Genome-wide association mapping and predictive modeling of wet bean mass in a diverse cacao collection

Abstract

Improving cacao yield, a key objective in post-domestication crop improvement, remains a primary goal for breeders, but progress is often hindered by the confounding effects of population structure. To overcome this, we analyzed 346 diverse cacao accessions using an ML-based association mapping framework (with and without population structure adjustment) and a phenotype-only ML prediction of yield. By correcting for population structure, our Bootstrap Forest-based GWAS revealed association signals that showed consistent enrichment for ribosome and protein-synthesis functions, and a recurrent subset of SNPs with high importance appeared across multiple yield components, including pod index and seed number. In parallel, a Neural Network model was utilized to identify cotyledon mass and length as the most powerful predictors for total wet bean mass (R² = 0.715 by repeated five-fold cross-validation), suggesting a practical, low-cost screening proxy for breeding). Collectively, this study delivers a robust genetic framework and a novel predictive tool to accelerate the development of high-yielding cacao varieties through the early identification of elite clones.

Plant phenotyping relevance

カカオの形質（湿重量収量）を、測定可能な種子形質から予測するニューラルネットワークを開発・検証しており、低コストな表現型スクリーニング手法が研究の中心です。

Code and data availability

The supplied blocks describe GWAS and ML analyses of cacao yield, but the phenotype/genotype data are from a cited prior study (Bekele et al. 2022, ICGT database), not a paper-specific deposit by these authors. No author analysis code, scripts, trained models, or supplementary data with an explicit public availability/

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