Improving power of genetic association studies by extreme phenotype sampling: a review and some new results

Extreme phenotype sampling is a selective genotyping design for genetic association studies where only individuals with extreme values of a continuous trait are genotyped for a set of genetic variants. Under financial or other limitations, this design is assumed to improve the power to detect associations between genetic variants and the trait, compared to randomly selecting the same number of individuals for genotyping. Here we present extensions of likelihood models that can be used for inference when the data are sampled according to the extreme phenotype sampling design. Computational methods for parameter estimation and hypothesis testing are provided. We consider methods for common variant genetic effects and gene-environment interaction effects in linear regression models with a normally distributed trait. We use simulated and real data to show that extreme phenotype sampling can be powerful compared to random sampling, but that this does not hold for all extreme sampling methods and situations.