title: On genome-wide association studies and their meta-analyses: lessons learned from osteoporosis studies.
abstract: BACKGROUND
  Genome-wide association studies (GWASs) and meta-analyses of GWASs have led to the identification of a number of promising genes for osteoporosis. However, inconsistent findings are seen among and between GWASs and meta-analyses, and inconsistencies have even been observed between meta-analyses whose samples overlapped to a large extent.
  
  
  OBJECTIVE
  We carefully evaluated the usefulness and limitations of GWASs and their meta-analyses, with an emphasis on understanding the reasons for inconsistent results.
  
  
  METHODS
  Based on published empirical data for osteoporosis, we performed a series of theoretical analyses using simulation studies.
  
  
  RESULTS
  The power of meta-analyses is limited to identifying a particular locus with modest effect size. In the situation in which individual GWASs were not included in the meta-analysis (ie, nonoverlap), the meta-analysis has rather limited power to replicate particular loci identified from the individual GWASs. Between-study heterogeneity may result in a power loss in meta-analyses, implying that adding heterogeneous samples into a meta-analysis may reduce the power, rather than having the anticipated effect of increasing power due to increased sample size.
  
  
  CONCLUSIONS
  Discordant findings in GWASs and meta-analyses are not unexpected, even for true susceptible genes. Contrary to the general belief, meta-analyses should not and cannot be used as a gold standard to evaluate the results of individual GWASs. Individual GWASs in homogeneous populations can detect true disease genes that meta-analyses may have low power to replicate.
authors.affiliation: Center for Bioinformatics and Genomics, and Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana 70112, USA.
authors.forename: Yong-Jun
authors.initials: YJ
authors.lastname: Liu
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.forename: Yufang
authors.initials: Y
authors.lastname: Pei
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Papasian
authors.forename: Hong-Wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D015519:Bone Density / Q000235:genetics; D003198:Computer Simulation; D056426:Genetic Loci; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D010024:Osteoporosis / Q000235:genetics* / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D013223:Statistics as Topic