IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v13y2014i5p11n1.html

Imputing genotypes using regularized generalized linear regression models

Author

Listed:
  • Wong William W.L.

    (Toronto Health Economics and Technology Assessment Collaborative, Leslie Dan Faculty of Pharmacy, University of Toronto, 6th Floor, Room 658, 144 College Street, Toronto M5S 3M2, ON, Canada)

  • Griesman Josh

    (Royal College of Surgeons in Ireland, 123 St. Stephens Green, Dublin 2, Ireland)

  • Feng Zeny Z.

    (Department of Mathematics and Statistics, University of Guelph, 50 Stone Road East, Guelph N1G2W1, ON, Canada)

Abstract

As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits. However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted by GLMNET, is proposed to impute missing genotypes. The method aims to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly the specification of the number of neighboring SNPs to be included for imputing the missing genotype. The performance of GLMNET-based method is compared to the conventional multinomial regression method and two phase-based methods: fastPHASE and BEAGLE. Two simulation scenarios are evaluated: a sparse-missing model, and a small-panel expansion model. The sparse-missing model simulates a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of individuals is only genotyped at a subset of the SNPs of the large panel. Each imputation method is tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Results show that the proposed GLMNET method outperforms the other methods in the small panel expansion scenario and fastPHASE performs slightly better than the GLMNET method in the sparse-missing scenario.

Suggested Citation

  • Wong William W.L. & Griesman Josh & Feng Zeny Z., 2014. "Imputing genotypes using regularized generalized linear regression models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 519-529, October.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:5:p:11:n:1
    DOI: 10.1515/sagmb-2012-0044
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2012-0044
    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-2012-0044?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    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. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    2. Hajime Shimao & Sung Joo Kim & Warut Khern-Am-Nuai & Maxime C. Cohen, 2025. "Revisiting the CEO Effect Through a Machine Learning Lens," Management Science, INFORMS, vol. 71(6), pages 5396-5408, June.
    3. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    4. Junyang Qian & Yosuke Tanigawa & Wenfei Du & Matthew Aguirre & Chris Chang & Robert Tibshirani & Manuel A Rivas & Trevor Hastie, 2020. "A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-30, October.
    5. Rangan Gupta & Christian Pierdzioch, 2024. "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
    6. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    7. Katarina M. Jørgensen & Ellen Færgestad Mosleth & Kristian Hovde Liland & Nancy B. Hopf & Rita Holdhus & Anne-Kristin Stavrum & Bjørn Tore Gjertsen & Jorunn Kirkeleit, 2018. "Gene Expression Response in Peripheral Blood Cells of Petroleum Workers Exposed to Sub-Ppm Benzene Levels," IJERPH, MDPI, vol. 15(11), pages 1-18, October.
    8. Mattia Zanon & Giovanni Sparacino & Andrea Facchinetti & Mark S. Talary & Andreas Caduff & Claudio Cobelli, 2013. "Regularised Model Identification Improves Accuracy of Multisensor Systems for Noninvasive Continuous Glucose Monitoring in Diabetes Management," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    9. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    10. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    11. Zhi Zhao & Manuela Zucknick, 2020. "Structured penalized regression for drug sensitivity prediction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 525-545, June.
    12. Immanuel Bayer & Philip Groth & Sebastian Schneckener, 2013. "Prediction Errors in Learning Drug Response from Gene Expression Data – Influence of Labeling, Sample Size, and Machine Learning Algorithm," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    13. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    14. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    15. Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    16. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    17. Karim Barigou & Stéphane Loisel & Yahia Salhi, 2020. "Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect," Risks, MDPI, vol. 9(1), pages 1-18, December.
    18. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    19. Samuel P Leighton & Rajeev Krishnadas & Kelly Chung & Alison Blair & Susie Brown & Suzy Clark & Kathryn Sowerbutts & Matthias Schwannauer & Jonathan Cavanagh & Andrew I Gumley, 2019. "Predicting one-year outcome in first episode psychosis using machine learning," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-14, March.
    20. Michael Funke & Helery Tasane, 2025. "Regional economic impacts of the Øresund cross-border fixed link: Cui Bono?," Regional Studies, Taylor & Francis Journals, vol. 59(1), pages 2573115-257, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:bpj:sagmbi:v:13:y:2014:i:5:p:11:n:1. 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.degruyterbrill.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.