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Animal genetics2025; 56(5); e70047; doi: 10.1111/age.70047

Evaluation of a targeted enrichment panel for gene editing detection and assessment of population variation in Thoroughbred horses.

Abstract: Gene editing and genome manipulation offer great promise for treating diseases in both humans and animals. There is a danger, however, that this technology could be used for other purposes such as performance enhancement. To detect such 'gene doping' events, we evaluated a targeted enrichment panel and next-generation sequencing to assess its reproducibility, sensitivity, and capability of variant detection on a wide variety of samples and biological matrices. The panel was verified against existing data for the myostatin gene, a PCR-based SNP panel, and whole genome sequencing in a subset of samples. As successful detection of seamless edits will rely on a detailed understanding of the natural population, we also screened over 170 Thoroughbreds and catalogued numerous novel variants. These included several resulting in coding alterations, and a structural variant. Samples spiked with transgenic cDNA-based material to simulate gene doping events were detected down to 3.2% mosaicism, giving confidence that mosaic mutations resulting from embryonic introduction of gene editing reagents can be detected using these methods. The ability of software packages to detect gene doping events was also assessed, including multiple genome alignment tools, variant callers, and structural variant callers. Freebayes performed strongest at SNP-based editing detection, and Delly and Manta had complementary advantages depending on the mutation type. For routine testing, a multi-faceted approach to calling should be taken to maximise the detection capabilities.
© 2025 Stichting International Foundation for Animal Genetics.
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Publication Date: 2025-10-13 PubMed ID: 41081551DOI: 10.1111/age.70047Google 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.

Plain Language Overview

  • This study evaluated a specialized genetic testing method to detect gene editing in Thoroughbred horses, focusing on identifying both natural genetic variation and artificially introduced changes that could be used for performance enhancement (“gene doping”).
  • The research demonstrated the effectiveness of the method in detecting small levels of edited genetic material and assessed various software tools to optimize reliable detection of gene edits in horse populations.

Introduction to the Research

  • Gene editing and genome manipulation have potential applications for treating diseases in humans and animals, but there is concern about misuse for non-therapeutic purposes such as enhancing athletic performance in horses.
  • Detecting unauthorized gene editing or “gene doping” in animals presents challenges because edits can be subtle and difficult to differentiate from natural genetic variation.
  • This research aimed to evaluate a targeted enrichment panel combined with next-generation sequencing (NGS) technology for reliably detecting gene editing in Thoroughbred horses.

Methods and Testing Approach

  • The targeted enrichment panel was designed to focus on specific gene regions, including the myostatin gene, which affects muscle growth and is often targeted for performance enhancement.
  • The panel’s performance was benchmarked against:
    • Existing genetic data for the myostatin gene
    • A PCR-based single nucleotide polymorphism (SNP) panel
    • Whole genome sequencing data from a subset of samples
  • The study included over 170 Thoroughbred horses to build a comprehensive catalog of natural genetic variation, including novel variants and structural variants—key for differentiating spontaneous from artificial edits.
  • To simulate gene doping events, horse DNA samples were spiked with transgenic cDNA to test the method’s sensitivity in detecting edited sequences, even when present at low levels (mosaicism as low as 3.2%).
  • Various bioinformatic tools were evaluated for their ability to detect gene editing from sequencing data, including:
    • Multiple genome alignment tools
    • Variant callers such as Freebayes
    • Structural variant callers like Delly and Manta

Key Findings

  • The targeted enrichment panel showed high reproducibility and sensitivity, effectively detecting gene edits in a variety of biological samples and matrices.
  • The extensive cataloging of variants in Thoroughbreds revealed numerous novel SNPs and at least one structural variant, highlighting the complexity of natural genetic diversity.
  • The detection limit for introduced gene edits was as low as 3.2% mosaicism, indicating that even minor edited cell populations (such as those arising from early embryonic gene editing) can be identified.
  • Freebayes outperformed other variant callers in detecting SNP-based gene edits, while Delly and Manta had complementary strengths in identifying structural variants, depending on mutation type.
  • For routine surveillance or testing, using a combination of variant calling software improves the probability of detecting different types of edits, suggesting a multi-faceted bioinformatics approach is optimal.

Implications and Conclusions

  • This research demonstrates the feasibility of detecting gene doping in horses using targeted sequencing panels combined with advanced computational analyses.
  • The comprehensive understanding of natural population genetics is critical to confidently identify artificial edits versus naturally occurring variants.
  • Multi-tool software pipelines enhance detection capabilities and should be implemented in monitoring programs for gene editing in animals involved in sports or breeding.
  • The approaches validated here can serve as a model for developing gene doping detection frameworks in other species or contexts where gene editing may be misused.

Cite This Article

APA
Maniego J, Swinburne J, Hincks P, Habershon-Butcher J, Given J, Ryder E. (2025). Evaluation of a targeted enrichment panel for gene editing detection and assessment of population variation in Thoroughbred horses. Anim Genet, 56(5), e70047. https://doi.org/10.1111/age.70047

Publication

Animal genetics
ISSN: 1365-2052
NlmUniqueID: 8605704
Country: England
Language: English
Volume: 56
Issue: 5
Pages: e70047

Researcher Affiliations

Maniego, Jillian
  • LGC Ltd, Cambridgeshire, UK.
Swinburne, June
  • LGC Ltd, Cambridgeshire, UK.
Hincks, Pamela
  • LGC Ltd, Cambridgeshire, UK.
Habershon-Butcher, Jocelyn
  • British Horseracing Authority, London, UK.
Given, James
  • British Horseracing Authority, London, UK.
Ryder, Edward
  • LGC Ltd, Cambridgeshire, UK.

MeSH Terms

  • Animals
  • Horses / genetics
  • Gene Editing / veterinary
  • Gene Editing / methods
  • Polymorphism, Single Nucleotide
  • High-Throughput Nucleotide Sequencing / veterinary
  • Myostatin / genetics
  • Reproducibility of Results

Grant Funding

  • British Horseracing Authority

References

This article includes 77 references
  1. Argentina breeds gene‐edited polo super ponies (2025). https://www.reuters.com/science/argentina‐breeds‐gene‐edited‐polo‐super‐ponies‐2025‐02‐04/. Accessed 28 April 2025.
  2. Bafunno V, Divella C, Sessa F, Tiscia GL, Castellano G, Gesualdo L. De novo homozygous mutation of the C1 inhibitor gene in a patient with hereditary angioedema. Journal of Allergy and Clinical Immunology 2013;132(3):748–750.e3.
    doi: 10.1016/j.jaci.2013.04.006google scholar: lookup
  3. Bailey E, Finno CJ, Cullen JN, Kalbfleisch T, Petersen JL. Analyses of whole‐genome sequences from 185 north American thoroughbred horses, spanning 5 generations. Scientific Reports 2024;14(1):22930.
    doi: 10.1038/s41598-024-73645-9google scholar: lookup
  4. Bi R, Li Y, Xu M, Zheng Q, Zhang DF, Li X. Direct evidence of CRISPR‐Cas9‐mediated mitochondrial genome editing. The Innovation 2022;3(6):100329.
    doi: 10.1016/j.xinn.2022.100329google scholar: lookup
  5. Boel A, de Saffel H, Steyaert W, Callewaert B, de Paepe A, Coucke PJ. CRISPR/Cas9‐mediated homology‐directed repair by ssODNs in zebrafish induces complex mutational patterns resulting from genomic integration of repair‐template fragments. Disease Models and Mechanisms 2018;11(10):dmm035352.
    doi: 10.1242/dmm.035352google scholar: lookup
  6. Carson AR, Smith EN, Matsui H, Brækkan SK, Jepsen K, Hansen JB. Effective filtering strategies to improve data quality from population‐based whole exome sequencing studies. BMC Bioinformatics 2014;15(1):125.
    doi: 10.1186/1471-2105-15-125google scholar: lookup
  7. Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G. The European variation archive: a FAIR resource of genomic variation for all species. Nucleic Acids Research 2022;50(D1):D1216–D1220.
    doi: 10.1093/nar/gkab960google scholar: lookup
  8. Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Current Protocols in Bioinformatics 2004;4:4.10.1–4.10.14.
    doi: 10.1002/0471250953.bi0410s05google scholar: lookup
  9. Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Källberg M. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 2016;32(8):1220–1222.
    doi: 10.1093/bioinformatics/btv710google scholar: lookup
  10. Cheung HW, Wong KS, Lin VYC, Wan TSM, Ho ENM. A duplex qPCR assay for human erythropoietin (EPO) transgene to control gene doping in horses. Drug Testing and Analysis 2021;13(1):113–121.
    doi: 10.1002/dta.2907google scholar: lookup
  11. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 2017;33(18):2938–2940.
    doi: 10.1093/bioinformatics/btx364google scholar: lookup
  12. Dall'Olio S, Scotti E, Fontanesi L, Tassinari M. Analysis of the 227 bp short interspersed nuclear element (SINE) insertion of the promoter of the myostatin (MSTN) gene in different horse breeds. Veterinaria Italiana 2014;50(3):193–197.
    doi: 10.12834/vetit.61.178.3google scholar: lookup
  13. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO. Twelve years of SAMtools and BCFtools. GigaScience 2021;10(2):giab008.
    doi: 10.1093/gigascience/giab008google scholar: lookup
  14. El Refaey M, Xu L, Gao Y, Canan BD, Adesanya TMA, Warner SC. In vivo genome editing restores dystrophin expression and cardiac function in dystrophic mice. Circulation Research 2017;121(8):923–929.
    doi: 10.1161/circresaha.117.310996google scholar: lookup
  15. Erbs V, Lorentz R, Eisenman B, Schaeffer L, Luppi L, Lindner L. Increased on‐target rate and risk of concatemerization after CRISPR‐enhanced targeting in ES cells. Genes 2023;14(2):401.
    doi: 10.3390/genes14020401/s1google scholar: lookup
  16. Furukawa R, Tozaki T, Mizukami K, Iwasaki Y, Kawate K, Kikuchi M. Mutation rate and spectrum of germline de novo mutations in a closed population of Thoroughbred horses. Journal of Equine Veterinary Science 154, 105682.
    doi: 10.1016/j.jevs.2025.105682google scholar: lookup
  17. Garrison E, Marth G. Haplotype‐based variant detection from short‐read sequencing. .
  18. Genovese G, Handsaker RE, Li H, Kenny EE, McCarroll SA. Mapping the human reference Genome's missing sequence by three‐way admixture in Latino genomes. American Journal of Human Genetics 93(3), 411–421.
    doi: 10.1016/j.ajhg.2013.07.002google scholar: lookup
  19. Han H, McGivney BA, Allen L, Bai D, Corduff LR, Davaakhuu G. Common protein‐coding variants influence the racing phenotype in galloping racehorse breeds. Communications Biology 5(1), 1320.
    doi: 10.1038/s42003-022-04206-xgoogle scholar: lookup
  20. Hanlon KS, Kleinstiver BP, Garcia SP, Zaborowski MP, Volak A, Spirig SE. High levels of AAV vector integration into CRISPR‐induced DNA breaks. Nature Communications 10(1), 4439.
    doi: 10.1038/s41467-019-12449-2google scholar: lookup
  21. Jiang Z, Haughan J, Moss KL, Stefanovski D, Ortved KF, Robinson MA. A quantitative PCR screening method for adeno‐associated viral vector 2‐mediated gene doping. Drug Testing and Analysis 14(5), 963–972.
    doi: 10.1002/dta.3152google scholar: lookup
  22. Johnson PL, Dodds KG, Bain WE, Greer GJ, McLean NJ, McLaren RJ. Investigations into the GDF8 g+6723G‐A polymorphism in New Zealand Texel sheep. Journal of Animal Science 87(6), 1856–1864.
    doi: 10.2527/jas.2008-1508google scholar: lookup
  23. Kalbfleisch TS, Rice ES, DePriest MS Jr, Walenz BP, Hestand MS, Vermeesch JR. Improved reference genome for the domestic horse increases assembly contiguity and composition. Communications Biology 1(1), 197.
    doi: 10.1038/s42003-018-0199-zgoogle scholar: lookup
  24. Kechin A, Borobova V, Boyarskikh U, Khrapov E, Subbotin S, Filipenko M. NGS‐PrimerPlex: high‐throughput primer design for multiplex polymerase chain reactions. PLoS Computational Biology 16(12), e1008468.
    doi: 10.1371/journal.pcbi.1008468google scholar: lookup
  25. Knaus BJ, Grünwald NJ. VcfR: an R package to manipulate and visualize VCF format data. BioRxiv [Preprint] .
    doi: 10.1101/041277google scholar: lookup
  26. Langmead B, Salzberg SL. Fast gapped‐read alignment with bowtie 2. Nature Methods 9(4), 357–359.
    doi: 10.1038/nmeth.1923google scholar: lookup
  27. Lefouili M, Nam K. The evaluation of Bcftools mpileup and GATK HaplotypeCaller for variant calling in non‐human species. Scientific Reports 12(1), 11331.
    doi: 10.1038/s41598-022-15563-2google scholar: lookup
  28. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34(18), 3094–3100.
    doi: 10.1093/bioinformatics/bty191google scholar: lookup
  29. Li H, Durbin R. Fast and accurate short read alignment with burrows‐wheeler transform. Bioinformatics 25(14), 1754–1760.
    doi: 10.1093/bioinformatics/btp324google scholar: lookup
  30. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N. The sequence alignment/map format and SAMtools. Bioinformatics 25(16), 2078–2079.
    doi: 10.1093/bioinformatics/btp352google scholar: lookup
  31. Lim K. Mitochondrial genome editing: strategies, challenges, and applications. BMB Reports 57(1), 19–29.
    doi: 10.5483/bmbrep.2023-0224google scholar: lookup
  32. Lin Y. Application of CRISPR/Cas9 system in establishing large animal models. Frontiers in Cell and Developmental Biology 10, 919155.
    doi: 10.3389/fcell.2022.919155/bibtexgoogle scholar: lookup
  33. Lord J, McMullan DJ, Eberhardt RY, Rinck G, Hamilton SJ, Quinlan-Jones E. Prenatal exome sequencing analysis in fetal structural anomalies detected by ultrasonography (PAGE): a cohort study. The Lancet 393(10173), 747–757.
    doi: 10.1016/s0140-6736(18)31940-8google scholar: lookup
  34. Luqman MW, Jenjaroenpun P, Spathos J, Shingte N, Cummins M, Nimsamer P. Long read sequencing reveals transgene concatemerization and vector sequences integration following AAV-driven electroporation of CRISPR RNP complexes in mouse zygotes. Frontiers in Genome Editing 7, 1582097.
    doi: 10.3389/fgeed.2025.1582097/bibtexgoogle scholar: lookup
  35. Mahmoud M, Gobet N, Cruz-Dávalos DI, Mounier N, Dessimoz C, Sedlazeck FJ. Structural variant calling: the long and the short of it. Genome Biology 20(1), 246.
    doi: 10.1186/s13059-019-1828-7/tables/2google scholar: lookup
  36. Maniego J, Giles O, Hincks P, Stewart G, Proudman C, Ryder E. Long-read sequencing assays designed to detect potential gene editing events in the myostatin gene revealed distinct haplotype signatures in the thoroughbred horse population. Animal Genetics 54(4), 470–482.
    doi: 10.1111/age.13332google scholar: lookup
  37. Maniego J, Harding C, Habershon-Butcher J, Hincks P, Ryder E. Administration and detection of a multi-target rAAV gene doping vector in horses using multiple matrices and molecular techniques. Gene Therapy 31(9–10), 477–488.
    doi: 10.1038/s41434-024-00462-0google scholar: lookup
  38. Maniego J, Pesko B, Habershon-Butcher J, Hincks P, Taylor P, Tozaki T. Use of mitochondrial sequencing to detect gene doping in horses via gene editing and somatic cell nuclear transfer. Drug Testing and Analysis 14(8), 1429–1437.
    doi: 10.1002/dta.3267google scholar: lookup
  39. Maniego J, Pesko B, Habershon-Butcher J, Huggett J, Taylor P, Scarth J. Screening for gene doping transgenes in horses via the use of massively parallel sequencing. Gene Therapy 29(5), 236–246.
    doi: 10.1038/s41434-021-00279-1google scholar: lookup
  40. McGivney BA, Han H, Corduff LR, Katz LM, Tozaki T, MacHugh DE. Genomic inbreeding trends, influential sire lines and selection in the global thoroughbred horse population. Scientific Reports 10(1), 466.
    doi: 10.1038/s41598-019-57389-5google scholar: lookup
  41. Mehravar M, Shirazi A, Nazari M, Banan M. Mosaicism in CRISPR/Cas9-mediated genome editing. Developmental Biology 445(2), 156–162.
    doi: 10.1016/j.ydbio.2018.10.008google scholar: lookup
  42. Meynert AM, Ansari M, FitzPatrick DR, Taylor MS. Variant detection sensitivity and biases in whole genome and exome sequencing. BMC Bioinformatics 15(1), 247.
    doi: 10.1186/1471-2105-15-247google scholar: lookup
  43. Mianné J, Codner GF, Caulder A, Fell R, Hutchison M, King R. Analysing the outcome of CRISPR-aided genome editing in embryos: screening, genotyping and quality control. Methods 121, 68–76.
    doi: 10.1016/j.ymeth.2017.03.016google scholar: lookup
  44. Monteys AM, Ebanks SA, Keiser MS, Davidson BL. CRISPR/Cas9 editing of the mutant huntingtin allele in vitro and in vivo. Molecular Therapy 25(1), 12–23.
    doi: 10.1016/j.ymthe.2016.11.010google scholar: lookup
  45. Moro LN, Viale DL, Bastón JI, Arnold V, Suvá M, Wiedenmann E. Generation of myostatin edited horse embryos using CRISPR/Cas9 technology and somatic cell nuclear transfer. Scientific Reports 10(1), 15587.
    doi: 10.1038/s41598-020-72040-4google scholar: lookup
  46. Mu W, Lu HM, Chen J, Li S, Elliott AM. Sanger confirmation is required to achieve optimal sensitivity and specificity in next-generation sequencing panel testing. Journal of Molecular Diagnostics 18(6), 923–932.
    doi: 10.1016/j.jmoldx.2016.07.006google scholar: lookup
  47. Munté E, Roca C, del Valle J, Feliubadaló L, Pineda M, Gel B. Detection of germline CNVs from gene panel data: benchmarking the state of the art. Briefings in Bioinformatics 26(1), 645.
    doi: 10.1093/bib/bbae645google scholar: lookup
  48. Musunuru K, Grandinette SA, Wang X, Hudson TR, Briseno K, Berry AM. Patient-specific in vivo gene editing to treat a rare genetic disease. New England Journal of Medicine 392, 2235–2243.
    doi: 10.1056/nejmoa2504747google scholar: lookup
  49. Ou J, Zhu LJ. trackViewer: a Bioconductor package for interactive and integrative visualization of multi-omics data. Nature Methods 16(6), 453–454.
    doi: 10.1038/s41592-019-0430-ygoogle scholar: lookup
  50. Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34(5), 867–868.
    doi: 10.1093/bioinformatics/btx699google scholar: lookup
  51. Perez G, Barber GP, Benet-Pages A, Casper J, Clawson H, Diekhans M. The UCSC genome browser database: 2025 update. Nucleic Acids Research 53(D1), D1243–D1249.
    doi: 10.1093/nar/gkae974google scholar: lookup
  52. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E. Genetic diversity in the modern horse illustrated from genome-wide SNP data. PLoS One 8(1), e54997.
    doi: 10.1371/journal.pone.0054997google scholar: lookup
  53. Pinzon-Arteaga C, Snyder MD, Lazzarotto CR, Moreno NF, Juras R, Raudsepp T. Efficient correction of a deleterious point mutation in primary horse fibroblasts with CRISPR-Cas9. Scientific Reports 10(1), 7411.
    doi: 10.1038/s41598-020-62723-3google scholar: lookup
  54. Poplin R, Ruano-Rubio V, DePristo MA, Fennell TJ, Carneiro MO, Van der Auwera GA. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv p. 201178.
    doi: 10.1101/201178google scholar: lookup
  55. Rajaby R, Liu DX, Au CH, Cheung YT, Lau AYT, Yang QY. INSurVeyor: improving insertion calling from short read sequencing data. Nature Communications 14(1), 3243.
    doi: 10.1038/s41467-023-38870-2google scholar: lookup
  56. Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28(18), i333–i339.
    doi: 10.1093/bioinformatics/bts378google scholar: lookup
  57. Ryder E, Gleeson D, Sethi D, Vyas S, Miklejewska E, Dalvi P. Molecular characterization of mutant mouse strains generated from the EUCOMM/KOMP-CSD ES cell resource. Mammalian Genome 24(7–8), 286–294.
    doi: 10.1007/s00335-013-9467-xgoogle scholar: lookup
  58. Sasani TA, Pedersen BS, Gao Z, Baird L, Przeworski M, Jorde LB. Large, three-generation human families reveal post-zygotic mosaicism and variability in germline mutation accumulation. eLife 8, e46922.
    doi: 10.7554/elife.46922google scholar: lookup
  59. Satsangi J, Jewell DP, Welsh K, Bunce M, Bell JI. Effect of heparin on polymerase chain reaction. Lancet 343, 1509–1510.
    doi: 10.1016/s0140-6736(94)92622-0google scholar: lookup
  60. Schuelke M, Wagner KR, Stolz LE, Hübner C, Riebel T, Kömen W. Myostatin mutation associated with gross muscle hypertrophy in a child. New England Journal of Medicine 350(26), 2682–2688.
    doi: 10.1056/nejmoa040933google scholar: lookup
  61. Suchy FP, Karigane D, Nakauchi Y, Higuchi M, Zhang J, Pekrun K. Genome engineering with Cas9 and AAV repair templates generates frequent concatemeric insertions of viral vectors. Nature Biotechnology 43(2), 204–213.
    doi: 10.1038/s41587-024-02171-wgoogle scholar: lookup
  62. Tourvas N, Ganopoulos I, Koubouris G, Kostelenos G, Manthos I, Bazakos C. Wild and cultivated olive tree genetic diversity in Greece: a diverse resource in danger of erosion. Frontiers in Genetics 14, 1298565.
    doi: 10.3389/fgene.2023.1298565/epubgoogle scholar: lookup
  63. Tozaki T, Hamilton NA. Control of gene doping in human and horse sports. Gene Therapy 29(3–4), 107–112.
    doi: 10.1038/s41434-021-00267-5google scholar: lookup
  64. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota K. Microfluidic quantitative PCR detection of 12 transgenes from horse plasma for gene doping control. Genes 11(4), 457.
    doi: 10.3390/genes11040457google scholar: lookup
  65. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota KI. Whole-genome resequencing using genomic DNA extracted from horsehair roots for gene-doping control in horse sports. Journal of Equine Science 31(4), 75–83.
    doi: 10.1294/jes.31.75google scholar: lookup
  66. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota KI. Rare and common variant discovery by whole-genome sequencing of 101 thoroughbred racehorses. Scientific Reports 11(1), 16057.
    doi: 10.1038/s41598-021-95669-1google scholar: lookup
  67. Tozaki T, Ohnuma A, Nakamura K, Hano K, Takasu M, Takahashi Y. Detection of indiscriminate genetic manipulation in thoroughbred racehorses by targeted resequencing for gene-doping control. Genes 13(9), 1589.
    doi: 10.3390/genes13091589google scholar: lookup
  68. Tozaki T, Ohnuma A, Takasu M, Nakamura K, Kikuchi M, Ishige T. Detection of non-targeted transgenes by whole-genome resequencing for gene-doping control. Gene Therapy 28(3–4), 199–205.
    doi: 10.1038/s41434-020-00185-ygoogle scholar: lookup
  69. Van der Auwera, G., O'Connor, B. & Safari. (2020) Genomics in the cloud: using docker, GATK, and WDL in Terra. O'Reilly Media, p. 300. https://www.oreilly.com/library/view/genomics‐in‐the/9781491975183/
  70. Van LTK, Hien HTD, Kieu HTT, Hieu NLT, Vinh LS, Hoa G. De novo homozygous variant of the SCN1A gene in a patient with severe Dravet syndrome complicated by acute encephalopathy. Neurogenetics 22(2), 133–136.
    doi: 10.1007/s10048-021-00636-7google scholar: lookup
  71. Van Nieuwerburgh F, Goetghebeur E, Vandewoestyne M, Deforce D. Impact of allelic dropout on evidential value of forensic DNA profiles using RMNE. Bioinformatics 25(2), 225–229.
    doi: 10.1093/bioinformatics/btn608google scholar: lookup
  72. Wilkin T, Hamilton NA, Cawley AT, Bhat S, Baoutina A. PCR-based equine gene doping test for the Australian horseracing industry. International Journal of Molecular Sciences 25(5), 2570.
    doi: 10.3390/ijms25052570google scholar: lookup
  73. Wright CF, Campbell P, Eberhardt RY, Aitken S, Perrett D, Brent S. Genomic diagnosis of rare pediatric disease in the United Kingdom and Ireland. New England Journal of Medicine 388(17), 1559–1571.
    doi: 10.1056/nejmoa2209046google scholar: lookup
  74. Yang W, Chen X, Li S, Li XJ. Genetically modified large animal models for investigating neurodegenerative diseases. Cell and Bioscience 11(1), 218.
    doi: 10.1186/s13578-021-00729-8google scholar: lookup
  75. Young AE, Mansour TA, McNabb BR, Owen JR, Trott JF, Brown CT. Genomic and phenotypic analyses of six offspring of a genome-edited hornless bull. Nature Biotechnology 38(2), 225–232.
    doi: 10.1038/s41587-019-0266-0google scholar: lookup
  76. Zhang X, Qiu M, Han B, Liao L, Peng X, Lin J. Generation and propagation of high fecundity gene edited fine wool sheep by CRISPR/Cas9. Scientific Reports 15(1), 2557.
    doi: 10.1038/s41598-025-86592-wgoogle scholar: lookup
  77. Zhang ZD, du J, Lam H, Abyzov A, Urban AE, Snyder M. Identification of genomic indels and structural variations using split reads. BMC Genomics 12, 375.
    doi: 10.1186/1471-2164-12-375google scholar: lookup

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