Optimizing the genomic bit budget: an information-theoretic framework for trait-enriched genotyping and stratified screening in Theobroma cacao

High-throughput genotyping has revolutionized horticultural breeding, yet the efficient utilization of genomic data remains a bottleneck for germplasm curation and downstream selection. Translating complex genomic information into cost-effective and readily applicable tools for clonally propagated crops requires a shift from maximizing marker density to optimizing information content. Here, we reinterpret cacao (Theobroma cacao L.) genotyping as an information-allocation problem and introduce an information-theoretic framework for designing minimalist, trait-enriched SNP barcodes. Using a diverse international collection from Trinidad (ICGT) and an independent field trial in Puerto Rico (USDA-ARS Tropical Agriculture Research Station, TARS), we compress a 500+ SNP panel into a 32-marker ‘CacaoCipher’ barcode that preserves pairwise genetic distance structure at coarse resolution while retaining trait-aligned signal for pod index and related yield components. Barcode-space axes correlate with agronomic traits measured across environments in a limited overlap subset, supporting the portability of key signals beyond the training setting. We further quantify a heuristic ‘genomic bit budget,’ showing how information is allocated across unique identification, ancestry structure, and trait variation. Together, this framework converts cacao germplasm from an analog collection of names into a compact digital code and provides a general template for designing low-cost, high-information marker panels for germplasm QC and stratified screening in clonally propagated crops.