• The Plant Pathology Journal
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• v.24 no.3
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• pp.337-351
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• 2008

Host-pathogen interaction in rice bacterial blight pathosystem was analyzed for a better understanding of their relationship and recognition of stable pathogenicity among the populations of Xanthomonas oryzae pv. oryzae. A total number of 52 bacterial strains isolated from diseased leaf samples collected from 12 rice growing states and one Union Territory of India, were inoculated on 16 rice varieties, each possessing known genes for resistance. Analysis of variance revealed that the host genotypes(G) accounted for largest(78.4%) proportion of the total sum of squares(SS), followed by 16.5% due to the pathogen isolates(I) and 5.1% due to the $I{\times}G$ interactions. Application of the Additive Main effects and Multiplicative Interaction(AMMI) model revealed that the first two interaction principal component axes(IPCA) accounted for 66.8% and 21.5% of the interaction SS, respectively. The biplot generated using the isolate and genotypic scores of the first two IPCAs revealed groups of host genotypes and pathogen isolates falling into four sectors. A group of five isolates with high virulence, high absolute IPCA-1 scores, moderate IPCA-2 scores, low AMMI stability index '$D_i$' values and minimal deviations from additive main effects displayed in AMMI biplot as well as response plot, were identified as possessing stable pathogenicity across 16 host genotypes. The largest group of 27 isolates with low virulence, small IPCA-1 as well as IPCA-2 scores, low $D_i$ values and minimal deviations from additive main effect predictions, possessed stable pathogenicity for low virulence. The AMMI analysis and biplot display facilitated in a better understanding of the host-pathogen interaction, adaptability of pathogen isolates to specific host genotypes, identification of isolates showing stable pathogenicity and most discriminating host genotypes, which could be useful in location specific breeding programs aiming at deployment of resistant host genotypes in bacterial blight disease control strategies.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.2
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• pp.65-67
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• 2003

This study was conducted to analyze the effects of genotypes, environments and interaction of G$\times$E on yields of sesame grown in seven different environments by AMMI analysis. Environments accounted for the largest (91 %) proportion of the sums of squares, followed by G$\times$E (8%) and genotypes (1%) Therefore, G$\times$E effects are theoretically eight times as important as G effects. G2 (Yanghukkae) has the largest IPCAI scores indicating higher G$\times$E interaction. G3 (Suwon 171) was near zero score of IPCAI suggesting higher stability than others in yield component. Most of environments except for Iksan area shows different G$\times$E effects by years, which means Iksan is optimal area for multi-environmental adaptation evaluation in sesame breeding programs. According to this experiment, it is concluded that maximization of grain yield through environments can be achieved by specific genotypes in specific environments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.5
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• pp.316-321
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• 2000

Blast severity data of 39 rice varieties at 11 sites in Korea from 1997 to 1999 were analyzed using AMMI model and pattern analysis. Genotype x Environment (G$\times$E) interaction sum of squares (SS) accounted for 12 % of the total SS. Eight genotype groups and seven location groups were identified based on blast reaction pattern. The data obtained from over 21 sites with 44 test varieties from 1981 to 1996 were also considered. These were compared with the 1997-1999 data using the G$\times$E analysis results. Majority of the variability in the Korean Rice Blast Nursery (KRBN) were attributable to variations due to genotypes. Variations of G$\times$E interaction were maintained though test sites were reduced from 21 to 11 sites. Broadly compatible biological discriminative varieties identified were Nagdongbyeo and Akibare while broadly incompatible biological discriminative varieties identified were Hangangchalbyeo and Seogwangbyeo. Key sites for future evaluation work could be selected from location groups. Each location group should be represented by the site with the strongest interaction pattern. Blast responses in Cheolwon, Gyehwa, Suwon, Iksan, and Icheon showed different patterns from other locations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.349-354
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• 2015

The AMMI (additive main effects and multiplicative interaction) and GGE (genotype main effect and genotype by environment interaction) biplot which were accounted for a substantial part of total sum of square in the analysis of variance suggested to be more appropriate models for explaining G $\times$ E interaction. The grain yield of total ten sesame genotypes was significantly affected by environment which explained 61% of total variation, whereas genotype and genotype x environment interaction (G $\times$ E) were explained 16%, 24% respectively. From the results of experiment, three genotypes Miryang49, Koppoom and Ansan were unstable, whereas other three genotypes Kyeongbuk18, Miryang50 and Kanghuk which were shorter projections to AEA ordinate were relatively stable over the environments. Yangbak which was closeness to the mean yield and short projection of the genotype marker lines was regarded as genotype indicating good performance with stability. Ansan, Miryang48 and Yangbaek showed the best performance in the environments of Naju, Suwon, Iksan and Andong. Similarly, genotype Miyrang47 exhibited the best performance in the environments of Chuncheon and Miryang. Andong is the closest to the ideal environment, and therefore, is the most desirable among eight environments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.3
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• pp.297-302
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• 2008

Application of genotype by environment ($G\;{\times}\;E$) interaction would be used for identifying optimum test condition of the varietal adaptation in the establishment of breeding purpose. Yield and yield components were used to perform additive main effect and multiplicative interaction (AMMI) analysis. Significant difference for $G\;{\times}\;E$ interaction were observed for all variable examined. For yield, 0.18 of total sum of squares corresponded to $G\;{\times}\;E$ interaction. Correlation analysis was carried out between genotypic scores of the first interaction principal component axis (IPCA 1) for agronomic characters. Significant correlations were observed between IPCA 1 for yield and capsule bearing stem length (CBSL), number of capsule per plant (NOC). The biplot of grain yield means for IPCA1 which accounted for 34% of the variation in total treatment sums of squares showed different reaction according to $G\;{\times}\;E$ interaction, genotypes and environments. Taegu showed relatively lower positive IPCA1 scores, and it also showed smaller coefficient variation of yield mean where it is recommendable as a optimal site for the sesame cultivar adaptation and evaluation trial. In case of variables, Yangbaek and M1 showed relatively lower IPCA1 scores, but the score direction showed opposite each other on the graph. Ansan, Miryang1, Miryang4, and Miryang6 seemed to be similar group in view of yield response against IPCA1 scores. These results will be helpful to select experimental site for sesame in Korea to minimize $G\;{\times}\;E$ interaction for the selection of promising genotype with higher stability.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.6
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• pp.453-458
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• 2001

The Rural Development Administration (RDA) of Korea now operates a system called Rice Variety Selection Tests (RVST), which are now being implemented in eight Agricultural Research and Extension Services located in eight province RVST's objective is to provide accurate yield estimates and to select well-adapted varieties to each province. Systematic evaluation of entries included in RVST is a highly important task to select the best-adapted varieties to specific location and to observe the performance of entries across a wide range of test sites within a region. The rice yield data in RVST for ordinary transplanting in Kangwon province during 1997-2000 were analyzed. The experiments were carried out in three replications of a random complete block design with eleven entries across five locations. Additive Main effects and Multiplicative Interaction (AMMI) model was employed to examine the interaction between genotype and environment (G$\times$E) in the biplot form. It was found that genotype variability was as high as 66％, followed by G$\times$E interaction variability, 21％, and variability by environment, 13％. G$\times$E interaction was partitioned into two significant (P＜0.05) principal components. Pattern analysis was used for interpretation on G$\times$E interaction and adaptibility. Major determinants among the meteorological factors on G$\times$E matrix were canopy minimum temperature, minimum relative humidity, sunshine hours, precipitation and mean cloud amount. Odaebyeo, Obongbyeo and Jinbubyeo were relatively stable varieties in all the regions. Furthermore, the most adapted varieties in each region, in terms of productivity, were evaluated.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.4
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• pp.317-324
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• 2017

The purpose of this study was to provide basic data on the genetic and environmental effects of stickiness in glutinous rice varieties. In our study, we analyzed the genotype-by-environment ($G{\times}E$) interactions of the stickiness using six glutinous rice varieties under six environmental conditions. AMMI (Additive Main Effects and Multiplicative Interaction) analysis results showed that genotype (variety, G), environment (cultivation region, E) and $G{\times}E$ interaction were highly significant (P < 0.001). Among all the variations of stickiness for glutinous rice varieties, the environmental effect was 24.5%, the genetic effect was 37.1%, and the $G{\times}E$ interaction effect was 28.9%. From the AMMI analysis, the IPCA1 scores of Aranghangchal (G6, IPCA1: 3.85) and Hwaseonchal (G4, IPCA1: -5.24) was lower than other varieties. On the other hand, the Sangjuchal (G1, IPCA1: -61.23) and Boseogchal (G2, IPCA1: 41.21) were highly affected by environmental effects. In this study, there were large differences in stickiness according to region of cultivation. In the future, it is considered that a precise study should be carried out on the environmental factors that may increase the stickiness of glutinous rice varieties.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.4
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• pp.333-345
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• 2017

The potato tuber is known as a rich source of essential nutrients, used throughout the world. Although potato-breeding programs share some priorities, the major objective is to increase the genetic potential for yield through breeding or to eliminate hazards that reduce yield. Glycoalkaloids, which are considered a serious hazard to human health, accumulate naturally in potatoes during growth, harvesting, transportation, and storage. Here, we used the AMMI (additive main effects and multiplicative interaction) and GGE (Genotype main effect and genotype by environment interaction) biplot model, to evaluate tuber yield stability and glycoalkaloid content in six potato cultivars across three locations during 2012/2013. The environment on tuber yield had the greatest effect and accounted for 33.0% of the total sum squares; genotypes accounted for 3.8% and $G{\times}E$ interaction accounted for 11.1% which is the nest highest contribution. Conversely, the genotype on glycoalkaloid had the greatest effect and accounted for 82.4% of the total sum squares), whereas environment and $G{\times}E$ effects on this trait accounted for only 0.4% and 3.7%, respectively. Furthermore, potato genotype 'Superior', which covers most of the cultivated area, exhibited high yield performance with stability. 'Goun', which showed lower glycoalkaloid content, was the most suitable and desirable genotype. Results showed that, while tuber yield was more affected by the environment, glycoalkaloid content was more dependent on genotype. Further, the use of the AMMI and GGE biplot model generated more interactive visuals, facilitated the identification of superior genotypes, and suggested decisions on a variety of recommendations for specific environments.

• Horticultural Science & Technology
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• v.35 no.3
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• pp.361-372
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• 2017

Bred and grown around the world, tomato (Solanum spp.) has highly valuable fruits containings various anti-oxidants such as lycopene, flavonoids, glutamine, and ${\beta}-carotene$. Several studies have explored, way in which to enhance the growth, management and quality of tomato, we focus on the management of growth for yield rather than quality. The expression of superior agronomic traits depends on where cultivars are grown. We evaluated 10 cultivars grown in three environment for their lycopene. HTL3137 ($70.48mg{\cdot}kg^{-1}$), which was grown in Yoeju in spring/summer, contained the highest lycopene content, while HTL10256 ($20.9mg{\cdot}kg^{-1}$), which was grown in Suwon in spring/summer, contain the least lycopene.Correlations between color components and lycopene content varied according to growing location and season. In spring/summer-grown tomatoes from Suwon, no significant correlation was observed between any color component (redness [R], greenness [G], blueness [B], luminosity, $L^*$, $a^*$, $b^*$, hue and chroma) and lycopene content. A correlation was observed between B and lycopene content in tomatoes grown in Yeoju during the same season. In tomatoes grown in Yeoju in fall/winter, significant correlations were found between lycopene content and G, luminosity, $L^*$, and hue. Variance in interactions between genotype, environment, and genotype ${\times}$ environment (G ${\times}$ E) using Minimum Norm Quadratic Unbiased Estimate (MINQUE) analysis indicated that lycopene content depends on genotype (51.33%), environment (49.13%), and G ${\times}$ E (21.43%). However, when the Additive Main Effects and Multiplicative Interaction (AMMI) was used, the G ${\times}$ E value was highest.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5067-5072
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• 2013

Background: The phosphatidylinositol 3-kinase (PI3K) pathway plays a significant role in apoptosis, cellular proliferation and motility. The aim of the present study was to analyze mutations and gene expression profiles of the PI3KCA gene to determine any role in breast carcinomas. Materials and Methods: We analyzed 38 breast cancers for mutations in the two PIK3CA hotspots in exons 9 and 20 by direct sequencing of DNA obtained from biopsy samples. We have also analyzed expression of the PI3KCA gene in 38 breast carcinoma tumor and corresponding control tissue samples at the mRNA level by RT-PCR. The Fisher's exact test ($2{\times}2$ only) was performed using MedCalc software for to examine associations with mRNA levels. Results: In the present study a total of 13 cases demonstrated somatic mutations. In 9/13 cases 1633 G>A (E545K) were found in exon 9, whereas in exon 20, 4/13 cases had 3140A>G mutation. Our combined analysis showed PI3KCA mutations present in 34% of human breast cancer patients. In our study, we have also clearly found significantly higher expression in breast cancer tissues in comparison with control tissues (p=0.001). Conclusions: PIK3CA mutation is an emerging tumor marker that, in the future, might be used in the process of choosing a treatment. The detection of PI3KCA mutation might have important clinical implications for diagnosis, progression and therapy.