Selecting SNPs for Two-Stage Genome-Wide Association Studies
Author Information
Author(s): Li Jing, Heather J Cordell, Mariza de Andrade, Marie-Claude Babron, Christopher W Bartlett, Joseph Beyene, Heike Bickeböller, Robert Culverhouse, Adrienne Cupples, E Warwick Daw, Josée Dupuis, Catherine T Falk, Saurabh Ghosh, Katrina A Goddard, Ellen L Goode, Elizabeth R Hauser, Lisa J Martin, Maria Martinez, Kari E North, Nancy L Saccone, Silke Schmidt, William Tapper, Duncan Thomas, David Tritchler, Veronica J Vieland, Ellen M Wijsman, Marsha A Wilcox, John S Witte, Qiong Yang, Andreas Ziegler, Laura Almasy, Jean W MacCluer
Primary Institution: Case Western Reserve University
Hypothesis
Can a two-stage design effectively reduce the number of SNPs needed for genome-wide association studies without sacrificing mapping precision?
Conclusion
The proposed method can significantly reduce the number of SNPs required in later stages of analysis while maintaining mapping precision.
Supporting Evidence
- The method can reduce the number of SNPs needed in later stages by half.
- Clustering nearby SNPs that are in high linkage disequilibrium can significantly lower genotyping costs.
- Results indicate that the two-stage design with clustering performs similarly or better than traditional methods.
Takeaway
This study shows a way to test fewer genetic markers in research about diseases, which saves money and time while still getting good results.
Methodology
A clustering algorithm was applied to select and prioritize SNPs based on their associations and correlations in a two-stage design.
Limitations
The method may not detect signals from rare alleles with small sample sizes.
Participant Demographics
1500 cases and 2000 controls were tested, with cases selected from affected families and controls from normal families.
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