Sandell Felix L, Rupprecht Christina, Himmelbauer Heinz, Dohm Juliane C
Institute of Computational Biology, Department of Biotechnology and Food Science, BOKU University, Muthgasse 18, 1190 Vienna, Austria.
Institute of Integrative Nature Conservation Research, Department of Ecosystem Management, Climate and Biodiversity, BOKU University, Gregor-Mendel-Straße 33, 1180 Vienna, Austria.
iScience. 2025 Jun 11;28(8):112835. doi: 10.1016/j.isci.2025.112835. eCollection 2025 Aug 15.
Cultivated beets (), 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 the flowering regulator APETALA1. Admixture profiles were analyzed to address open questions regarding the genomic history, provenance, and dispersal of wild beets. Our findings provide exciting possibilities for targeted breeding and show advances in variation analysis using machine learning.
栽培甜菜（包括糖用甜菜）是重要作物，已有多项研究采用全基因组测序来探索基因组变异。我们对数百个已测序的甜菜种质运用机器学习方法“随机森林”，识别出区分野生甜菜和驯化甜菜的基因组变异，平均准确率达98.4%。相关基因涉及糖分积累与运输（如SUC4）、对线虫的抗性以及根系生长。领先种子公司的现代育种系与公共种子库种质的区分准确率为98.5%，揭示出与真菌抗性相关的强烈信号，可能源自意大利野生甜菜。我们还按公司区分了种质，发现了受选择的基因，特别是开花调控因子APETALA1。分析了混合图谱，以解决有关野生甜菜基因组历史、起源和传播的未决问题。我们的研究结果为定向育种提供了令人兴奋的可能性，并展示了机器学习在变异分析方面的进展。
