 ridge regression but t-distribution prior (rather than Gaussian) 
for the marker effects ; variance comes from an inverse-chi-square distribution instead of being fixed. Estimation 
via Gibbs sampling.
Bayes C: uses a “rounded spike” (low-variance Gaussian) at origin many small 
effects can contribute to polygenic component, reduces the dimensionality of 
the model (makes Gibbs sampling feasible).
Bayes R: Hierarchical Bayesian mixture model with 4 Gaussian components, with 
variances scaled by 0, 0.00