Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

Abstract

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

Using soybean historical field trial data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors

“…The results suggested improvements in the response to selection mostly for Denmark, where the CRS ratio was 0.93. More recently, S mith et al . (2021) proposed a new way to define groups of environments that exhibit minimal COI based on FA models.…”

“…According to C immyt (1989) p. 58, “MEs are broad, not necessarily contiguous areas, defined by similar biotic and abiotic stresses, cropping system requirements … “. Another definition is a group of environments that share the same winning genotypes (K ang 2020; G auch and Z obel 1997), or that within ME there is minimal crossover interaction (COI) among the genotypes grown among environments (S mith et al . 2021).…”

“…(2012) explored historical (2001-2009) METs data to determine MEs according to five subdivision systems (climate, altitude, geographic, country, and yield-level), and concluded there was enough genotype by subregion interaction relative to genotypic variance to justify the selection for the low and high-yielding sub-regions separately. Other methodologies such as the additive main effects and multiplicative interaction (AMMI) model (B ustos -K orts 2017; G auch and Z obel 1997) and factor analytic (FA) models (S mith et al . 2021; B ustos -K orts 2017; S mith et al .…”

Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials

“…Within the paradigm of the factor analytic linear mixed model (FALMM) approach for MET analysis, the aim of both the design and analysis is the reliable prediction of the variety effects for individual environments (VE effects). These are the baseline predictions which can then be summarised across environments in meaningful ways, for example, using the interaction class approach of Smith et al ( 2021b ).…”

“…It is used in many plant breeding programs within Australia and overseas due to its flexibility and ability to handle unbalanced datasets. Additionally, it provides reliable predictions of the VE effects which can then be summarised in an informative manner using the selection tools of Smith and Cullis ( 2018 ) and Smith et al ( 2021b ).…”