Identifying Genes for a Complex Disease Using a Two-Stage Approach
Author Information
Author(s): Pankratz Nathan, 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: Indiana University
Hypothesis
Can a two-stage classification approach identify susceptibility genes for a complex disease using simulated data?
Conclusion
The study successfully identified additional susceptibility loci by using a two-stage classification approach.
Supporting Evidence
- Significant association with the DR locus was found for affection status.
- Two additional loci were identified through a genome-wide association analysis.
- Transformation of skewed trait distributions improved the power to detect linkage.
- Logistic regression and multifactor dimensionality reduction performed comparably.
- Prediction accuracy improved from 86% to 89% after two rounds of genetic analyses.
Takeaway
Researchers used a special method to find genes that might cause a disease by looking at fake data, and they found some new genes that could be important.
Methodology
The study used simulated data to perform linkage analysis and association studies without prior knowledge of the underlying model.
Potential Biases
The absence of linkage disequilibrium for some SNPs may have led to false negatives.
Limitations
The study relied on simulated data, which may not fully represent real-world complexities.
Statistical Information
P-Value
p = 10-6 and p = 6 × 10-16
Statistical Significance
p<0.05
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