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Life after the screen: making sense of many P-values
S Schmidt1, Y Shao, E R Hauser
1Center for Human Genetics, Duke University Medical Center, Box 3468, Durham, North Carolina 27710, USA.
Genetic Epidemiology
|January 17, 2002
Summary
Combining multiple analytical methods improves complex trait analysis by integrating complementary genome data. This approach enhances the identification of key regions for further study and reduces false positives in genetic linkage analysis.
Area of Science:
- Genetics
- Statistical genetics
- Bioinformatics
Background:
- Complex traits require sophisticated analytical methods for genome screening.
- Different analytical approaches yield complementary information from genetic data.
- Genome screening on extended pedigrees is crucial for understanding heritability.
Purpose of the Study:
- To evaluate the utility of combining p-values from multiple analytical methods and adjacent markers.
- To assess the effectiveness of the truncated product method for complex trait analysis.
- To improve the visualization of priority regions and reduce false-positive linkage signals.
Main Methods:
- Utilized a multiple analytic approach for genome screen data.
- Employed the truncated product method for combining p-values.
- Accounted for the correlation structure among p-values across methods and markers.
Main Results:
- The combined p-value approach aids in prioritizing regions for follow-up genetic analysis.
- This method effectively reduces the number of false-positive linkage signals.
- Complementary information from different methods enhances the overall analysis.
Conclusions:
- Combining analytical methods and p-values is beneficial for complex trait genome screening.
- The truncated product method offers a robust strategy for integrating genetic data.
- This approach enhances the accuracy and efficiency of linkage analysis in genetic studies.