Predictive computational obesity risk framework through integration of gene expression profiles and genetic risk score.

We aimed to predict obesity risk with genetic data, specifically, obesity-associated gene expression profiles. Genetic risk score was computed. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used. Linear regression and built support vector machine models predicted obesity risk using gene expression profiles and the genetic risk score with a new mathematical method. Microarrays n = 90 whole blood peripheral human samples were assayed by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. Fasting morning blood samples (2.5 mL) were collected from healthy controls and overweight participants using PAXgeneTM RNA (Qiagen, Valencia, CA) tubes and frozen at -80ºC. Total RNA was extracted and purified with RNA PAXgene kit (Qiagen, Valencia, CA) and stored at -80oC. RNA quantity, purity, and integrity were assessed via spectrometry and by RNA 6000 Nano LabChip kit on a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Total RNA passed quality control criteria prior to microarray analysis. Grant Information: 1ZIANR000018 NIH Intramural Research Program, NINR, DHHS; Henderson, W.A. (PI) The Rockefeller University, Heilbrunn Nurse Scholar Award; NIH Office of Workforce Diversity; Joseph, P.V. (PI) NIH Intramural Research Training Award; Fourie, N.F.