Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

• Published in: Nature communications Vol. 12; no. 1; pp. 3551 - 14
• Main Authors: Badet, Thomas, Fouché, Simone, Hartmann, Fanny E., Zala, Marcello, Croll, Daniel
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 45; 45/23; 45/43; 49; 631/114/1305; 631/181/2474; 631/208/212; 631/326/193; Arabidopsis - genetics; Ascomycota - genetics; Ascomycota - pathogenicity; Assemblies; Base composition; Chromosome rearrangements; Chromosomes - genetics; Chromosomes - metabolism; Computer Simulation; Crossing Over, Genetic; Deoxyribonucleic acid; DNA; Eukaryota - genetics; Evolution & development; Evolution, Molecular; Genes, Duplicate; Genetic crosses; Genetic Variation; Genome; Genome-Wide Association Study; Genomes; Genomic instability; Genomics - methods; Humanities and Social Sciences; INDEL Mutation; Learning algorithms; Machine Learning; Meiosis; Meiosis - genetics; Models, Genetic; multidisciplinary; Nucleotide sequence; Nucleotides; Offspring; Pathogens; Pedigree; Pesticide resistance; Pesticides; Phylogeny; Polymorphism, Genetic; Polymorphism, Single Nucleotide; Retroelements - genetics; Science; Science (multidisciplinary); Sequence Inversion; Species; Structural stability; Telomeres; Variation
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-23862-x

Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes. Structural variation in genomes of the same species is frequent but what drives the rearrangements remains unclear. Machine-learning of rearrangement patterns among telomere-to-telomere assemblies can accurately identify regions of intrinsic DNA instability in a eukaryotic pathogen.