• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.42 no.5
• /
• pp.604-608
• /
• 1997

Ten selected soybean genotypes, consisting of nine from a pedigree breeding programme and one recommended variety, were evaluated in nine different locations and over two years for stability of yield performance. Variance component analysis revealed that soybean regional yield trials should be performed at more locations rather than in more years. Five stability parameters, which were coefficient of variability, regression coefficient, deviation parameter, variance component for genotype$\times$environment interaction, and ecovalence, were employed in the evaluation. Significant genotype$\times$environment interaction was present with respect to soybean yield. The highest average yield over nine locations and two years was shown in Suwon 145, which was considered to be stable in all stability statistics. In rank correlation among stability parameters, there were highly significant correlations among stability parameters derived from three Eberhart and Russell's, Plaisted's, and Wricke's methods. Due to the different ranking of genotypes by different stability parameters, a comprehensive method should be employed to identify the promising genotype as well as to characterize the relationship between genotype and environment.

• Asian-Australasian Journal of Animal Sciences
• /
• v.16 no.9
• /
• pp.1247-1253
• /
• 2003

Selection for increased milk production in dairy cows has often resulted in a higher incidence of disease and thus incurred a greater health costs. Considerable interests have been shown in breeding dairy cattle for disease resistance in recent years. This paper discusses the limitations of breeding dairy cattle for genetic resistance in six parts: 1) complexity of disease resistance, 2) difficulty in estimating genetic parameters for planning breeding programs against disease, 3) undesirable relationship between production traits and disease, 4) disease as affected by recessive genes, 5) new mutation of the pathogens, and 6) variable environmental factors. The hidden problems of estimating genetic and phenotypic parameters involving disease incidence were examined in terms of categorical nature, non-independence, heterogeneity of error variance, non-randomness, and automatic relationship between disease and production traits. In light of these limitations, the prospect for increasing genetic resistance by conventional breeding methods would not be so bright as we like. Since the phenomenon of disease is the result of a joint interaction among host genotype, pathogen genotype and environment, it becomes essential to adopt an integrated approach of increasing genetic resistance of the host animals, manipulating the pathogen genotypes, developing effective vaccines and drugs, and improving the environmental conditions. The advances in DNA-based technology show considerable promise in directly manipulating host and pathogen genomes for genetic resistance and producing vaccines and drugs for prevention and medication to promote the wellbeing of the animals.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.11
• /
• pp.6673-6680
• /
• 2013

Objective: Insulin resistance (IR) is an established risk factor for colorectal cancer (CRC). Given that CRC and IR physiologically overlap and the calpain-10 gene (CAPN10) is a candidate for IR, we explored the association between CAPN10 and CRC risk. Methods: Blood samples of 400 case-control pairs were genotyped, and the lifestyle and dietary habits of these pairs were recorded and collected. Unconditional logistic regression (LR) was used to assess the effects of CAPN10 SNP43 and SNP19, and environmental factors. Both generalized multifactor dimensionality reduction (GMDR) and the classification and regression tree (CART) were used to test gene-environment interactions for CRC risk. Results: The GA+AA genotype of SNP43 and the Del/Ins+Ins/Ins genotype of SNP19 were marginally related to CRC risk (GA+AA: OR = 1.35, 95% CI = 0.92-1.99; Del/Ins+Ins/Ins: OR = 1.31, 95% CI = 0.84-2.04). Notably, a high-order interaction was consistently identified by GMDR and CART analyses. In GMDR, the four-factor interaction model of SNP43, SNP19, red meat consumption, and smoked meat consumption was the best model, with a maximum cross-validation consistency of 10/10 and testing balance accuracy of 0.61 (P < 0.01). In LR, subjects with high red and smoked meat consumption and two risk genotypes had a 6.17-fold CRC risk (95% CI = 2.44-15.6) relative to that of subjects with low red and smoked meat consumption and null risk genotypes. In CART, individuals with high smoked and red meat consumption, SNP19 Del/Ins+Ins/Ins, and SNP43 GA+AA had higher CRC risk (OR = 4.56, 95%CI = 1.94-10.75) than those with low smoked and red meat consumption. Conclusions: Though the single loci of CAPN10 SNP43 and SNP19 are not enough to significantly increase the CRC susceptibility, the combination of SNP43, SNP19, red meat consumption, and smoked meat consumption is associated with elevated risk.

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.861-869
• /
• 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
• /
• v.16 no.16
• /
• pp.6953-6961
• /
• 2015

Background: Tobacco and alcohol contain or may generate carcinogenic compounds related to cancers. CYP1A1 enzymes act upon these carcinogens before elimination from the body. The aim of this study was to investigate whether CYP1A1 T3801C polymorphism modulates the relationship between tobacco and alcohol-associated head and neck cancer (HNC) susceptibility among the northeast Indian population. Materials and Methods: One hundred and seventy histologically confirmed HNC cases and 230 controls were included within the study. The CYP1A1 T3801C polymorphism was determined using PCR-RFLP, and the results were confirmed by DNA sequencing. Logistic regression (LR) and multifactor dimensionality reduction (MDR) approaches were applied for statistical analysis. Results: The CYP1A1 CC genotype was significantly associated with HNC risk (P=0.045). A significantly increased risk of HNC (OR=6.09; P<0.0001) was observed in individuals with combined habits of smoking, alcohol drinking and tobacco-betel quid chewing. Further, gene-environment interactions revealed enhanced risks of HNC among smokers, alcohol drinkers and tobacco-betel quid chewers carrying CYP1A1 TC or CC genotypes. The highest risk of HNC was observed among smokers (OR=7.55; P=0.009) and chewers (OR=10.8; P<0.0001) carrying the CYP1A1 CC genotype. In MDR analysis, the best model for HNC risk was the three-factor model combination of smoking, tobacco-betel quid chewing and the CYP1A1 variant genotype (CVC=99/100; TBA=0.605; P<0.0001); whereas interaction entropy graphs showed synergistic interaction between tobacco habits and CYP1A1. Conclusions: Our results confirm that the CYP1A1 T3801C polymorphism modifies the risk of HNC and further demonstrated importance of gene-environment interaction.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.14
• /
• pp.5767-5772
• /
• 2015

Background: Polymorphisms in the MDM2 309 (T>G) and TP53 72 (G>C) genes are reported to increase the susceptibility to head and neck cancer (HNC) in various populations. The risk for HNC is also strongly associated with etiologic habits such as smoking, alcohol consumption and/or chewing of betel quid (BQ). In a case-control study, we investigated the significance of the above polymorphisms alone, and upon interaction with one another as well as with various etiologic habits in determining HNC risk in a Northeast Indian population. Materials and Methods: Genotyping at 309 MDM2 and 72 TP53 in 122 HNC patients and 86 cancer free healthy controls was performed by PCR using allele specific primers, and the results were confirmed by DNA sequencing. Results: Individuals with the GG mutant allele of MDM2 showed a higher risk for HNC in comparison to those with the TT wild type allele (OR=1.9, 95%CI: 1.1-3.3) (p=0.022). The risk was further increased in females by ~4-fold (OR=4.6, 95% CI: 1.1-19.4) (P=0.04). TP53 polymorphism did not contribute to HNC risk alone; however, interaction between the TP53 GC and MDM2 GG genotypes resulted in significant risk (OR=4.9, 95% CI: 0.2-105.1) (p=0.04). Smokers, BQ- chewers and alcohol consumers showed statistically significant and dose-dependent increase in HNC risk, irrespective of the MDM2 genotype. Conclusions: MDM2 genotype could serve as an important predictive biomarker for HNC risk in the population of Northeast India.

• Korean Journal of Plant Resources
• /
• v.10 no.2
• /
• pp.151-158
• /
• 1997

This study was carried out to identify how soybean seed oil is influenced by climatic factors and to investigate how genotypes differ in their responses. Twelve lines selected were studied in 13 environments of North Carolina. Responses of oil concentration and total seed oil to climatic variables were investigated using a linear regression model. The best response models were determined. There were wide climatic effects in oil concentration and total seed oil. The lowest oil concentration environment was characterized by the most HTD and the smallest VADTRg and the lowest total oil environment was distinguished by the largest VADTRa and the smallest VMnDT. For oil concentration, most lines except for NC107 responded negatively to MxDT, HTD, ADT, and ADTRg, although they had different degrees of sensitivities, indication that warmer temperature may result in decreased oil concentration. All lines responded positively to VMnDT, VADTRg, and ADRa, although they had different degrees of sensitivities, suggesting that larger variation in minimum daily temperature and average daily temperature range and more average daily rain may result in increased oil concentration. Eleven lines had best response models with 1 to 3 variables. However, although NC109 did not show a significant sensitivity to any variable, it had the best response model with 2 significant variables, demonstrating that an interaction between 2 variables might be more critical in determining oil concentration than one variable.