Dissecting the Clinical Relevance of Polygenic Risk Score for Obesity – A Cross-sectional, Longitudinal Analysis
Manu Shivakumar is a graduate student in the Graduate Group in Genomics and Computational Biology (GCB). He is interested in biomedical informatics, cancer genomics, disease-disease networks and risk prediction.
Background: Obesity is a global pandemic disease whose prevalence is increasing worldwide. The clinical relevance of a polygenic risk score (PRS) for obesity has not been fully elucidated in Asian populations.
Method: We utilized a comprehensive health check-up database from the Korean population in conjunction with genotyping to generate PRS for BMI (PRS-BMI). We conducted a phenome-wide association (PheWAS) analysis and observed the longitudinal association of BMI with PRS-BMI.
Results: PRS-BMI was generated by PRS-CS. Adding PRS-BMI to a model predicting ten-year BMI based on age, sex, and baseline BMI improved the model’s accuracy (p=0.003). In a linear mixed model of longitudinal change in BMI with aging, higher deciles of PRS were directly associated with changes in BMI. In the PheWAS, significant associations were observed for metabolic syndrome, bone density, and fatty liver. In the lean body population, those having the top 20% PRS-BMI had higher BMI and body fat mass along with better metabolic trait profiles compared to the bottom 20%. A bottom-20% PRS-BMI was a risk factor for metabolically unhealthy lean body (odds ratio 3.092, 95% confidence interval 1.707-6.018, p<0.001), with adjustment for age, sex and BMI.
Conclusions: Genetic predisposition to obesity as defined by PRS-BMI was significantly associated with obesity-related disease or trajectory of obesity. Low PRS-BMI might be a risk factor associated with a metabolically unhealthy lean body. Better understanding the mechanisms of these relationships may allow tailored intervention in obesity or early selection of populations at risk of metabolic disease.
KeywordsPolygenic risk score; Obesity; Body mass index; Metabolic syndrome; Korean population; Electronic health record database
To understand health and disease today, we need new thinking and novel science —the kind we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. LEARN MORE ABOUT US