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Evolutionary bioinformatics online2018; 14; 1176934318775106; doi: 10.1177/1176934318775106

Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data.

Abstract: Animal domestication gives rise to gradual changes at the genomic level through selection in populations. Selective sweeps have been traced in the genomes of many animal species, including humans, cattle, and dogs. However, little is known regarding positional candidate genes and genomic regions that exhibit signatures of selection in domestic horses. In addition, an understanding of the genetic processes underlying horse domestication, especially the origin of Chinese native populations, is still lacking. In our study, we generated whole genome sequences from 4 Chinese native horses and combined them with 48 publicly available full genome sequences, from which 15 341 213 high-quality unique single-nucleotide polymorphism variants were identified. Kazakh and Lichuan horses are 2 typical Asian native breeds that were formed in Kazakh or Northwest China and South China, respectively. We detected 1390 loss-of-function (LoF) variants in protein-coding genes, and gene ontology (GO) enrichment analysis revealed that some LoF-affected genes were overrepresented in GO terms related to the immune response. Bayesian clustering, distance analysis, and principal component analysis demonstrated that the population structure of these breeds largely reflected weak geographic patterns. Kazakh and Lichuan horses were assigned to the same lineage with other Asian native breeds, in agreement with previous studies on the genetic origin of Chinese domestic horses. We applied the composite likelihood ratio method to scan for genomic regions showing signals of recent selection in the horse genome. A total of 1052 genomic windows of 10 kB, corresponding to 933 distinct core regions, significantly exceeded neutral simulations. The GO enrichment analysis revealed that the genes under selective sweeps were overrepresented with GO terms, including "negative regulation of canonical Wnt signaling pathway," "muscle contraction," and "axon guidance." Frequent exercise training in domestic horses may have resulted in changes in the expression of genes related to metabolism, muscle structure, and the nervous system.
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Publication Date: 2018-06-04 PubMed ID: 29899660PubMed Central: PMC5990873DOI: 10.1177/1176934318775106Google 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 paper explores the selective breeding of domestic horses and its impact on their genomic level. The focus is on Chinese native horses, their genetic history, potential signatures of selection, and identification of distinct regions and genes that may have been affected by selective breeding.

Study Overview and Methodology

In this study, the researchers collected whole genome sequences from four Chinese native horses and supplemented this data with 48 publicly available full genome sequences. Through this, they identified 15,341,213 high-quality unique single-nucleotide polymorphism variants. Loss-of-function (LoF) variants prevalent in these sequences were also examined, particularly those related to protein-coding genes.

  • Two particular Asian breeds, Kazakh and Lichuan horses, were studied in-depth. These breeds originated from different geographical areas in China (Kazakh or Northwest China and South China, respectively).
  • The team conducted a Gene Ontology (GO) enrichment analysis to identify overrepresented GO terms in LoF-affected genes, which notably pointed towards immune response functions.
  • The researchers applied multiple analysis methods, including Bayesian clustering, distance analysis, and principal component analysis, to comprehend the population structure of these breeds and the geographical correlates.

Findings

The study reveals that the population structure of Kazakh and Lichuan horses reflects weak geographical patterns, and these breeds were found to be part of the same lineage with other Asian native breeds. This aligns with previous studies on the genetic origin of Chinese domestic horses.

  • Moreover, the research employs the composite likelihood ratio method to scan the horse genome for regions showing signals of recent selection. The analysis identifies 1052 genomic windows of 10 kB each, corresponding to 933 distinct core regions, significantly exceeding neutral simulations.
  • Further GO enrichment analysis reveals that the genes under selective sweeps were associated with GO terms such as “negative regulation of canonical Wnt signaling pathway,” “muscle contraction,” and “axon guidance.” These findings suggest that the frequent exercise training of domestic horses might have led to changes in gene expression related to metabolism, muscle structure, and the nervous system.

Implications

This study contributes valuable insights into the impact of domestication and selective breeding on the genomic structure of horses, particularly Chinese native breeds. It identifies potential genomic regions and genes that may have been significantly impacted by the breeding selection. The findings of this research could offer a better understanding of the genetic processes underlying horse domestication, fostering improvements in breeding strategies.

Cite This Article

APA
Zhang C, Ni P, Ahmad HI, Gemingguli M, Baizilaitibei A, Gulibaheti D, Fang Y, Wang H, Asif AR, Xiao C, Chen J, Ma Y, Liu X, Du X, Zhao S. (2018). Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data. Evol Bioinform Online, 14, 1176934318775106. https://doi.org/10.1177/1176934318775106

Publication

Evolutionary bioinformatics online
ISSN: 1176-9343
NlmUniqueID: 101256319
Country: United States
Language: English
Volume: 14
Pages: 1176934318775106
PII: 1176934318775106

Researcher Affiliations

Zhang, Cheng
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Ni, Pan
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Ahmad, Hafiz Ishfaq
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Gemingguli, M
  • College of Animal Science, Tarim University, Alar, China.
Baizilaitibei, A
  • College of Animal Science, Tarim University, Alar, China.
Gulibaheti, D
  • College of Animal Science, Tarim University, Alar, China.
Fang, Yaping
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Wang, Haiyang
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Asif, Akhtar Rasool
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Xiao, Changyi
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Chen, Jianhai
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Ma, Yunlong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Liu, Xiangdong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Du, Xiaoyong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Zhao, Shuhong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.

Conflict of Interest Statement

Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Citations

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