Genetic mapping of recurrent exertional rhabdomyolysis in a population of North American Thoroughbreds

K. L. Fritz, M. E. McCue, S. J. Valberg, A. K. Rendahl, J. R. Mickelson

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Recurrent exertional rhabdomyolysis is a heritable disorder that results in painful skeletal muscle cramping with exercise in up to 10% of all Thoroughbred racehorses. Here, we report a genome-wide association study with 48 282 SNPs analyzed among 48 case and 37 control Thoroughbreds. The most significant SNPs spanned approximately 13 Mb on ECA16, and the P-value of the most significant SNP after correcting for population structure was 8.0 × 10-6. This region on ECA16 was further evaluated by genotyping 247 SNPs in both the initial population and a second population of 34 case and 98 control Thoroughbreds. Several SNPs across the 13-Mb region on ECA16 showed significance when each population was analyzed separately; however, the exact positions of the most significant SNPs within this region on ECA16 varied between populations. This variability in location may be attributed to lack of power owing to insufficient sample sizes within each population individually, or to the relative distribution of long, conserved haplotypes, characteristic of the Thoroughbred breed. Future genome-wide association studies with additional horses would likely improve the power to resolve casual loci located on ECA16 and increase the likelihood of detecting any additional loci on other chromosomes contributing to disease susceptibility.

Original languageEnglish (US)
Pages (from-to)730-738
Number of pages9
JournalAnimal Genetics
Volume43
Issue number6
DOIs
StatePublished - Dec 2012

Keywords

  • Equine SNP chip
  • genome-wide association study
  • skeletal muscle
  • tying up
  • whole-genome association study

Fingerprint

Dive into the research topics of 'Genetic mapping of recurrent exertional rhabdomyolysis in a population of North American Thoroughbreds'. Together they form a unique fingerprint.

Cite this