Incorporating genetic marker information into the analysis of twin survival data: a simulation study

Iachine, I. A., Christensen, K., Yashin, A. I.
Research Report of the Department of Statistics and Demography 26
Odense, Odense University (1999)


Traditional methods of linkage analysis of sib-pair data for quantitative traits are not always appropriate for the analysis of survival data because of censoring and truncation conditions. In contrast, methods of survival analysis can deal with censoring and truncation but lack the tools required to analyze genetic marker data. In this paper we suggest a method for linkage analysis of sib-pair survival data with genetic marker information that combines both approaches. It is based on a correlated frailty model which includes the effect of a major gene that may be in genetic linkage with one or more genetic markers. The model includes additional frailty components associated with effects of polygenes, shared and non-shared environment and allows for semiparametric estimation of model's characteristics. We study the properties of the estimates of the recombination distance using simulated data on survival times and genetic markers. In particular, we investigate the sample size required for detecting linkage under different censoring conditions, levels of relative risks associated with the major gene, and frailty effects.
The Max Planck Institute for Demographic Research (MPIDR) in Rostock is one of the leading demographic research centers in the world. It's part of the Max Planck Society, the internationally renowned German research society.