A Bayesian Method for Evaluating and Discovering Disease Loci Associations
Author Information
Author(s): Jiang Xia, Barmada M. Michael, Cooper Gregory F., Becich Michael J.
Primary Institution: University of Pittsburgh
Hypothesis
Can a Bayesian network posterior probability (BNPP) method effectively evaluate and discover disease loci associations in genome-wide association studies?
Conclusion
The BNPP method provides a way to compute the posterior probability of complex multi-locus hypotheses and is effective for discovering disease loci associations.
Supporting Evidence
- The BNPP method outperformed traditional p-value methods in evaluating and discovering disease loci.
- Results confirmed previous findings in the literature and identified new associations.
- The method effectively handles complex multi-locus hypotheses.
Takeaway
This study introduces a new method to help scientists find connections between genes and diseases by looking at many genes at once, rather than just one at a time.
Methodology
The study developed a Bayesian network posterior probability (BNPP) method to compute the posterior probability of multi-locus models using directed acyclic graphs.
Limitations
The assessment of prior probabilities can be challenging and may affect the results.
Participant Demographics
The study involved 1411 participants, including 861 with late-onset Alzheimer's disease and 550 without.
Digital Object Identifier (DOI)
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