This journal article from Postdoctoral Researcher Owen Powell, Associate Investigator Kai Voss-Fels and Chief Investigators David Jordan, Graeme Hammer and Mark Cooper explores the ability of novel methods to improve the prediction of plant traits across environments, breeding cycles, and populations.
Predicting plant traits becomes more difficult whenever interactions among genes (GxG) and between genes and the environment (GxE) result in changes in the value of genes and their alleles, which are known as non-stationary effects. This is a challenge for plant breeding as current methods struggle to predict these non-stationary effects, which makes it difficult to accurately rank variety performance for selection decisions.
A key part of prediction methods is the gene-to-phenotype map, which defines the physiological paths from genes to the observable traits (phenotypes) of a plant. The current model used in plant breeding is called the infinitesimal model, which connects genes directly to observable traits of interest, such as crop yield in a particular environment. However, this gene-to-phenotype map makes it difficult to adjust predictions across environments, breeding cycles and populations. In this paper, the researchers discuss the use of hierarchical gene-to-phenotype maps that incorporate information from intermediate traits and environmental measures to enable accurate adjustments of predictions of genetic values.
Research has found that the development of hierarchical gene-to-phenotype maps can improve short-term prediction accuracy across environments. However, whether these accuracy benefits of hierarchical gene-to-phenotype maps within breeding cycles translate into improved genetic gain of breeding programs over longer time frames is still unknown. As part of the Centre for Plant Success, the research team, in collaboration with CIs Christine Beveridge, Kevin Burrage, Diane Donovan and Daniel Ortiz-Barrientos, is developing computational and methodological capabilities to answer this, and related questions.
Hierarchical G2P Maps for Plant Breeding. Examples of three multi-trait hierarchical G2P maps with the explicit specification of interactions. Hierarchical G2P maps incorporating knowledge of trait interactions (+, λ) can be used to adjust phenotypes and increase the accuracy of the estimation of gene effects (u), gene interactions, and genetic correlations (rg) between traits. Gene effects (u) can be directly assigned to trait phenotypes (y) or indirectly assigned via linear trait relationships (+) or non-linear trait interactions (λ). A, D, and E indicate additive, dominance, and epistatic functional genetic effects, respectively. Non-genetic effects of trait phenotypes are represented by e. (A) Representation of a G2P map with gene interactions and linear relationship between trait phenotypes, (B) Representation of current Crop Growth Model – Whole Genome Prediction (CGM -WGP) G2P maps with additive genetic effects and non-linear trait interactions, and (C) Representation of potential G2P maps with both gene interactions and non-linear trait interactions.
READ THE ARTICLE:
Powell, O.M., Voss-Fels, K.P., Jordan, D.R., Hammer, G. and Cooper, M. (2021), Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction. Frontiers in Plant Science. doi: 10.3389/fpls.2021.663565
