Genomic structure of nucleotide diversity among Lyon rat models of metabolic syndrome.

BACKGROUND: The metabolic syndrome (MetS), a complex disorder involving hypertension, obesity, dyslipidemia and insulin resistance, is a major risk factor for heart disease, stroke, and diabetes. The Lyon Hypertensive (LH), Lyon Normotensive (LN) and Lyon Low-pressure (LL) rats are inbred strains simultaneously derived from a common outbred Sprague Dawley colony by selection for high, normal, and low blood pressure, respectively. Further studies found that LH is a MetS susceptible strain, while LN is resistant and LL has an intermediate phenotype. Whole genome sequencing determined that, while the strains are phenotypically divergent, they are nearly 98% similar at the nucleotide level. Using the sequence of the three strains, we applied an approach that harnesses the distribution of Observed Strain Differences (OSD), or nucleotide diversity, to distinguish genomic regions of identity-by-descent (IBD) from those with divergent ancestry between the three strains. This information was then used to fine-map QTL identified in a cross between LH and LN rats in order to identify candidate genes causing the phenotypes. RESULTS: We identified haplotypes that, in total, contain at least 95% of the identifiable polymorphisms between the Lyon strains that are likely of differing ancestral origin. By intersecting the identified haplotype blocks with Quantitative Trait Loci (QTL) previously identified in a cross between LH and LN strains, the candidate QTL regions have been narrowed by 78%. Because the genome sequence has been determined, we were further able to identify putative functional variants in genes that are candidates for causing the QTL. CONCLUSIONS: Whole genome sequence analysis between the LH, LN, and LL strains identified the haplotype structure of these three strains and identified candidate genes with sequence variants predicted to affect gene function. This approach, merged with additional integrative genetics approaches, will likely lead to novel mechanisms underlying complex disease and provide new drug targets and therapies.