Survival Analysis Methods in Genetic Epidemiology

Mapping genes for complex human diseases is a challenging problem because many such diseases are the result of both genetic and environmental risk factors. Many also exhibit phenotypic heterogeneity, such as variable age of onset. Information on the variable age of disease onset is often a good indicator for disease heterogeneity. The incorporation of such information together with environmental risk factors in genetic analysis should lead to more powerful tests. Because of the problem of censoring, survival analysis methods have proved to be very useful for genetic analysis. In this paper, I review some recent methodological developments on integrating modern survival analysis methods and human genetics in order to rigorously incorporate both the age of onset and the environmental covariate data into aggregation analysis, segregation analysis, linkage analysis, association analysis, and gene risk characterization. I also briefly discuss the issue of ascertainment correction and survival analysis methods for high-dimensional genomic data. Finally, I outline several areas that need further methodological developments.