Methylomes as key features for predicting recombination in some plant species.

Peñuela M, Finke J, Rocha C

Published: 8 March 2024 in Plant molecular biology
Keywords: CHH methylation, Extra Trees, Machine learning, Model plants, Plant breeding
Pubmed ID: 38457042
DOI: 10.1007/s11103-023-01396-8

Knowing how chromosome recombination works is essential for plant breeding. It enables the design of crosses between different varieties to combine desirable traits and create new ones. This is because the meiotic crossovers between homologous chromatids are not purely random, and various strategies have been developed to describe and predict such exchange events. Recent studies have used methylation data to predict chromosomal recombination in rice using machine learning models. This approach proved successful due to the presence of a positive correlation between the CHH context cytosine methylation and recombination rates in rice chromosomes. This paper assesses the question if methylation can be used to predict recombination in four plant species: Arabidopsis, maize, sorghum, and tomato. The results indicate a positive association between CHH context methylation and recombination rates in certain plant species, with varying degrees of strength in their relationships. The CG and CHG methylation contexts show negative correlation with recombination. Methylation data was key effectively in predicting recombination in sorghum and tomato, with a mean determination coefficient of 0.65 ± 0.11 and 0.76 ± 0.05, respectively. In addition, the mean correlation values between predicted and experimental recombination rates were 0.83 ± 0.06 for sorghum and 0.90 ± 0.05 for tomato, confirming the significance of methylomes in both monocotyledonous and dicotyledonous species. The predictions for Arabidopsis and maize were not as accurate, likely due to the comparatively weaker relationships between methylation contexts and recombination, in contrast to sorghum and tomato, where stronger associations were observed. To enhance the accuracy of predictions, further evaluations using data sets closely related to each other might prove beneficial. In general, this methylome-based method holds great potential as a reliable strategy for predicting recombination rates in various plant species, offering valuable insights to breeders in their quest to develop novel and improved varieties.