An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction

By Weikai Yan, Nicholas A. Tinker. 2005

351 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/f314d5e54c8eff317f1ab350d571e55681c29133

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Jose Crossa
2. Juan Burgueño
3. Weikai Yan
4. P. Cornelius
5. Kuang Hongyu
6. Rong‐Cai Yang
7. R. Mohammadi
8. Baoluo Ma

In Plant Science

1. Jose Crossa
2. Juan Burgueño
3. P. Cornelius
4. Weikai Yan
5. Kuang Hongyu
6. Rong‐Cai Yang
7. R. Mohammadi
8. Baoluo Ma

Citations in the corpus

• GGE biplot and AMMI analysis of barley yield performance in Iran (2017, methodology): “If genotype effect (G) is sizable, PC1 scores will be highly correlated with G and PC2 will be controlled by GE interaction and (Yan and Tinker 2005).”
• Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, methodology): “Yan and Tinker (2005) argued that the covariate-eff ect biplot would enable one to determine if GE for yield can be exploited by indirect selection for the other traits.”
• Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment (2008, background): “…investigating the role of genotype, environment and GEI effects in yield-trial experiments is the genotype main effects and genotype 9 environment interaction (GGE) biplot (Yan et al. 2000, 2001, 2007; Yan and Kang 2002; Yan and Tinker 2005; Samonte et al. 2005; Dehghani et al. 2006; Gauch 2006).”
• Biplot analysis of multi-environment trial data: Principles and applications (2006, methodology): “From the above discussions, four questions need to be asked to correctly interpret a biplot (Yan and Tinker 2005a).”
• GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition (2013, methodology): “Therefore all data contain some error, but according to Yan and Hunt (2002) and Yan and Tinker (2005) as the biplot shows and makes conclusions on the overall pattern of the whole dataset; these predictions are probably more reliable than individual observations.”
• AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, methodology): “This difference is the main topic of discussion among authors concerned with the effectiveness of the two models in depicting adaptive responses of genotypes over environments (Yan and Tinker, 2005; Gauch, 2006; Yan and Tinker, 2006; Yan et. al., 2007; Gauch et al., 2008).”
• GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data (2007, methodology): “" Centering = 0 " was used in studying QTL-by-environment interactions (Yan and Tinker, 2005a) and genotypic covariate-by-environment interactions (Yan and Tinker, 2005b).”
• Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.) (2018, background): “Which-won-where graphs identified through analysis of GGE biplot facilitate the visual comparison of distance between genotypes and environments and helps identify the representativeness of environments and their discriminating ability (Yan and Tinker 2005, 2006).”