Joint Analysis for Genome-Wide Association Studies in Family-Based Designs
Author Information
Author(s): Sha Qiuying, Zhang Zhaogong, Zhang Shuanglin
Primary Institution: Department of Mathematical Sciences, Michigan Technological University
Hypothesis
Can we do joint analysis in FTSA as Skol et al. did for the two-stage approach in case-control studies?
Conclusion
The proposed joint analysis approach is robust to population stratification and is consistently more powerful than FTSA.
Supporting Evidence
- The joint analysis was consistently more powerful than FTSA and AFTSA across different simulation sets.
- Using the admixture screening test instead of traditional screening tests increased power in the presence of population stratification.
- The proposed method is robust to population stratification.
Takeaway
This study created a new way to analyze genetic data from families that helps find genetic links to diseases more accurately.
Methodology
The study used simulation studies to compare the performance of the joint analysis with FTSA and AFTSA under various population structures.
Potential Biases
The screening test may be subject to bias caused by population stratification.
Limitations
The PC approach may not be as strongly resistant to stratification bias as other methods.
Participant Demographics
The study involved nuclear families with children, considering different population structures including homogeneous, structured, and admixture populations.
Digital Object Identifier (DOI)
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