title: A mixed two-stage method for detecting interactions in genomewide association studies.
abstract: Genomewide association studies (GWAS) are being conducted to unravel the genetic etiology of complex diseases, in which complex epistasis may play an important role. One-stage method in which interactions are tested using all samples at one time may be computationally problematic, may have low power as the number of markers tested increases and may not be cost-efficient. A common two-stage method may be a reasonable and powerful approach for detecting interacting genes using all samples in both two stages. In this study, we introduce an alternative two-stage method, in which some promising markers are selected using a proportion of samples in the first stage and interactions are then tested using the remaining samples in the second stage. This two-stage method is called mixed two-stage method. We then investigate the power of both one-stage method and mixed two-stage method to detect interacting disease loci for a range of two-locus epistatic models in a case-control study design. Our results suggest that mixed two-stage method may be more powerful than one-stage method if we choose about 30% of samples for single-locus tests in the first stage, and identify less than and equal to 1% of markers for follow-up interaction tests. In addition, we compare both two-stage methods and find that our two-stage method will lose power because we only use part of samples in both two stages.
authors.affiliation: Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.
authors.forename: Yijun
authors.initials: Y
authors.lastname: Zuo
authors.forename: Guolian
authors.initials: G
authors.lastname: Kang
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D004843:Epistasis, Genetic; D005787:Gene Frequency; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012306:Risk