PedGenie: meta genetic association testing in mixed family and case-control designs

BMC Bioinformatics. 2007 Nov 15:8:448. doi: 10.1186/1471-2105-8-448.


Background: PedGenie software, introduced in 2006, includes genetic association testing of cases and controls that may be independent or related (nuclear families or extended pedigrees) or mixtures thereof using Monte Carlo significance testing. Our aim is to demonstrate that PedGenie, a unique and flexible analysis tool freely available in Genie 2.4 software, is significantly enhanced by incorporating meta statistics for detecting genetic association with disease using data across multiple study groups.

Methods: Meta statistics (chi-squared tests, odds ratios, and confidence intervals) were calculated using formal Cochran-Mantel-Haenszel techniques. Simulated data from unrelated individuals and individuals in families were used to illustrate meta tests and their empirically-derived p-values and confidence intervals are accurate, precise, and for independent designs match those provided by standard statistical software.

Results: PedGenie yields accurate Monte Carlo p-values for meta analysis of data across multiple studies, based on validation testing using pedigree, nuclear family, and case-control data simulated under both the null and alternative hypotheses of a genotype-phenotype association.

Conclusion: PedGenie allows valid combined analysis of data from mixtures of pedigree-based and case-control resources. Added meta capabilities provide new avenues for association analysis, including pedigree resources from large consortia and multi-center studies.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Case-Control Studies*
  • Chi-Square Distribution
  • Confidence Intervals
  • Family
  • Genetic Linkage
  • Genetic Testing / methods*
  • Genetic Testing / statistics & numerical data
  • Genotype
  • Humans
  • Likelihood Functions
  • Meta-Analysis as Topic*
  • Models, Genetic
  • Odds Ratio
  • Pedigree*
  • Phenotype
  • Predictive Value of Tests
  • Research Design
  • Software*