title: Prioritized subset analysis: improving power in genome-wide association studies.
abstract: BACKGROUND
  Genome-wide association studies (GWAS) are now feasible for studying the genetics underlying complex diseases. For many diseases, a list of candidate genes or regions exists and incorporation of such information into data analyses can potentially improve the power to detect disease variants. Traditional approaches for assessing the overall statistical significance of GWAS results ignore such information by inherently treating all markers equally.
  
  
  METHODS
  We propose the prioritized subset analysis (PSA), in which a prioritized subset of markers is pre-selected from candidate regions, and the false discovery rate (FDR) procedure is carried out in the prioritized subset and its complementary subset, respectively.
  
  
  RESULTS
  The PSA is more powerful than the whole-genome single-step FDR adjustment for a range of alternative models. The degree of power improvement depends on the fraction of associated SNPs in the prioritized subset and their nominal power, with higher fraction of associated SNPs and higher nominal power leading to more power improvement. The power improvement can be substantial; for disease loci not included in the prioritized subset, the power loss is almost negligible.
  
  
  CONCLUSIONS
  The PSA has the flexibility of allowing investigators to combine prior information from a variety of sources, and will be a useful tool for GWAS.
authors.affiliation: Department of Biostatistics, Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. chun.li@vanderbilt.edu
authors.forename: Chun
authors.initials: C
authors.lastname: Li
authors.forename: Mingyao
authors.initials: M
authors.lastname: Li
authors.forename: Ethan M
authors.initials: EM
authors.lastname: Lange
authors.forename: Richard M
authors.initials: RM
authors.lastname: Watanabe
mesh_terms: D003198:Computer Simulation; D005787:Gene Frequency; D008040:Genetic Linkage; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers