The research team introduced dynamicGP, a computational approach that facilitates the prediction of trait dynamics across development in crops for which time-series phenotypic measurements for multiple genotypes are available from HTP platforms. “We demonstrated that dynamicGP is an efficient computational approach to predict genotype-specific dynamics for multiple traits. This is achieved by combining genomic prediction with dynamic mode decomposition (DMD)”, says David Hobby, researcher at the Max Planck Institute of Molecular Plant Physiology and one of the first authors of the study.
Using genetic markers and data from high-throughput phenotyping of a maize multi-parent advanced generation inter-cross population and an Arabidopsis thaliana diversity panel, the researchers showed that dynamicGP outperforms a state-of-the-art genomic prediction approach for multiple traits. “We found that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy, shedding light on a factor that affect the predictability of traits over developmental trajectory”, says Dr. Marc Heuermann, researcher at the IPK Leibniz Institute and also one of the first authors of the study.
Therefore, dynamicGP paves the way for interrogating and integrating the dynamical interactions between genotype and phenotype over crop development to improve the prediction accuracy of agronomically relevant traits. Future developments of dynamicGP can rely on extensions of DMD to consider effects of environmental factors. These will facilitate further refinements of the proposed approach that are expected to have very substantial impact on breeding crop varieties adapted to particular regions as well as precision agriculture.
Nature Plants
Predicting plant trait dynamics from genetic markers
17-Apr-2025