Literature-driven extraction and computational prediction of causal statements linking genetic variants to biological...

Understanding the mechanistic basis of pathogenic genetic variants requires reconstructing the molecular pathways connecting the variant, via a chain of molecular intermediates, to a disease-causing biological process and phenotype. However, a literature-wide assembly of causal networks connecting variants, molecular pathways, biological processes and phenotypes has not been previously available. To create such a resource, we developed an automated pathway reconstruction approach building on the Integrated Network and Dynamical Reasoning Assembler (INDRA) system which extracts causal mechanistic statements (positive regulation, phosphorylation, complex formation, etc.) by combining structured databases and literature mining. We traversed INDRA statements extracted from publications to identify those describing a genetic variant resulting in a protein point mutation. We then reconstructed directed paths (consisting of one or more linked INDRA statements) connecting this variant to a term representing a biological process, phenotype or disease within the same publication. This resulted in a directed multigraph obtained from 25,862 paths for variants in 2,561 proteins. Each node in this graph corresponds to an ontology-grounded molecular or process term and each edge is explicitly linked to supporting literature evidence, enabling full auditability of inferred mechanisms. To leverage the assembled networks, we trained a classification model to predict likely downstream biological processes or specific disease associations for protein variants. As features to the model, we integrated molecular annotations (including protein sequence features, ClinVar pathogenicity labels, and UniProt domain mappings) in combination with representations from the ESM2 transformer-based protein language model. The performance achieved by this model shows promise for reconstructing causal mechanistic statements associated with function of genetic variants, a framing of the variant effect prediction task that goes significantly beyond simple assessment of pathogenicity. This integrative framework enables the mechanistic interpretation of known variants and prediction of functional relevance for variants lacking prior phenotypic annotation.