Single nucleotide polymorphisms (SNPs), the most common genetic markers in genome-wide association studies, are usually in linkage disequilibrium (LD) with each other within a small genomic region. Both single- and two-marker-based LD mapping methods have been developed by taking advantage of the LD structures. In this study, a more general LD mapping framework with an arbitrary number of markers has been developed to further improve LD mapping and its detection power. This method is referred as multi-marker linkage disequilibrium mapping (mmLD). For the parameter estimation, we implemented a two-phase estimation procedure: first, haplotype frequencies were estimated for known markers, then, haplotype frequencies were updated to include the unknown quantitative trait loci based on estimates from the first step. For the hypothesis testing, we proposed a novel sequential likelihood ratio test procedure, which iteratively removed haplotypes with zero frequency and subsequently determined the proper degree of freedom. To compare the proposed mmLD method with other existing mapping methods, e.g. the adjusted single-marker LD mapping and the SKAT_C, we performed extensive simulations under various scenarios. The simulation results demonstrated that the mmLD has the same or higher power than the existing methods, while maintaining the correct type I errors. We further applied the mmLD to a public data set, GAW17, to investigate its applicability. The result showed the good performance of mmLD. We concluded that this improved mmLD method will be useful for future genome-wide association studies and genetic association analyses.