  Bayesian approaches to variable selection have been widely used for
quantitative trait locus (QTL) mapping. The Markov chain Monte Carlo (MCMC)
algorithms for that aim are often difficult to be implemented for
high-dimensional variable selection problems, such as the ones arising in
epistatic analysis. Variational approximation is an alternative to MCMC, and
variational linear regression (VLR) is an effective solution for the variable
selection problems, but lacks accuracy in some QTL mapping problems where there
are many more variables than samples. In this paper, we propose an effective
method with aim to improve the accuracy of VLR in the case of above by
dynamically reducing components (variable or markers) with known effects (zero
or fixed). We show that the proposed method can greatly improve the accuracy of
VLR with little increase in computational cost. The method is compared with
several other variational methods used for QTL mapping, and simulation results
show that its performance is higher than those methods when applied in
high-dimensional cases.
