A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles

• Published in: PloS one 2018-05, Vol.13 (5), p.e0197843-e0197843
• Main Authors: Joseph, Paule V, Wang, Yupeng, Fourie, Nicolaas H, Henderson, Wendy A
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Bioinformatics; Biology and Life Sciences; Body mass; Body Mass Index; Body size; Cardiovascular diseases; Cohort Studies; Complications and side effects; Computation; Computer applications; Consent; Correlation analysis; Deoxyribonucleic acid; Diabetes; Disease; DNA; Female; Gastrointestinal surgery; Gene expression; Gene Expression Profiling; Gene loci; Genetic aspects; Genetic Predisposition to Disease; Genetics; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Genotype; Health aspects; Humans; Male; Mathematical models; Medicine and Health Sciences; Metabolic diseases; Methods; Middle Aged; Models, Statistical; Obesity; Obesity - epidemiology; Obesity - genetics; Optimization; Physical Sciences; Polymorphism, Single Nucleotide; Prediction models; Predictive Value of Tests; Prevention; Regression analysis; Research and Analysis Methods; Risk; Risk Factors; Single-nucleotide polymorphism; Studies; Support vector machines; Transcriptome; United States - epidemiology; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0197843

Recent large-scale genome-wide association studies have identified tens of genetic loci robustly associated with Body Mass Index (BMI). Gene expression profiles were also found to be associated with BMI. However, accurate prediction of obesity risk utilizing genetic data remains challenging. In a cohort of 75 individuals, we integrated 27 BMI-associated SNPs and obesity-associated gene expression profiles. Genetic risk score was computed by adding BMI-increasing alleles. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used that excluded some SNPs. Linear regression and support vector machine models were built to predict obesity risk using gene expression profiles and the genetic risk score. An adjusted R2 of 0.556 and accuracy of 76% was achieved for the linear regression and support vector machine models, respectively. In this paper, we report a new mathematical method to predict obesity genetic risk. We constructed obesity prediction models based on genetic information for a small cohort. Our computational framework serves as an example for using genetic information to predict obesity risk for specific cohorts.