Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs

Pour-Aboughadareh, Alireza; Khalili, Mohsen; Poczai, Péter; Olivoto, Tiago

doi:10.3390/plants11030414

Cited by 42 publications

(21 citation statements)

References 85 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1). G × E interaction effect has important value for agricultural breeding, and it reflects trait variation that cannot be explained by individual G and E effects [34,35]. In this study, the G × E interaction effect of flowering time-related traits was significant (Table 1), which would facilitate the selection of stable varieties for breeding in different growth environments.…”

Section: Discussionmentioning

confidence: 75%

Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits

Shi

Wang

et al. 2022

BMC Plant Biol

View full text Add to dashboard Cite

Background Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize.

show abstract

Section: Discussionmentioning

confidence: 75%

Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits

Shi

Wang

et al. 2022

BMC Plant Biol

View full text Add to dashboard Cite

show abstract

“…Also, the GGE biplot involves a group of bioplot-based platforms for the interpretation of interactions present between the genotype and the environment. In general, in both GGE biplot and AMMI, the graphical images are used to answer the critical queries about G x E evaluation on a visual basis ( Pour-Aboughadareh et al, 2022 ). In addition, in both the biplot analyses, the results are further interpreted based on the criteria mentioned by Khan et al (2021) .…”

Section: Methodsmentioning

confidence: 99%

Ascertaining yield and grain protein content stability in wheat genotypes having the Gpc-B1 gene using univariate, multivariate, and correlation analysis

et al. 2022

View full text Add to dashboard Cite

The high performance and stability of wheat genotypes for yield, grain protein content (GPC), and other desirable traits are critical for varietal development and food and nutritional security. Likewise, the genotype by environment (G × E) interaction (GEI) should be thoroughly investigated and favorably utilized whenever genotype selection decisions are made. The present study was planned with the following two major objectives: 1) determination of GEI for some advanced wheat genotypes across four locations (Ludhiana, Ballowal, Patiala, and Bathinda) of Punjab, India; and 2) selection of the best genotypes with high GPC and yield in various environments. Different univariate [Eberhart and Ruessll’s models; Perkins and Jinks’ models; Wrike’s Ecovalence; and Francis and Kannenberg’s models], multivariate (AMMI and GGE biplot), and correlation analyses were used to interpret the data from the multi-environmental trial (MET). Consequently, both the univariate and multivariate analyses provided almost similar results regarding the top-performing and stable genotypes. The analysis of variance revealed that variation due to environment, genotype, and GEI was highly significant at the 0.01 and 0.001 levels of significance for all studied traits. The days to flowering, plant height, spikelets per spike, grain per spike, days to maturity, and 1000-grain weight were specifically affected by the environment, whereas yield was mainly affected by the environment and GEI. Genotypes, on the other hand, had a greater impact on the GPC than environmental conditions. As a result, a multi-environmental investigation was necessary to identify the GEI for wheat genotype selection because the GEI was very significant for all of the evaluated traits. Yield, 1000-grain weight, spikelet per spike, and days to maturity were observed to have positive correlations, implying the feasibility of their simultaneous selection for yield enhancement. However, GPC was observed to have a negative correlation with yield. Patiala was found to be the most discriminating environment for both yield and GPC and also the most effective representative environment for GPC, whereas Ludhiana was found to be the most effective representative environment for yield. Eventually, two NILs (BWL7508, and BWL7511) were selected as the top across all environments for both yield and GPC.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Modeling the GEI in METs can help in determining the adaptability and stability of genotypes across a wide range of environments. However, this concept has been defined in different ways in interpreting GEI (Gauch and Zobel, 1996), resulting in an increasing number of stability statistics (Pour-Aboughadareh et al, 2022a). Huehn (1996) classified stability statistics into parametric and non-parametric methods.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, a genotype is considered stable if its ranking remains largely constant across environments (Flores et al, 1998) and the addition or deletion of one or a few genotypes has no significant effect on the results (Huehn, 1990). Several parametric and non-parametric methods and models have been developed to analyze the extent of GEI and determine the yield performance and stability of genotypes (reviewed by Pour-Aboughadareh et al, 2022a). Each of these methods has its own set of strengths and weaknesses in describing the phenomenon of GEI, so a combination of statistics from both approaches may provide a better understanding of GEI (Becker and Leon, 1988).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dissection of genotype-by-environment interaction and simultaneous selection for grain yield and stability in faba bean (Vicia fabaL.)

Gela

Khazaei

Podder

et al. 2022

Preprint

View full text Add to dashboard Cite

Increasing faba bean production is indispensable to supply the growing demand for plant-based protein on the global scale. A thorough understanding of genotype (G) by environment (E) interaction (GEI) patterns is critical to developing high-yielding varieties with wider adaptation. Thirteen faba bean genotypes were evaluated in 15 environments from 2019 to 2020 in western Canada to estimate their yield stability using different stability statistics. The combined analysis of variance and additive main effects and multiplicative interaction (AMMI) analysis revealed that G, E, and GEI effects were highly significant (P<0.001), indicating differential responses of the genotypes across the environments, enabling the stability analysis. The result of the model comparison found the best linear unbiased prediction (BLUP) to outperform AMMI models. The BLUP-based biplot of the weighted average of absolute scores (WAASB) stability and mean grain yield identified AO1155 (Navi), 1089-1-2, 1310-5, DL Tesoro, and 1239-1 as high-yielding and stable genotypes. The correlation analysis revealed that most of the stability parameters had a strong association with grain yield and with each other, indicating that they should be used in combination with one another to select genotypes with high yield. Overall, the WAASB superiority index (WAASBY) and the average sum of ranks of all stability statistics identified the same genotypes in terms of high yielding and stability, and genotype AO1155 is considered the most stable and highest yielding among the tested genotypes. Genotypes with stable yields across environments would be beneficial for faba bean genetic improvement programs globally. Keywords: Yield stability, WAASB, AMMI model, BLUP, Stability analysis, Western Canada

show abstract

Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs

Cited by 42 publications

References 85 publications

Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits

Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits

Ascertaining yield and grain protein content stability in wheat genotypes having the Gpc-B1 gene using univariate, multivariate, and correlation analysis

Dissection of genotype-by-environment interaction and simultaneous selection for grain yield and stability in faba bean (Vicia fabaL.)

Contact Info

Product

Resources

About