Phenome-wide screening of GWAS data reveals the complex causal architecture of obesity.

Autor: García-Marín LM; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia., Campos AI; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia., Kho PF; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia., Martin NG; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia., Cuéllar-Partida G; University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia. g.cuellarpartida@uq.edu.au.; 23andMe, Inc, Sunnyvale, CA, USA. g.cuellarpartida@uq.edu.au., Rentería ME; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. miguel.renteria@qimrberghofer.edu.au.; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia. miguel.renteria@qimrberghofer.edu.au.; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia. miguel.renteria@qimrberghofer.edu.au.
Jazyk: angličtina
Zdroj: Human genetics [Hum Genet] 2021 Aug; Vol. 140 (8), pp. 1253-1265. Date of Electronic Publication: 2021 May 31.
DOI: 10.1007/s00439-021-02298-9
Abstrakt: 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.
Databáze: MEDLINE
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