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Structural equation modeling of genotype x environment interaction
An understanding of genotypic and environmental causes of genotype x environment interaction (GEI) of complex traits is a difficult task. Despite numerous methodological developments for analyzing GEI, there is a need for a more comprehensive approach that is capable of incorporating the underlying sequential biological processes of complex traits. A structural equation modeling (SEM) approach for the analysis of GEI is proposed that allows us to account for the underlying biological processes by incorporating intermediate traits associated with those processes and quantifying the interrelationships among them. Among three models developed in this study: SEM-RESIDS, SEM-AMMI and SEM-Mixed AMMI, the first uses residuals obtained from main effect models as GEI whereas the later two use pattern-rich GEI considering the first few multiplicative interaction components obtained from singular value decomposition of GEI. The uses of these models are demonstrated by employing them in analyzing GEI in winter and durum wheat trials. Overall it was concluded that the SEM approach had a distinct advantage over other methods in providing a comprehensive understanding of GEI compensation effects among yield components and in decomposing the total effects of yield components GEI and cross product covariates on grain yield GEI, into their direct and indirect effects. Specifically, SEM-AMMI is useful when both environments and genotypes are fixed and the purpose of the statistical analysis is to assess the combined effects of genotypic and environmental covariates on yield and yield components GEI and to identify which set of genotypic materials do relatively well in which set of environments. It is also demonstrated that SEM-Mixed AMMI is useful to separately model genotypic latent variables associated with GEI assuming genotypes as random and environments as fixed, and thus to identify subsets of genotypic covariates and yield components which are the most sensitive to the environments in order to enhance the capability of selecting a set of relatively stable genetic markers and yield components for further evaluation. ^
Agriculture, Agronomy|Biology, Biostatistics|Agriculture, Plant Culture
Dhungana, Prabhakar, "Structural equation modeling of genotype x environment interaction" (2004). ETD collection for University of Nebraska - Lincoln. AAI3152605.