PHACTn enables training-free, context-independent inference of nucleotide variant tolerance across the genome

Yildirim, Ceren, Kuru, Nurdan, Adebali, Ogün (September 2026) PHACTn enables training-free, context-independent inference of nucleotide variant tolerance across the genome. bioRxiv. ISSN 2692-8205 (Submitted)

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Abstract

Accurate prediction of single-nucleotide variant (SNV) tolerability across the entire human genome remains a fundamental challenge in computational genomics, particularly for non-coding regions where the regulatory landscape is vast and poorly understood. Machine learning classifiers suffer from data circularity and demographic bias, while genomic language models demand massive computational resources and offer little biological interpretability. Here, we present PHACTn (Phylogeny-Aware Computing of Tolerance for nucleotide variants), a training-free, parameter-minimal method that infers nucleotide variant tolerability by traversing the mammalian phylogenetic tree and explicitly modeling the evolutionary independence of observed substitutions and their distance from the query species. With only 4 interpretable parameters, no training and no GPU requirement, PHACTn outperforms all evaluated tools on non-coding variants curated from both the ClinVar, and on non-coding variants potentially responsible for selected Mendelian diseases curated from OMIM. Additionally, it achieves state-of-the-art performance on variants within the informative range of alignment-based inference. These results establish that principled probabilistic phylogenetic modeling captures evolutionary constraint signals that large-scale sequence models fail to recover, offering a powerful, accessible, and mechanistically transparent alternative for genome-wide variant effect prediction.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > quantitative biology
CSHL Authors:	Kuru, Nurdan
Communities:	CSHL labs > Siepel lab CSHL Post Doctoral Fellows
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	14 September 2026
Date Deposited:	02 Oct 2026 15:52
Last Modified:	02 Oct 2026 15:52
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/42334

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