Computing Individual Risks based on Family History in Genetic Disease in the Presence of Competing Risks
G Nuel (UPMC, LPMA), Antoine Lefebvre (UPMC, LPMA), O Bouaziz (MAP5)

TL;DR
This paper introduces a Bayesian network-based method to compute individual genetic disease risks from family history, accounting for competing risks like death, with application to breast cancer risk modeling.
Contribution
It presents a novel approach combining belief propagation and survival analysis to estimate time-dependent genotype posterior distributions and risks.
Findings
Effective computation of individual risks using Bayesian networks.
Incorporation of competing risks in genetic risk assessment.
Application to breast cancer risk modeling with real data.
Abstract
When considering a genetic disease with variable age at onset (ex: diabetes , familial amyloid neuropathy, cancers, etc.), computing the individual risk of the disease based on family history (FH) is of critical interest both for clinicians and patients. Such a risk is very challenging to compute because: 1) the genotype X of the individual of interest is in general unknown; 2) the posterior distribution P(X|FH, T > t) changes with t (T is the age at disease onset for the targeted individual); 3) the competing risk of death is not negligible. In this work, we present a modeling of this problem using a Bayesian network mixed with (right-censored) survival outcomes where hazard rates only depend on the genotype of each individual. We explain how belief propagation can be used to obtain posterior distribution of genotypes given the FH, and how to obtain a time-dependent posterior hazard…
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Taxonomy
TopicsGenetics, Bioinformatics, and Biomedical Research
