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. 2022 Dec 29;13(1):7973.
doi: 10.1038/s41467-022-35553-2.

Genomics and phenomics of body mass index reveals a complex disease network

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Genomics and phenomics of body mass index reveals a complex disease network

Jie Huang et al. Nat Commun. .

Abstract

Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity.

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Conflict of interest statement

C.J.O’D. is employed by Novartis Institutes for Biomedical Research. P.D.R. has received research grant support from the following for-profit companies: Bristol Myers Squib, Lysulin Inc; and has consulted with Intercept Pharmaceuticals and Boston Heart Diagnostics. D.S. has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. L.S.P. declares that there is no duality of interest associated with this manuscript. With regard to potential conflicts of interest, L.S.P. has served on Scientific Advisory Boards for Boehringer Ingelheim and Janssen, and has or had research support from Merck, Pfizer, Eli Lilly, Novo Nordisk, Sanofi, PhaseBio, Roche, Abbvie, Vascular Pharmaceuticals, Janssen, Glaxo SmithKline, and the Cystic Fibrosis Foundation. L.S.P. is also a cofounder and Officer and Board member and stockholder of a company, Diasyst, Inc., which markets software aimed to help improve diabetes management. S.M.D receives research support from Renalytix AI and Novo Nordisk to the University of Pennsylvania and consulting fees from Calico Labs. A.-L.B. is co-scientific founder of and is supported by Scipher Medicine, Inc. G.D.S. is a member of the Scientific Advisory Boards of Relation Therapeutics and Insitro. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Summary of phenome-wide Mendelian randomization analysis using GRSBMI.
Red dots represent a statistically significant positive association with GRSBMI ( ↑ GRSBMI = ↑ disease risk), and black dots represent a statistically significant negative association in the MR analysis after multiple testing correction (two-sided test with nominal p-value less than 4.02 × 10−5). Top 100 associations with lowest p-values in the MR analysis are labeled including 98 positive associations (colored font) and two negative associations (black font) from 13 systems. Abbreviations (clockwise): Circulatory: circulatory system; Co: congenital anomalies; Derm: dermatologic; Endo/Metab: endocrine/metabolic; Hema: hematopoietic; Infect: infectious diseases; Injuries: injuries & poisonings; Mental: mental disorders; Neuro: neurological; Respir: respiratory; Sym: symptoms.
Fig. 2
Fig. 2. Phenotypic network map.
Nodes represent phecodes at the three-digit level and the links represent significant disease-disease associations (ϕ-correlation). Node size is proportional to network connectivity (degree). Individual disease communities (A-G) are described in the Supplementary Materials.

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