Meta-analysis of Genetic Association Studies Supports a Contribution of Common Variants to Susceptibility to Common Disease

Nat Genet. 2003 Feb;33(2):177-82. doi: 10.1038/ng1071. Epub 2003 Jan 13.

Abstract

Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Alleles
  • Data Interpretation, Statistical
  • False Positive Reactions
  • Follow-Up Studies
  • Genetic Predisposition to Disease*
  • Genetic Variation*
  • Genetics, Population
  • Genome, Human
  • Humans
  • Linkage Disequilibrium
  • Polymorphism, Single Nucleotide
  • Publication Bias
  • Reproducibility of Results