Joint Modeling of Linkage and Association Using Affected Sib-Pair Data
Author Information
Author(s): Chen Ming-Huei, Cui Jing, Guo Chao-Yu, Cupples L Adrienne, Van Eerdewegh Paul, Dupuis Josée, Yang Qiong
Primary Institution: Boston University
Hypothesis
Can joint modeling of linkage and association effectively identify SNPs that explain linkage signals for rheumatoid arthritis?
Conclusion
The study found that TDT is the most powerful method for testing association in the presence of linkage, while HST and LAMP are more powerful for testing whether a SNP explains all the linkage evidence.
Supporting Evidence
- TDT was found to be the most powerful method for testing association in the presence of linkage.
- HST and LAMP were more powerful than Millstein-a for testing whether a SNP explains all the linkage evidence.
- The study compared several existing methods and proposed a new method called HSTDT.
Takeaway
Researchers looked at different ways to find genetic markers that might explain why some people get rheumatoid arthritis, and they found that some methods work better than others.
Methodology
The study used 100 replicates of simulated affected sib-pair data to compare various methods for detecting linkage and association.
Potential Biases
The study noted slightly inflated type I error rates for HSTDT due to high LOD scores.
Limitations
The findings may not apply to study designs other than nuclear families with two affected children and parental genotypes available.
Participant Demographics
The study focused on nuclear families with two affected children.
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