that many different phenotypes can be analyzed by HBCGM when a larger number of strains are characterized. Since additional strains must be genetically analyzed, the new computational tool was used to examine the impact that incorporating allelic data from an additional strain had on the genetic map. Allelic data derived from whole genome sequencing data obtained from an additional strain resulted in the formation of ~30,000 additional haplotype blocks, which represented 5–6% of the total number of blocks formed. Surprisingly, new genetic variation present in the added strain was responsible for ~50% of the newly formed blocks [14]. Genotyping arrays that characterize known SNP alleles can provide useful information for QTL analysis [13, 29], but ~15,000 additional blocks produced by new sites of genetic variation would not be identified with array-based genotyping data, which only characterizes previously known SNPs. The unique genotypic variants that could be responsible for outlier phenotypic responses would be missed if the allelic data generated by these genotyping arrays was used [14]. We have already demonstrated that whole genome sequencing data can be used to produce comprehensive genetic maps, which can be used for HBCGM studies [14]. Since the pattern of genetic variation across a large
