Once a candidate locus has been identified as interesting from a GWAS, haplotypes can be helpful to fine-map association signals. Further, understanding specific haplotypes that are most significant can help design sequencing studies and direct investigators towards underlying susceptibility variants. We provide hapcLite, a version of hapConstructor, which uses standard distribution theory to assess significance within the iterative haplotype building. This version is only appropriate when the design does *not* include family-based data. HapcLite automatically and iteratively builds and tests multi-locus SNP sets in a case-control framework, using standard chi-squared testing at each step. It begins with single SNPs, and expands these in a search to find SNP-haplotypes that optimize the association signal. Multi-SNP sets considered at any step in the process need not be contiguous; the SNP sets are built based on the significance of the preceding steps’ SNP subsets. It is a useful tool for exploring multi-locus associations in candidate genes and regions using a structured process.
We note that when the data include individuals from families, a Monte Carlo framework is necessary and the original hapConstructor analysis should be used.