The efficiency of single SNP and SNP-set analysis in genome-wide association studies

Abstract

The objective of this research is to compare and identify effective methods for the identification of gene loci associated with a disease outcome in the analysis of genome-wide data. We evaluate three methods, namely single SNP analysis, Sequence Kernel Association Test (SKAT) and the recently proposed Generalized Higher Criticism (GHC). The simulated data used in this research were constructed from a control data set in a study of Crohn's disease. True positive (TP) and false positive rate (FP) were evaluated under different genetic models for disease with significant thresholds adjusted for multiple hypothesis testing based on the permutation method. The findings are mixed with all three methods giving similar TP rates under some disease models and different rates for other models. Overall, GHC is shown to be preferable in terms of error rates but it is disadvantageous in terms of computational efficiency.

Keywords

• single snp analysis
• sequence kernel association test
• generalized higher criticism
• permutation test