From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

ABSTRACT: Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment

SUBMITTER: Wei Z 

PROVIDER: S-EPMC2748686 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature