Metabogenomics reveals four candidate regions involved in the pathophysiology of Equine Metabolic Syndrome.

Published: 29-05-2020| Version 1 | DOI: 10.17632/zs5h47xsxc.1
Laura Patterson Rosa


An analogous condition to human metabolic syndrome, Equine Metabolic Syndrome (EMS) is defined by several clinical signs including obesity, hyperinsulinemia, and peripheral insulin dysregulation (ID). Affected horses may also exhibit hypertension, hyperlipemia and systemic inflammation. Measures of ID typically comprise the gold-standard for diagnosis in veterinary care. Yet, the dynamic nature of insulin homeostasis and complex procedures of typical assays make accurate quantification of ID and EMS challenging. This work aimed to investigate new strategies for identification of biochemical markers and correlated genes in EMS. To quantify EMS risk within this population, we utilized a composite score derived from nine common diagnostic variables. We applied a global liquid chromatography/mass spectroscopy approach (HPLC/MS) to whole plasma collected from 49 Arabian horses, resulting in 3392 high-confidence features and identification of putative metabolites in public databases. We performed a genome wide association analysis with genotypes from the 670k Affymetrix Equine SNP array utilizing EMS-correlated metabolites as phenotypes. We discovered four metabolite features significantly correlated with EMS score (P < 1.474e-5). GWAs for these features results (P = 6.787 x 10−7, Bonferroni) identified four unique candidate regions (r2 > 0.4) containing 63 genes. Significant genomic markers capture 43.52% of the variation in the original EMS score phenotype. The identified genomic loci provide insight into the pathways controlling variation in EMS and the origin of genetic predisposition to the condition. Rapid, feasible and accurate diagnostic tools derived from metabogenomics can be translated into measurable benefits in the timeline and quality of preventative management practices to preserve health in horses. Here we have submitted the raw genotypes (Plink files) for the 49 Arabian horses included in the Metabogenomics study.