We present JAWAMix5, an out-of-core open-source toolkit for association mapping using high-throughput sequence data. Taking advantage of its HDF5-based implementation, JAWAMix5 stores genotype data on disk and accesses them as though stored in main memory. Therefore, it offers a scalable and fast analysis without concerns about memory usage, whatever the size of the dataset. We have implemented eight functions for association studies, including standard methods (linear models, linear mixed models, rare variants test, analysis in nested association mapping design and local variance component analysis), as well as a novel Bayesian local variance component analysis. Application to real data demonstrates that JAWAMix5 is reasonably fast compared with traditional solutions that load the complete dataset into memory, and that the memory usage is efficient regardless of the dataset size.