New approaches are needed to explore the different ways in which genes affect the human life span. One needs to assess the genetic effects themselves, as well as gene-environment interactions and sex dependency. In this paper, we present a new model that combines both genotypic and demographic information in the estimation of the genetic influence on life spans. Based on Cox's proportional hazard assumption, the model measures the risks for each gene as well as for gene-environment and gene-sex interactions, while controlling for confounding factors. A two-step MLE is introduced to obtain a non-parametric form of the baseline hazard function. The model is applied to genotypic data from Italian centenarian studies to estimate relative risks of candidate genes, risks due to interactions and initial frequencies of different genes in the population. Results from models that either do or do not take into consideration individual heterogeneity are compared. It is shown that ignoring the existence of heterogeneity can lead to a systematic underestimation of genetic effects and effects due to interactions.