Phenome-Wide Identification of Therapeutic Genetic Targets, Leveraging Knowledge Graphs, Graph Neural Networks, and UK Biobank Data

Authors

Lawrence Middleton
Ioannis Melas
Chirag Vasavda
Arwa Raies
Benedek Rozemberczki
Ryan S Dhindsa
Justin S Dhindsa
Blake Weido
Quanli Wang
Andrew R Harper
Gavin Edwards
Slavé Petrovski
Dimitrios Vitsios

Language

English

Publication Date

5-10-2024

Journal

Science Advances

DOI

10.1126/sciadv.adj1424

PMID

38718126

PMCID

PMC11078195

PubMedCentral® Posted Date

5-8-2024

PubMedCentral® Full Text Version

Post-print

Abstract

The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca's Biological Insights Knowledge Graph and numerous tabular datasets, to assess gene-disease probabilities throughout the phenome. We use graph neural networks, capturing the graph's holistic structure, and train them on hundreds of balanced datasets via a robust semi-supervised learning framework to provide gene-disease probabilities across the human exome. Mantis-ML 2.0 incorporates natural language processing to automate disease-relevant feature selection for thousands of diseases. The enhanced models demonstrate a 6.9% average classification power boost, achieving a median receiver operating characteristic (ROC) area under curve (AUC) score of 0.90 across 5220 diseases from Human Phenotype Ontology, OpenTargets, and Genomics England. Notably, Mantis-ML 2.0 prioritizes associations from an independent UK Biobank phenome-wide association study (PheWAS), providing a stronger form of triaging and mitigating against underpowered PheWAS associations. Results are exposed through an interactive web resource.

Keywords

Humans, Algorithms, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Neural Networks, Computer, Phenomics, Phenotype, UK Biobank, United Kingdom

Published Open-Access

yes

Recommended Citation

Middleton, Lawrence; Melas, Ioannis; Vasavda, Chirag; et al., "Phenome-Wide Identification of Therapeutic Genetic Targets, Leveraging Knowledge Graphs, Graph Neural Networks, and UK Biobank Data" (2024). Faculty and Staff Publications. 2359.
https://digitalcommons.library.tmc.edu/baylor_docs/2359