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Abstract: quantitative trait loci (QTL) and their additive, dominance and epistatic effects play a critical role in complex trait variation. It is often infeasible to detect multiple interacting QTL due to main effects often being confounded by interaction effects. Positioning interacting QTL within a small region is even more difficult. We present a variance component approach nested in an empirical Bayesian method, which simultaneously takes into account additive, dominance and epistatic effects due to multiple interacting QTL. The covariance structure used in the variance component approach is based on combined linkage disequilibrium and linkage (LDL) information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously fine map interacting QTL using the proposed approach. The present method combined with LDL information can efficiently detect QTL and their dominance and epistatic effects, making it possible to simultaneously fine map main and epistatic QTL.

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