Although they are usually considered as highly similar entities, the methodological foundation, preferred phenotypes and the genotypic representations used in HBCGM and conventional murine GWAS are quite distinct (Table 2). In a murine GWAS, marker SNPs are selected to represent the pattern of genetic variation across the genome, which are utilized to identify the causative genetic loci for measured phenotypic differences in an inbred strain panel. The poor performance (low statistical power for detection) of murine GWAS in several simulations [2, 3] is partly attributable to the fact that the pattern of genetic variation within a genomic region of an arbitrarily determined size (usually 20–60 kb) is represented by a selected SNP. The selected SNP is unlikely to be the causative factor for the analyzed trait, and it may not even be in linkage disequilibrium (LD) with a potentially causative SNP in that region. The use of selected SNPs to cover an arbitrarily sized genomic region does not produce a robust genetic map [9], and some of the problems with genetic association studies result from the incorrect representation of the
