sk)
- handles different genetic architectures (few large, many small effects)
- scale to large data (e.g. sparse LD)

In the Bayesian multiple regression model the posterior density of the 
model parameters depend on the likelihood of the data given 
the parameters and a prior probability for the model parameters
The prior density of marker effects defines whether the model will 
induce variable selection and shrinkage or shrinkage only. 
Also, the choice of prior will define the extent and type of shrinkag