Advances in high-throughput phenotyping (HTP) platforms, combined with genotyping technologies, have revolutionized the breeding of crop varieties with desirable traits through genomic prediction. However, a significant knowledge gap remains in understanding how multiple traits are expressed at different stages of the plant’s growth cycle. A research team from the IPK Leibniz Institute and the Max Planck Institute of Molecular Plant Physiology has developed a computational approach to address this challenge, with their findings published in Nature Plants.
The phenome of a plant encompasses all traits expressed at any given time, shaped by the interplay of genetic factors, environmental conditions, and their complex interactions. Understanding how the phenome evolves over time can help predict specific traits at various stages of crop development. This is particularly challenging due to the intricate relationships between traits and how the phenomes of different genotypes change throughout the plant’s life cycle, according to a press release.
Traditional genomic prediction (GP) in crops relies on training machine learning models using trait data collected from a population of genotypes at a single time point, based on genetic markers. However, current GP methods have yet to address the dynamic nature of trait expression across the entire growth period of the plant, leaving a gap in predicting the evolution of multiple traits over time.
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.
By combining genetic markers and high-throughput phenotyping data from a maize multi-parent advanced generation inter-cross population and an Arabidopsis thaliana diversity panel, the researchers demonstrated that dynamicGP outperforms current genomic prediction methods 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.
DynamicGP opens new avenues for exploring and integrating the dynamic interactions between genotype and phenotype throughout crop development, enhancing the prediction accuracy of agronomically important traits. Future advancements in dynamicGP could include extensions of DMD to account for environmental factors. These improvements are expected to significantly impact the breeding of regionally adapted crop varieties and advance precision agriculture practices.
