Semi-Parametric Survival Estimation for pedigrees

TL;DR

This paper introduces a semi-parametric method for estimating survival functions in pedigrees, accounting for ungenotyped individuals and ascertainment bias, with applications to hereditary diseases like amyloidosis.

Contribution

It presents a novel semi-parametric approach combining belief propagation and EM algorithms for survival estimation in pedigrees with partial genotyping.

Findings

Method effectively estimates survival functions in simulated data.

Application to hereditary amyloidosis demonstrates practical utility.

Corrects for ascertainment bias using phenotypic data from relatives.

Abstract

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.