Identifying Susceptibility Genes Using Joint Tests
Author Information
Author(s): Joshua Millstein, Kimberly D. Siegmund, David V. Conti, W. James Gauderman
Primary Institution: National Oceanic and Atmospheric Administration/National Marine Fisheries Service, Alaska Fisheries Science Center
Hypothesis
Can joint tests of association and linkage effectively identify susceptibility genes while accounting for gene interactions?
Conclusion
The study successfully identified four disease loci through a novel method that accounts for interactions while maintaining power for detecting main effects.
Supporting Evidence
- The study identified significant joint effects of linkage and association in four disease regions.
- A highly significant p-value was observed for SNP B03T3056.
- The method controlled the false discovery rate at a significance level of 0.05.
Takeaway
The researchers looked for genes that might cause diseases by checking how different genes work together, and they found some important ones.
Methodology
The study used a candidate gene approach analyzing SNP data from affected sib-parent nuclear families and performed likelihood ratio tests for association and linkage.
Potential Biases
Potential biases due to population stratification and the assumptions of the model regarding risk patterns.
Limitations
The study faced challenges in detecting multilocus effects due to low power and the complexity of interactions across populations.
Participant Demographics
Participants included affected sib-pair-parent nuclear families from three populations: Aipotu, Kaarangar, and Danacaa.
Statistical Information
P-Value
1.98 × 10-34
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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