A New Explained-Variance-Based Genetic Risk Score for Predictive Modeling of Disease Risk

• Activity Number: 580
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ronglin Che*+ and Alison Anne Motsinger-Reif
• Companies: North Carolina State University and North Carolina State University
• Keywords: effect size ; explained variance, ; polygenic ; predictive modeling ; simple count genetic risk score ; weighted genetic risk score

Abstract

The goal of association mapping is to identify genetic variants that predict disease. As the field of human genetics matures, many association studies have shown that for many diseases, risk is explained by a large number of variants that each only conveys minor risk. This is prompting the use of genetic risk scores (GRS) in building predictive models, where information across several variants is combined for predictive modeling. In our study, we compare the performance of four previously proposed GRS methods (a simple count GRS, an odds ratio weighted GRS, a direct logistic regression GRS, a polygenic GRS), and present a new explained variance weighted method. We compare the methods using a wide range of simulations in two steps, with a range of the number of deleterious single nucleotide polymorphisms (SNPs) explaining disease risk, genetic modes, baseline penetrances, sample sizes, odds ratios and minor allele frequencies. Several measures of model performance were compared including overall power, C-statistic and AIC. Our results show the relative performance of methods differs significantly, with the new explained variance weighted GRS generally performing favorably to others.