Variation analysis using random forests reveals domestication patterns and breeding trends in sugar beet.

ABSTRACT: Cultivated beets (Beta vulgaris), including sugar beet, are important crops, and several studies employed whole genome sequencing to explore genomic variation. We applied the machine learning method "random forests" on hundreds of sequenced beet accessions and identified genomic variants that distinguish wild from domesticated beets at a mean accuracy of 98.4%. Associated genes were involved in sugar accumulation and transport (e.g., SUC4), nematode resistance, and root growth. Modern breeding lines from leading seed companies were distinguished from public seed bank accessions at 98.5% accuracy, revealing a strong signal linked to fungal resistance, likely originating from Italian wild beets. We also differentiated accessions by company, uncovering genes under selection, notably th

SUBMITTER: Sandell FL 

PROVIDER: S-EPMC12307672 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature