Multiplicative Interaction Analysis Research Articles

Phenotypic Stability Analysis of Yield Components in Cashew (Anacardium occidentale L.) Using Additive Main Effect and Multiplicative Interaction (AMMI) and GGE Biplot Analyses

Perennial poor fruit-set and variability in tree yield are among major problems of cashew nut production. Thus, development of improved stable genotypes would be a sustainable strategy to address this perpetual problem in order to boost income and livelihood of many smallholder farmers of this important commodity crop. Here, we have applied additive main effect and multiplicative interaction (AMMI) and genotype, genotype by environment (GGE) biplot analysis to a 3-year multi-locational trial data on nine yield component characters of cashew to evaluate phenotypic stability across diverse environments. Variance analysis showed significant variability in the cashew genotypes and strong influence of genotype by environment (GxE) on tree yield as none of the genotypes was stable for any of the yield components across locations. GxE data showed that a substantial portion of the variation was explained by the genotype (highly heritable), accounting for between 10% and 87% of the variation, while the environment accounted for between 0.7% and 37%. Data showed significant higher values of interaction (GxE) than the respective values for environment, and were mostly captured and could be explained by the first principal component axis (IPCA 1) for all the yield component characters.There was an inverse relationship between stability and yield as the best three yielding genotypes (KT_26, IW_222 and IW_31) were found to be the most unstable. Among the yield component tested, hermaphrodite flowers per panicle, nuts per panicle, nuts per tree, nut weight, and tree fruiting efficiency were identified to be critical components for nut yield. Although there was wide variation between the three environments evaluated, the data effectively identified two mega-environments (ME), and two superior genotypes (IW_222 and KT_26) suitable for these two mega-environments. The GxE complex exposes the short-comings of broad recommendations of common agronomic-husbandry technologies across diverse cashew ecologies as each mega-environment would require specific adaptable technologies for optimal plant output. Above all, the data presented here underscore the importance of multi-locational evaluation of genotypes for varietal development in cashew.

Genotype by Environment Effects on Potato Mini-Tuber Seed Production in an Aeroponics System

In order to evaluate the environmental effect on plant development and mini-tuber production of a diverse group of potato genotypes grown under an aeroponic system, a G × E interaction experiment was carried out in greenhouses located at CIP’s experimental stations in La Molina (Lima) and Huancayo (Junín). Five contrasting environments were set-up and evaluated. A combined Analysis of Variance was performed for the variables “days to tuber set”, “days to senescence” and “plant height”. An Additive Main Effects and Multiplicative Interaction (AMMI) Analysis was performed for yield variables: mini-tuber “weight” and “number of mini-tubers” per plant. There was a high variation in all the responses to the treatments. “Days to tuber set” was influenced by genetic responses, temperature and greenhouse Photosynthetically Active Radiation intensity. Considerable increases in the length of the vegetative cycle and plant height were recorded for all genotypes, and these were particularly notable in the warmer coastal environments. AMMI analysis showed that yield variables were primarily influenced by the genotypic effect followed by the genotype by environment interaction effect. The Venturana variety (T2) was the best performing genotype with a total average mini-tuber “weight” of 644 g per plant while the Chucmarina variety (T1) performed best for the variable “number of mini-tubers” with an overall average of 60.2 mini-tubers per plant. Both showed stability across different environments for these variables. The advanced clones T3 (395434.1), T5 (397077.16) and T6 (397073.16) showed stability for yield variables, but their performance was below the overall average of the trial. It is recommended that the environment and management should ideally be tailored to the genotype as this will result in significant yield gains.

Additive Main Effects and Multiplicative Interactions (AMMI) Analysis of Grain Yield in Barley Genotypes Across Environments

The study was carried out to analyze grain yield performances of nine varieties and sixteen advanced barley genotypes across eight environments of Southeastern Anatolia, Turkey, between 2003-2007 growing seasons. The experimental layout was randomized complete block design with four replications. Additive main effects and multiplicative interactions analysis (AMMI) revealed that the yield performances of genotypes were under the main environmental effects of genotype by environmental interactions. The first two principal component axes (IPCA 1 and IPCA 2) were significant (P<0.01) and cumulatively contributed to 61.07% of the total genotype by environmental interaction. According to the AMMI biplot analysis, genotype G17 and G21 had the desirable characteristics of high or moderate stability with high grain yield and were thus recommended for commercial release in Turkey and genotypes G7, G9 and the G8 proved to be valuable sources for yield stability in barley breeding programs.

Influence of Location, Year, Potato Rotation, and Chemical Seed Treatment on Incidence and Severity of Silver Scurf on Progeny Tubers

A three-year study was conducted in 1999, 2001, and 2002 to examine the influence of seed-borne inoculum and fludioxonil+mancozeb seed treatment on silver scurf (caused by Helminthosporium solani) development on progeny tubers at six locations under different potato rotations in the semi-arid U.S. Pacific Northwest. Disease-free pre-nuclear seed and diseased generation 3 seed was either treated or not treated with fludioxonil plus mancozeb, planted, and progeny tubers were harvested and then evaluated for silver scurf incidence and severity. Experiments were conducted in the southern Columbia Basin (Oregon), northern Columbia Basin (Washington), central Oregon, southern Oregon, western Idaho, and eastern Idaho under short ( 5 years) potato rotations over the three years for a total of 19 location-year-rotation combinations. Significant differences were observed among years and locations with disease incidence being highest in central Oregon. Progeny tubers from untreated generation 3 seed had significantly higher silver scurf incidence (18.4 %) and severity (1.3) compared to untreated progeny tubers from pre-nuclear seed (1.2 % and 0.04 for incidence and severity, respectively). Seed treatment with fludioxonil+mancozeb reduced incidence (3.8 %) and severity (0.2) significantly compared to the untreated control (15.8 % and 1.1 for incidence and severity, respectively). Significant (P < 0.0001) interactions between treatments and location-year-rotation were observed and additive main effects multiplicative interaction analysis discriminated those with high incidence, severity, and variability. These data indicate that seed, not soil, is the primary source of progeny tuber infection in the field in the Pacific Northwest. For long term storage, purchase of clean seed is an essential component for managing silver scurf.

Using Additive Main Effect and Multiplicative Interaction Model for Exploration of Yield Stability in Some Lentil (Lens Culinaris Medik.) Genotypes

AbstractThe additive main effect and multiplicative interaction (AMMI) analysis has been indicated to be effective in interpreting complex genotype by environment (GE) interactions of lentil (Lens culinarisMedik.) multienvironmental trials. Eighteen improved lentil genotypes were grown in 12 semiarid environments in Iran from 2007 to 2009. Complex GE interactions are difficult to understand with ordinary analysis of variance (ANOVA) or conventional stability methods. Combined analysis of variance indicated the genotype by location interaction (GL) and three way interactions (GYL) were highly significant. FGH1and FGH2tests indicated the five significant components; FRatioshowed three significant components and F-Gollob detected seven significant components. The RMSPD (root mean square predicted difference) values of validation procedure indicated seven significant components. Using five components in AMMI stability parameters (EVFI, SIP-CFI, AMGEFI and DFI) indicated that genotypes G5 and G6 were the most stable genotypes while considering three components in of AMMI stability parameters (EVFII, SIPCFII, AMGEFII and DFII) showed that genotypes G8 and G18 were the most stable genotypes. Also genotypes G2, G5 and G18 were the most stable genotypes according to AMMI stability parameters which calculated from seven components (EVFIII, SIP-CFIII, AMGEFIII and DFIII). Among these stable genotypes, only genotypes G2 (1365.63 kg × ha-1), G11 (1374.13 kg × ha′1) and G12 (1334.73 kg × ha-1) had high mean yield and so could be regarded as the most favorable genotype. These genotypes are therefore recommended for release as commercial cultivars.

Vibrational spectra, electronic absorption, nonlinear optical properties, evaluation of bonding, chemical reactivity and thermodynamic properties of ethyl 4-(1-(2-(hydrazinecarbonothioyl)hydrazono)ethyl)-3,5-dimethyl-1H-pyrrole-2-carboxylate molecule by ab initio HF and density functional methods

In this work, detailed vibrational spectral analysis of ethyl 4-(1-(2-(hydrazinecarbonothioyl)hydrazono)ethyl)-3,5-dimethyl-1H-pyrrole-2-carboxylate (EHCHEDPC) molecule has been carried out using FT-IR spectroscopy and potential energy distribution (PED). Theoretical calculations were performed by ab initio RHF and density functional theory (DFT) method, using 6-31G(d,p) and 6-311+G(d,p) basis sets. The other carried outwork cover: structural, thermodynamic properties, electronic transitions, bonding, multiple interaction, chemical reactivity and hyperpolarizability analysis. The results of the calculation were applied to the simulated spectra of (EHCHEDPC), which show excellent agreement with observed spectra. The vibrational analysis shows red shift in both group, the proton donor (pyrrole NH) and proton acceptor (CO of ester) indicating the presence of intermolecular hydrogen bonding. Time dependent density functional theory (TD-DFT) has been used to find electronic excitations and their nature. The results of natural bond orbital (NBOs) analysis show the charges transfer and delocalization in various intra- and intermolecular interactions. The binding energy of intermolecular multiple interactions is calculated to be 12.54kcalmol−1 using QTAIM calculation. The electronic descriptors analyses reveal the investigated molecule used as precursor for heterocyclic derivatives synthesis. First hyperpolarizability (β0) has been computed to evaluate non-linear optical (NLO) response.

English

The objectives of the present study were: (i) to evaluate the genotypic performances of 45 soybean genotypes with the future finality of recommendation of varieties for the State of S&atilde;o Paulo, Brazil; (ii) to determine the stability and adaptability of the genotypes and compare the performance and accuracy of the Wricke&rsquo;s ecovalence, additive main effects and multiplicative interaction analysis (AMMI), GGE-Biplot and harmonic mean of the relative performance of genotypic values (MHPRVG) methods; (iii) to evaluate the phenotypic, genotypic and environmental correlations among the traits of 45 genotypes in three environments. The exploration of genotype-by-environment interaction (GEI) allowed the identification of 21 genotypes with high mean grain yield, representing different relative maturity groups and stability levels to the environments. This group was subdivided by crop cycle, in which the genotypes 18, 36, 20, 34 and 33 were early cycles (108 to 125 days), while genotypes 11, 22, 44 (CD 219), 24, 23, 14, 32, 1, 12, 39, 30, 38, 7 and 26 were medium cycles (126 to 135 days) and genotypes 25 and 37 were late cycles (&ge; 136 days). The interpretations obtained from the ecovalence, AMMI and GGE-biplot methods were more similar than those from the MHPRVG method. This was due to the method&rsquo;s properties, which assigns more weight to grain yield and little weight to the adaptability and stability parameters. The genotypic and environmental correlations among traits enhanced the interpretations of the genotype x environmental interactions. &nbsp; Key words: Glycine max, stability and adaptability, additive main effects and multiplicative interaction analysis (AMMI), GGE-biplot, harmonic mean of the relative performance of genotypic values (MHPRVG), restricted maximum likelihood/best linear unbiased procedure analysis (REML/BLUP).

Brazilian Spring Wheat Homogeneous Adaptation Regions can be Dissected in Major Megaenvironments

ABSTRACTThe objectives of this study were to investigate the pattern of genotype environment interaction and identify megaenvironments (ME), essential test locations, and suitable genotypes for each ME. The genotype plus genotype by environment interaction (GGE) biplot and additive main effects and multiplicative interaction analysis (AMMI) were used to demonstrate the potential of using a graphical biplot to analyze the genotype by environment interaction (GEI) in data of multienvironment trials (MET). These trials of the Central Cooperative Agricultural Research (COODETEC) evaluated 36 advanced breeding lines and 27 check cultivars for 3 years (2008–2010) at 12 test locations. Yield data were analyzed using the genotype plus GGE and AMMI biplot methods. The test environments were classified into two ME (i.e. ME1: Castro, Guarapuava, Não‐Me‐Toque, Abelardo Luz, and Cachoeira do Sul; and ME2: Campo Mourão, Dourados, Palmital, Palotina, Ponta Porã, and Rolândia. In ME1, the locations Guarapuava, Cachoeira do Sul, and Abelardo Luz were classified as ideal, while in ME2, Ponta Porã, Dourados and Palotina were close to ideal. Guarapuava was the essential test location in ME1; however the identification of an essential test location in ME2 was unclear. Superior cultivars and advanced lines were identified in both ME that could be valuable for spring wheat (Triticum aestivum L.) improvement or cultivars released.

Effects of Early and Late Harvest on Agronomic Performance and Stability of Late Blight Resistant (R-gene Free) Potato Genotypes

Late blight is an important constraint to potato production and genotype resistance is an effective disease control mesure. Ten late blight resistant potato genotypes (R-gene free) were assessed for yield performance and stability at early (90 days) and late harvest (120 days) at two locations in Kenya during two years. Significant differences (P ≤ 0.05) in area under disease progress curves (AUDPC) were detected among potato genotypes. Resistant genotypes free of R-genes had significantly (P ≤ 0.05) higher yield at late than early harvest, perhaps due to increased tuber bulking period. The rank of genotypes for AUDPC, late blight resistance, and tuber yield varied across seasons and locations (environment). Additive main effects and multiplicative interaction (AMMI) analysis of tuber yield and late blight resistance resulted in significant (P ≤ 0.05) effects of genotypes (G) and environments (E). The proportion of genotypic variance was larger than the environmental variance and the G × E interactions. For tuber yield, the G, E, and G × E interactions accounted for 42.9, 39.6 and 17.5%; and 53.4, 29.7, and 16.9% at early and late harvests, respectively. For AUDPC, G, E, and G × E accounted for 80.2, 5.0, and 14.8%; as well as 82.3, 4.6, and 13% for early and late harvests, respectively. The resistance of potato genotypes without R-genes varied. Selective deployment of resistant genotypes can improve potato tuber yield.

Performance and stability of homogeneous and heterogeneous broccoli (Brassica oleracea L. var. italica Plenck) varieties in organic and low-input conditions

Organic (OA) and low-input (LI) farming rely on genotypes with high adaptability that maintain good performance over a broad range of agronomic and environmental conditions. Two synthetic varieties of Brassica oleracea var. italica Plenck (broccoli) were developed from a landrace. Their performance and stability under LI and OA farming conditions were then assessed and compared to a F1 hybrid variety. Identical experiments were carried out over a period of 2 years in three locations in Italy having different management and pedo-climatic conditions. Initially, an analysis of variance, carried out using a linear mixed model (LMM), with “Genotype” (“G”) and “Location” (“L”) as fixed factors and “Year” (“Y”) as a random factor, showed that the “Genotype” effect was significant for days to heading (DH), head number (HN), plant diameter (PD), plant vigour (PV) and plant height (PH). The “L” effect was significant for PD and PV. “G × L” interaction was significant for DH, PV and for yield. To obtain a better understanding of entry performances across years and locations, each location—year combination was considered as “Environment” and the additive main effects and multiplicative interaction analysis was used to dissect the “G × E” interaction. Synthetic varieties had good performances and always had a higher stability than the F1 hybrid. The data discussed in this study suggest that heterogeneous varieties developed from adapted materials are suitable for OA and LI because of their stability.

Genotype × environment interactions and yield stability of stress-tolerant open-pollinated maize varieties in the Eastern Cape province, South Africa

Genotype × environment interactions are inherent in multilocational trials and complicate identification of superior genotypes. The aim of the study was to determine yield performance and stability of 13 maize genotypes in five locations of the Eastern Cape province, South Africa. The genotypes assessed were: ZM305, ZM423, ZM501, ZM525, ZM621, ZM627 and Obatanpa from the International Maize and Wheat Improvement Centre; BR993 and COMP4 from the International Institute of Tropical Agriculture; four local checks, AFRIC 1, Okavango, Nelson's Choice, and PAN 6479. Yield data were subjected to analysis of variance (ANOVA) and additive main effects and multiplicative interaction (AMMI) analysis. According to ANOVA, genotype × environment interaction was significant. PAN 6479 (5.21 t ha-1) and ZM525 (5.19 t ha-1) were the highest-yielding varieties, whereas Okavango (4.08 t ha-1) showed the lowest yield. Based on AMMI analysis, BR993 and Obatanpa were the least stable genotypes. PAN 6479 and ZM525 showed specific adaptations to high potential environments. Obatanpa was adapted to environments with low-yielding potential, since improvement of the environment did not improve its yield. ZM501 was the most stable genotype, and showed no significant yield difference to PAN 6479. ZM501 can therefore be assigned to several environments in the Eastern Cape.

Association of DNA repair and cell cycle gene variations with breast cancer risk in Northeast Indian population: a multiple interaction analysis

Polymorphisms in DNA repair and cell cycle genes contribute to increased breast cancer (BC) risk. Their association and interaction in relation to betel quid and tobacco chewing habits need exhaustive multi-analytical investigation to explain BC predisposition due to DNA damage. Polymorphism in TP53-72Arg>Pro, RAD51-135G>C, BRCA2, and CCND1-G870A were examined in 204 BC cases and 217 controls from Northeast Indian population. Multifaceted analytic approaches were used to explore relationships between polymorphisms, tobacco history, and BC susceptibility. Betel quid chewing was identified as the predominant risk factor. CCND-AA and dominant model showed protection towards BC in betel quid chewer (BQC) [(0.28 (0.10-0.77), 0.01 and 0.32 (0.12-0.81), 0.01)] and non-betel quid chewers (NBQC) [(0.26 (0.09-0.78), 0.01 and 0.37 (0.16-0.87), 0.02)]. TP53-Pro/Pro genotype showed protection towards BC in NBQC (0.29 (0.10-0.81), p=0.01) and (0.51 (0.32-0.80), p=0.003, respectively). RAD51-C allele was associated with BC risk (2.03 (1.26-3.30) 0.002) in BQC. Two BQC cases had BRCA2 8415G>T:K2729N mutation in Exon18. MDR analysis showed best four locus model with TBA 0.6765 (0.005) and CVC of 10/10 in NBQC. Interaction diagram concurred the interactions between TP53 and RAD51 (1.32 %) with independent effect (1.89 %) of CCND1in NBQC. In CART analysis, BQC with CCND1 GG genotype were at risk (OR=33.0; 95 % CI=6.08-179.07), p<0.001) followed by combination of BQC, CCND1, No-Smk, and Alc (OR=42.00; 95 % CI=5.11-345.11, p<0.001). Risk was also observed in BQC, CCND1, No-Smk, Non-Alc, and TP53 combination (OR=14.84; 95 % CI=3.13-70.34, p<0.001) and BQC, CCND1, No-Smk, Non-Alc, TP53 (OR=9.40; 95 % CI=1.99-44.34, p<0.001). NBQC group showed risk with combination of NBQC and TP53 (OR=5.54; 95 % CI=1.11-27.42, p=0.03). Genetic variants in DNA repair and cell cycle genes contribute to BC risk through gene-gene and gene-environmental interactions.

Rank correlation among different statistical models in ranking of winter wheat genotypes

Several statistical methods have been developed for analyzing genotype×environment (GE) interactions in crop breeding programs to identify genotypes with high yield and stability performances. Four statistical methods, including joint regression analysis (JRA), additive mean effects and multiplicative interaction (AMMI) analysis, genotype plus GE interaction (GGE) biplot analysis, and yield–stability (YSi) statistic were used to evaluate GE interaction in 20 winter wheat genotypes grown in 24 environments in Iran. The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield, stability and yield–stability. Three kinds of genotypic ranks (yield ranks, stability ranks, and yield–stability ranks) were determined with each method. The results indicated the presence of GE interaction, suggesting the need for stability analysis. With respect to yield, the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated (P<0.01). For stability ranking, the rank correlations ranged from 0.53 (GGE–YSi; P<0.05) to 0.97 (JRA–YSi; P<0.01). AMMI distance (AMMID) was highly correlated (P<0.01) with variance of regression deviation (S2di) in JRA (r=0.83) and Shukla stability variance (σ2) in YSi (r=0.86), indicating that these stability indices can be used interchangeably. No correlation was found between yield ranks and stability ranks (AMMID, S2di, σ2, and GGE stability index), indicating that they measure static stability and accordingly could be used if selection is based primarily on stability. For yield–stability, rank correlation coefficients among the statistical methods varied from 0.64 (JRA–YSi; P<0.01) to 0.89 (AMMI–YSi; P<0.01), indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance. Based on the results, it can be concluded that YSi was closely correlated with (i) JRA in ranking genotypes for stability and (ii) AMMI for integrating yield and stability.

Synthesis, molecular structure, photoluminescence, multiple interaction, chemical reactivity and first hyperpolarizability analysis of ethyl 2-cyano-3-{5-(4-methylbenzenesulfonyl)-hydrazonomethyl]-1H-pyrrol-2-yl}-acrylate: Experimental and quantum chemical approaches

A detailed spectroscopic, molecular structure, photoluminescence, multiple interaction, chemical reactivity and first hyperpolarizability analysis of a newly synthesized ethyl 2-cyano-3-{5-[(4-methylbenzenesulfonyl)-hydrazonomethyl]-1H-pyrrol-2-yl}-acrylate (ECMHPA) has been carried out. TD-DFT is used to find various electronic excitations and their nature within molecule. The emission spectra show photoluminescence behavior of title molecule in yellow region. The red shift in both the proton donor (pyrrole NH) and proton acceptor (SO) group designates the presence of intermolecular hydrogen bonding N1H2⋯O49 and N46H47⋯O4. The binding energy of dimer formation has been calculated to be 12.12kcal/mol after basis set superposition error (BSSE) correction. The global electrophilicity index (ω=5.44eV) shows that ECMHPA is a strong electrophile. First hyperpolarizability (β0) of ECMHPA has been computed to evaluate non-linear optical (NLO) response of the investigated molecule.

Herbage yield stability of 38 genotypes of sainfoin (Onobrychis sativa) across five environments of Iran

The aim of this study was to compare some stability analyses methods as additive main effect and the multiplicative interaction analysis (AMMI), Eberhart-Russel regression, cultivar superiority (Pi), ecovalence (Wi), and stability variance analysis in classification of dry matter (DM) yield in 38 sainfoin (Onobrychis sativa L.) genotypes across five environments of Iran over 2 years (2010 and 2011). Results of combined analysis of variance showed significant differences effects of genotypes, environments, and genotype × environment interaction. In comparison among various stability methods results showed that five of the 38 tested genotypes as 3 (Sarab), 8 (Khomin), 24 (Khonsar), 37 (Divandara) and 38 (Songor) with average values of 7.18 to 7.85 t had high and stable DM yield and wide-general adaptability. Other tested cultivars had unstable yield and narrow (specific) adaptability.

Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments

Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments

Genotype by environment interaction and stability analysis for rice genotypes under Boro condition

Genotype (G)?Environment (E) interaction of nine rice genotypes possessing cold tolerance at seedling stage tested over four environments was analyzed to identify stable high yielding genotypes suitable for boro environments. The genotypes were grown in a randomized complete block design with three replications. The genotype ? environment (G?E) interaction was studied using different stability statistics viz. Additive Main effects and Multiplicative Interaction (AMMI), AMMI stability value (ASV), rank-sum (RS) and yield stability index (YSI). Combined analysis of variance shows that genotype, environment and G?E interaction are highly significant. This indicates possibility of selection of stable genotypes across the environments. The results of AMMI (additive main effect and multiplicative interaction) analysis indicated that the first two principal components (PC1-PC2) were highly significant (P&lt;0.05). The partitioning of TSS (total sum of squares) exhibited that the genotype effect was a predominant source of variation followed by G?E interaction and environment. The genotype effect was nine times higher than that of the G?E interaction, suggesting the possible existence of different environment groups. The first two interaction principal component axes (IPCA) cumulatively explained 92 % of the total interaction effects. The study revealed that genotypes GEN6 and GEN4 were found to be stable based on all stability statistics. Grain yield (GY) is positively and significantly correlated with rank-sum (RS) and yield stability index (YSI). The above mentioned stability statistics could be useful for identification of stable high yielding genotypes and facilitates visual comparisons of high yielding genotype across the multi-environments.

The application of AMMI model for barley cultivars evaluation in multi-year trials

The interpretation of new varieties performance is disturbed under the influence of genotype-by-environment interaction. Among several methods used for understanding this effect, one of the most frequently used methods is Additive Main Effects and Multiplicative Interaction (AMMI) analysis. In this study we used AMMI method with the aim to estimate the genotype - environment interaction of 14 barley genotypes, and to identify barley genotypes that have high and stable performance in different environments. The trials were conducted during 11 growing seasons (1995/96 - 2005/06), arranged in a randomized complete block (RCB) design with four replications in location Rimski Sancevi. The results showed that the influence of environment (seasons), genotypes and their interaction on barley grain yield were significant (p &lt; 0.01). Based on AMMI method, two-rowed variety Novosadski 317 and the six-rowed variety Novosadski 331 can be distinguished due their high and stable yields.

Risk of GWAS-identified genetic variants for breast cancer in a Chinese population: a multiple interaction analysis

Genome-wide association studies (GWASs) of breast cancer (BC) have identified multiple risk variants. However, the multiple interactions among these variants are still not well established. In this study, we utilized the multi-analytic strategy combing random forest (RF), multifactor dimensionality reduction (MDR), and logistic regression approaches to investigate the high-order interactions among ten genetic variants recently identified by GWAS in 477 BC patients and 534 healthy controls. Expectedly, six variants, rs1219648, rs3757318, rs1926657, rs6556756, rs2046210, and rs4973768, were significantly associated with BC risk under independent analysis. In RF analysis, rs3757318, rs2046210, and rs4973768 were ranked as the top three important risk factors and were selected as the best set which taking interactions into consideration. Subsequently, the MDR analysis of the ten variants found that the three-factor model including rs3757318, rs2046210, and rs4973768 interpret the best interaction model with the maximized testing accuracy of 0.6183 and cross-validation consistency of 10/10. Intriguingly, cumulative effect was observed in the manner of dose-dependent with increasing numbers of risk alleles (P(trend) = 9.80 × 10(-5)), and the individuals carrying 4-6 risk alleles had a threefold higher risk of BC than carrying 0 risk alleles (OR 3.27, 95 % CI 1.96-5.48). Our findings emphasized the proof of principle that multiple interactions of genetic variants, including rs3757318, rs2046210, and rs4973768 may play important roles in the susceptibility of BC though the biological mechanisms underlying the observed associations need to be elucidated.

Additive main effects and multiplicative interaction (AMMI) analysis of GxE interactions in rice-blast pathosystem to identify stable resistant genotypes

Genotype x environment interaction (GEI) of 42 rice genotypes tested over nine seasons was analyzed to identify stable resistance to blast disease incited by Magnaporthe oryzae. The genotypes were raised in uniform blast nursery in a randomized complete block design with three replications. The GEI was analyzed following the regression models as well as additive main effects and multiplicative interaction (AMMI) model. AMMI analysis of variance revealed that the first two interaction principal component axes (IPCA) explained 37.28 and 33.47% of the interaction effects in 14.63 and 14.02% of interaction degrees of freedom, respectively and rest of the five IPCAs were noisy. Integrating biplot display and genotypic stability statistics enabled five groupings of genotypes based on similarities in their performance across environments. The biplot generated using the environment and genotype scores for the first two IPCAs revealed the positioning of the five host genotype groups (HG) into four sectors. HG-1 constituting of 28 genotypes exhibiting low stability index (Di values), low IPCA-1 as well as IPCA-2 scores and low mean disease scores across seasons of testing, were identified as possessing stable resistance to the disease. Although, both regression and AMMI models were equally potential in partitioning of GEI, AMMI analysis and the biplot display were more informative in differentiating genotype response over environments, describing specific and non-specific resistance of genotypes, identifying most discriminating environments and thus could be useful to plant pathologists as well as breeders in supporting breeding program decisions. Key words: Additive main effects and multiplicative interaction (AMMI) model, rice blast disease, Magnaporthe oryzae, regression model, stable resistance.