Abstract
The study of gene-based genetic associations has gained conceptual popularity recently. Biologic insight into the etiology of a complex disease can be gained by focusing on genes as testing units. Several gene-based methods (e.g., minimum p-value (or maximum test statistic) or entropy-based method) have been developed and have more power than a single nucleotide polymorphism (SNP)-based analysis. The objective of this study is to compare the performance of the entropy-based method with the minimum p-value and single SNP–based analysis and to explore their strengths and weaknesses. Simulation studies show that: 1) all three methods can reasonably control the false-positive rate; 2) the minimum p-value method outperforms the entropy-based and the single SNP–based method when only one disease-related SNP occurs within the gene; 3) the entropy-based method outperforms the other methods when there are more than two disease-related SNPs in the gene; and 4) the entropy-based method is computationally more efficient than the minimum p-value method. Application to a real data set shows that more significant genes were identified by the entropy-based method than by the other two methods.
Highlights
Single nucleotide polymorphism (SNP)–based genomewide association studies (GWAS) have been a popular and successful method to identify disease-related SNPs
Gene- or region-based analysis may have higher power to identify the causal variants that affect the complex disease, because it takes into consideration the correlations among SNPs within a single gene
The simplest method for gene-based analysis is the SNPbased method, in which each genotyped SNP is tested for association, and multiple testing corrections based on the Bonferroni procedure are applied to control the type-I error rate
Summary
Single nucleotide polymorphism (SNP)–based genomewide association studies (GWAS) have been a popular and successful method to identify disease-related SNPs. The simplest method for gene-based analysis is the SNPbased method, in which each genotyped SNP is tested for association, and multiple testing corrections based on the Bonferroni procedure are applied to control the type-I error rate. The SNP-based method for gene-based analysis has low power when the causal variants are highly correlated with one or more genotyped SNPs and when the causal SNPs are not genotyped. Several methods have been developed to analyze multiple SNPs within the same gene simultaneously. These methods include Fisher’s method for combining p-values by a logarithm function of p-values and the minP (minimum p-value) or maxT (maximum test statistics) method in which the significance level can be determined by the observed p-value. The empirical pvalue must be calculated by using permutation, because the limiting distributions of Fisher’s statistic and minP (maxT) statistic are unknown under the null hypothesis that the gene is not associated with the disease
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
