Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes.

ABSTRACT: The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction method

SUBMITTER: Choi S 

PROVIDER: S-EPMC5287117 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature