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"es 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
SONGKLANAKARIN JOURNAL OF SCIENCE & TECHNOLOGY
Published by : Prince of Songkla University Contributions welcome at : http://rdo.psu.ac.th
By using our website, you acknowledge that you have read and understand our Cookie Policy and Privacy Policy.