Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.

Abstract

Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced rapid market expansion but remains understudied in terms of its fundamental biology due to historical prohibitions. This pioneering study implements GS and ML to optimize cannabinoid profiles in cannabis breeding. We analyzed a representative population of drug-type cannabis accessions, quantifying major cannabinoids and utilizing high-density genotyping with 250K SNPs for GS. Our evaluations of various models-including ML algorithms, statistical methods, and Bayesian approaches-highlighted Random Forest's superior predictive accuracy for single and multi-trait genomic predictions, particularly for THC, CBD, and their precursors. Multi-trait analyses elucidated complex genetic interdependencies and identified key loci crucial to cannabinoid biosynthesis. These results demonstrate the efficacy of integrating GS and ML in developing cannabis varieties with tailored cannabinoid profiles.