Authors
Ahn, E., Park, S., Bhatt, J., Lim, S., Meinhardt, L. W.
Abstract
This study used single-SNP association analysis and machine learning to investigate the genetic basis of three agronomic traits, including coffee bean production, leaf rust incidence, and green bean yield, in two Coffea canephora populations (Premature and Intermediate). A publicly available dataset was utilized, identifying substantial differences between two populations in the overall number and genomic location of significantly associated SNPs. The Premature population exhibited numerous significant associations for all three traits, while the Intermediate population showed fewer associations, primarily for leaf rust incidence. Single-SNP association analysis identified significant SNPs, and Bootstrap Forest models were used to assess SNP importance for phenotype prediction. Several candidate genes were identified near significant and/or highly important SNPs, including genes with known roles in plant defense (e.g., RPP13-like, NB-ARC, and CERK1 genes for leaf rust resistance) and a putative caffeine synthase gene associated with green bean yield in the Premature population. Through these findings, the significant differences in the two populations emphasize the population-specific genetic architecture of essential traits in C. canephora and provide valuable targets for future functional studies and targeted breeding efforts.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 03 Nov 2025.
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