(1) Risk prediction by kernels and (2) Ranking SNPs

Usman Roshan

Disease risk prediction

• Can we better predict disease risk with non-linear kernels?

• What value of the regularization parameter C should one pick?

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

Type 1 diabetes (linear kernel)

Type 1 diabetes (polynomial degree 2 kernel)

Type 1 diabetes (both kernels)

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

Ranking SNPs

• Chi-square• Similar to other univariate statistics

– "Ranking SNPs by different tests"

• Can we rank with multivariate methods?• Rank SNPs for population structure using

PCA– PCA correlated SNPs

• Can we rank with the SVM discriminant? To answer this we have to do simulation.

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)

Rank of causal SNPs

Rank of causal SNPs

Rank of causal SNPs (real data)

Risk prediction on simulated data

Risk prediction on simulated data

Risk prediction on simulated data