Linkage analysis with gene-environment interaction

Model illustration and performance of ordered subset analysis

Silke Schmidt, Mike Schmidt, Xuejun Qin, Eden R Martin, Elizabeth R. Hauser

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The ordered subset analysis (OSA) method allows for the incorporation of covariates into the linkage analysis of a dichotomous disease phenotype in order to reduce genetic heterogeneity. Complex human diseases may involve gene-environment (G × E) interactions, which represent a special form of heterogeneity. Here, we present results of a simulation study to evaluate the performance of OSA when the disease-generating mechanism includes G × E interaction, in the absence of main effects of gene and environment. First, the complex simulation models are illustrated graphically. Second, we show that OSA is underpowered to detect small to moderate interaction effects, consistent with previous evaluations of other linkage analysis methods. When interaction effects are large enough to produce substantial marginal effects, standard linkage methods have sufficient power to detect significant baseline linkage evidence in the entire dataset. The power of OSA to improve upon a high baseline lod score is then strongly dependent on the underlying genetic model, especially the susceptibility allele frequency. If significant, OSA identifies family subsets that are more efficient for follow-up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E) = 10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per-genotype efficiency in the subset is 20-30% greater than in the entire dataset.

Original languageEnglish
Pages (from-to)409-422
Number of pages14
JournalGenetic Epidemiology
Volume30
Issue number5
DOIs
StatePublished - Jul 1 2006
Externally publishedYes

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Gene-Environment Interaction
Genotype
Lod Score
Genetic Heterogeneity
Genetic Models
Gene Frequency
Genes
Phenotype
Datasets

Keywords

  • Complex human disease
  • Genetic heterogeneity
  • Simulation

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Linkage analysis with gene-environment interaction : Model illustration and performance of ordered subset analysis. / Schmidt, Silke; Schmidt, Mike; Qin, Xuejun; Martin, Eden R; Hauser, Elizabeth R.

In: Genetic Epidemiology, Vol. 30, No. 5, 01.07.2006, p. 409-422.

Research output: Contribution to journalArticle

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