Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models Linfeng Wang, Susana Campino, Taane G. Clark, Jody E. Phelan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8920054/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Aug, 2026 Read the published version in BMC Bioinformatics → Version 1 posted 13 You are reading this latest preprint version Abstract Motivation: Positive selection is a key evolutionary force in Mycobacterium tuberculosis, driving the emergence of adaptive mutations that influence drug resistance, transmissibility, and virulence of tuberculosis. Phylogenetic trees capture the hierarchical evolutionary relationships among isolates, making them an ideal framework for detecting such adaptive signals. Here, we present a phylogeny-guided graph attention network approach, coupled with a novel method for converting SNP-annotated phylogenetic trees into graph structures suitable for graph neural network processing. Results: Using a dataset of 1,000 M. tuberculosis isolates, representing the four main lineages, and 249 single-nucleotide variants (84 resistance-associated and 165 neutral) spanning 61 drug-resistance genes, we constructed graphs where nodes represented individual isolates and edges reflected phylogenetic distances. To reduce noise and highlight local evolutionary structure, we pruned edges between isolates separated by more than seven internal nodes. Node features were encoded as binary indicators of SNP presence or absence, and the graph attention network (GAT) architecture comprised two attention layers with a residual connection, followed by global attention pooling and a multilayer perceptron classifier. The model achieved an accuracy of 0.81 on the held-out test set, and application to 146 WHO-classified "uncertain" variants identified 28 high-confidence candidates with convergent occurrence across multiple lineages, consistent with adaptive evolution. These variants included: eis c.-37G>T (kanamycin, amikacin), embA c.-12C>T (ethambutol) and rpoA Thr187Ala (rifampicin). These findings demonstrate the feasibility of transforming phylogenetic trees into graph neural network-compatible structures and utility of attention-based models for detecting signals of positive selection, supporting genomic surveillance and prioritising candidate variants for experimental validation. Full Text Additional Declarations No competing interests reported. Supplementary Files supplementaryinformationdraft8.docx sampleENAID.csv Cite Share Download PDF Status: Published Journal Publication published 06 Aug, 2026 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 27 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Editor invited by journal 05 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 02 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

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