A new computational approach called dynamicGP combines genomic prediction with dynamic mode decomposition to predict plant traits during development. This method addresses the challenge of predicting how a plant's observable traits (phenotype) change over time, which is influenced by genetic factors, environmental conditions, and their interactions.
Researchers at the Max Planck Institute of Molecular Plant Physiology and the Leibniz Institute of Plant Genetics and Crop Plant Research have demonstrated that dynamicGP offers more accurate predictions than previous methods. By using genetic markers and high-throughput phenotyping data from maize and Arabidopsis thaliana, dynamicGP can predict the totality of traits. The ability to predict traits with less heritability variation over time enables more reliable statements about predictability throughout development.
dynamicGP facilitates the study of interactions between genotype and phenotype, paving the way for improved prediction accuracy of agronomically relevant traits. Future developments could incorporate environmental factors, refining the approach and significantly impacting the breeding of plant varieties adapted to specific regions and improving precision agriculture.