Common protein-coding variants influence the racing phenotype in galloping racehorse breeds.
Haige HanBeatrice A McGivneyLucy AllenDongyi BaiLeanne R CorduffGantulga DavaakhuuJargalsaikhan DavaasambuuDulguun DorjgotovThomas J HallAndrew J HemmingsAmy R HoltbyTuyatsetseg JambalBadarch JargalsaikhanUyasakh JargalsaikhanNaveen Kumar KadriDavid E MacHughHubert PauschCarol ReadheadDavid WarburtonManglai DugarjaviinEmmeline W HillPublished in: Communications biology (2022)
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.
Keyphrases
- genome wide
- copy number
- skeletal muscle
- high intensity
- single cell
- bioinformatics analysis
- genome wide identification
- dna methylation
- physical activity
- genetic diversity
- genome wide analysis
- binding protein
- protein protein
- amino acid
- insulin resistance
- resistance training
- climate change
- rna seq
- machine learning
- electronic health record
- adipose tissue
- deep learning
- risk assessment