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Scientific reports2024; 14(1); 27648; doi: 10.1038/s41598-024-79014-w

A genome-wide association study of the racing performance traits in Yili horses based on Blink and FarmCPU models.

Abstract: Racing performance traits are the main indicators for evaluating the performance and value of sport horses. The aim of this study was to identify the key genes for racing performance traits in Yili horses by performing a genome-wide association study (GWAS). Breeding values for racing performance traits were calculated for Yili horses (n = 827) using an animal model. Genome-wide association analysis of racing performance traits in horses (n = 236) was carried out using the Blink, and FarmCPU models in GAPIT software, and genes within the significant regions were functionally annotated. The results of GWAS showed that a total of 24 significant SNP markers (P < 6.05 × 10) and 22 suggestive SNP markers (P < 1.21 × 10) were identified. Among them, the Blink associated 16 significant SNP loci and FarmCPU associated 12 significant SNP loci. A total of 127 candidate genes (50 significant) were annotated. Among these, CNTN6 (motor coordination), NIPA1 (neuronal development), and DCC (dopamine pathway maturation) may be the main candidate genes affecting speed traits. SHANK2 (neuronal synaptic regulation), ISCA1 (mitochondrial protein assembly), and KCNIP4 (neuronal excitability) may be the main candidate genes affecting ranking score traits. A common locus (ECA1: 22698579) was significantly associated with racing performance traits, and the function of the genes at this locus needs to be studied in depth. These findings will provide new insights into the detection and selection of genetic variants for racing performance and will help to accelerate the genetic improvement of Yili horses.
© 2024. The Author(s).
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Publication Date: 2024-11-12 PubMed ID: 39532956PubMed Central: PMC11557848DOI: 10.1038/s41598-024-79014-wGoogle Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article involves a detailed study on the racing performance of Yili horses, an uncommon breed, based on their genetic factors. The researchers conducted a genome-wide association study (GWAS), which identified key genes that affect the horses’ racing performance.

Research Methodology

  • The research was conducted on a group of 827 Yili horses. Breeding values for their racing performance traits were calculated using a specific animal model.
  • The genome-wide association analysis was conducted on a subgroup of 236 horses, using the Blink and FarmCPU models in GAPIT software.
  • The significant regions and genes were then functionally annotated to determine their relevance to the horse’s racing characteristics.

Research Findings

  • This study identified a total of 24 significant and 22 suggestive SNP markers through the GWAS.
  • Blink model associated 16 significant SNP loci, and FarmCPU associated 12 significant SNP loci.
  • 127 candidate genes were annotated, with 50 considered significant. From these, they were able to identify certain main genes affecting speed and ranking score traits.
  • CNTN6, NIPA1, and DCC are thought to be the critical genes influencing speed traits, while SHANK2, ISCA1, and KCNIP4 may be the primary candidate genes affecting ranking score traits.
  • A common genetic locus (ECA1: 22698579) was significantly associated with racing performance traits, indicating that further research in this area is needed.

Implications

  • The study has filled a void in the understanding of genetic factors affecting racing performance in Yili horses.
  • These findings will help in identifying and selecting genetic variants for racing performance, which can potentially improve the breeding values of these horses.
  • The detail about the significant genetic features identified will help in future research and in-depth functional study.

Cite This Article

APA
Wang C, Zeng Y, Wang J, Wang T, Li X, Shen Z, Meng J, Yao X. (2024). A genome-wide association study of the racing performance traits in Yili horses based on Blink and FarmCPU models. Sci Rep, 14(1), 27648. https://doi.org/10.1038/s41598-024-79014-w

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 14
Issue: 1
Pages: 27648
PII: 27648

Researcher Affiliations

Wang, Chuankun
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
Zeng, Yaqi
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
  • Xinjiang Key Laboratory of Horse Breeding and Exercise Physiology, Urumqi, 830052, China.
Wang, Jianwen
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
  • Xinjiang Key Laboratory of Horse Breeding and Exercise Physiology, Urumqi, 830052, China.
Wang, Tongliang
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
Li, Xueyan
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
Shen, Zhehong
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
Meng, Jun
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China. junm86@163.com.
  • Xinjiang Key Laboratory of Horse Breeding and Exercise Physiology, Urumqi, 830052, China. junm86@163.com.
Yao, Xinkui
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China. yxk61@126.com.
  • Xinjiang Key Laboratory of Horse Breeding and Exercise Physiology, Urumqi, 830052, China. yxk61@126.com.

MeSH Terms

  • Animals
  • Horses / genetics
  • Horses / physiology
  • Genome-Wide Association Study
  • Polymorphism, Single Nucleotide
  • Breeding / methods
  • Phenotype
  • Physical Conditioning, Animal
  • Quantitative Trait Loci

Grant Funding

  • PT2311 / and The Innovation Environment (Talent, Base) Construction Project of Xinjiang
  • 2022A02013 / Development of Key Technologies for the Horse Industry in Xinjiang

Conflict of Interest Statement

The authors declare no competing interests.

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Citations

This article has been cited 5 times.
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