Increasing the Power of Association Studies by Imputation-based Sparse Tag SNP Selection

Abstract

In low- and medium-budget association studies, a limited number of tag SNPs are selected out of a large set of available SNPs previously typed in an initial cohort. These tag SNPs are then typed in a larger set of control and affected individuals. Current association studies pick the set of tag SNPs based on the correlation criterion. Here we show that association studies that use tag SNPs selected according to their imputation accuracy are more powerful than those relying on tag SNPs selected by the correlation criterion. The advantage is particularly striking when the set of tag SNPs is sparse; thus, picking tag SNPs to maximize the imputation accuracy will increase the effectiveness of future association studies without additional cost.