Using Bayesian Methods to Study Rheumatoid Arthritis Genetics
Author Information
Author(s): Kwon Soonil, Wang Dai, Guo Xiuqing
Primary Institution: Medical Genetics Institute, Cedars-Sinai Medical Center
Hypothesis
Can an iterative Bayesian variable selection method improve the identification of genetic markers associated with rheumatoid arthritis in cases where the number of SNPs greatly exceeds the number of samples?
Conclusion
The iterative Bayesian variable selection method successfully identified genetic associations with rheumatoid arthritis, particularly when the number of SNPs was much larger than the sample size.
Supporting Evidence
- The iterative Bayesian variable selection method was able to identify two trait loci associated with rheumatoid arthritis.
- IBVS performed better than traditional stepwise variable selection when the number of samples was greater than the number of SNPs.
- IBVS was able to identify associations even when the sample size was small compared to the number of SNPs.
Takeaway
Researchers used a smart math method to find genes linked to rheumatoid arthritis, even when they had way more genes than people in their study.
Methodology
The study applied an iterative Bayesian variable selection method to simulated case-control data to identify SNPs associated with rheumatoid arthritis.
Limitations
The method may struggle to identify associations with very rare alleles due to low sample sizes.
Participant Demographics
The study included 1500 nuclear families with affected sibling pairs and 2000 unrelated control subjects.
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