 Solution

Introduce independent component indicators $z_i\mid q\sim \mathrm{Bernoulli}(q)$, and auxiliary slab effects $u_i\sim N(0,V)$, independent of the indicators. Set ${\theta }_i=z_i{u}_i$. Then inactive genes have exactly zero effect, while active genes have the stipulated normal distribution. With $V>0$ supplied as known data and elicited positive beta shapes, one suitable WinBUGS model isThe normal arguments are precisions, not variances. Supply all observed y values, monitor q, theta and optionally z, and assess Markov chain Monte Carlo convergence diagnostics. The proper auxiliary prior remains defined for inactive genes, where $u_i$ is not informed by the likelihood. This represents the exact point-null mixture prior, rather than replacing its point mass by a narrow continuous spike. Conditional on the indicators, $q\mid z$ has beta distribution $\mathrm{Beta}(a_q+{\sum }_i{z}_i,b_q+N-{\sum }_i{z}_i)$, providing a useful check on the update.