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bioRxiv · 10.1101/2025.09.23.677995

Machine learning and multi-omic analysis reveal contrasting recombination landscape of A and C subgenomes of winter oilseed rape

Montero-Tena, J. A.·Zanini, S. F.·Yildiz, G.·Kox, T.·Abbadi, A.·Snowdon, R. J.·Golicz, A. A.
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Abstract

Meiotic recombination is essential for generating genetic diversity, driving plant evolution, and enabling crop improvement, yet its uneven distribution across genomes constrains breeding efforts. Here, we investigated the multi-omic landmarks that shape the recombination landscape in Brassica napus by integrating epigenomic, genomic and transcriptomic data with recombination maps derived from large multi-parental rapeseed populations. Predictive machine-learning accurately predicted recombination rates and hotspot location using only feature information. Recombination was generally suppressed in centromeres and other repeat-rich, methylated regions and enriched in gene-dense, transcriptionally active domains. Proxies for chromatin configuration--such as DNA methylation, transposable elements or genes-- consistently achieved the highest predictive power with the random forest algorithm. We discovered distinct recombination landscape patterns between subgenomes, with crossovers clustering near subtelomeres in the A subgenome and more evenly spread across the C subgenome. Models trained on A-subgenome data outperformed those based on the C subgenome, although combining both subgenomes improved overall accuracy.

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Montero-Tena, J. A., Zanini, S. F., Yildiz, G., Kox, T., Abbadi, A., Snowdon, R. J., Golicz, A. A.. 2025-09-25. Machine learning and multi-omic analysis reveal contrasting recombination landscape of A and C subgenomes of winter oilseed rape. https://doi.org/10.1101/2025.09.23.677995

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