The development of good statistical methodologies is one of the key elements of successful QTL mapping. Particularly, innovative and complex multi-parent mapping populations (MPPs), drive the need to develop suitable statistical tools for QTL mapping. The popularity and success of using MPPs for genetic studies can be explained because they often provide a bridge between linkage mapping and association mapping approaches. However, the difficulty in QTL mapping for MPP designs is dealing with the multitude of design options and the corresponding genetic architectures. This project aims to develop statistical models for the QTL mapping in a wide range of MPPs, using identity-by-descent (IBD) probabilities as genetic predictors in the mixed model approach to estimate genetic variance and effects. This generic model approach can be extended to more advanced models to unravel epistasis and QTL-by-environment interaction (QEI) in multi-cross type MPPs and multi-environment MPP (MET-MPP) designs. The statistical models and tools developed in this project will be applied to and verified by the analysis of a wide range of simulated and empirical MPP designs including nested association mapping (NAM), diallel, multi-parent advanced generation inter-cross (MAGIC) populations, and other sophisticated MPPs with known pedigree information in diverse species like maize, cowpea, soybean, barley, tomato, cucumber, and Arabidopsis.