A Bayesian method to estimate variant-induced disease penetrance.
PLoS Genet
; 16(6): e1008862, 2020 06.
Article
en En
| MEDLINE
| ID: mdl-32569262
ABSTRACT
A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Penetrancia
/
Polimorfismo de Nucleótido Simple
/
Síndrome de Brugada
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Canal de Sodio Activado por Voltaje NAV1.5
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Modelos Genéticos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
PLoS Genet
Asunto de la revista:
GENETICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos