Impact of SNP Density on Trait Detection in Genetic Studies
Author Information
Author(s): Alison P Klein, Ya-Yu Tsai, Priya Duggal, Elizabeth M Gillanders, Michael Barnhart, Rasika A Mathias, Ian P Dusenberry, Amy Turiff, Peter S Chines, Janet Goldstein, Robert Wojciechowski, Wayne Hening, Elizabeth W Pugh, Joan E Bailey-Wilson
Primary Institution: Inherited Disease Research Branch, NHGRI/NIH, Baltimore, MD, USA
Hypothesis
How does altering the density of SNP marker sets impact the power to detect trait loci and the frequency of false positives in linkage analyses?
Conclusion
Higher SNP map density increases information content but does not consistently improve the ability to detect trait loci.
Supporting Evidence
- Information content increased with higher SNP map density.
- Power to detect trait loci showed modest improvement with denser SNP maps.
- False positive results were similar across different SNP densities in simulated data.
- More false positives were observed in the COGA dataset with denser SNP maps.
- LD between markers may lead to increased false positives.
- Power was dependent on disease prevalence for the traits studied.
Takeaway
Using more SNP markers can help find genetic traits better, but sometimes it can also lead to mistakes in results.
Methodology
The study used simulated and COGA datasets to analyze SNP density effects on trait detection and false positives.
Potential Biases
Potential bias due to missing parental data and the assumption of linkage equilibrium.
Limitations
The presence of linkage disequilibrium may lead to false positives, and results may vary based on disease prevalence.
Participant Demographics
Analysis was limited to white/non-Hispanic families in the COGA dataset.
Statistical Information
P-Value
0.000049
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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