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author Huang, Jie
Huffman, Jennifer E.
Huang, Yunfeng
Do Valle, Ítalo
Assimes, Themistocles L.
Raghavan, Sridharan
Voight, Benjamin F.
Liu, Chang
Barabási, Albert-László
Huang, Rose D. L.
Hui, Qin
Nguyen, Xuan-Mai T.
Ho, Yuk-Lam
Djousse, Luc
Lynch, Julie A.
Vujkovic, Marijana
Tcheandjieu, Catherine
Tang, Hua
Damrauer, Scott M.
Reaven, Peter D.
Miller, Donald
Phillips, Lawrence S.
Ng, Maggie C. Y.
Graff, Mariaelisa
Haiman, Christopher A.
Loos, Ruth J. F.
North, Kari E.
Yengo, Loic
Smith, George Davey
Saleheen, Danish
Gaziano, J. Michael
Rader, Daniel J.
Tsao, Philip S.
Cho, Kelly
Chang, Kyong-Mi
Wilson, Peter W. F.
Sun, Yan V.
O’Donnell, Christopher J.
author_facet Huang, Jie
Huffman, Jennifer E.
Huang, Yunfeng
Do Valle, Ítalo
Assimes, Themistocles L.
Raghavan, Sridharan
Voight, Benjamin F.
Liu, Chang
Barabási, Albert-László
Huang, Rose D. L.
Hui, Qin
Nguyen, Xuan-Mai T.
Ho, Yuk-Lam
Djousse, Luc
Lynch, Julie A.
Vujkovic, Marijana
Tcheandjieu, Catherine
Tang, Hua
Damrauer, Scott M.
Reaven, Peter D.
Miller, Donald
Phillips, Lawrence S.
Ng, Maggie C. Y.
Graff, Mariaelisa
Haiman, Christopher A.
Loos, Ruth J. F.
North, Kari E.
Yengo, Loic
Smith, George Davey
Saleheen, Danish
Gaziano, J. Michael
Rader, Daniel J.
Tsao, Philip S.
Cho, Kelly
Chang, Kyong-Mi
Wilson, Peter W. F.
Sun, Yan V.
O’Donnell, Christopher J.
author_sort Huang, Jie
collection PubMed
description 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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spelling pubmed-97983562022-12-29 Genomics and phenomics of body mass index reveals a complex disease network Huang, Jie Huffman, Jennifer E. Huang, Yunfeng Do Valle, Ítalo Assimes, Themistocles L. Raghavan, Sridharan Voight, Benjamin F. Liu, Chang Barabási, Albert-László Huang, Rose D. L. Hui, Qin Nguyen, Xuan-Mai T. Ho, Yuk-Lam Djousse, Luc Lynch, Julie A. Vujkovic, Marijana Tcheandjieu, Catherine Tang, Hua Damrauer, Scott M. Reaven, Peter D. Miller, Donald Phillips, Lawrence S. Ng, Maggie C. Y. Graff, Mariaelisa Haiman, Christopher A. Loos, Ruth J. F. North, Kari E. Yengo, Loic Smith, George Davey Saleheen, Danish Gaziano, J. Michael Rader, Daniel J. Tsao, Philip S. Cho, Kelly Chang, Kyong-Mi Wilson, Peter W. F. Sun, Yan V. O’Donnell, Christopher J. Nat Commun Article 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. Nature Publishing Group UK 2022-12-29 /pmc/articles/PMC9798356/ /pubmed/36581621 http://dx.doi.org/10.1038/s41467-022-35553-2 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Huang, Jie
Huffman, Jennifer E.
Huang, Yunfeng
Do Valle, Ítalo
Assimes, Themistocles L.
Raghavan, Sridharan
Voight, Benjamin F.
Liu, Chang
Barabási, Albert-László
Huang, Rose D. L.
Hui, Qin
Nguyen, Xuan-Mai T.
Ho, Yuk-Lam
Djousse, Luc
Lynch, Julie A.
Vujkovic, Marijana
Tcheandjieu, Catherine
Tang, Hua
Damrauer, Scott M.
Reaven, Peter D.
Miller, Donald
Phillips, Lawrence S.
Ng, Maggie C. Y.
Graff, Mariaelisa
Haiman, Christopher A.
Loos, Ruth J. F.
North, Kari E.
Yengo, Loic
Smith, George Davey
Saleheen, Danish
Gaziano, J. Michael
Rader, Daniel J.
Tsao, Philip S.
Cho, Kelly
Chang, Kyong-Mi
Wilson, Peter W. F.
Sun, Yan V.
O’Donnell, Christopher J.
Genomics and phenomics of body mass index reveals a complex disease network
title Genomics and phenomics of body mass index reveals a complex disease network
title_full Genomics and phenomics of body mass index reveals a complex disease network
title_fullStr Genomics and phenomics of body mass index reveals a complex disease network
title_full_unstemmed Genomics and phenomics of body mass index reveals a complex disease network
title_short Genomics and phenomics of body mass index reveals a complex disease network
title_sort genomics and phenomics of body mass index reveals a complex disease network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798356/
https://www.ncbi.nlm.nih.gov/pubmed/36581621
http://dx.doi.org/10.1038/s41467-022-35553-2
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