Bayesian Approach to Gene Mapping
Author Information
Author(s): Michael D Swartz, Sanjay Shete
Primary Institution: University of Texas M. D. Anderson Cancer Center
Hypothesis
How much impact do prior values have on a Bayesian method when analyzing a null region of the chromosome?
Conclusion
The study suggests using a prior probability of inclusion of 0.25 to control for false positives in null regions.
Supporting Evidence
- Using a prior probability of inclusion of 0.5 detected 3 of 59 alleles as significant from Replicate 1.
- Using a prior probability of inclusion of 0.25 reduced the false-positive rate to 1%.
- SSGS without including a dependence structure indicated all alleles at dense microsatellite locus 2.
Takeaway
This study looked at how to find genes related to diseases using a special method, and it found that using certain numbers can help avoid mistakes when there are no genes to find.
Methodology
The study used stochastic search gene suggestion (SSGS) combined with hierarchical priors and conditional logistic regression to analyze genetic data.
Limitations
The study's findings may not apply to regions with high linkage disequilibrium.
Participant Demographics
Families from Replicates 1 and 2 of Problem 3 were analyzed.
Statistical Information
P-Value
0.009
Statistical Significance
p<0.05
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