Journal of Veterinary Internal Medicine · 2019 · 65 citations · 60 references
Life SciencesAnimal SciencesGeneticsEquine MedicineEquine ScienceMedical GeneticsAnimal GeneticsGenome-wide Association StudiesGenotype-phenotype Association AnalysisInsulin ResistanceMetabolic DisordersMetabolic SyndromeCandidate Gene SelectionMetabolic Syndrome SusceptibilityPleiotropic Effects
BACKGROUND: Ponies are highly susceptible to metabolic derangements including hyperinsulinemia, insulin resistance, and adiposity. HYPOTHESIS/OBJECTIVES: Genetic loci affecting height in ponies have pleiotropic effects on metabolic pathways and increase the susceptibility to equine metabolic syndrome (EMS). ANIMALS: Two hundred ninety-four Welsh ponies and 529 horses. METHODS: Retrospective study of horses phenotyped for metabolic traits. Correlations between height and metabolic traits were assessed by Pearson's correlation coefficients. Complementary genome-wide analysis methods were used to identify a region of interest (ROI) for height and metabolic traits, determine the fraction of heritability contributed by the ROI, and identify candidate genes. RESULTS: There was an inverse relationship between height and baseline insulin (-0.26) in ponies. Genomic signature of selection and association analyses for both height and insulin identified the same ~1.3 megabase region on chromosome 6 that contained a shared ancestral haplotype between these traits. The ROI contributed ~40% of the heritability for height and ~20% of the heritability for insulin. High-mobility group AT-hook 2 was identified as a candidate gene, and Sanger sequencing detected a c.83G>A (p.G28E) variant associated with height in Shetland ponies. In our cohort of ponies, the A allele had a frequency of 0.76, was strongly correlated with height (-0.75), and was low to moderately correlated with metabolic traits including: insulin (0.32), insulin after an oral sugar test (0.25), non-esterified fatty acids (0.19), and triglyceride (0.22) concentrations. CONCLUSIONS AND CLINICAL IMPORTANCE: These data have important implications for identifying individuals at risk for EMS.
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