  In genetics it is often of interest to discover single nucleotide
polymorphisms (SNPs) that are directly related to a disease, rather than just
being associated with it. Few methods exist, however, addressing this so-called
`true sparsity recovery' issue. In a thorough simulation study, we show that
for moderate or low correlation between predictors, lasso-based methods perform
well at true sparsity recovery, despite not being specifically designed for
this purpose. For large correlations, however, more specialised methods are
needed. Stability selection and direct effect testing perform well in all
situations, including when the correlation is large.
