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A powerful test for ordinal trait genetic association analysis

Author

Listed:
  • Xue Yuan
  • Zhang Sanguo

    (School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China)

  • Wang Jinjuan

    (LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China)

  • Ding Juan

    (School of Mathematics and Statistics, Guangxi Normal University, Guilin, China)

  • Li Qizhai

    (LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 55, Beijing 100190, China, Phone: +86-10-82541839)

Abstract

Response selective sampling design is commonly adopted in genetic epidemiologic study because it can substantially reduce time cost and increase power of identifying deleterious genetic variants predispose to human complex disease comparing with prospective design. The proportional odds model (POM) can be used to fit data obtained by this design. Unlike the logistic regression model, the estimated genetic effect based on POM by taking data as being enrolled prospectively is inconsistent. So the power of resulted Wald test is not satisfactory. The modified POM is suitable to fit this type of data, however, the corresponding Wald test is not optimal when the genetic effect is small. Here, we propose a new association test to handle this issue. Simulation studies show that the proposed test can control the type I error rate correctly and is more powerful than two existing methods. Finally, we applied three tests to Anticyclic Citrullinated Protein Antibody data from Genetic Workshop 16.

Suggested Citation

  • Xue Yuan & Zhang Sanguo & Wang Jinjuan & Ding Juan & Li Qizhai, 2019. "A powerful test for ordinal trait genetic association analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-9, April.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:2:p:9:n:2
    DOI: 10.1515/sagmb-2017-0066
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    References listed on IDEAS

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    1. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    2. Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
    3. Cosslett, Stephen R, 1981. "Maximum Likelihood Estimator for Choice-Based Samples," Econometrica, Econometric Society, vol. 49(5), pages 1289-1316, September.
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