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

Wang, Lan; Perez, Jeremiah; Heard-Costa, Nancy; Chu, Audrey Y; Joehanes, Roby; Munson, Peter J; Levy, Daniel; Fox, Caroline S; Cupples, L Adrienne; Liu, Ching-Ti.
Int J Obes (Lond); 2018 Sep 19.
Artigo em Inglês | MEDLINE | ID: mdl-30232418


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.


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.


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.


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.
Selo DaSilva