title: An efficient weighted tag SNP-set analytical method in genome-wide association studies.
abstract: BACKGROUND
  Single-nucleotide polymorphism (SNP)-set analysis in Genome-wide association studies (GWAS) has emerged as a research hotspot for identifying genetic variants associated with disease susceptibility. But most existing methods of SNP-set analysis are affected by the quality of SNP-set, and poor quality of SNP-set can lead to low power in GWAS.
  
  
  RESULTS
  In this research, we propose an efficient weighted tag-SNP-set analytical method to detect the disease associations. In our method, we first design a fast algorithm to select a subset of SNPs (called tag SNP-set) from a given original SNP-set based on the linkage disequilibrium (LD) between SNPs, then assign a proper weight to each of the selected tag SNP respectively and test the joint effect of these weighted tag SNPs. The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test. We also compare the powers of the weighted tag SNP-set-based test based on four types of tag SNP-sets. The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.
  
  
  CONCLUSIONS
  From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS. We also designed a faster algorithm of selecting tag SNPs which include most of information of original SNP-set, and a better weighted function which can describe the status of each tag SNP in GWAS.
authors: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. 890612@163.com.
authors: Bin
authors: B
authors: Yan
authors: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. Shudongwang2013@sohu.com.|College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China. Shudongwang2013@sohu.com.|State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. Shudongwang2013@sohu.com.
authors: Shudong
authors: S
authors: Wang
authors: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. jiahuaqian0407@sina.com.
authors: Huaqian
authors: H
authors: Jia
authors: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. zyx.liuxing@163.com.
authors: Xing
authors: X
authors: Liu
authors: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. wangelxz@126.com.
authors: Xinzeng
authors: X
authors: Wang
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results