Browsing by Subject "Factorial regression"

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Extending Finlay-Wilkinson regression with environmental covariates
(2023) Piepho, Hans‐Peter; Blancon, Justin
Finlay–Wilkinson regression is a popular method for analysing genotype–environment interaction in series of plant breeding and variety trials. It involves a regression on the environmental mean, indexing the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This paper reviews the development of such methods. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Two new methods are proposed for obtaining such synthetic covariates, which may be integrated into genotype‐specific regression models, that is, criss‐cross regression and a factor‐analytic approach. The main advantage of such explicit modelling is that predictions can be made also for new environments where trials have not been conducted. A published dataset is employed to illustrate the proposed methods.
Regression approaches for modeling genotype-environment interaction and making predictions into unseen environments
(2026) Hrachov, Maksym; Piepho, Hans-Peter; Rahman, Niaz Md. Farhat; Malik, Waqas Ahmed; Hrachov, Maksym; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany; Piepho, Hans-Peter; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany; Rahman, Niaz Md. Farhat; Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh; Malik, Waqas Ahmed; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses. When observable environmental covariates are used, this is also known as factorial regression. Early work along these lines can be traced back to Stringfield & Salter (1934) and Yates & Cochran (1938), who proposed a method nowadays best known as Finlay-Wilkinson regression. This method, in turn, has close ties with regression on latent environmental covariates and factor-analytic variance-covariance structures for genotype-environment interaction. Extensions of these approaches – reduced rank regression, kernel- or kinship-based approaches, random coefficient regression, and extended Finlay-Wilkinson regression – will be the focus of this paper. Our objective is to demonstrate how seemingly disparate methods are very closely linked and fall within a common model-based prediction framework. The framework considers environments as random throughout, with genotypes also modeled as random in most cases. We will discuss options for assessing uncertainty of predictions, including cross validation and model-based estimates of uncertainty, the latter one being estimated using our new suggested approach. The methods are illustrated using a long-term rice variety trial dataset from Bangladesh.