scholarly journals Model selection in additive main effect and multiplicative interaction model in durum wheat

Genetika ◽  
2012 ◽  
Vol 44 (2) ◽  
pp. 325-339 ◽  
Author(s):  
Naser Sabaghnia ◽  
Rahmatollah Karimizadeh ◽  
Mohtasham Mohammadi
Keyword(s):  
Durum Wheat ◽  
Interaction Model ◽  
Principal Component ◽  
Wheat Genotype ◽  
Ge Interaction ◽  
Stability Parameters ◽  
Data Set ◽  
Group Iii ◽  
Main Effect ◽  
Ammi Model

The study included data set of 20 durum wheat genotype across 15 rain-fed environments. A combined analysis of variance showed that the genotypes differed significantly for seed yield and GE (year ? location) interaction. Cross validations procedure and four various F-tests including FGollob, FRatio, FGH1 and FGH2 are used for testing the GE interaction principal component analysis (IPCA) axes and indicated that two, four, six or seven axes could be significant. According to EV1, D1, AMGE1 and SIPC1 parameters, genotypes G3, G7 and G17 were the most stable genotypes while based on EV4, D4, SIPC4 and AMGE4 parameters, genotype G13 was the most stable genotype. The hierarchical clustering showed that the twenty one studied the AMMI stability parameters and mean yield could be divided into four distinct groups. Group III contains mean yield, SIPC4, SIPC6 and SIPC8 which were computed from four, six or eight IPCAs. In conclusion, G13 (DON-MD 81- 36) was found to be the most stable genotype as well as high mean yield performance (2592.45 kg ha-1) and so is recommended for commercial release in semi-arid areas of Iran. Also, the SIPC-based stability parameters of the AMMI model was found to be useful in detecting the yield stability of the genotypes studied.

scholarly journals THE USE OF AMMI MODEL FOR INTERPRETING GENOTYPE × ENVIRONMENT INTERACTION IN DURUM WHEAT

2017 ◽  
Vol 54 (5) ◽  
pp. 670-683 ◽  
Author(s):  
REZA MOHAMMADI ◽  
MOHAMMAD ARMION ◽  
ESMAEIL ZADHASAN ◽  
MALEK MASOUD AHMADI ◽  
AHMED AMRI
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Air Temperature ◽  
Heading Date ◽  
Principal Component ◽  
High Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Breeding Lines ◽  
Ammi Model

SUMMARYDurum wheat (Triticum durum) is one of the most important cereal crops in the Mediterranean region; however, its cultivation suffers from low yield due to environmental constrains. The main objectives of this study were to (i) assess genotype × environment (GE) interaction for grain yield in rainfed durum wheat and to (ii) analyse the relationships of GE interaction with genotypic/meteorological variables by the additive main effects and multiplicative interaction (AMMI) model. Grain yield and some related traits were evaluated in 25 durum wheat genotypes (landrace, breeding line, old and new varieties) in 12 rainfed environments differing in winter air temperature. The AMMI analysis of variance indicated that the environment had highest contribution (84.3% of total variation) to the variation in grain yield. The first interaction principal component axis (IPCA1) explained 77.5% of GE interaction sum of squares (SS), and its effect was 5.5 times greater than the genotype effect, indicating that the IPCA1 contributed remarkably to the total GE interaction. Large GE interaction for grain yield was detected, indicating specific adaptation of genotypes. While the postdictive success method indicated AMMI-4 as the best model, the predictive success one suggested AMMI-1. The AMMI biplot analysis confirmed a rank change interaction among the locations, indicating the presence of strong and unpredictable rank-change location-by-year interactions for locations. In contrast to landraces and old varieties, the breeding lines with high yield performance had high phenotypic plasticity under varying environmental conditions. Results indicated that the GE interaction was associated with the interaction of heading date, plant height, rainfall, air temperature and freezing days.

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

2014 ◽  
Vol 67 (1) ◽  
pp. 45-59
Author(s):  
Naser Sabaghnia ◽  
Rahmatollah Karimizadeh ◽  
Mohtasham Mohammadi
Keyword(s):  
Analysis Of Variance ◽  
Lens Culinaris ◽  
Interaction Model ◽  
Stability Parameters ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Semiarid Environments ◽  
Main Effect ◽  
Ammi Analysis ◽  
Validation Procedure

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.

scholarly journals Choosing components in the additive main effect and multiplicative interaction (AMMI) models

2006 ◽  
Vol 63 (2) ◽  
pp. 169-175 ◽  
Author(s):  
Carlos Tadeu dos Santos Dias ◽  
Wojtek Janusz Krzanowski
Keyword(s):  
Simulated Data ◽  
User Needs ◽  
Data Set ◽  
Multiplicative Interaction ◽  
Main Effect ◽  
Ammi Model

The additive main effect and multiplicative interaction (AMMI) models allows analysts to detect interactions between rows and columns in a two-way table. However, there are many methods proposed in the literature to determine the number of multiplicative components to include in the AMMI model. These methods typically give different results for any particular data set, so the user needs some guidance as to which methods to use. In this paper we compare four commonly used methods using simulated data based on real experiments, and provide some general recommendations.

Biplot analysis of genotype-environment interaction in durum wheat using the AMMI model

2006 ◽  
Vol 54 (4) ◽  
pp. 459-467 ◽  
Author(s):  
E. Farshadfar ◽  
J. Sutka
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Field Experiments ◽  
Block Design ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Biplot Analysis ◽  
Randomized Block Design ◽  
Total Variability ◽  
Ammi Model

The genotype by environment (GE) interaction is a major problem in the study of quantitative traits because it complicates the interpretation of genetic experiments and makes predictions difficult. In order to quantify GE interaction effects on the grain yield of durum wheat and to determine stable genotypes, field experiments were conducted with ten genotypes for four consecutive years in two different conditions (irrigated and rainfed) in a completely randomized block design with three replications in each environment. Combined analysis of variance exhibited significant differences for the GE interaction, indicating the possibility of stable entries. The results of additive main effect and multiplicative interaction (AMMI) analysis revealed that 12% of total variability was justified by the GE interaction, which was six times more than that of genotype. Ordination techniques displayed high differences for the interaction principal components (IPC1, IPC2 and IPC3), indicating that 92.5% of the GE sum of squares was justified by AMMI1, AMMI2 and AMMI3, i.e. 4.5 times more than that explained by the linear regression model. The results of the AMMI model and biplot analysis showed two stable genotypes with high grain yield, due to general adaptability to both rainfed and irrigated conditions, and one with specific adaptation.

scholarly journals Provenance by Site Interaction of Pinus densiflora in Korea

2008 ◽  
Vol 57 (1-6) ◽  
pp. 131-139 ◽  
Author(s):  
In-Sik Kim ◽  
Hae-Yun Kwon ◽  
Keun-Ok Ryu ◽  
Wan Yong Choi
Keyword(s):  
Regression Model ◽  
Pinus Densiflora ◽  
Interaction Model ◽  
Height Growth ◽  
Seed Transfer ◽  
Gxe Interaction ◽  
Main Effect ◽  
Ammi Model ◽  
Interaction Term ◽  
Component Scores

Abstract Thirty-six provenances of Pinus densiflora were evaluated for stability and adaptability for height growth at 11 test sites in Korea. The data were obtained from measurements at age 6 and analyzed using linear regression model and AMMI (additive main effect and multiplicative interaction) model. There was significant provenance by site interaction effect (p < 0.011). The interaction term explained 7.1% of total variation. While the regression model accounted for 15.8% of GxE interaction term, the AMMI model accounted for 74.9% with four PCA values. Most of the provenances were not significantly different from the unity (b =1.0), except for Inje (1), Jungsun (4), Bongwha (5), Koryung (26), Hamyang (30) and Seoguipo (36). Adaptability of provenances to the test sites was estimated with mean height growth and first AMMI component scores (IPCA 1). Inje (1), Bongwha (5), Taean (20) and Seoguipo (36) were specifically adapted to the high yielding environments. Considering the first and second AMMI components (IPCA 1 and IPCA 2, respectively) scores, Whachun (2), Samchuk (10), Joongwon (14) and Buan (29) provenances were more stable than others. The implication of GxE interaction was discussed in view of seed transfer and delineation of seed zones.

Stability analysis of seed yield in safflower genotypes in Iran

2009 ◽  
Vol 57 (2) ◽  
pp. 185-195 ◽  
Author(s):  
A. Abdulahi ◽  
S. Pourdad ◽  
R. Mohammadi
Keyword(s):  
Stability Index ◽  
High Yield ◽  
Ge Interaction ◽  
Stability Parameters ◽  
Yield Performance ◽  
New Approach ◽  
Superior Genotype ◽  
Breeding Programmes ◽  
Ammi Model ◽  
The Stability

To assess the stability and yield performance of safflower genotypes and to identify subregions within Iran, a set of experiments was conducted at six locations during 2003–2005. AMMI model analysis and some stability parameters derived from the grain yield were used. AMMI analysis showed differences between genotypes and environments and the GE interaction was highly significant, indicating that the agro-climatic environmental conditions were different, and that there was a differential response of the genotypes to the environments. The first two IPCA components of the GE interaction explained 51.5% of the GE interaction. According to the AMMI model, G16 was the most superior genotype in 15 out of 18 environments. The biplot of IPCA1 and IPCA2 showed that the six locations represent different environments, and mega-environments in Iran were identified for safflower breeding programmes. Due to the great fluctuation observed when selecting genotypes through stability parameters, it was not possible to distinguish stable genotypes clearly. In addition, when calculating these parameters high yield performance is not considered. So the Yield and Stability Index (YSI) can be recommended as a new approach to facilitate genotype selection, where genotypes with low values of YSI are the best. According to YSI the genotypes G16, G2, G9 and G1 can be selected. These genotypes were also selected using the AMMI model.

scholarly journals Additive Main Effect and Multiplicative Interaction Analysis for Grain Yield of Chickpea (Cicer arietinum L.) in Iran

ISRN Agronomy ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Sayyed Hossain Sabaghpour ◽  
Farhang Razavi ◽  
Seyyedeh Fatemeh Danyali ◽  
Davood Tobe ◽  
Asghar Ebadi
Keyword(s):  
Grain Yield ◽  
Cicer Arietinum ◽  
Interaction Analysis ◽  
Principal Component ◽  
High Rate ◽  
Ge Interaction ◽  
Environment Analysis ◽  
Multiplicative Interaction ◽  
Main Effect ◽  
Final Yield

Selection of chickpea (Cicer arietinum L.) cultivars with wide adaptability across diverse farming environments is important before recommending them to achieve a high rate of cultivar adoption. Multienvironment trials including 3 years and 5 locations for 17 genotypes of autumn chickpea were carried out in Iran. Additive main effect and multiplicative interaction (AMMI) were used to understand the GE interaction pattern. Analysis of variance of grain yield showed that 68.36% of the total sum of squares was attributable to environmental effects, only 15.9% to genotypic effects and 13.55% to GE interaction effects. Biplot of the first principal component and mean grain yields for genotypes and environments revealed that high yielding genotypes were not stable cultivars regarding final yield. The AMMI2 mega-environment analysis identified four chickpea megaenvironments in Iran. The first megaenvironment contained locations, Ghachsaran and Lorestan, where genotype Arman was the winner; the second megaenvironment contained locations Gorgan, where genotype FLIP 98-126C was superior. The tertiary megaenvironment contained locations in Ilam, where genotype FLIP 98-82C was superior and the location of Kermanshah made up the other megaenvironment, with FLIP 98-201C as superior.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Sign in / Sign up

Export Citation Format

Share Document