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Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass Index
Paule V Joseph1, Rosario B Jaime-Lara1, Yupeng Wang2
1Sensory Science and Metabolism Unit, Biobehavioral Branch, Division of Intramural Research, National Institute of Nursing Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20892, USA.
None:
Both genetic and environmental factors are suggested to influence overweight and obesity risks. Although individual loci and genes have been frequently shown to be associated with body mass index (BMI), the overall interaction of these genes and their role in BMI remains underexplored. Data were collected in 90 healthy, predominately Caucasian participants (51% female) with a mean age of 26.00 ± 9.02 years. Whole blood samples were assayed by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. We integrated and analyzed the clinical and microarray gene expression data from those individuals to understand various systematic gene expression patterns underlying BMI. Conventional differential expression analysis identified seven genes RBM20, SEPT12, AX748233, SLC30A3, WTIP, CASP10, and OR12D3 associated with BMI. Weight gene co-expression network analysis among 4,647 expressed genes identified two gene modules associated with BMI. These two modules, with different extents of gene connectivity, are enriched for catabolic and muscle system processes respectively, and tend to be regulated by zinc finger transcription factors. A total of 246 hub genes were converted to non-hub genes, and 286 non-hub genes were converted to hub genes between normal and overweight individuals, revealing the network dynamics underlying BMI. A total of 28 three-way gene interactions were identified, suggesting the existence of high-order gene expression patterns underlying BMI. Our study demonstrated a variety of systematic gene expression patterns associated with BMI and thus provided novel understanding regarding the genetic factors for overweight and obesity risks on system levels.
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