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Home / Equine Research Bank / J Anim Breed Genet / Genotype imputation accuracy in multiple equine breeds from ...
Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie2018; 135(6); 420-431; doi: 10.1111/jbg.12358

Genotype imputation accuracy in multiple equine breeds from medium- to high-density genotypes.

Abstract: Genotype imputation is now a key component of genomic analyses as it increases the density of available genotypes within a population. However, many factors can influence imputation accuracy. The aim of this study was to assess and compare the accuracy of imputation of high-density genotypes (Affymetrix Axiom Equine genotyping array, 670,806 SNPs) from two moderate-density genotypes (Illumina Equine SNP50 BeadChip, 54,602 SNPs and Illumina Equine SNP70 BeadChip, 65,157 SNPs), using single-breed or multiple-breed reference sets. Genotypes were available from five groups of horse breeds: Arab (AR, 1,207 horses), Trotteur Français (TF, 979 horses), Selle Français (SF, 1,979 horses), Anglo-Arab (AA, 229 horses) and various foreign sport horses (FH, 209 horses). The proportions of horses genotyped with the high-density (HD) chip in each breed group were 10% in AA, 15% in AR and FH, 30% in TF and 57% in SF. A validation set consisting of one-third of the horses genotyped with the HD chip was formed and their genotypes deleted. Two imputation strategies were compared, one in which the reference population consisted only of horses from the same breed group as in the validation set, and another with horses from all breed groups. For the first strategy, concordance rates (CRs) ranged from 97.8% (AR) to 99.0% (TF) and correlations (r²) from 0.94 (AR) to 0.99 (TF). For the second strategy, CR ranged from 97.4% (AR) to 98.9% (TF) and r² from 0.93 (AR) to 0.99 (TF). Overall, the results show a small advantage of within-breed imputation compared with multi-breed imputation. Adding horses from different breed groups to the reference population does not improve the accuracy of imputation. Imputation provides an accurate means of combining data sets from different genotyping platforms, now necessary with the increasing use of the recently developed Affymetrix Axiom Equine genotyping array.
© 2018 The Authors. Journal of Animal Breeding and Genetics Published by Blackwell Verlag GmbH.
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Publication Date: 2018-10-09 PubMed ID: 30298946DOI: 10.1111/jbg.12358Google 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.

This study focused on the accuracy of genotype imputation across different horse breeds using two medium-density genotyping arrays to predict a high-density array. The research indicated that imputation is slightly more accurate when a single breed is used as reference, rather than multiple breeds.

Research Background

  • Genotype imputation is a method used to predict unknown genotypes using known genotypes. It is critical in genomic analyses because it increases the density of available genotypes in a population.
  • Previous studies indicated that several factors, including genotyping density and reference population, could influence imputation accuracy.
  • The study aimed to assess and compare the accuracy of imputation of high-density genotypes from two medium-density genotypes, using either single-breed or multiple-breed reference sets.

Methodology

  • The researchers used the Affymetrix Axiom Equine genotyping array (670,806 SNPs) to represent high-density genotypes and the Illumina Equine SNP50 BeadChip (54,602 SNPs) and Illumina Equine SNP70 BeadChip (65,157 SNPs) to represent medium-density genotypes.
  • Genotypes were available from five groups of horse breeds, and a validation set was created by deleting the genotypes of one-third of horses genotyped with the high-density chip.
  • Two imputation strategies were tested: one using a reference population from the same breed group as the validation set, and another using a reference population from all breed groups.

Results and Conclusions

  • The researchers found that the concordance rates (CRs) and correlations in genotype imputation across different breeds varied, with the single breed reference slightly outperforming the multi-breed reference.
  • These results show a minor advantage of within-breed imputation over multi-breed imputation, demonstrating that adding horses from different breed groups to the reference population does not necessarily improve imputation accuracy.
  • The study concludes that genotype imputation is an accurate method of combining data sets from different genotyping platforms, which is especially important given the growing use of the Affymetrix Axiom Equine genotyping array in the field.

Cite This Article

APA
Chassier M, Barrey E, Robert C, Duluard A, Danvy S, Ricard A. (2018). Genotype imputation accuracy in multiple equine breeds from medium- to high-density genotypes. J Anim Breed Genet, 135(6), 420-431. https://doi.org/10.1111/jbg.12358

Publication

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
ISSN: 1439-0388
NlmUniqueID: 100955807
Country: Germany
Language: English
Volume: 135
Issue: 6
Pages: 420-431

Researcher Affiliations

Chassier, Marjorie
  • Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative, Département Sciences du Vivant, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.
Barrey, Eric
  • Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative, Département Sciences du Vivant, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.
Robert, Céline
  • Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative, Département Sciences du Vivant, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.
  • Ecole Nationale Vétérinaire d'Alfort, Maisons Alfort, France.
Duluard, Arnaud
  • Département élevage et santé animale, Le Trot, Paris, France.
Danvy, Sophie
  • Institut Français du Cheval et de l'Equitation, Pôle développement, Innovation et Recherche, Exmes, France.
Ricard, Anne
  • Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative, Département Sciences du Vivant, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.
  • Institut Français du Cheval et de l'Equitation, Pôle développement, Innovation et Recherche, Exmes, France.

MeSH Terms

  • Animals
  • Breeding
  • Genomics / methods
  • Genotype
  • Horses / genetics
  • Linkage Disequilibrium
  • Polymorphism, Single Nucleotide

Grant Funding

  • Institut Franu00e7ais du Cheval et de l'Equitation
  • the Institut National de la Recherche Agronomique (INRA)
  • the Fonds Eperon

Citations

This article has been cited 8 times.
  1. Reich P, Falker-Gieske C, Pook T, Tetens J. Development and validation of a horse reference panel for genotype imputation. Genet Sel Evol 2022 Jul 4;54(1):49.
    doi: 10.1186/s12711-022-00740-8pubmed: 35787788google scholar: lookup
  2. Mdyogolo S, MacNeil MD, Neser FWC, Scholtz MM, Makgahlela ML. Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa. Trop Anim Health Prod 2022 Feb 8;54(2):90.
    doi: 10.1007/s11250-022-03102-0pubmed: 35133512google scholar: lookup
  3. Jenkins CA, Schofield EC, Mellersh CS, De Risio L, Ricketts SL. Improving the resolution of canine genome-wide association studies using genotype imputation: A study of two breeds. Anim Genet 2021 Oct;52(5):703-713.
    doi: 10.1111/age.13117pubmed: 34252218google scholar: lookup
  4. Dugué M, Dumont Saint Priest B, Crichan H, Danvy S, Ricard A. Genomic Correlations Between the Gaits of Young Horses Measured by Accelerometry and Functional Longevity in Jumping Competition. Front Genet 2021;12:619947.
    doi: 10.3389/fgene.2021.619947pubmed: 33584826google scholar: lookup
  5. Ricard A, Duluard A. Genomic analysis of gaits and racing performance of the French trotter. J Anim Breed Genet 2021 Mar;138(2):204-222.
    doi: 10.1111/jbg.12526pubmed: 33249655google scholar: lookup
  6. Wallis N, Raffan E. The Genetic Basis of Obesity and Related Metabolic Diseases in Humans and Companion Animals. Genes (Basel) 2020 Nov 20;11(11).
    doi: 10.3390/genes11111378pubmed: 33233816google scholar: lookup
  7. 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.
    doi: 10.1186/s12864-025-12256-8pubmed: 41318401google scholar: lookup
  8. Ricard A, Crevier-Denoix N, Pourcelot P, Crichan H, Sabbagh M, Dumont-Saint-Priest B, Danvy S. Genetic analysis of geometric morphometric 3D visuals of French jumping horses. Genet Sel Evol 2023 Sep 18;55(1):63.
    doi: 10.1186/s12711-023-00837-8pubmed: 37723416google scholar: lookup
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