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Genetics, selection, evolution : GSE2022; 54(1); 49; doi: 10.1186/s12711-022-00740-8

Development and validation of a horse reference panel for genotype imputation.

Abstract: Genotype imputation is a cost-effective method to generate sequence-level genotypes for a large number of animals. Its application can improve the power of genomic studies, provided that the accuracy of imputation is sufficiently high. The purpose of this study was to develop an optimal strategy for genotype imputation from genotyping array data to sequence level in German warmblood horses, and to investigate the effect of different factors on the accuracy of imputation. Publicly available whole-genome sequence data from 317 horses of 46 breeds was used to conduct the analyses. Results: Depending on the size and composition of the reference panel, the accuracy of imputation from medium marker density (60K) to sequence level using the software Beagle 5.1 ranged from 0.64 to 0.70 for horse chromosome 3. Generally, imputation accuracy increased as the size of the reference panel increased, but if genetically distant individuals were included in the panel, the accuracy dropped. Imputation was most precise when using a reference panel of multiple but related breeds and the software Beagle 5.1, which outperformed the other two tested computer programs, Impute 5 and Minimac 4. Genome-wide imputation for this scenario resulted in a mean accuracy of 0.66. Stepwise imputation from 60K to 670K markers and subsequently to sequence level did not improve the accuracy of imputation. However, imputation from higher density (670K) was considerably more accurate (about 0.90) than from medium density. Likewise, imputation in genomic regions with a low marker coverage resulted in a reduced accuracy of imputation. Conclusions: The accuracy of imputation in horses was influenced by the size and composition of the reference panel, the marker density of the genotyping array, and the imputation software. Genotype imputation can be used to extend the limited amount of available sequence-level data from horses in order to boost the power of downstream analyses, such as genome-wide association studies, or the detection of embryonic lethal variants.
© 2022. The Author(s).
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Publication Date: 2022-07-04 PubMed ID: 35787788PubMed Central: PMC9252005DOI: 10.1186/s12711-022-00740-8Google Scholar: Lookup
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  • Journal Article

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 aims to create an effective strategy for genotype imputation for German warmblood horses and evaluate the impact of various contributing factors on imputation accuracy. Genotype imputation can enhance genomics studies’ effectiveness, given the accuracy of the imputation is adequate, and this work is positioned to extend the limited sequence-level data available from horses for downstream analyses.

Objective and Methodology

  • The study aims at devising an optimal strategy for genotype imputation from the genotyping array data to sequence level in German warmblood horses.
  • It also aims to scrutinize the influence of different factors like the reference panel’s size and composition, marker density of the genotyping array, and imputation software on the accuracy of imputation.
  • The research utilized publicly available whole-genome sequence data from 317 horses of 46 breeds for data analysis.

Results and Observations

  • The analysis shows that imputation accuracy depends on the reference panel’s size and composition.
  • The accuracy of imputation using Beagle 5.1 software ranged from 0.64 to 0.70 for horse chromosome 3, depending on the reference panel.
  • Imputation accuracy generally increased with the size of the reference panel, yet it decreased if the reference panel contained genetically distant individuals.
  • Narrowed down, the study found that imputation was notably accurate when a reference panel of multiple but closely related breeds and the software Beagle 5.1 was employed.
  • However, the process of stepwise imputation from 60K to 670K markers to sequence level did not result in any tangible improvement in the accuracy of imputation.

Conclusions and Significance

  • The research concludes that the size and composition of the reference panel, the marker density of the genotyping array, and the imputation software had significant influences on the accuracy of imputation in horses.
  • According to the investigation, genotype imputation can indeed be used to supplement the limited sequence-level data from horses to increase downstream analysis power like genome-wide association studies or the identification of lethal variants.
  • By offering an optimal strategy for genotype imputation in horses, the research has potential to optimize the use of genomics for understanding genetic conditions and developing more advanced treatments.

Cite This Article

APA
Reich P, Falker-Gieske C, Pook T, Tetens J. (2022). Development and validation of a horse reference panel for genotype imputation. Genet Sel Evol, 54(1), 49. https://doi.org/10.1186/s12711-022-00740-8

Publication

Genetics, selection, evolution : GSE
ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 54
Issue: 1
Pages: 49
PII: 49

Researcher Affiliations

Reich, Paula
  • Department of Animal Sciences, Georg-August-University Göttingen, 37077, Göttingen, Germany. paula.reich@agr.uni-goettingen.de.
Falker-Gieske, Clemens
  • Department of Animal Sciences, Georg-August-University Göttingen, 37077, Göttingen, Germany.
  • Center for Integrated Breeding Research (CiBreed), Georg-August-University Göttingen, 37075, Göttingen, Germany.
Pook, Torsten
  • Department of Animal Sciences, Georg-August-University Göttingen, 37077, Göttingen, Germany.
  • Center for Integrated Breeding Research (CiBreed), Georg-August-University Göttingen, 37075, Göttingen, Germany.
Tetens, Jens
  • Department of Animal Sciences, Georg-August-University Göttingen, 37077, Göttingen, Germany.
  • Center for Integrated Breeding Research (CiBreed), Georg-August-University Göttingen, 37075, Göttingen, Germany.

MeSH Terms

  • Animals
  • Dogs
  • Genome-Wide Association Study
  • Genomics
  • Genotype
  • Horses / genetics
  • Records
  • Software

Conflict of Interest Statement

The authors declare that they have no competing interests.

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Citations

This article has been cited 4 times.
  1. Zheng M, Liao J, Li Z, Xu Z, Jiang Z, Tan L, Fu R, Xu H, Li Z, Zhang X, Nie Q. Evaluation of the selection of key individuals for genotype imputation in Chinese yellow-feathered chicken. Poult Sci 2023 Jun 29;102(10):102901.
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  2. Chessari G, Reich P, Criscione A, Falker-Gieske C, Mastrangelo S, Tumino S, Bordonaro S, Marletta D, Tetens J. Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds. BMC Genomics 2025 Nov 29;26(1):1086.
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  3. Tan HZ, Stuart KC, Vi T, Whibley A, Bailey S, Brekke P, Santure AW. High Imputation Accuracy Can Be Achieved Using a Small Reference Panel in a Natural Population With Low Genetic Diversity. Mol Ecol Resour 2025 Nov;25(8):e70024.
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  4. Reich P, Möller S, Stock KF, Nolte W, von Depka Prondzinski M, Reents R, Kalm E, Kühn C, Thaller G, Falker-Gieske C, Tetens J. Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes. Genet Sel Evol 2024 Jun 13;56(1):45.
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