IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v15y2016i5p431-445n5.html
   My bibliography  Save this article

Evaluation of low-template DNA profiles using peak heights

Author

Listed:
  • Steele Christopher D.

    (University College London – UGI, Darwin Building Gower Street, London WC1E 6BT, United Kingdom of Great Britain and Northern Ireland)

  • Greenhalgh Matthew

    (Orchid Cellmark Ltd., Abingdon Business Park, Blacklands Way, Abingdon OX14 1YX, United Kingdom of Great Britain and Northern Ireland)

  • Balding David J.

    (University of Melbourne – Centre for Systems Genomics, School of BioSciences and School of Mathematics and Statistics, Melbourne, Victoria, Australia)

Abstract

In recent years statistical models for the analysis of complex (low-template and/or mixed) DNA profiles have moved from using only presence/absence information about allelic peaks in an electropherogram, to quantitative use of peak heights. This is challenging because peak heights are very variable and affected by a number of factors. We present a new peak-height model with important novel features, including over- and double-stutter, and a new approach to dropin. Our model is incorporated in open-source R code likeLTD. We apply it to 108 laboratory-generated crime-scene profiles and demonstrate techniques of model validation that are novel in the field. We use the results to explore the benefits of modeling peak heights, finding that it is not always advantageous, and to assess the merits of pre-extraction replication. We also introduce an approximation that can reduce computational complexity when there are multiple low-level contributors who are not of interest to the investigation, and we present a simple approximate adjustment for linkage between loci, making it possible to accommodate linkage when evaluating complex DNA profiles.

Suggested Citation

  • Steele Christopher D. & Greenhalgh Matthew & Balding David J., 2016. "Evaluation of low-template DNA profiles using peak heights," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(5), pages 431-445, October.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:5:p:431-445:n:5
    DOI: 10.1515/sagmb-2016-0038
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2016-0038
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2016-0038?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. R. G. Cowell & T. Graversen & S. L. Lauritzen & J. Mortera, 2015. "Analysis of forensic DNA mixtures with artefacts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 1-48, January.
    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. Fernando Rojas & Peter Wanke & Víctor Leiva & Mauricio Huerta & Carlos Martin-Barreiro, 2022. "Modeling Inventory Cost Savings and Supply Chain Success Factors: A Hybrid Robust Compromise Multi-Criteria Approach," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    2. Villacorta, Pablo J. & Verdegay, José L., 2016. "FuzzyStatProb: An R Package for the Estimation of Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i08).
    3. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
    4. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
    5. William Lim & Gaurav Khemka & David Pitt & Bridget Browne, 2019. "A method for calculating the implied no-recovery three-state transition matrix using observable population mortality incidence and disability prevalence rates among the elderly," Journal of Population Research, Springer, vol. 36(3), pages 245-282, September.
    6. Shi, Yanlin, 2022. "A closed-form estimator for the Markov switching in mean model," Finance Research Letters, Elsevier, vol. 44(C).
    7. Olschewski, Sebastian & Diao, Linan & Rieskamp, Jörg, 2021. "Reinforcement learning about asset variability and correlation in repeated portfolio decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    8. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    9. Moraes, Ricardo Kalil & Wanke, Peter Fernandes & Faria, João Ricardo, 2021. "Unveiling the endogeneity between social-welfare and labor efficiency: Two-stage NDEA neural network approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    10. Scheiter, Simon & Savadogo, Patrice, 2016. "Ecosystem management can mitigate vegetation shifts induced by climate change in West Africa," Ecological Modelling, Elsevier, vol. 332(C), pages 19-27.
    11. Marchetti, Dalmo & Wanke, Peter, 2020. "Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    12. Sebastián Cano-Berlanga & José-Manuel Giménez-Gómez, 2018. "On Chinese stock markets: How have they evolved over time?," Annals of Operations Research, Springer, vol. 266(1), pages 499-510, July.
    13. You, Kisung & Suh, Changhee, 2022. "Parameter estimation and model-based clustering with spherical normal distribution on the unit hypersphere," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    14. Joel E. Cohen & Christina Bohk & Roland Rau, 2018. "Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(29), pages 773-842.
    15. Nölte, Anja & Yousefpour, Rasoul & Hanewinkel, Marc, 2020. "Changes in sessile oak (Quercus petraea) productivity under climate change by improved leaf phenology in the 3-PG model," Ecological Modelling, Elsevier, vol. 438(C).
    16. Prudentia Zikalala & Isaya Kisekka & Mark Grismer, 2019. "Calibration and Global Sensitivity Analysis for a Salinity Model Used in Evaluating Fields Irrigated with Treated Wastewater in the Salinas Valley," Agriculture, MDPI, vol. 9(2), pages 1-33, February.
    17. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
    18. Su, Zheng’e & Zhao, Jin & Marek, Thomas H. & Liu, Ke & Harrison, Matthew Tom & Xue, Qingwu, 2022. "Drought tolerant maize hybrids have higher yields and lower water use under drought conditions at a regional scale," Agricultural Water Management, Elsevier, vol. 274(C).
    19. Antunes, Jorge & Tan, Yong & Wanke, Peter & Jabbour, Charbel Jose Chiappetta, 2023. "Impact of R&D and innovation in Chinese road transportation sustainability performance: A novel trigonometric envelopment analysis for ideal solutions (TEA-IS)," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    20. Sarah Riman & Hari Iyer & Peter M Vallone, 2021. "Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-30, September.

    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:bpj:sagmbi:v:15:y:2016:i:5:p:431-445:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.