Bayesian Analysis of Genetic Factors in Rheumatoid Arthritis
Author Information
Author(s): Oh Cheongeun, Heather J Cordell, Mariza de Andrade, Marie-Claude Babron, Christopher W Bartlett, Joseph Beyene, Heike Bickeböller, Robert Culverhouse, Adrienne Cupples, E Warwick Daw, Josée Dupuis, Catherine T Falk, Saurabh Ghosh, Katrina A Goddard, Ellen L Goode, Elizabeth R Hauser, Lisa J Martin, Maria Martinez, Kari E North, Nancy L Saccone, Silke Schmidt, William Tapper, Duncan Thomas, David Tritchler, Veronica J Vieland, Ellen M Wijsman, Marsha A Wilcox, John S Witte, Qiong Yang, Andreas Ziegler, Laura Almasy, Jean W MacCluer
Primary Institution: University of Medicine and Dentistry of New Jersey
Hypothesis
Can Bayesian variable selection identify genetic markers linked to rheumatoid factor IgM in rheumatoid arthritis?
Conclusion
The study found strong evidence for main effects on chromosome 6 and marginal evidence for epistatic interactions between chromosomes 6 and 16, 6 and 19, and 6 and 21.
Supporting Evidence
- Bayesian methods allow for the consideration of multiple models and their probabilities.
- Perfect sampling overcomes issues related to MCMC convergence and initialization.
- The study analyzed data from 511 multiplex families to identify genetic markers.
Takeaway
Researchers used a special method to find genes that might cause rheumatoid arthritis by looking at family data. They found important clues on chromosome 6.
Methodology
The study used Bayesian variable selection and perfect sampling to analyze genetic data from rheumatoid arthritis patients.
Limitations
The study may miss important loci that influence traits primarily through epistasis due to its methodology.
Participant Demographics
The study involved 511 multiplex families with rheumatoid arthritis.
Statistical Information
Statistical Significance
p<0.05
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