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Genetic association studies using high-throughput genotyping and sequencing technologies have identified a large number of genetic variants associated with complex human diseases. These findings have provided an unprecedented opportunity to identify individuals in the population at high risk for disease who carry causal genetic mutations and hold great promise for early intervention and individualized medicine. While interest is high in building risk prediction models based on recent genetic findings, it is crucial to have appropriate statistical measurements to assess the performance of a genetic risk prediction model. Predictiveness curves were recently proposed as a graphic tool for evaluating a risk prediction model on the basis of a single continuous biomarker. The curve evaluates a risk prediction model for classification performance as well as its usefulness when applied to a population. In this article, we extend the predictiveness curve to measure the collective contribution of multiple genetic variants. We further propose a nonparametric, U-statistics-based measurement, referred to as the U-Index, to quantify the performance of a multi-locus predictiveness curve. In particular, a global U-Index and a partial U-Index can be used in the general population and a subpopulation of particular clinical interest, respectively. Through simulation studies, we demonstrate that the proposed U-Index has advantages over several existing summary statistics under various disease models. We also show that the partial U-Index can have its own uniqueness when rare variants have a substantial contribution to disease risk. Finally, we use the proposed predictiveness curve and its corresponding U-Index to evaluate the performance of a genetic risk prediction model for nicotine dependence. [ABSTRACT FROM AUTHOR]

Translation studies have been initiated to assess the combined effect of genetic loci from recently accomplished genome-wide association studies and the existing risk factors for early disease prediction. We propose a bagging optimal receiver operating characteristic (ROC) curve method to facilitate this research. Through simulation and real data application, we compared the new method with the commonly used allele counting method and logistic regression, and found that the new method yields a better performance. The new method was applied on the Wellcome Trust data set to form a predictive genetic test for rheumatoid arthritis. The formed test reached an area under the curve (AUC) value of 0.7. [ABSTRACT FROM AUTHOR]