Meinhardt, L. W. | bioRxiv Science

bioRxiv2025

Genomic Prediction and SNP Importance Analysis for Agronomic Traits in Two Coffea canephora Populations

This study reveals that distinct breeding populations of Coffea canephora can achieve agronomic success through fundamentally different biological strategies. To uncover this, we performed a comparative genomic analysis of two populations ( Premature and Intermediate), integrating single-SNP association, machine learning (Bootstrap Forest), and Gene Ontology (GO) pathway analysis. The genetic architecture of the Premature population was linked to specialized metabolic pathways, including lipid modification and processes within the organelle lumen, a finding supported by the identification of a putative caffeine synthase 3 gene. In contrast, traits in the Intermediate population were governed by variation in core cellular machinery, with significant enrichment for pathways related to actin cytoskeleton regulation and salicylic acid signaling. This discovery provides a new biological context for important candidate genes involved in disease resistance (e.g., RPP13-like, NB-ARC, CERK1). These findings demonstrate that population-specific biological routes underpin agronomic performance, providing a powerful foundation for designing more targeted breeding programs in coffee. We release population-specific, ranked SNP lists and GO gene sets as a reusable resource to enable meta-analyses and benchmarking in coffee and perennial crops.

genetics↗

bioRxiv2025

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

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 (R{superscript 2} = 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.

genomics↗