91-106
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• 2013

Exserohilum turcicum is considered serious destructive disease of maize (Zea mays L.) in North Korea. This study aimed to understand genetic inheritance and combining ability of newly bred lines of maize tolerant to E. turcicum by diallel crosses. Three diallel sets for two different ecological regions and one agronomic trait; eastern (E), northern (N) and stay green (SG) involving 29 inbred lines were tested in eight locations of 2000 and 2001. E. turcicum infections were under natural conditions, respectively. Lines used were selected for high yield potential in test crosses with good agronomic traits and tolerance to biotic and abiotic stresses. Selection for race specific high resistance to biotic stresses was avoided to select quantitatively inherited genes. Host plant responses to E. turcicum were rated on a scale of 1 (highly tolerant) to 9 (highly susceptible). Highly significant variations were recorded in all trials. General combining ability (GCA) mean square was roughly twice that of specific combining ability (SCA). The genotype (G) by environment (E) interaction was highly significant. The overall results of genetic studies in three diallel sets show that genetic control for inbred tolerance to E. turcicum is polygenic and quantitatively inherited. New inbreds; E-3, N-1 and SG-4 confer better tolerance to E. turcicum than the widely used inbreds; Mo17, and B73. Proper use of genetic information from this study shall increase of corn production under high E. turcicum infection in the Far Eastern Regions of Korea and China.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.4
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• pp.337-341
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• 2004

Morphological and genetic diversity of thirty nine safflower germplasm were collected and evaluated by Principal Component Analysis (PCA) and Random Amplified Polymorphic DNA (RAPD) method. Stem length and seeding to flowering days of the safflower germplasm showed $26\~117cm\;and\;76\~179$ days of variation respectively. USA originated germplasm showed higher oil content as $39\%$, but that of Japanese showed lower as $26\%$. PCA made three different cluster groups according to some agronomic characteristics of safflower. Korea originated germplasm showed similar cluster group with that of collected from USA in the PCA of stem length. But in the seeding to flowering days, it showed similar cluster pattern with that of collected from Japan rather than USA. In the experiment of RAPD analysis, total five primers showed polymorphism at the several chromosomal loci. Korea, China Japan and South Central Asia originated germplasm were differently classified with USA and South West Asia originated germplasm with lower similarity coefficient value (0.47). Most of Korea originated germplasm were grouped with South Central Asia originated germplasm with higher similarity coefficient value (0.74) conferring similar genetic background between both of them. China and Japan originated germplasm were dendrogramed with Korea originated germplasm at the 0.65 and 0.50 similarity coefficient values respectively. Some common results were expected from both of PCA and RAPD analysis, but lower genetic heritability caused by relative higher portion of environmental variance and environment by genotype interaction at the expression of those of agronomic characteristics made constraint to find any reliable results.