The contribution of myostatin (MSTN) and additional modifying genetic loci to race distance aptitude in Thoroughbred horses racing in different geographic regions.


Journal

Equine veterinary journal
ISSN: 2042-3306
Titre abrégé: Equine Vet J
Pays: United States
ID NLM: 0173320

Informations de publication

Date de publication:
Sep 2019
Historique:
received: 14 03 2018
accepted: 14 11 2018
pubmed: 4 1 2019
medline: 7 1 2020
entrez: 4 1 2019
Statut: ppublish

Résumé

Race distance aptitude in Thoroughbred horses is highly heritable and is influenced largely by variation at the myostatin gene (MSTN). In addition to MSTN, we hypothesised that other modifying loci contribute to best race distance. Using 3006 Thoroughbreds, including 835 'elite' horses, which were >3 years old, had race records and were sampled from Europe/Middle-East, Australia/New Zealand, North America and South Africa, we performed genome-wide association (GWA) tests and separately developed a genomic prediction algorithm to comprehensively catalogue additive genetic variation contributing to best race distance. 48,896 single-nucleotide polymorphism (SNP) genotypes were generated from high-density SNP genotyping arrays. Heritability estimates, tests of GWA and genomic prediction models were derived for the phenotypes: average race distance, best race distance for elite, nonelite and all winning horses. Heritability estimates were high ( The nongenetic influence of owner/trainer decisions on placement of horses in suitable races could not be controlled. MSTN is the single most important genetic contributor to best race distance in the Thoroughbred. Employment of genetic prediction models will lead to more accurate placing of horses in races that are best suited to their inherited genetic potential for distance aptitude.

Sections du résumé

BACKGROUND BACKGROUND
Race distance aptitude in Thoroughbred horses is highly heritable and is influenced largely by variation at the myostatin gene (MSTN).
OBJECTIVES OBJECTIVE
In addition to MSTN, we hypothesised that other modifying loci contribute to best race distance.
STUDY DESIGN METHODS
Using 3006 Thoroughbreds, including 835 'elite' horses, which were >3 years old, had race records and were sampled from Europe/Middle-East, Australia/New Zealand, North America and South Africa, we performed genome-wide association (GWA) tests and separately developed a genomic prediction algorithm to comprehensively catalogue additive genetic variation contributing to best race distance.
METHODS METHODS
48,896 single-nucleotide polymorphism (SNP) genotypes were generated from high-density SNP genotyping arrays. Heritability estimates, tests of GWA and genomic prediction models were derived for the phenotypes: average race distance, best race distance for elite, nonelite and all winning horses.
RESULTS RESULTS
Heritability estimates were high (
MAIN LIMITATIONS CONCLUSIONS
The nongenetic influence of owner/trainer decisions on placement of horses in suitable races could not be controlled.
CONCLUSIONS CONCLUSIONS
MSTN is the single most important genetic contributor to best race distance in the Thoroughbred. Employment of genetic prediction models will lead to more accurate placing of horses in races that are best suited to their inherited genetic potential for distance aptitude.

Identifiants

pubmed: 30604488
doi: 10.1111/evj.13058
doi:

Substances chimiques

Myostatin 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

625-633

Subventions

Organisme : Science Foundation Ireland
ID : 11/PI/1166
Pays : Ireland
Organisme : Plusvital Ltd

Informations de copyright

© 2019 EVJ Ltd.

Auteurs

E W Hill (EW)

Plusvital Ltd, Dun Laoghaire, Co. Dublin, Ireland.
UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland.

B A McGivney (BA)

Plusvital Ltd, Dun Laoghaire, Co. Dublin, Ireland.

M F Rooney (MF)

School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute (TBSI), Trinity College Dublin, Dublin, Ireland.

L M Katz (LM)

UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland.

A Parnell (A)

UCD Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin, Ireland.

D E MacHugh (DE)

UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland.
UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland.

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