title: Risk prediction using genome-wide association studies.
abstract: Over the last few years, many new genetic associations have been identified by genome-wide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biology, and risk prediction. Recently, there has been some skepticism regarding the prospects of risk prediction using GWAS, primarily motivated by the fact that individual effect sizes of variants associated with the phenotype are mostly small. However, there have also been arguments that many disease-associated variants have not yet been identified; hence, prospects for risk prediction may improve if more variants are included. From a risk prediction perspective, it is reasonable to average a larger number of predictors, of which some may have (limited) predictive power, and some actually may be noise. The idea being that when added together, the combined small signals results in a signal that is stronger than the noise from the unrelated predictors. We examine various aspects of the construction of models for the estimation of disease probability. We compare different methods to construct such models, to examine how implementation of cross-validation may influence results, and to examine which single nucleotide polymorphisms (SNPs) are most useful for prediction. We carry out our investigation on GWAS of the Welcome Trust Case Control Consortium. For Crohn's disease, we confirm our results on another GWAS. Our results suggest that utilizing a larger number of SNPs than those which reach genome-wide significance, for example using the lasso, improves the construction of risk prediction models.
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. clk@fhcrc.org
authors.forename: Charles
authors.initials: C
authors.lastname: Kooperberg
authors.forename: Michael
authors.initials: M
authors.lastname: LeBlanc
authors.forename: Valerie
authors.initials: V
authors.lastname: Obenchain
mesh_terms: D016022:Case-Control Studies; D003424:Crohn Disease / Q000453:epidemiology / Q000235:genetics; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D017720:Molecular Epidemiology; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors