Abstract
BackgroundSingle-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.ResultsIn 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.ConclusionsFrom 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.
Highlights
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
We proposed a weighted tag SNP-set analytical method involving the selection of tag SNP-set from original SNP-set and the description of status of each tag SNP
The selected 2 4 5 7 9 10 13 15 16 23 29 31 34 37 40 58 59 60 61 62 tag SNPs 64 65 67 68 69 72 75 79 80 81 83 85 89 91 94 103 108. This is an example with 169 original SNPs and each number represents a tag SNP
Summary
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. Max-single is the simplest method using the maximum χ2 statistic of all SNPs to compute the p-value of the SNP-set [9]. Fan and Knapp [10] used a numerical dosage scheme to score each marker genotype and compared the mean genotype score vectors between the cases and controls by Hotelling’s T2 statistic. Compared with the former, the later makes full use of the LD information, but the degree of freedom of Hotelling’s T2 increases greatly. The principal component analysis (PCA) was first applied to analyze the association
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