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

Yidong Zhang; Xilin Jiang; Alexander J Mentzer; Gil McVean; Gerton Lunter

doi:10.1016/j.xgen.2023.100371

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

Cell Genom. 2023 Aug 1;3(8):100371. doi: 10.1016/j.xgen.2023.100371. eCollection 2023 Aug 9.

Authors

Yidong Zhang^{1

2

3}, Xilin Jiang^{1

4

5

6

7

8}, Alexander J Mentzer^{1

5}, Gil McVean¹, Gerton Lunter^{9

10}

Affiliations

¹ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
² Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK.
³ Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100006, China.
⁴ Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
⁵ Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK.
⁶ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁷ Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0SR, UK.
⁸ Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BB, UK.
⁹ MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
¹⁰ Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, the Netherlands.

Abstract

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.

Keywords: UK Biobank; multimorbidity; topic modeling; topic-GWAS; treeLFA.