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Genetics and Epigenetics

Integrating genetic, transcriptional, and biological information provides insights into obesity

International Journal of Obesity volume 43, pages 457–467 (2019)Cite this article

Subjects

Abstract

Objective

Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks.

Subjects

We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4303), as well as implemented an integrative analysis of these data and existing biological information.

Results

Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist–hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin-mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair, and degradation of regulatory proteins are cancer related.

Conclusions

Our integrative analysis of genetic, transcriptional, and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.

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Acknowledgements

This research was in part support by the grant NIH R01 DK089256, NIGMS T32GM074905 and by NHLBI contracts N01-HC-25195 and HHSN268201500001I.

Author information

Author notes

  1. These authors contributed equally: Lan Wang, Jeremiah Perez.

Authors and Affiliations

  1. Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA

    Lan Wang, Jeremiah Perez, L. Adrienne Cupples & Ching-Ti Liu

  2. Department of Medicine, Boston University, Boston, MA, 02118, USA

    Nancy Heard-Costa

  3. Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA

    Audrey Y. Chu, Daniel Levy & Caroline S. Fox

  4. The Framingham Heart Study, Framingham, MA, 01702, USA

    Audrey Y. Chu, Daniel Levy, Caroline S. Fox & L. Adrienne Cupples

  5. Hebrew SeniorLife, Harvard Medical School, Boston, MA, 02131, USA

    Roby Joehanes

  6. Mathematical and Statistical Computing Laboratory, Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, MD, 20892, USA

    Peter J. Munson

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  1. Lan Wang
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Correspondence to Ching-Ti Liu.

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Audrey Y. Chu and Caroline S. Fox have moved to Merck & Co., Inc.

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Wang, L., Perez, J., Heard-Costa, N. et al. Integrating genetic, transcriptional, and biological information provides insights into obesity. Int J Obes 43, 457–467 (2019). https://doi.org/10.1038/s41366-018-0190-2

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