Synthesis of Genetic Association Studies for Pertinent Gene-Disease Associations Requires Appropriate Methodological and Statistical Approaches

J Clin Epidemiol. 2008 Jul;61(7):634-45. doi: 10.1016/j.jclinepi.2007.12.011.

Abstract

Objective: The aim of the study was to consider statistical and methodological issues affecting the results of meta-analysis of genetic association studies for pertinent gene-disease associations. Although the basic statistical issues for performing meta-analysis are well described in the literature, there are remaining methodological issues.

Study design and setting: An analysis of our database and a literature review were performed to assess issues such as departure of Hardy-Weinberg equilibrium, genetic contrasts, sources of bias (replication validity, early extreme contradictory results, differential magnitude of effect in large versus small studies, and "racial" diversity), utility of cumulative and recursive cumulative meta-analyses. Gene-gene-environment interactions and methodological challenges of genome-wide association studies are discussed.

Results: Departures from Hardy-Weinberg equilibrium can be handled using sensitivity analysis or correction procedures. A spectrum of genetic models should be investigated in the absence of biological justification. Cumulative and recursive cumulative meta-analyses are useful to explore heterogeneity in risk effect in time. Exploration of bias leading to heterogeneity provides insight to postulated genetic effects. In the presence of bias, results should be interpreted with caution.

Conclusions: Meta-analysis provides a robust tool to investigate contradictory results in genetic association studies by estimating population-wide effects of genetic risk factors in diseases and explaining sources of bias and heterogeneity.

Publication types

  • Review

MeSH terms

  • Epistasis, Genetic
  • Genetic Diseases, Inborn*
  • Genetic Markers
  • Genetic Predisposition to Disease
  • Humans
  • Meta-Analysis as Topic
  • Models, Genetic*
  • Models, Statistical*

Substances

  • Genetic Markers