IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0201889.html
   My bibliography  Save this article

Population structure, relatedness and ploidy levels in an apple gene bank revealed through genotyping-by-sequencing

Author

Listed:
  • Bjarne Larsen
  • Kyle Gardner
  • Carsten Pedersen
  • Marian Ørgaard
  • Zoë Migicovsky
  • Sean Myles
  • Torben Bo Toldam-Andersen

Abstract

In recent years, new genome-wide marker systems have provided highly informative alternatives to low density marker systems for evaluating plant populations. To date, most apple germplasm collections have been genotyped using low-density markers such as simple sequence repeats (SSRs), whereas only a few have been explored using high-density genome-wide marker information. We explored the genetic diversity of the Pometum gene bank collection (University of Copenhagen, Denmark) of 349 apple accessions using over 15,000 genome-wide single nucleotide polymorphisms (SNPs) and 15 SSR markers, in order to compare the strength of the two approaches for describing population structure. We found that 119 accessions shared a putative clonal relationship with at least one other accession in the collection, resulting in the identification of 272 (78%) unique accessions. Of these unique accessions, over half (52%) share a first-degree relationship with at least one other accession. There is therefore a high degree of clonal and family relatedness in the Danish apple gene bank. We find significant genetic differentiation between Malus domestica and its supposed primary wild ancestor, M. sieversii, as well as between accessions of Danish origin and all others. Using the GBS approach allowed us to estimate ploidy levels, which were in accordance with flow cytometry results. Overall, we found strong concordance between analyses based on the genome-wide SNPs and the 15 SSR loci. However, we argue that GBS is superior to traditional SSR approaches because it allows detection of a much more detailed population structure and can be further exploited in genome-wide association studies (GWAS). Finally, we compare GBS with SSR for the purpose of identifying clones and pedigree relations in a diverse apple gene bank and discuss the advantages and constraints of the two approaches.

Suggested Citation

  • Bjarne Larsen & Kyle Gardner & Carsten Pedersen & Marian Ørgaard & Zoë Migicovsky & Sean Myles & Torben Bo Toldam-Andersen, 2018. "Population structure, relatedness and ploidy levels in an apple gene bank revealed through genotyping-by-sequencing," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
  • Handle: RePEc:plo:pone00:0201889
    DOI: 10.1371/journal.pone.0201889
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201889
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0201889&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0201889?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Butts, Carter T., 2008. "network: A Package for Managing Relational Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i02).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bender-deMol, Skye & Morris, Martina & Moody, James, 2008. "Prototype Packages for Managing and Animating Longitudinal Network Data: dynamicnetwork and rSoNIA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i07).
    2. Groenen, Patrick J. F. & van de Velden, Michel, 2016. "Multidimensional Scaling by Majorization: A Review," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i08).
    3. Milad Abbasiharofteh & Tom Broekel, 2021. "Still in the shadow of the wall? The case of the Berlin biotechnology cluster," Environment and Planning A, , vol. 53(1), pages 73-94, February.
    4. Shulgin, Sergey & Zinkina, Julia & Korotayev, Andrey, 2017. "“Neighbors in values”: A new dataset of cultural distances between countries based on individuals’ values, and its application to the study of global trade," Research in International Business and Finance, Elsevier, vol. 42(C), pages 966-985.
    5. Mengyu Yu & Mazie Krehbiel & Samantha Thompson & Tatjana Miljkovic, 2020. "An exploration of gender gap using advanced data science tools: actuarial research community," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 767-789, May.
    6. Olivier Sire & Salim Lardjane, 2014. "VORTEX, a dedicated tool for the development of territorial intelligence based on a systemic approach of the social network for innovation in Brittany [VORTEX, un outil d'intelligence territoriale ," Working Papers hal-01098549, HAL.
    7. Francesca Mateo & Zhengcheng He & Lin Mei & Gorka Ruiz de Garibay & Carmen Herranz & Nadia García & Amanda Lorentzian & Alexandra Baiges & Eline Blommaert & Antonio Gómez & Oriol Mirallas & Anna Garri, 2022. "Modification of BRCA1-associated breast cancer risk by HMMR overexpression," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Caimo, Alberto & Friel, Nial, 2014. "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i02).
    9. Paula Ianishi & Oilson Alberto Gonzatto Junior & Marcos Jardel Henriques & Diego Carvalho do Nascimento & Gabriel Kamada Mattar & Pedro Luiz Ramos & Anderson Ara & Francisco Louzada, 2022. "Probability on Graphical Structure: A Knowledge-Based Agricultural Case," Annals of Data Science, Springer, vol. 9(2), pages 327-345, April.
    10. repec:jss:jstsof:24:i07 is not listed on IDEAS
    11. Suesse Thomas & Chambers Ray, 2018. "Using Social Network Information for Survey Estimation," Journal of Official Statistics, Sciendo, vol. 34(1), pages 181-209, March.
    12. Johannes Pol, 2019. "Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 845-875, October.
    13. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2013. "ergm.userterms: A Template Package for Extending statnet," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i02).
    14. Johannes VAN DER POL, 2016. "The modelling of networks using Exponential Random Graph Models: an introduction," Cahiers du GREThA (2007-2019) 2016-22, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    15. Georgia Pollard & James Ward & Philip Roetman, 2018. "Typically Diverse: The Nature of Urban Agriculture in South Australia," Sustainability, MDPI, vol. 10(4), pages 1-18, March.
    16. Alfonso Langle-Flores & Zinthia López-Vázquez & Rosa María Chávez-Dagostino & Adriana Aguilar-Rodríguez, 2022. "COVID-19 Impacts on Whale-Watching Collaboration Networks," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    17. Elina H. Hwang & David Krackhardt, 2020. "Online Knowledge Communities: Breaking or Sustaining Knowledge Silos?," Production and Operations Management, Production and Operations Management Society, vol. 29(1), pages 138-155, January.
    18. Johannes van Der Pol, 2017. "Introduction to network modeling using Exponential Random Graph models (ERGM)," Working Papers hal-01284994, HAL.
    19. Vasaf, Esmaeil & Sanatkhani, Mahboobeh, 2014. "Dynamic of Publication Network in German Photovoltaic Industry," MPRA Paper 65453, University Library of Munich, Germany.
    20. Juan D Montoro-Pons & Manuel Cuadrado-García, 2021. "Analyzing online search patterns of music festival tourists," Tourism Economics, , vol. 27(6), pages 1276-1300, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0201889. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.