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Predicting the Performance of Genotypes by Multi-Environment Trial Analyses
The VSNi Team
21 January 2021
Predicting the performance of novel plant genotypes in different environments is essential for developing cultivars with superior, economically important traits (such as yield) and resilience to environmental stresses (such as drought).
Case Study The Radiata Pine Breeding Company Ltd. runs breeding programmes to improve the productivity and quality of wood from radiata pine trees for the Australasian forestry industry. Candidate pine trees (i.e., different genotypes) are tested in a multi-environment trial (MET), a series of experiments conducted across a range of geographic locations in New Zealand and Australia, over multiple years. The set of experiments within and across years is designed to provide a range of growing conditions or “environments” that differ on climate, soil, and sometimes management. From these trials, the genetic and environmental effects on the performance of the candidate pine trees can be assessed and estimated. This information is then used by radiata pine breeders to select trees for future crossing, with the aim of further genetic improvement, and by growers of radiata pine to select cultivars predicted to perform well at their land. |
Plant breeders use MET data to evaluate genotypes across a range of environments. However, the relative performance of the genotypes often varies from environment to environment, a phenomenon known as the genotype by environment (G×E) interaction. This lack of consistency, is a good thing, as it can be exploited to identify genotypes that perform well in all environments (i.e., are suitable for broad use) and those with exceptional performance in specific environments (i.e., are well suited for use in certain growing conditions). These are the concepts of broad and specific adaptability that are common in many breeding programs.
A Brief History of MET Analysis
Limitation: It does not provide information on the nature and architecture of the G×E interaction.
Limitation: They do not provide simple numerical summaries that are useful for plant selection and they do not handle unbalanced data well.
Advantages: They help to understand the architecture and dynamics of the GxE interaction and handle unbalanced and messy data very well. |
Today, linear mixed models (LMM) are widely used in the analysis of MET data. The linear mixed model framework accommodates the analysis of genetically and/or experimentally correlated data, heterogeneous variances and unbalanced data sets simultaneously, enabling the accurate prediction of genotype performance within all environments in the data set. In addition, the ability to formally test statistical hypotheses provides greater insight into the nature of the G×E interaction.
A state-of-the-art method for analysing MET data involves a LMM that adopts factor analytic (FA) variance structures for the G×E effects. The FA model provides a parsimonious, yet flexible, method of describing the G×E interaction. For example, it allows for genetic variance heterogeneity across trials and different genetic correlation between each pair of trials. It can also be extended to include genetic relationship information (e.g., pedigree data) so that the genetic effects can be partitioned into additive and non-additive components. Furthermore, it typically has higher predictive accuracy than alternative models when there is a substantial G×E interaction. In addition, its parametrization for BLUP effects and variance component is useful to identify those genotypes with broad or specific adaptability.
A powerful, efficient and reliable software solution for fitting FA or other complex LMM is ASReml and its R library version ASReml-R. Both are particularly well suited to the analysis of MET data with a large number of genotypes and trials. ASReml has been widely cited in scientific publications, and it is broadly used by many industries for their commercial operations. ASReml and ASReml-R are popular choices by MET data analysts due to:
- Flexible syntax that makes fitting complex linear mixed models possible and easy.
- An efficient statistical algorithm for fitting the linear mixed model, which makes it feasible to analyse large and complex data sets.
- Strong theoretical and statistical background providing reliable estimation and inference.
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