The Use of the Restricted Partition Method with Case-Control Data

BACKGROUND/AIMS

Many diseases important to public health are not due solely to a single mutation or environmental insult. Instead, complex interactions among multiple genes and environmental exposures likely play crucial roles in the etiology of diverse phenotypes from schizophrenia to chemotherapy response. The Restricted Partition Method (RPM) was designed to detect qualitative genetic and environmental factors contributing to a quantitative trait, even if the contribution is predominantly presented as an interaction (displaying little or no signal in univariate analyses). Although the RPM was developed with the expectation that trait values would be drawn from normal distributions, the algorithm will function if the quantitative trait values are replaced with 0's or 1's indicating control or case status. The aim of this study is to evaluate the performance of the RPM on case-control data.

METHODS

Case-control data simulated for this study and data provided to the Pharmcogenetics Research Network Analysis Workshop 2005 were used to assess power and type I error for the RPM in this setting.

RESULTS

For the tested data, the RPM displayed good power and type I error very close to nominal rates.

CONCLUSIONS

The RPM is an appropriate method for the analysis of case-control data.

Exploiting linkage disequilibrium in population isolates

A comparison of haplotype frequencies between unrelated cases and controls from population isolates identifies a strong signal for the presence/absence of the discrete phenotype in an interval on chromosome 6 bounded by markers 34 and 35. We define a dissimilarity index, D, which is sensitive to differences in allele distributions and differences in the patterns of linkage disequilibrium between cases and controls. We describe two statistical methods to utilize D: a method appropriate for a single moderately sized sample and a sequential approach appropriate for multiple small independent samples.

Covariates in linkage analysis

We apply a novel technique to detect significant covariates in linkage analysis using a logistic regression approach. An overall test of linkage is first performed to determine whether there is significant perturbation from the expected 50% sharing under the hypothesis of no linkage; if the overall test is significant, the importance of the individual covariate is assessed. In addition, association analyses were performed. These methods were applied to simulated data from multiple populations, and detected correct marker linkages and associations. No population heterogeneity was detected. These methods have the advantages of using all sib pairs and of providing a formal test for heterogeneity across populations.