Non-parametric estimation of survival in age-dependent genetic disease and application to the transthyretin-related hereditary amyloidosis

Flora Alarcon,Violaine Planté-Bordeneuve,Grégory Nuel,Malin Olsson

doi:10.1371/journal.pone.0203860

Abstract

In genetic diseases with variable age of onset, survival function estimation for the mutation carriers as well as estimation of the modifying factors effects are essential to provide individual risk assessment, both for mutation carriers management and prevention strategies. In practice, this survival function is classically estimated from pedigrees data where most genotypes are unobserved. In this article, we present a unifying Expectation-Maximization (EM) framework combining probabilistic computations in Bayesian networks with standard statistical survival procedures in order to provide mutation carrier survival estimates. The proposed approach allows to obtain previously published parametric estimates (e.g. Weibull survival) as particular cases as well as more general Kaplan-Meier non-parametric estimates, which is the main contribution. Note that covariates can also be taken into account using a proportional hazard model. The whole methodology is both validated on simulated data and applied to family samples with transthyretin-related hereditary amyloidosis (a rare autosomal dominant disease with highly variable age of onset), showing very promising results.

Highlights

In monogenic diseases with variable age of onset, an accurate estimation of the survival function for the mutation carriers is essential
In the context of genetic diseases with variable age of onset, geneticists usually focus on the penetrance function, that is the cumulative risk of being affected by a given age for mutation
Since in this paper one aims at exploiting standard statistical survival analysis, we will rather consider the survival function defined by: SðtÞ 1⁄4 Pðthe disease is not diagnosed before age tÞ

Summary

Introduction

In monogenic diseases with variable age of onset, an accurate estimation of the survival function for the mutation carriers is essential. Since potential factors (e.g. genetic or environmental factors) can modify this age of onset, it is important to identify these factors and estimate their effects. These estimations are usually combined into a proportional hazard model that is typically used to provide individual risk assessment as well as to establish prevention strategies. In the context of genetic diseases with variable age of onset, geneticists usually focus on the penetrance function, that is the cumulative risk of being affected by a given age for mutation.

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