Strain survey and genetic analysis of vasoreactivity in mouse aorta.


Understanding the genetic influence on vascular reactivity is important for identifying genes underlying impaired vascular function. The purpose of this study was to characterize the genetic contribution to intrinsic vascular function and to identify loci associated with phenotypic variation in vascular reactivity in mice. Concentration response curves to phenylephrine (PE), potassium chloride (KCl), acetylcholine (ACh), and sodium nitroprusside (SNP) were generated in aortic rings from male mice (12 wk old) from 27 inbred mouse strains. Significant strain-dependent differences were found for both maximal responses and sensitivity for each agent, except for SNP Max (%). Strain differences for maximal responses to ACh, PE, and KCl varied by two- to fivefold. On the basis of these large strain differences, we performed genome-wide association mapping (GWAS) to identify loci associated with variation in responses to these agents. GWAS for responses to ACh identified four significant and 19 suggestive loci. Several suggestive loci for responses to SNP, PE, and KCl (including one significant locus for KCl EC50) were also identified. These results demonstrate that intrinsic endothelial function, and more generally vascular function, is genetically determined and associated with multiple genomic loci. Furthermore, these results are supported by the finding that several genes residing in significant and suggestive loci for responses to ACh were previously identified in rat and/or human quantitative trait loci/GWAS for cardiovascular disease. This study represents the first step toward the unbiased comprehensive discovery of genetic determinants that regulate intrinsic vascular function, particularly endothelial function.

DOI: 10.1152/physiolgenomics.00054.2016

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@article{Kim2016StrainSA, title={Strain survey and genetic analysis of vasoreactivity in mouse aorta.}, author={Seung Kyum Kim and Joshua J. Avila and Michael P Massett}, journal={Physiological genomics}, year={2016}, volume={48 11}, pages={861-873} }