Higher Order AMMI (HO-AMMI) analysis: A novel stability model to study genotype-location interactions

Abstract

Additive main effects and multiplicative interaction (AMMI) model is most widely used to analyze genotype x environment interactions (GEI) wherein interaction effects of location is masked by year effect. Hence, presently available models are not able to estimate interaction effects of genotype x location (GLI) and genotype x year (GYI) separately. Moreover, genotype ranking differs as number of years of evaluation vary making selection of genotype for target location difficult. In the present study, a novel stability model i.e., Higher-order-AMMI (HO-AMMI) analysis which can calculate GLI without the confounding effect of GYI and GLYI has been proposed. GEI of AMMI model and all 2-way interactions of HO-AMMI model follow χ2 distribution, whereas 3-way interaction (GLYI) of HO-AMMI follow noncentral χ2 distribution. With increase in number of years of evaluation contribution of GLI towards total variation increased whereas in AMMI model contribution of GEI towards total variation decreased. Variation explained by multiplicative components is higher in HO-AMMI compared to AMMI model. Genotypes were ranked using GL, GY and GL+GY+GLY interactions of HO-AMMI and GEI of AMMI for stability and yield and compared their ranks with field ranking. Correlation and linear regression analysis have indicated high association of GLI (HO-AMMI) with field ranking with high R2 values. Further, HO-AMMI model was able to remove the confounding effect of GYI and GLYI on GLI for accurate identification of genotype for target location irrespective of number of years of evaluation. Hence, HO-AMMI model can be used under multi-environment trials(MET) for selecting genotypes efficiently.

AMMI and GGE biplot analysis of genotype by environment interaction for yield and yield contributing traits in confectionery groundnut

“…The study determined that genotypes G37 (PBS 29233) and G42 (PBS 29230) were desirable as they displayed high pod yield and stability with consistent performance under different environmental conditions, which was in agreement with reports of 28 . The genotypes PBS 29079B (HKW, PWP, SP) and Girnar 2 (NPP) exhibited superior performance, however, their lack of stability suggests that their performance was inconsistent and unpredictable.…”

“…Furthermore, it should be noted that the impact of the environment on genotypes is not consistent, as different genotypes display distinct responses in terms of yield and associated traits under different environmental circumstances. This, phenomena has been extensively observed by several authors 20 – 28 .…”

Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations

“…This indicates that the AMMI stability model is adequate for comprehending the GEI 27 . The multiplicative component of AMMI models consists of the singular value/multiplication factor of IPCA, the genotype eigenvector, and the environment eigenvector 17 . The IPCA1 and IPCA 2 were highly significant for pod yield and RUE, with IPCA1 accounting for 86.99 and 73.33 percent of the GEI for pod yield and RUE, respectively, whereas IPCA 2 accounted for 13.01 and 26.27 percent of the GEI, indicating a significant contribution of environment on genotype and trait expression performance.…”

Multi-Phenotyping and Genotyping of Spanish Groundnuts from Diverse Crosses to Identify Stable donors for Fresh Seed Dormancy and Selection of High-Yielding Stable Genotypes in the genetic background of dormant lines-BLUP, YREM, and AMMI model based stability

“…Further, the signi cant contribution of genotypes and GSI Signal to total variation among the genotypes across seasons, for yield and related traits, justify the application of AMMI and BLUP models to our data to understand patterns of GSI. The multiplicative component of AMMI models consists of the singular value/multiplication factor of IPCA, the genotype eigenvector, and the environment eigenvector (Ajay et al, 2022). In the present study, the rst PCs explained 68% (HKW) to 87% (PYLP) of GSI, indicating that most variation was captured by the rst component, whereas second component accounted for 12.60 to 31.80 percent of the GEI, indicating a signi cant contribution of environment on genotype and trait expression performance.…”