A Comprehensive Genetic Approach for Improving Prediction of Skin Cancer Risk in Humans.

Vazquez AI, de Los Campos G, Klimentidis YC, Rosa GJ, Gianola D, Yi N, Allison DB.

Source

University of Alabama at Birmingham;

Abstract

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PC, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PC or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for non-genetic covariates to AUCs of 0.58 (pedigree), 0.62 (PC), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of SNPs in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.