Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Samvida S Venkatesh; Habib Ganjgahi; Duncan S Palmer; Kayesha Coley; Gregorio V Linchangco Jr; Qin Hui; Peter Wilson; Yuk-Lam Ho; Kelly Cho; Kadri Arumäe; Million Veteran Program; Estonian Biobank Research Team; Laura B L Wittemans; Christoffer Nellåker; Uku Vainik; Yan V Sun; Chris Holmes; Cecilia M Lindgren; George Nicholson

doi:10.1038/s41467-024-49998-0

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Nat Commun. 2024 Jul 10;15(1):5801. doi: 10.1038/s41467-024-49998-0.

Authors

Samvida S Venkatesh^{1

2}, Habib Ganjgahi^{3

4}, Duncan S Palmer^{3

5}, Kayesha Coley⁶, Gregorio V Linchangco Jr^{7

8}, Qin Hui^{7

8}, Peter Wilson^{8

9}, Yuk-Lam Ho¹⁰, Kelly Cho^{10

11}, Kadri Arumäe¹²; Million Veteran Program; Estonian Biobank Research Team; Laura B L Wittemans^{13

14}, Christoffer Nellåker^{3

14}, Uku Vainik^{12

15

16}, Yan V Sun^{7

8}, Chris Holmes^{4

17

18}, Cecilia M Lindgren^{19

20

21

22}, George Nicholson²³

Collaborators

Andres Metspalu, Lili Milani, Tõnu Esko, Reedik Mägi, Mari Nelis, Georgi Hudjashov

Affiliations

¹ Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK. samvida@well.ox.ac.uk.
² Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. samvida@well.ox.ac.uk.
³ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
⁴ Department of Statistics, University of Oxford, Oxford, UK.
⁵ Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
⁶ Department of Population Health Sciences, University of Leicester, Leicester, UK.
⁷ Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
⁸ Atlanta VA Health Care System, Decatur, GA, USA.
⁹ Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
¹⁰ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA.
¹¹ Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
¹² Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia.
¹³ Novo Nordisk Research Centre Oxford, Oxford, UK.
¹⁴ Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
¹⁵ Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia.
¹⁶ Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada.
¹⁷ Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK.
¹⁸ The Alan Turing Institute, London, UK.
¹⁹ Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK. cecilia.lindgren@bdi.ox.ac.uk.
²⁰ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. cecilia.lindgren@bdi.ox.ac.uk.
²¹ Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK. cecilia.lindgren@bdi.ox.ac.uk.
²² Broad Institute of Harvard and MIT, Cambridge, MA, USA. cecilia.lindgren@bdi.ox.ac.uk.
²³ Department of Statistics, University of Oxford, Oxford, UK. george.nicholson@stats.ox.ac.uk.

Abstract

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

MeSH terms

Adiposity* / genetics
Adult
Aged
Body Mass Index*
Electronic Health Records*
Estonia
Female
Genetic Predisposition to Disease
Genome-Wide Association Study*
Humans
Male
Middle Aged
Obesity* / genetics
Phenotype
Polymorphism, Single Nucleotide*
United Kingdom
United States

Abstract

MeSH terms

Grants and funding