Genetic risk-prediction in the post-GWAS era

Large genome-wide association studies are now consistently pointing towards extremely polygenic models for complex diseases. Such models may involve thousands of susceptibility markers, each conferring only a modest risk,  but collectively they could be explaining substantial variation in disease-risks within populations. Further, a few large studies of gene-environment interactions indicate that genetic and environmental risk-factors may broadly act in a multiplicative fashion on the risk of a number of different cancers and possibly other diseases. In this talk, I will explore how under such emerging models for disease architecture, the performance of polygenic risk prediction models are expected to improve in the future with increasing sample size,  incorporation of functional information and integration of next generation sequencing or genotyping technologies that can provide coverage for low frequency and rare variants. Further, using results from recent studies on bladder and breast cancers, I will illustrate potential implications for multiplicative gene-environment interactions for targeted prevention for these two malignancies both of which have modifiable risk-factors. These analyses will highlight both challenges and opportunities for using genetic information for personalized disease prevention. The statistical tools we develop for assessing "yield" of future genetic studies can be broadly useful for understanding sample size requirements, efficient study designs and optimal statistical methods for conducting population-based studies with  "-omics" technology.