Comparison of Linkage and Association Strategies for Quantitative Traits
Author Information
Author(s): Matthew B McQueen, Amy Murphy, Peter Kraft, Jessica Su, Ross Lazarus, Nan M Laird, Christoph Lange, Kristel Van Steen
Primary Institution: Harvard School of Public Health
Hypothesis
Will denser SNP genome scans provide better power to uncover genes of modest effect size compared to traditional methods?
Conclusion
The study found that while linkage and association analyses provided different results, both strategies may be useful in high-density genome-wide scans.
Supporting Evidence
- The study used a dataset with 11,555 SNPs and quantitative trait information.
- Linkage analysis resulted in a LOD score of 3.55 for the ECB21 phenotype.
- The strongest association was found for SNP TSC0053776 with a p-value of 0.0011.
Takeaway
Researchers looked at how two different methods for studying genes worked on the same data, and found that they sometimes gave different answers.
Methodology
The study used PBAT for association analysis and MERLIN for linkage analysis on SNP data from the COGA dataset.
Potential Biases
Potential for false positives in association results due to chance.
Limitations
The analysis was not optimally performed, which may have affected the linkage signal.
Participant Demographics
Approximately 1,614 subjects from 143 families.
Statistical Information
P-Value
0.00003
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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