Robustness of inference on measured covariates to misspecification of genetic random effects in family studies

Ruth M. Pfeiffer, Allan Hildesheim, Mitchell H. Gail, David Pee, Chien Jen Chen, Alisa M. Goldstein, Scott R. Diehl

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Family studies to identify disease-related genes frequently collect only families with multiple cases. It is often desirable to determine if risk factors that are known to influence disease risk in the general population also play a role in the study families. If so, these factors should be incorporated into the genetic analysis to control for confounding. Pfeiffer et al. [2001 Biometrika 88: 933-948] proposed a variance components or random effects model to account for common familial effects and for different genetic correlations among family members. After adjusting for ascertainment, they found maximum likelihood estimates of the measured exposure effects. Although it is appealing that this model accounts for genetic correlations as well as for the ascertainment of families, in order to perform an analysis one needs to specify the distribution of random genetic effects. The current work investigates the robustness of the proposed model with respect to various misspecifications of genetic random effects in simulations. When the true underlying genetic mechanism is polygenic with a small dominant component, or Mendelian with low allele frequency and penetrance, the effects of misspecification on the estimation of fixed effects in the model are negligible. The model is applied to data from a family study on nasopharyngeal carcinoma in Taiwan.

Original languageEnglish (US)
Pages (from-to)14-23
Number of pages10
JournalGenetic Epidemiology
Issue number1
StatePublished - Jan 2003

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)


  • Ascertainment
  • Conditional logistic regression
  • Correlated binary data
  • Misspecified model
  • Nested random effects model


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