Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

Abstract

Motivation: Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability.

Results: We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information.

Availability and implementation: EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.

Figure 1.

EmbedPVP Model Workflow. (A) Generates background knowledge from different ontologies. (B) Generates embeddings for diseases (D_i) and genes (G_i) using various embedding methods. (C) Calculates phenotype–genotype similarity using the scoring function associated with the selected embedding method, considering the maximum similarity score for multiple genes associated with the phenotype, and then averages this similarity with pathogenicity prediction. V_i represents variant i, MS_vi is max phenotype similarity for variants, GP_vi is genotype prediction (CADD), and S_vi is the final weighted score of phenotypes and genotypes for the variants.