Abstract
Objective
In the present study, we sought to identify causal relationships between obesity and other complex traits and conditions using a data-driven hypothesis-free approach that uses genetic data to infer causal associations.
Methods
We leveraged available summary-based genetic data from genome-wide association studies on 1498 phenotypes and applied the latent causal variable method (LCV) between obesity and all traits.
Results
We identified 110 traits causally associated with obesity. Of those, 109 were causal outcomes of obesity, while only leg pain in calves was a causal determinant of obesity. Causal outcomes of obesity included 26 phenotypes associated with cardiovascular diseases, 22 anthropometric measurements, nine with the musculoskeletal system, nine with behavioural or lifestyle factors including loneliness or isolation, six with respiratory diseases, five with body bioelectric impedances, four with psychiatric phenotypes, four related to the nervous system, four with disabilities or long-standing illness, three with the gastrointestinal system, three with use of analgesics, two with metabolic diseases, one with inflammatory response and one with the neurodevelopmental disorder ADHD, among others. In particular, some causal outcomes of obesity included hypertension, stroke, ever having a period of extreme irritability, low forced vital capacity and forced expiratory volume, diseases of the musculoskeletal system, diabetes, carpal tunnel syndrome, loneliness or isolation, high leukocyte count, and ADHD.
Conclusions
Our results indicate that obesity causally affects a wide range of traits and comorbid diseases, thus providing an overview of the metabolic, physiological, and neuropsychiatric impact of obesity on human health.
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Data availability
Summary-level data used in the present study are publicly available at the Complex Traits Genomics Virtual Lab (https://genoma.io/) web platform.
Code availability
Code used as part of the present manuscript is web platform-based work, which is available at the Complex Traits Genomics Virtual Lab (https://genoma.io/) web platform. Links and details to access the pipeline used in the present study are provided in the Methods section of this manuscript.
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Acknowledgements
AIC and LMGM are supported by UQ Research Training Scholarships from The University of Queensland (UQ). MER thanks the National Health and Medical Research Council and Australian Research Council’s support through a Research Fellowship (APP1102821).
Funding
L.M.G.M and A.I.C. are supported by UQ Research Training Scholarships from The University of Queensland (UQ). M.E.R. thanks support of the NHMRC and Australian Research Council (ARC) through a Research Fellowship (GNT1102821). P.F.K. is supported by an Australian Government Research Training Program Scholarship from Queensland University of Technology (QUT).
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MER and GC-P conceived and directed the study. LMG-M performed the statistical and bioinformatics analyses, with support and input from AIC, P-FK, NGM, GC-P and MER. LMG-M wrote the first draft of the paper and integrated input and feedback from all co-authors.
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GC-P contributed to this study while employed at The University of Queensland. He is now an employee of 23andMe Inc., and he may hold stock or stock options. All other authors declare having no conflicts of interest.
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This study was approved by the Human Research Ethics Committee of the QIMR Berghofer Medical Research Institute.
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García-Marín, L.M., Campos, A.I., Kho, PF. et al. Phenome-wide screening of GWAS data reveals the complex causal architecture of obesity. Hum Genet 140, 1253–1265 (2021). https://doi.org/10.1007/s00439-021-02298-9
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DOI: https://doi.org/10.1007/s00439-021-02298-9