Improvements in HBCGM methodology should enable a wider range of biomedical traits to be evaluated. The previous HBCGM algorithm [7] had significant limitations that inhibited our ability to analyze many phenotypes. (1) The genetic map only covered ~15% of the genes in the murine genome. (2) The haplotype block construction algorithm did not allow for overlapping blocks within a region, which enabled a causative block to easily be missed (producing false negative results) if the algorithm selected another block with fewer haplotypes and fewer SNPs that was preferred by the algorithm. (3) All analyses used a single haplotype map that incorporated all available allelic data for all strains, yet phenotypic data was usually available for a subset of the strains in a typical mapping experiment. Inclusion of irrelevant alleles can disrupt haplotypic patterns that are uniform among the strains of interest. To overcome these problems, a ‘next-generation’ computational genetic mapping program with three advanced features was developed [14]. (1) By merging two large SNP datasets, which included SNPs generated from analysis of the complete genomic sequence of multiple inbred strains
