1. (1) Risk prediction by kernels and (2) Ranking SNPs Usman Roshan

2. Disease risk prediction Can we better predict disease risk with non-linear kernels? What value of the regularization parameter C should one pick?

3. Experimental design WTCCC type 1 diabetes GWAS Select 90% of case and controls as training and remaining as test Learn SVM on training and predict on test

4. Type 1 diabetes (linear kernel)

5. Type 1 diabetes (polynomial degree 2 kernel)

6. Type 1 diabetes (both kernels)

7. Kernels Are there kernels that can predict risk better than the linear model? –Previous kernels in bioinformatics for sequence data Can we combine kernels or automatically learn kernels? –Multiple kernel learning

8. Ranking SNPs Chi-square Similar to other univariate statistics –"Ranking SNPs by different tests""Ranking SNPs by different tests" Can we rank with multivariate methods? Rank SNPs for population structure using PCA –PCA correlated SNPsPCA correlated SNPs Can we rank with the SVM discriminant? To answer this we have to do simulation.

9. Ranking SNPs Experimental design: –Simulate GWAS with known causal SNPs –Rank SNPs in simulated data and examine number of causal variants in top ranked SNPs –Bonferroni correction: 0.05/(number of SNPs)

10. Rank of causal SNPs

12. Rank of causal SNPs (real data)

13. Risk prediction on simulated data