Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

Xilin Jiang; Martin Jinye Zhang; Yidong Zhang; Arun Durvasula; Michael Inouye; Chris Holmes; Alkes L Price; Gil McVean

doi:10.1038/s41588-023-01522-8

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

Nat Genet. 2023 Nov;55(11):1854-1865. doi: 10.1038/s41588-023-01522-8. Epub 2023 Oct 9.

Authors

Xilin Jiang^{1

2

3

4

5

6}, Martin Jinye Zhang^#^{7

8}, Yidong Zhang^#^{9

10

11}, Arun Durvasula^#^{7

8

12

13}, Michael Inouye^{14

15

16

17

18

19

20}, Chris Holmes^{9

21

20}, Alkes L Price^{22

23

24}, Gil McVean²⁵

Affiliations

¹ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. xilinjiang@hsph.harvard.edu.
² Department of Statistics, University of Oxford, Oxford, UK. xilinjiang@hsph.harvard.edu.
³ Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. xilinjiang@hsph.harvard.edu.
⁴ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. xilinjiang@hsph.harvard.edu.
⁵ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. xilinjiang@hsph.harvard.edu.
⁶ Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK. xilinjiang@hsph.harvard.edu.
⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁸ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁹ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
¹⁰ Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
¹¹ Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
¹² Department of Genetics, Harvard Medical School, Cambridge, MA, USA.
¹³ Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA.
¹⁴ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁵ Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
¹⁶ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁷ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
¹⁸ British Heart Foundation Cambridge Centre of Research Excellence, Department of Clinical Medicine, University of Cambridge, Cambridge, UK.
¹⁹ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
²⁰ The Alan Turing Institute, London, UK.
²¹ Department of Statistics, University of Oxford, Oxford, UK.
²² Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.
²³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. aprice@hsph.harvard.edu.
²⁴ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.
²⁵ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. gil.mcvean@bdi.ox.ac.uk.

^# Contributed equally.

Abstract

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

MeSH terms

Biological Specimen Banks
Comorbidity
Genetic Predisposition to Disease*
Genome-Wide Association Study / methods
Humans
Multifactorial Inheritance / genetics
Population Health*
Risk Factors
United Kingdom / epidemiology

Abstract

MeSH terms

Grants and funding