IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v14y2015i3p253-264n3.html
   My bibliography  Save this article

Bayes factors based on robust TDT-type tests for family trio design

Author

Listed:
  • Yuan Min

    (School of mathematics, University of Science and Technology of China, Hefei 230026, Anhui, P.R. China)

  • Pan Xiaoqing

    (Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, Anhui, P.R. China)

  • Yang Yaning

    (Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, Anhui, P.R. China)

Abstract

Adaptive transmission disequilibrium test (aTDT) and MAX3 test are two robust-efficient association tests for case-parent family trio data. Both tests incorporate information of common genetic models including recessive, additive and dominant models and are efficient in power and robust to genetic model specifications. The aTDT uses information of departure from Hardy-Weinberg disequilibrium to identify the potential genetic model underlying the data and then applies the corresponding TDT-type test, and the MAX3 test is defined as the maximum of the absolute value of three TDT-type tests under the three common genetic models. In this article, we propose three robust Bayes procedures, the aTDT based Bayes factor, MAX3 based Bayes factor and Bayes model averaging (BMA), for association analysis with case-parent trio design. The asymptotic distributions of aTDT under the null and alternative hypothesis are derived in order to calculate its Bayes factor. Extensive simulations show that the Bayes factors and the p-values of the corresponding tests are generally consistent and these Bayes factors are robust to genetic model specifications, especially so when the priors on the genetic models are equal. When equal priors are used for the underlying genetic models, the Bayes factor method based on aTDT is more powerful than those based on MAX3 and Bayes model averaging. When the prior placed a small (large) probability on the true model, the Bayes factor based on aTDT (BMA) is more powerful. Analysis of a simulation data about RA from GAW15 is presented to illustrate applications of the proposed methods.

Suggested Citation

  • Yuan Min & Pan Xiaoqing & Yang Yaning, 2015. "Bayes factors based on robust TDT-type tests for family trio design," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 253-264, June.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:3:p:253-264:n:3
    DOI: 10.1515/sagmb-2014-0051
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2014-0051
    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-2014-0051?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. Yuan Min & Tian Xin & Zheng Gang & Yang Yaning, 2009. "Adaptive Transmission Disequilibrium Test for Family Trio Design," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-22, June.
    2. Valen E. Johnson, 2008. "Properties of Bayes Factors Based on Test Statistics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 354-368, June.
    3. Valen E. Johnson, 2005. "Bayes factors based on test statistics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 689-701, November.
    4. Gang Zheng & Qizhai Li & Ao Yuan, 2014. "Some Statistical Properties of Efficiency Robust Tests with Applications to Genetic Association Studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 762-774, September.
    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. Xiaoquan Wen, 2017. "Robust Bayesian FDR Control Using Bayes Factors, with Applications to Multi-tissue eQTL Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 28-49, June.
    2. Xiaoquan Wen, 2014. "Bayesian model selection in complex linear systems, as illustrated in genetic association studies," Biometrics, The International Biometric Society, vol. 70(1), pages 73-83, March.
    3. Jianhua Hu & Valen E. Johnson, 2009. "Bayesian model selection using test statistics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 143-158, January.
    4. Mariusz Czekala & Zbigniew Kurylek, 2021. "Inversions Distribution and Testing Correlation Changes for Rates of Return," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 633-650.
    5. Wang, Linglu & Li, Qizhai & Li, Zhaohai & Zheng, Gang, 2011. "Bayes factors in the presence of population stratification," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 836-841, July.
    6. Magris Martin & Iosifidis Alexandros, 2021. "Approximate Bayes factors for unit root testing," Papers 2102.10048, arXiv.org, revised Feb 2021.
    7. Zhang, Jingsi & Jiang, Wenxin & Shao, Xiaofeng, 2013. "Bayesian model selection based on parameter estimates from subsamples," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 979-986.
    8. van Doorn, Johnny & Ly, Alexander & Marsman, Maarten & Wagenmakers, Eric-Jan, 2019. "Bayesian estimation of Kendall’s τ using a latent normal approach," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 268-272.
    9. Heyard, Rachel & Held, Leonhard, 2019. "The quantile probability model," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 84-99.
    10. Michael J. Daniels & Arkendu S. Chatterjee & Chenguang Wang, 2012. "Bayesian Model Selection for Incomplete Data Using the Posterior Predictive Distribution," Biometrics, The International Biometric Society, vol. 68(4), pages 1055-1063, December.
    11. Mary Meyer & Amber Hackstadt & Jennifer Hoeting, 2011. "Bayesian estimation and inference for generalised partial linear models using shape-restricted splines," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 867-884.

    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:14:y:2015:i:3:p:253-264:n:3. 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.