Longitudinal genomic prediction of cacao disease resilience identifies robust witches’ broom disease targets

Authors: Baek, Insuck; UPADHYAY, RAKESH - Bowie State University; Kim, Moon; Meinhardt, Lyndel; Ahn, Ezekiel

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/27/2026
Publication Date: 6/3/2026
Citation: Baek, I., Upadhyay, R., Kim, M.S., Meinhardt, L.W., Ahn, E.J. 2026. Longitudinal genomic prediction of cacao disease resilience identifies robust witches’ broom disease targets. Frontiers in Plant Science. 17. Article 1837102. https://doi.org/10.3389/fpls.2026.1837102.
DOI: https://doi.org/10.3389/fpls.2026.1837102

Interpretive Summary: Assessing a cacao tree's disease resistance usually relies on single-season snapshots, which often confuse temporary environmental luck with true genetic strength. To solve this, we tracked the cacao genome and disease records over multiple harvests as continuous "longitudinal trajectories." We discovered that resistance to witches' broom disease (WBD) is highly stable over time and can be reliably predicted using this long-term genomic tracking, unlike frosty pod rot (FPR) which is heavily dependent on fluctuating microclimates. By tracking these temporal changes, we developed a multi-trait ranking system that reliably identifies elite trees balancing tough defense with high pod production. Ultimately, this research provides cacao breeders and agricultural scientists with a powerful predictive tool to prioritize truly resilient germplasm early in the breeding cycle, accelerating the development of stable, disease-resistant varieties and ensuring reliable long-term harvests for farmers and a secure global chocolate supply.

Technical Abstract: Genomic prediction (GP) in perennial crops is frequently hindered by treating continuous phenotypes as static, single-point summaries. To address this, we evaluated the genomic predictability of repeated-harvest disease and yield trajectories in Theobroma cacao using 102 accessions across four harvests, extracting baseline intercepts and temporal slopes for healthy pod rate, frosty pod rot (FPR), and witches' broom disease (WBD). Linear mixed-model analyses and rigorous relatedness-blocked cross-validation revealed substantial accession-level temporal repeatability, highest for WBD-derived traits. GP models demonstrated that branch and flower WBD components yielded the strongest and most robust predictive accuracies, significantly outperforming FPR targets across alternative model specifications and both Criollo and Matina reference genomes. Finally, a Pareto-based multi-trait selection index identified a rank-stable candidate shortlist optimizing both disease resilience and productivity. This establishes that longitudinal trajectory targets provide a superior, stable GP framework over static trait summaries for prioritizing resilient germplasm in perennial breeding programs.