title: Genomewide association studies: history, rationale, and prospects for psychiatric disorders.
abstract: OBJECTIVE
  The authors conducted a review of the history and empirical basis of genomewide association studies (GWAS), the rationale for GWAS of psychiatric disorders, results to date, limitations, and plans for GWAS meta-analyses.
  
  
  METHODS
  A literature review was carried out, power and other issues discussed, and planned studies assessed.
  
  
  RESULTS
  Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.
  
  
  CONCLUSIONS
  GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.
authors.forename: Sven
authors.initials: S
authors.lastname: Cichon
authors.forename: Nick
authors.initials: N
authors.lastname: Craddock
authors.forename: Mark
authors.initials: M
authors.lastname: Daly
authors.forename: Stephen V
authors.initials: SV
authors.lastname: Faraone
authors.forename: Pablo V
authors.initials: PV
authors.lastname: Gejman
authors.forename: John
authors.initials: J
authors.lastname: Kelsoe
authors.forename: Thomas
authors.initials: T
authors.lastname: Lehner
authors.forename: Douglas F
authors.initials: DF
authors.lastname: Levinson
authors.forename: Audra
authors.initials: A
authors.lastname: Moran
authors.forename: Pamela
authors.initials: P
authors.lastname: Sklar
authors.forename: Patrick F
authors.initials: PF
authors.lastname: Sullivan