Two-level Bayesian interaction analysis for survival data incorporating pathway information.
Biometrics
; 79(3): 1761-1774, 2023 09.
Article
em En
| MEDLINE
| ID: mdl-36524727
ABSTRACT
Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single-level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower-level gene-gene interactions and higher-level pathway-pathway interactions simultaneously. Significantly advancing from the existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with effective priors to accommodate two-level selection as well as censoring. Its computational efficiency is much desirable for high-dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. The application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.
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ECOS
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Bases de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China