An IBD-based mixed model approach for QTL mapping in multiparental populations

Abstract Multiparental populations (MPPs) have become popular for QTL detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercrosses (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach. The estimated parameters are random allelic effects associated with the parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the IBD-based mixed model approach could increase true positive rates and mapping resolutions of QTLs in comparison to a widely used benchmark association mapping method. Successful analyses of various real data cases confirmed the wide applicability of our IBD-based mixed model methodology.