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Using Bayesian networks to discover relations between genes, environment, and disease
Chengwei Su1, Angeline Andrew, Margaret R Karagas
1Thayer School of Engineering, Dartmouth College, Hanover, NH, USA. mark.borsuk@dartmouth.edu.
Biodata Mining
|March 22, 2013
Summary
Bayesian networks (BNs) can model complex gene-environment-disease links. Algorithms effectively uncover genetic and environmental factors, even with missing data, aiding disease research.
Area of Science:
- Computational biology
- Genetics
- Epidemiology
Background:
- Bayesian networks (BNs) offer a graphical framework for complex systems.
- Understanding gene-environment interactions is crucial for disease etiology.
Purpose of the Study:
- To review Bayesian network applicability for discovering gene-environment-disease relationships.
- To explore algorithms for learning network structures from observational data.
Main Methods:
- Description of the Bayesian network approach and learning algorithms.
- Application of BNs to bladder cancer data, including handling missing data via the EM algorithm.
- Exploration of network structures beyond strict causal interpretation.
Main Results:
- BNs provide a comprehensible framework for genetic and environmental disease factors.
- Algorithms show minor performance differences on complete data.
- Handling missing data and non-causal structures enhances discovery of gene-gene and gene-environment interactions.
Conclusions:
- Bayesian networks are powerful for genetic dissection and prognostic models.
- Challenges remain in handling continuous variables, unmeasured factors, and prior knowledge incorporation.
- Robustness evaluation of conclusions is essential.
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