Singular‐Value Partitioning in Biplot Analysis of Multienvironment Trial Data

By Weikai Yan. Agronomy Journal, 2002

628 citations recorded by the source corpus.

Multienvironment trials (MET) are conducted every year for all major crops throughout the world, and best use of the information contained in MET data for cultivar evaluation and recommendation has been an important issue in plant breeding and agricultural research. A genotype main effect plus genotype × environment interaction (GGE) biplot based on MET data allows visualizing (i) the which-won-where pattern of the MET, (ii) the interrelationship among test environments, and (iii) the ranking of genotypes based on both mean performance and stability. Correct visualization of these aspects, however, requires appropriate singular-value (SV) partitioning between the genotype and environment eigenvectors. This paper compares four SV scaling methods. Genotype-focused scaling partitions the entire SV to the genotype eigenvectors; environment-focused scaling partitions the entire SV to the environment eigenvectors; symmetrical scaling splits the SV symmetrically between the genotype and the environment eigenvectors; and equal-space scaling splits the SV such that genotype markers and environment markers take equal biplot space. It is recommended that the genotype-focused scaling be used in visualizing the interrelationship and comparison among genotypes and the environment-focused scaling be used in visualizing the interrelationship and comparison among environments. All scaling methods are equally valid in visualizing the which-won-where pattern of the MET data, but the symmetric scaling is preferred because it has all properties intermediate between the genotype- and the environment-focused scaling methods.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/3ee24c4bf2aa160a98af834bbd7a89b26699eba8

How researchers see this paper

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

In Genetics

In Plant Science

Citations in the corpus

AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia (2009, methodology): “The GGE biplots were constructed from the first two principal components (PC1 and PC2) derived by subjecting the environment-centered yield data (which contains G and GE) to singular valued composition (SVD) (Yan, 2002; Yan et al., 2000).”
Effects of year, site, genotype and their interactions on various soybean isoflavones (2003, methodology): “It is the ‘‘average environment coordinate’’ view of the bi-plot, which is suitable for simultaneous visualization of both mean performance and stability of the genotypes (Yan, 2001, 2002; Yan and Kang, 2002).”
GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data (2007, methodology): “2 except that it is based on environment-focused scaling (Yan, 2002), that is, the singular values were entirely partitioned into the environment scores ( " SVP = 2 " ) so that it is appropriate for studying the relationships among test environments.”
GGE-Biplot Analysis of Multi-Environment Yield Trials in Bread Wheat (2006, methodology): “Yield performance and stability of genotypes were evaluated by an average environment coordination (AEC) method (Yan, 2001; Yan and Hunt, 2002; Yan, 2002).”
Methods to evaluate wheat cultivar testing environments and improve cultivar selection protocols (2006, background): “Interactions between cultivar and environment often confound the genetic differences that affect yield among wheat cultivars (Brennan and Byth, 1979; Baril, 1992; Yan, 2002).”
Statistical Analysis of Yield Trials by AMMI and GGE (2006, methodology): “Perhaps the availability of GGE software offering numerous kinds of graphs, but not implementing cross validation, has contributed to this outcome ( Yan, 2001, 2002 ).”
Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.) (2018, methodology): “GGE stands for genotype main effect (G) and genotype by environment interaction (GE) method and was developed by Yan (2002) for graphical analysis of MET.”
Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype ◊ environment interaction and seasonal adaptation (2011, methodology): “The polygon view of the GGE biplot and AMMI model for sugar yield (Fig 1) indicates the best genotype(s) for each season(s) (Yan 2002).”
Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application (2017, methodology): “The cosine of the angle approximates the correlation coefficient (Yan 2002), which is equivalent to the type-B genetic correlation in the study carried out by Ding et al. (2008b).”
Biplot analysis of multi-environment trial data: Principles and applications (2006, methodology): “Among these, two methods are particularly useful: column-metric preserving and rowmetric preserving (Gabriel 2002; Yan 2002).”
GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. (2012, background): “The cosine of the angle between the vectors of two environments approximates the correlation coefficient between them (Kroonenberg, 1995; Yan, 2002).”
A heritability-adjusted GGE biplot for test environment evaluation (2010, background): “2, 3, 4 and 5 were based on environmentfocused singular value partitioning (‘‘SVP = 2’’, Yan 2002) so that they are appropriate for test environment evaluation.”