Analyze Diet
Animals : an open access journal from MDPI2023; 13(13); doi: 10.3390/ani13132227

Application of Nanopore Sequencing for High Throughput Genotyping in Horses.

Abstract: Nanopore sequencing is a third-generation biopolymer sequencing technique that relies on monitoring the changes in an electrical current that occur as nucleic acids are passed through a protein nanopore. Increasing quality of reads generated by nanopore sequencing systems encourages their application in genome-wide polymorphism detection and genotyping. In this study, we employed nanopore sequencing to identify genome-wide polymorphisms in the horse genome. To reduce the size and complexity of genome fragments for sequencing in a simple and cost-efficient manner, we amplified random DNA fragments using a modified DOP-PCR and sequenced the resulting products using the MinION system. After initial filtering, this generated 28,426 polymorphisms, which were validated at a 3% error rate. Upon further filtering for polymorphism and reproducibility, we identified 9495 SNPs that reflected the horse population structure. To conclude, the use of nanopore sequencing, in conjunction with a genome enrichment step, is a promising tool that can be practical in a variety of applications, including genotyping, population genomics, association studies, linkage mapping, and potentially genomic selection.
Publication Date: 2023-07-06 PubMed ID: 37444025PubMed Central: PMC10340048DOI: 10.3390/ani13132227Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • 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.

This research paper discusses the use of Nanopore sequencing for genotyping in horses, effectively identifying nearly 10,000 single nucleotide polymorphisms (SNPs) that reflect the horse population structure.

Introduction to Nanopore Sequencing

  • Nanopore sequencing is a novel biopolymer sequencing technique. It identifies nucleic acids by measuring changes in electrical current as these molecules pass through a protein nanopore.
  • Due to advances in the technique which have improved the quality of reads, nanopore sequencing is increasingly being used for detecting genome-wide polymorphisms and genotyping.

Methodology

  • For this study, nanopore sequencing was used to identify genome-wide polymorphisms in the horse genome.
  • The researchers reduced the size and complexity of genome fragments to be sequenced using a modified DOP-PCR technique. This resulted in random DNA fragments being amplified.
  • Following this, the amplified DNA fragments were then sequenced using the MinION system.

Results

  • The initial screening process led to the detection of 28,426 polymorphisms. These were validated with a 3% error rate.
  • Upon further filtering for polymorphism and reproducibility, 9495 single nucleotide polymorphisms (SNPs) were isolated. These SNPs reflect the horse population structure.

Conclusion

  • The study concluded that the use of nanopore sequencing, partnered with a genome enrichment step, presents a promising tool for genotyping horses.
  • Furthermore, the technique could have practical implications in various applications, such as population genomics, association studies, linkage mapping, and potentially genomic selection.

Cite This Article

APA
Gurgul A, Jasielczuk I, Szmatoła T, Sawicki S, Semik-Gurgul E, Długosz B, Bugno-Poniewierska M. (2023). Application of Nanopore Sequencing for High Throughput Genotyping in Horses. Animals (Basel), 13(13). https://doi.org/10.3390/ani13132227

Publication

ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 13
Issue: 13

Researcher Affiliations

Gurgul, Artur
  • Center of Experimental and Innovative Medicine, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Jasielczuk, Igor
  • Center of Experimental and Innovative Medicine, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Szmatoła, Tomasz
  • Center of Experimental and Innovative Medicine, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Sawicki, Sebastian
  • Department of Animal Reproduction, Anatomy and Genomics, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Semik-Gurgul, Ewelina
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Krakowska 1, 32-083 Balice, Poland.
Długosz, Bogusława
  • Department of Animal Reproduction, Anatomy and Genomics, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Bugno-Poniewierska, Monika
  • Department of Animal Reproduction, Anatomy and Genomics, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.

Grant Funding

  • POIR.01.03.01-00-0005/17 / National Center for Research and Development

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 36 references
  1. Slatko BE, Gardner AF, Ausubel FM. Overview of Next-Generation Sequencing Technologies.. Curr Protoc Mol Biol 2018 Apr;122(1):e59.
    doi: 10.1002/cpmb.59pmc: PMC6020069pubmed: 29851291google scholar: lookup
  2. Ari Ş, Arikan M. Next-Generation Sequencing: Advantages, Disadvantages, and Future. Plant Omics: Trends and Applications 2016;pp. 109–135.
  3. Athanasopoulou K, Boti MA, Adamopoulos PG, Skourou PC, Scorilas A. Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics.. Life (Basel) 2021 Dec 26;12(1).
    doi: 10.3390/life12010030pmc: PMC8780579pubmed: 35054423google scholar: lookup
  4. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications.. Nat Biotechnol 2021 Nov;39(11):1348-1365.
    doi: 10.1038/s41587-021-01108-xpmc: PMC8988251pubmed: 34750572google scholar: lookup
  5. Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P, Reily AD, Courtney L, Kruchowski SS, Tomlinson C, Strong C, Delehaunty K, Fronick C, Courtney B, Rock SM, Belter E, Du F, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, Chen W, Yan L, Higginbotham J, Cardenas M, Waligorski J, Applebaum E, Phelps L, Falcone J, Kanchi K, Thane T, Scimone A, Thane N, Henke J, Wang T, Ruppert J, Shah N, Rotter K, Hodges J, Ingenthron E, Cordes M, Kohlberg S, Sgro J, Delgado B, Mead K, Chinwalla A, Leonard S, Crouse K, Collura K, Kudrna D, Currie J, He R, Angelova A, Rajasekar S, Mueller T, Lomeli R, Scara G, Ko A, Delaney K, Wissotski M, Lopez G, Campos D, Braidotti M, Ashley E, Golser W, Kim H, Lee S, Lin J, Dujmic Z, Kim W, Talag J, Zuccolo A, Fan C, Sebastian A, Kramer M, Spiegel L, Nascimento L, Zutavern T, Miller B, Ambroise C, Muller S, Spooner W, Narechania A, Ren L, Wei S, Kumari S, Faga B, Levy MJ, McMahan L, Van Buren P, Vaughn MW, Ying K, Yeh CT, Emrich SJ, Jia Y, Kalyanaraman A, Hsia AP, Barbazuk WB, Baucom RS, Brutnell TP, Carpita NC, Chaparro C, Chia JM, Deragon JM, Estill JC, Fu Y, Jeddeloh JA, Han Y, Lee H, Li P, Lisch DR, Liu S, Liu Z, Nagel DH, McCann MC, SanMiguel P, Myers AM, Nettleton D, Nguyen J, Penning BW, Ponnala L, Schneider KL, Schwartz DC, Sharma A, Soderlund C, Springer NM, Sun Q, Wang H, Waterman M, Westerman R, Wolfgruber TK, Yang L, Yu Y, Zhang L, Zhou S, Zhu Q, Bennetzen JL, Dawe RK, Jiang J, Jiang N, Presting GG, Wessler SR, Aluru S, Martienssen RA, Clifton SW, McCombie WR, Wing RA, Wilson RK. The B73 maize genome: complexity, diversity, and dynamics.. Science 2009 Nov 20;326(5956):1112-5.
    doi: 10.1126/science.1178534pubmed: 19965430google scholar: lookup
  6. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species.. PLoS One 2011 May 4;6(5):e19379.
  7. De Donato M, Peters SO, Mitchell SE, Hussain T, Imumorin IG. Genotyping-by-sequencing (GBS): a novel, efficient and cost-effective genotyping method for cattle using next-generation sequencing.. PLoS One 2013;8(5):e62137.
  8. Gurgul A, Miksza-Cybulska A, Szmatoła T, Jasielczuk I, Piestrzyńska-Kajtoch A, Fornal A, Semik-Gurgul E, Bugno-Poniewierska M. Genotyping-by-sequencing performance in selected livestock species.. Genomics 2019 Mar;111(2):186-195.
    doi: 10.1016/j.ygeno.2018.02.002pubmed: 29427639google scholar: lookup
  9. Telenius H, Carter NP, Bebb CE, Nordenskjöld M, Ponder BA, Tunnacliffe A. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer.. Genomics 1992 Jul;13(3):718-25.
    doi: 10.1016/0888-7543(92)90147-Kpubmed: 1639399google scholar: lookup
  10. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes.. Nat Rev Genet 2009 Jun;10(6):381-91.
    doi: 10.1038/nrg2575pubmed: 19448663google scholar: lookup
  11. Zhang H, Sachdev PS, Wen W, Kochan NA, Crawford JD, Brodaty H, Slavin MJ, Reppermund S, Draper B, Zhu W, Kang K, Trollor JN. Gray matter atrophy patterns of mild cognitive impairment subtypes.. J Neurol Sci 2012 Apr 15;315(1-2):26-32.
    doi: 10.1016/j.jns.2011.12.011pubmed: 22280946google scholar: lookup
  12. Szmatoła T, Gurgul A, Ropka-Molik K, Jasielczuk I, Zabek T, Bugno-Poniewierska M. Characteristics of runs of homozygosity in selected cattle breeds maintained in Poland. Livest. Sci. 2016;188:72–80.
  13. Meuwissen T, Hayes B, Goddard M. Genomic selection: A paradigm shift in animal breeding. Anim. Front. 2016;6:6–14.
    doi: 10.2527/af.2016-0002google scholar: lookup
  14. Panetto J.C.d.C., Machado M.A., da Silva M.V.G.B., Barbosa R.S., dos Santos G.G., Leite R.d.M.H., Peixoto M.G.C.D.. Parentage assignment using SNP markers, inbreeding and population size for the Brazilian Red Sindhi cattle. Livest. Sci. 2017;204:33–38.
  15. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor.. Bioinformatics 2018 Sep 1;34(17):i884-i890.
  16. Li H. Minimap2: pairwise alignment for nucleotide sequences.. Bioinformatics 2018 Sep 15;34(18):3094-3100.
  17. Garrison E.P., Marth G.T.. Haplotype-based variant detection from short-read sequencing. arXiv 2012.
    doi: 10.48550/arXiv.1207.3907google scholar: lookup
  18. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R. The variant call format and VCFtools.. Bioinformatics 2011 Aug 1;27(15):2156-8.
  19. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor.. Genome Biol 2016 Jun 6;17(1):122.
    doi: 10.1186/s13059-016-0974-4pmc: PMC4893825pubmed: 27268795google scholar: lookup
  20. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007 Sep;81(3):559-75.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  21. Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data.. Mol Ecol Resour 2014 Jan;14(1):209-14.
    doi: 10.1111/1755-0998.12157pubmed: 23992227google scholar: lookup
  22. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information.. Mol Ecol Resour 2009 Sep;9(5):1322-32.
  23. Li YL, Liu JX. StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods.. Mol Ecol Resour 2018 Jan;18(1):176-177.
    doi: 10.1111/1755-0998.12719pubmed: 28921901google scholar: lookup
  24. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.. Mol Ecol 2005 Jul;14(8):2611-20.
  25. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K.. Mol Ecol Resour 2015 Sep;15(5):1179-91.
    doi: 10.1111/1755-0998.12387pmc: PMC4534335pubmed: 25684545google scholar: lookup
  26. Kõressaar T, Lepamets M, Kaplinski L, Raime K, Andreson R, Remm M. Primer3_masker: integrating masking of template sequence with primer design software.. Bioinformatics 2018 Jun 1;34(11):1937-1938.
    doi: 10.1093/bioinformatics/bty036pubmed: 29360956google scholar: lookup
  27. Kono N, Arakawa K. Nanopore sequencing: Review of potential applications in functional genomics.. Dev Growth Differ 2019 Jun;61(5):316-326.
    doi: 10.1111/dgd.12608pubmed: 31037722google scholar: lookup
  28. Lamb HJ, Hayes BJ, Randhawa IAS, Nguyen LT, Ross EM. Genomic prediction using low-coverage portable Nanopore sequencing.. PLoS One 2021;16(12):e0261274.
  29. Delahaye C, Nicolas J. Sequencing DNA with nanopores: Troubles and biases.. PLoS One 2021;16(10):e0257521.
  30. Sahlin K, Medvedev P. Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis.. Nat Commun 2021 Jan 4;12(1):2.
    doi: 10.1038/s41467-020-20340-8pmc: PMC7782715pubmed: 33397972google scholar: lookup
  31. Dorfner M, Ott T, Ott P, Oberprieler C. Long-read genotyping with SLANG (Simple Long-read loci Assembly of Nanopore data for Genotyping).. Appl Plant Sci 2022 May-Jun;10(3):e11484.
    doi: 10.1002/aps3.11484pmc: PMC9215276pubmed: 35774992google scholar: lookup
  32. Malmberg MM, Spangenberg GC, Daetwyler HD, Cogan NOI. Assessment of low-coverage nanopore long read sequencing for SNP genotyping in doubled haploid canola (Brassica napus L.).. Sci Rep 2019 Jun 18;9(1):8688.
    doi: 10.1038/s41598-019-45131-0pmc: PMC6582154pubmed: 31213642google scholar: lookup
  33. Brouard JS, Boyle B, Ibeagha-Awemu EM, Bissonnette N. Low-depth genotyping-by-sequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation.. BMC Genet 2017 Apr 5;18(1):32.
    doi: 10.1186/s12863-017-0501-ypmc: PMC5382419pubmed: 28381212google scholar: lookup
  34. Jasielczuk I, Gurgul A, Szmatoła T, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. Linkage disequilibrium, haplotype blocks and historical effective population size in Arabian horses and selected Polish native horse breeds. Livest. Sci. 2020;239:104095.
  35. Mackowski M, Mucha S, Cholewinski G, Cieslak J. Genetic diversity in Hucul and Polish primitive horse breeds. Arch. Anim. Breed. 2015;58:23–31.
    doi: 10.5194/aab-58-23-2015google scholar: lookup
  36. Tabata Y, Matsuo Y, Fujii Y, Ohta A, Hirota K. Rapid detection of single nucleotide polymorphisms using the MinION nanopore sequencer: a feasibility study for perioperative precision medicine.. JA Clin Rep 2022 Mar 4;8(1):17.
    doi: 10.1186/s40981-022-00506-7pmc: PMC8897523pubmed: 35244794google scholar: lookup

Citations

This article has been cited 2 times.
  1. Moustakli E, Christopoulos P, Potiris A, Zikopoulos A, Mavrogianni D, Karampas G, Kathopoulis N, Anagnostaki I, Domali E, Tzallas AT, Drakakis P, Stavros S. Long-Read Sequencing and Structural Variant Detection: Unlocking the Hidden Genome in Rare Genetic Disorders. Diagnostics (Basel) 2025 Jul 17;15(14).
    doi: 10.3390/diagnostics15141803pubmed: 40722552google scholar: lookup
  2. Liu X, Zheng J, Ding J, Wu J, Zuo F, Zhang G. When Livestock Genomes Meet Third-Generation Sequencing Technology: From Opportunities to Applications. Genes (Basel) 2024 Feb 15;15(2).
    doi: 10.3390/genes15020245pubmed: 38397234google scholar: lookup