SIMLINK Enables Accurate Variant Pathogenicity Prediction through Modeling the Gene-Variant-Feature Association Structure

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ID: 324545
2026
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Abstract
Abstract Motivation Predicting variant pathogenicity is crucial for clinical genetics. Existing approaches face two primary limitations. First, biologically, data for pathogenicity prediction often lacks explicit modeling of the gene-variant-feature association structure. A single gene can harbor multiple variants, and each variant can be characterized by multiple features intrinsically associated with its parent gene. Current methods fail to explicitly model the gene-variant-feature association, thus limiting their performance. Second, methodologically, the variant-pathogenicity association is often assumed to comprise a linear component alongside a nonlinear one. However, current methods typically do not explicitly model the linear component, often failing to disentangle the linear component that might be better addressed with a linear approach. Results To overcome these limitations, we introduce simultaneous modeling of linear and nonlinear components of knowledge graph (SIMLINK). This novel approach leverages a knowledge graph to model gene-variant-feature associations and a linear model to isolate the linear component. We begin by constructing a variant-centered knowledge graph, comprising over 8 million triplets, which explicitly models the associations between genes, variants, and features. Subsequently, the linear and nonlinear components are learned using a combination of linear and graph neural networks. We train SIMLINK on ClinVar variants. Benchmarking experiments on independent test sets demonstrate its superior prediction on both missense and synonymous variants compared to state-of-the-art methods, including CADD and AlphaMissense. We evaluate the impact of allele frequencies on prediction performance. Applied to variants implicated in Autism Spectrum Disorder, SIMLINK effectively distinguished between high- and low-confidence variants, and critically, the genes harboring top-ranked variants are highly pathogenic. Availability and implementation The source code is freely available at https://github.com/Chen-LuWang/SIMLINK.
Reference Key
openalex_W7202139167 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hong-Dong Li, Chenlu Wang, Dongfang Yan, Wenkui Huang, Zongxuan Li, Shaokai Wang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag601
URL
https://doi.org/10.1093/bioinformatics/btag601
Keywords Keywords not found

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