An adaptive strategy for association analysis of common or rare variants using entropy theory

Abstract

Advances in DNA sequencing technology have been promoting the development of sequencing studies to identify rare variants associated with complex traits. Adaptive strategy can be effective to reduce the noise provided by non-causal variants. However, the existing adaptive strategies depend on many assumptions. In this paper, we proposed a new adaptive strategy using entropy theory for association analysis. This entropy-based strategy is based on the magnitude of association between variants and disease and does not depend on the detailed association pattern with causal variants. We considered multi-marker test and Sum test with collapsing method to construct the entropy-based adaptive strategy. Using simulation studies, we investigated the performance of our method for rare variant analyses as well as for common variant analyses with multi-marker test and compared it with several existing adaptive strategies. The results showed that our method can improve the power and achieve good performance when there is a large number of non-causal variants and effects of causal variants are in the same direction for rare variant.