title: 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.
authors.affiliation: School of Mathematics and Computational Science, Huaihua University, Hunan, China.|Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
authors.forename: Yu-Mei
authors.initials: YM
authors.lastname: Li
authors.affiliation: Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Xu
authors.affiliation: School of Mathematics and Computational Science, Huaihua University, Hunan, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiang
authors.affiliation: Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.|Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangdong, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Peng
authors.affiliation: Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
authors.forename: Hong-Wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D003198:Computer Simulation; D019277:Entropy; D056726:Genetic Association Studies / Q000379:methods*; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic
chemical_list: D005819:Genetic Markers