Common protein-coding variants influence the racing phenotype in galloping racehorse breeds

Haige Han, Beatrice A. McGivney, Lucy Allen, Dongyi Bai, Leanne R. Corduff, Gantulga Davaakhuu, Jargalsaikhan Davaasambuu, Dulguun Dorjgotov, Thomas J. Hall, Andrew J. Hemmings, Amy R. Holtby, Tuyatsetseg Jambal, Badarch Jargalsaikhan, Uyasakh Jargalsaikhan, Naveen K. Kadri, David E. MacHugh, Hubert Pausch, Carol Readhead, David Warburton, Manglai Dugarjaviin, Emmeline W. Hill
2022
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Abstract

Selection for system-wide morphological, physiological, and metabolic adaptations has led to extreme athletic phenotypes among geographically diverse horse breeds. Here, we identify genes contributing to exercise adaptation in racehorses by applying genomics approaches for racing performance, an end-point athletic phenotype. Using an integrative genomics strategy to first combine population genomics results with skeletal muscle exercise and training transcriptomic data, followed by whole-genome resequencing of Asian horses, we identify protein-coding variants in genes of interest in galloping racehorse breeds (Arabian, Mongolian and Thoroughbred). A core set of genes, G6PC2, HDAC9, KTN1, MYLK2, NTM, SLC16A1 and SYNDIG1, with central roles in muscle, metabolism, and neurobiology, are key drivers of the racing phenotype. Although racing potential is a multifactorial trait, the genomic architecture shaping the common athletic phenotype in horse populations bred for racing provides evidence for the influence of protein-coding variants in fundamental exercise-relevant genes. Variation in these genes may therefore be exploited for genetic improvement of horse populations towards specific types of racing.

Acknowledgments

We thank the Khentii herdsmen and Henry Seebach for assistance with collection of the Mongolian Racing horse samples; horse owners for agreement for the use of samples in research; Jonathan O’Grady (O’Grady Advisors) for assistance compiling the Thoroughbred race record phenotypes; and Gillian McHugo for assistance with data visualisation. This work was supported by National Key R&D Program of China (Grant Code 2017YFE0108700); National Natural Science Foundation of China (Grant Code 3191101008,31960657); Science Foundation Ireland (Grant Code 19FFP6879); National Institutes of Health (Grant Code NIEHS-1D43ES02286201); Royal Agricultural University Cirencester Fund; Plusvital Ltd.

     Type        Acknowledgement
     Publication    Communications Biology, 5, 1320