title: The choice of null distributions for detecting gene-gene interactions in genome-wide association studies.
abstract: BACKGROUND
  In genome-wide association studies (GWAS), the number of single-nucleotide polymorphisms (SNPs) typically ranges between 500,000 and 1,000,000. Accordingly, detecting gene-gene interactions in GWAS is computationally challenging because it involves hundreds of billions of SNP pairs. Stage-wise strategies are often used to overcome the computational difficulty. In the first stage, fast screening methods (e.g. Tuning ReliefF) are applied to reduce the whole SNP set to a small subset. In the second stage, sophisticated modeling methods (e.g., multifactor-dimensionality reduction (MDR)) are applied to the subset of SNPs to identify interesting interaction models and the corresponding interaction patterns. In the third stage, the significance of the identified interaction patterns is evaluated by hypothesis testing.
  
  
  RESULTS
  In this paper, we show that this stage-wise strategy could be problematic in controlling the false positive rate if the null distribution is not appropriately chosen. This is because screening and modeling may change the null distribution used in hypothesis testing. In our simulation study, we use some popular screening methods and the popular modeling method MDR as examples to show the effect of the inappropriate choice of null distributions. To choose appropriate null distributions, we suggest to use the permutation test or testing on the independent data set. We demonstrate their performance using synthetic data and a real genome wide data set from an Aged-related Macular Degeneration (AMD) study.
  
  
  CONCLUSIONS
  The permutation test or testing on the independent data set can help choosing appropriate null distributions in hypothesis testing, which provides more reliable results in practice.
authors.affiliation: Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.forename: Xiang
authors.initials: X
authors.lastname: Wan
authors.forename: Zengyou
authors.initials: Z
authors.lastname: He
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yang
authors.forename: Hong
authors.initials: H
authors.lastname: Xue
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
mesh_terms: D003198:Computer Simulation; D015870:Gene Expression; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics; D015233:Models, Statistical; D058011:Multifactor Dimensionality Reduction / Q000379:methods*; D020641:Polymorphism, Single Nucleotide