Integrative Machine Learning Approaches for Identifying Loci Associated with Anthracnose Resistance in Strawberry.

ABSTRACT: Anthracnose, predominantly caused by Colletotrichum fructicola, severely reduces yield in Fragaria × ananassa production. We assessed ensemble machine learning (ML) frameworks to improve genomic prediction (GP) of resistance using a training population of 300 individuals from six full-sib families. Genotyping with the Axiom^® 50K FanaSNP array and phenotyping by AUDPC after artificial inoculation enabled evaluation of five algorithms-G-BLUP, LASSO, LightGBM, Random Forest, and XGBoost-combined with informed feature selection and resampling-based data augmentation (3×, 5×). Ensemble ML models consistently outperformed linear approaches, with Random Forest, LightGBM, and XGBoost achieving the highest accuracies. Marker prioritization revealed that a reduced SNP

SUBMITTER: Jang YJ 

PROVIDER: S-EPMC12473499 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature