Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Journal article

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Authors: Zhang, Y; Jiang, X; Mentzer, AJ; McVean, G; Lunter, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cell Press
• Journal: Cell Genomics
• Volume: 3
• Issue: 8
• Pages: 100371-100371
• Article number: 100371
• Publication date: 2023-08-01
• DOI: 10.1016/j.xgen.2023.100371
• EISSN: 2666-979X
• ISSN: 2666-979X
• Language: English
• Keywords: Ontology; Genotype; Biobank; Computer science; Artificial intelligence; Data science; Genome-wide association study; Genetic association; Latent Dirichlet allocation; Single-nucleotide polymorphism; Computational biology; Biology; Information retrieval; Genetics; Topic model; Variety (cybernetics)