An efficient weighted tag SNP-set analytical method in genome-wide association studies

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• 作者：Bin Yan (1)
  Shudong Wang (1) (2) (3)
  Huaqian Jia (1)
  Xing Liu (1)
  Xinzeng Wang (1)
  
  1. College of Mathematics and Systems Science ; Shandong University of Science and Technology ; Qingdao ; Shandong ; 266590 ; China
  2. College of Computer and Communication Engineering ; China University of Petroleum ; Qingdao ; Shandong ; 266580 ; China
  3. 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
  
• 关键词：Association test ; GWAS ; Linkage disequilibrium ; SNP ; set ; Tag SNP
• 刊名：BMC Genetics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：16
• 期：1
• 全文大小：976 KB
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• 刊物主题：Life Sciences, general; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics; Genetics and Population Dynamics;
• 出版者：BioMed Central
• ISSN：1471-2156

文摘

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.