Modeling the genomic architecture of adiposity and anthropometrics across the lifespan

Abstract Obesity-related conditions are among the leading causes of preventable death and are increasing in prevalence worldwide. Body size and composition are complex traits that are challenging to characterize due to environmental and genetic influences, longitudinal variation, heterogeneity betwe...

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Main Authors: Christopher H. Arehart, Meng Lin, Raine A. Gibson, Colorado Center for Personalized Medicine, Sridharan Raghavan, Christopher R. Gignoux, Maggie A. Stanislawski, Andrew D. Grotzinger, Luke M. Evans
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-62730-w
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author Christopher H. Arehart
Meng Lin
Raine A. Gibson
Colorado Center for Personalized Medicine
Sridharan Raghavan
Christopher R. Gignoux
Maggie A. Stanislawski
Andrew D. Grotzinger
Luke M. Evans
author_facet Christopher H. Arehart
Meng Lin
Raine A. Gibson
Colorado Center for Personalized Medicine
Sridharan Raghavan
Christopher R. Gignoux
Maggie A. Stanislawski
Andrew D. Grotzinger
Luke M. Evans
author_sort Christopher H. Arehart
collection DOAJ
description Abstract Obesity-related conditions are among the leading causes of preventable death and are increasing in prevalence worldwide. Body size and composition are complex traits that are challenging to characterize due to environmental and genetic influences, longitudinal variation, heterogeneity between sexes, and differing health risks based on adipose distribution. Here, we construct a 4-factor genomic structural equation model using 18 measures, unveiling shared and distinct genetic architectures underlying birth size, abdominal size, adipose distribution, and adiposity. Multivariate genome-wide associations reveal the adiposity factor is enriched specifically in neural tissues and pathways, while adipose distribution is enriched more broadly across physiological systems. In addition, polygenic scores for the adiposity factor predict many adverse health outcomes, while those for body size and composition predict a more limited subset. Finally, we characterize the factors’ genetic correlations with obesity-related traits and examine the druggable genome by constructing a bipartite drug-gene network to identify potential therapeutic targets.
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spelling doaj-art-2181dbcb95644e08a1941f32205e2bfc2025-08-20T03:43:10ZengNature PortfolioNature Communications2041-17232025-08-0116111610.1038/s41467-025-62730-wModeling the genomic architecture of adiposity and anthropometrics across the lifespanChristopher H. Arehart0Meng Lin1Raine A. Gibson2Colorado Center for Personalized MedicineSridharan Raghavan3Christopher R. Gignoux4Maggie A. Stanislawski5Andrew D. Grotzinger6Luke M. Evans7Institute for Behavioral Genetics, University of Colorado BoulderDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical CampusInstitute for Behavioral Genetics, University of Colorado BoulderDepartment of Veterans Affairs Eastern Colorado Health Care SystemDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical CampusDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical CampusInstitute for Behavioral Genetics, University of Colorado BoulderInstitute for Behavioral Genetics, University of Colorado BoulderAbstract Obesity-related conditions are among the leading causes of preventable death and are increasing in prevalence worldwide. Body size and composition are complex traits that are challenging to characterize due to environmental and genetic influences, longitudinal variation, heterogeneity between sexes, and differing health risks based on adipose distribution. Here, we construct a 4-factor genomic structural equation model using 18 measures, unveiling shared and distinct genetic architectures underlying birth size, abdominal size, adipose distribution, and adiposity. Multivariate genome-wide associations reveal the adiposity factor is enriched specifically in neural tissues and pathways, while adipose distribution is enriched more broadly across physiological systems. In addition, polygenic scores for the adiposity factor predict many adverse health outcomes, while those for body size and composition predict a more limited subset. Finally, we characterize the factors’ genetic correlations with obesity-related traits and examine the druggable genome by constructing a bipartite drug-gene network to identify potential therapeutic targets.https://doi.org/10.1038/s41467-025-62730-w
spellingShingle Christopher H. Arehart
Meng Lin
Raine A. Gibson
Colorado Center for Personalized Medicine
Sridharan Raghavan
Christopher R. Gignoux
Maggie A. Stanislawski
Andrew D. Grotzinger
Luke M. Evans
Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
Nature Communications
title Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
title_full Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
title_fullStr Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
title_full_unstemmed Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
title_short Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
title_sort modeling the genomic architecture of adiposity and anthropometrics across the lifespan
url https://doi.org/10.1038/s41467-025-62730-w
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