  Mendelian diseases are determined by a single mutation in a given gene.
However, in the case of diseases with late onset, the age at onset is variable;
it can even be the case that the onset is not observed in a lifetime.
Estimating the survival function of the mutation carriers and the effect of
modifying factors such as the sex, mutation, origin, etc, is a task of
importance, both for management of mutation carriers and for prevention. In
this work, we present a semi-parametric method based on a proportional to
estimate the survival function using pedigrees ascertained through affected
individuals (probands). Not all members of the pedigree need to be genotyped.
The ascertainment bias is corrected by using only the phenotypic information
from the relatives of the proband, and not of the proband himself. The method
manage ungenotyped individuals through belief propagation in Bayesian networks
and uses an EM algorithm to compute a Kaplan-Meier estimator of the survival
function. The method is illustrated on simulated data and on a samples of
families with transthyretin-related hereditary amyloidosis, a rare autosomal
dominant disease with highly variable age of onset.
