Combining Multiple Family-Based Association Studies
Author Information
Author(s): Tang Hua, Peng Jie, Wang Pei, Coram Marc, Hsu Li
Primary Institution: Stanford University School of Medicine
Hypothesis
Can combining data from multiple studies improve the statistical power of genome-wide association studies?
Conclusion
The proposed method allows for improved statistical power in genome-wide association studies by combining data from multiple studies.
Supporting Evidence
- The study demonstrated that TDT statistics at tightly linked markers are correlated.
- Combining data sets with non-overlapping markers can improve statistical power.
- Results indicated possible gene × sex interaction in rheumatoid arthritis transmission.
Takeaway
This study shows that by using data from different studies together, researchers can find genetic links to diseases more easily, even when each study has a small number of participants.
Methodology
The study used a multipoint transmission-disequilibrium test (TDT) method to analyze simulated rheumatoid arthritis data from multiple studies.
Potential Biases
Potential biases may arise from population stratification and differences in allele frequencies among studies.
Limitations
The method may incur a loss of power compared to pooled TDT with complete marker data, and combining studies with different genetic factors may not improve power.
Participant Demographics
The study involved 1500 nuclear families with both parents and two affected children genotyped.
Statistical Information
P-Value
0.0011, 0.02, 0.61
Statistical Significance
p<0.05
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