Shudong Wang1, Sicheng He1, Fayou Yuan1, Xinjie Zhu1. 1. College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao Shandong, China.
Abstract
MOTIVATION: Effective tagging single-nucleotide polymorphism (SNP)-set selection is crucial to SNP-set analysis in genome-wide association studies (GWAS). Most of the existing tagging SNP-set selection methods cannot make full use of the information hidden in common or rare variants associated diseases. It is noticed that some SNPs have overlapping genetic information owing to linkage disequilibrium (LD) structure between SNPs. Therefore, when testing the association between SNPs and disease susceptibility, it is sufficient to elect the representative SNPs (called tag SNP-set or tagSNP-set) with maximum information. RESULTS: It is proposed a new tagSNP-set selection method based on LD information between SNPs, namely TagSNP-Set with Maximum Information. Compared with classical SNP-set analytical method, our method not only has higher power, but also can minimize the number of selected tagSNPs and maximize the information provided by selected tagSNPs with less genotyping cost and lower time complexity. CONTACT: hesicheng12@163.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Effective tagging single-nucleotide polymorphism (SNP)-set selection is crucial to SNP-set analysis in genome-wide association studies (GWAS). Most of the existing tagging SNP-set selection methods cannot make full use of the information hidden in common or rare variants associated diseases. It is noticed that some SNPs have overlapping genetic information owing to linkage disequilibrium (LD) structure between SNPs. Therefore, when testing the association between SNPs and disease susceptibility, it is sufficient to elect the representative SNPs (called tag SNP-set or tagSNP-set) with maximum information. RESULTS: It is proposed a new tagSNP-set selection method based on LD information between SNPs, namely TagSNP-Set with Maximum Information. Compared with classical SNP-set analytical method, our method not only has higher power, but also can minimize the number of selected tagSNPs and maximize the information provided by selected tagSNPs with less genotyping cost and lower time complexity. CONTACT: hesicheng12@163.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Miguel Seral-Cortes; Sergio Sabroso-Lasa; Pilar De Miguel-Etayo; Marcela Gonzalez-Gross; Eva Gesteiro; Cristina Molina-Hidalgo; Stefaan De Henauw; Frederic Gottrand; Christina Mavrogianni; Yannis Manios; Maria Plada; Kurt Widhalm; Anthony Kafatos; Éva Erhardt; Aline Meirhaeghe; Diego Salazar-Tortosa; Jonatan Ruiz; Luis A Moreno; Luis Mariano Esteban; Idoia Labayen Journal: Sci Rep Date: 2021-02-04 Impact factor: 4.379