IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v10y2018i1d10.1007_s12561-017-9189-9.html
   My bibliography  Save this article

A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression

Author

Listed:
  • Clemontina A. Davenport

    (Duke University Medical Center)

  • Arnab Maity

    (North Carolina State University)

  • Patrick F. Sullivan

    (University of North Carolina at Chapel Hill)

  • Jung-Ying Tzeng

    (North Carolina State University
    National Cheng-Kung University
    National Taiwan University)

Abstract

Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a single nucleotide polymorphism-set on multiple, possibly correlated, binary responses. We develop a score-based test using a non-parametric modeling framework that jointly models the global effect of the marker set. We account for the non-linear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrate our methods using the Clinical Antipsychotic Trials of Intervention Effectiveness antibody study data and the CoLaus study data.

Suggested Citation

  • Clemontina A. Davenport & Arnab Maity & Patrick F. Sullivan & Jung-Ying Tzeng, 2018. "A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 117-138, April.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9189-9
    DOI: 10.1007/s12561-017-9189-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-017-9189-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-017-9189-9?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. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
    2. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Debajyoti Sinha & Michael Parzen & Steven Lipshultz, 2009. "Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 3-20, January.
    3. Yang Zhao & Feng Chen & Rihong Zhai & Xihong Lin & Nancy Diao & David C Christiani, 2012. "Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, 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. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    2. Min Cai & Hui Dai & Yongyong Qiu & Yang Zhao & Ruyang Zhang & Minjie Chu & Juncheng Dai & Zhibin Hu & Hongbing Shen & Feng Chen, 2013. "SNP Set Association Analysis for Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
    3. Arnab Maity & Xihong Lin, 2011. "Powerful Tests for Detecting a Gene Effect in the Presence of Possible Gene–Gene Interactions Using Garrote Kernel Machines," Biometrics, The International Biometric Society, vol. 67(4), pages 1271-1284, December.
    4. Long Qu & Tobias Guennel & Scott L. Marshall, 2013. "Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 883-892, December.
    5. Chakraborty, Sounak, 2009. "Bayesian binary kernel probit model for microarray based cancer classification and gene selection," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4198-4209, October.
    6. Yunxuan Jiang & Karen N. Conneely & Michael P. Epstein, 2018. "Robust Rare-Variant Association Tests for Quantitative Traits in General Pedigrees," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 491-505, December.
    7. Glen McGee & Ander Wilson & Thomas F. Webster & Brent A. Coull, 2023. "Bayesian multiple index models for environmental mixtures," Biometrics, The International Biometric Society, vol. 79(1), pages 462-474, March.
    8. Hui Dai & Yang Zhao & Cheng Qian & Min Cai & Ruyang Zhang & Minjie Chu & Juncheng Dai & Zhibin Hu & Hongbing Shen & Feng Chen, 2013. "Weighted SNP Set Analysis in Genome-Wide Association Study," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
    9. Paul Little & Li Hsu & Wei Sun, 2023. "Associating somatic mutation with clinical outcomes through kernel regression and optimal transport," Biometrics, The International Biometric Society, vol. 79(3), pages 2705-2718, September.
    10. Manuel Gomes & Edmond S.-W. Ng & Richard Grieve & Richard Nixon & James Carpenter & Simon G. Thompson, 2012. "Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials," Medical Decision Making, , vol. 32(2), pages 350-361, March.
    11. Zhang Hongmei & Gan Jianjun, 2012. "A Reproducing Kernel-Based Spatial Model in Poisson Regressions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-26, October.
    12. Ghosh, Debashis, 2014. "An asymptotically minimax kernel machine," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 33-38.
    13. Xia Zheng & Yaohua Rong & Ling Liu & Weihu Cheng, 2021. "A More Accurate Estimation of Semiparametric Logistic Regression," Mathematics, MDPI, vol. 9(19), pages 1-12, September.
    14. Jennifer A. Sinnott & Tianxi Cai, 2013. "Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling," Biometrics, The International Biometric Society, vol. 69(4), pages 861-873, December.
    15. Thomas Suesse & Ivy Liu, 2013. "Modelling Strategies for Repeated Multiple Response Data," International Statistical Review, International Statistical Institute, vol. 81(2), pages 230-248, August.
    16. Xiang Zhan & Anna Plantinga & Ni Zhao & Michael C. Wu, 2017. "A fast small‐sample kernel independence test for microbiome community‐level association analysis," Biometrics, The International Biometric Society, vol. 73(4), pages 1453-1463, December.
    17. Lulu Cheng & Inyoung Kim & Herbert Pang, 2016. "Bayesian Semiparametric Model for Pathway-Based Analysis with Zero-Inflated Clinical Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 641-662, December.
    18. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.
    19. Tianxi Cai & Giulia Tonini & Xihong Lin, 2011. "Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction," Biometrics, The International Biometric Society, vol. 67(3), pages 975-986, September.
    20. Nadezhda M Belonogova & Gulnara R Svishcheva & James F Wilson & Harry Campbell & Tatiana I Axenovich, 2018. "Weighted functional linear regression models for gene-based association analysis," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-14, January.

    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:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9189-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.