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

Accurate Estimation of Quantitative Trait Locus Effects with Epistatic by Improved Variational Linear Regression