Impact of Marker Density on Association Mapping Accuracy
Author Information
Author(s): Zhang Weihua, Lau Winston, Hu Cheng, Kuo Tai-Yue
Primary Institution: The Institute of Cancer Research
Hypothesis
How does marker density affect the accuracy of association mapping?
Conclusion
Decreasing SNP density compromises the accuracy of association mapping, but a multi-marker approach improves prediction accuracy.
Supporting Evidence
- Fourteen out of 126 regions showed nominal significant association with RA.
- Five regions remained significant after Bonferroni correction.
- Excluding rare SNPs significantly decreased the power and accuracy in mapping rare disease loci.
Takeaway
This study shows that using more genetic markers helps scientists find disease-related genes more accurately, but if there are fewer markers, it gets harder to find them.
Methodology
The study analyzed simulated SNP data using the Malecot model and composite likelihood approach on chromosome 6.
Limitations
The study used simulated data, which may not fully represent real-world scenarios.
Participant Demographics
1500 cases with rheumatoid arthritis and 2000 unaffected controls.
Statistical Information
P-Value
<10-12
Statistical Significance
p<0.05
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