Biology subjects

Hobby, D.

Publications and source records attributed to Hobby, D..

3 recordsLinked to original sources

bioRxiv2025

KineticGP: a computational framework for genomic prediction of leaf photosynthesis traits

Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.

systems biology↗

bioRxiv2025

Comparative analysis of genomic prediction approaches for multiple time-resolved traits in maize

Ability to accurately predict multiple growth-related traits over plant developmental trajectories has the potential to revolutionize crop breeding and precision agriculture. Despite increased availability of time-resolved data for multiple traits from high-throughput phenotyping platforms of model plants and crops, genomic prediction is largely applied to a small number of traits, often neglecting their dynamics. Here, we compared and contrasted the performance of MegaLMM and dynamicGP as well as their hybrid variants that can handle high-dimensional temporal data for multi-trait genomic prediction. The comparative analysis made use of time series for 50 geometric, colour, and texture traits in a maize multiparent advanced generation inter-cross (MAGIC) population. The performance of the approaches was assessed using snapshot accuracy and longitudinal accuracy, providing insight into the ability to predict multiple traits at a single time point or the dynamics of individual traits over the considered time domain, respectively. We found that MegaLMM outperforms dynamicGP in terms of snapshot accuracy, while dynamicGP proved superior in terms of longitudinal accuracy. This study paves the way for careful investigation of factors that affect the capacity to predict dynamics of multiple traits from genetic markers alone.

bioinformatics↗

bioRxiv2025

Advances in multi-trait genomic prediction approaches: Classification, comparative analysis, and perspectives

Traits in any organism are not independent, but show considerable integration, observed in a form of couplings and trade-offs. Therefore, improvement in one trait may affect other traits, often in undesired direction. To account for this problem, crop breeding increasingly relies on multi-trait genomic prediction (MT-GP) approaches that leverage the availability of genetic markers from different populations along with advances in high-throughput precision phenotyping. While significant progress has been made to jointly model multiple traits using a variety of statistical and machine learning approaches, there is no systematic comparison of advantages and shortcomings of the existing classes of MT-GP models. Here, we fill this knowledge gap by first classifying the existing MT-GP models and briefly summarizing their general principles, modeling assumptions, and potential limitations. We then perform an extensive comparative analysis with ten traits measured in an Oryza sativa diversity panel using cross-validation scenarios relevant in breeding practice. Finally, we discuss directions that can enable the building of next generation MT-GP models in addressing pressing challenges in crop breeding.

bioinformatics↗