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Set‐based tests for genetic association in longitudinal studies

Author

Listed:
  • Zihuai He
  • Min Zhang
  • Seunggeun Lee
  • Jennifer A. Smith
  • Xiuqing Guo
  • Walter Palmas
  • Sharon L. R. Kardia
  • Ana V. Diez Roux
  • Bhramar Mukherjee

Abstract

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within‐subject correlation, a feature that is desirable for longitudinal analysis. In addition, a joint test incorporating gene–time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large‐scale genome‐wide association studies (GWAS). The proposed methods are evaluated through extensive simulation studies and illustrated using data from the Multi‐Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate substantial gain in power using LGRF when compared with two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing gene‐based tests using the average value of repeated measurements as the outcome.

Suggested Citation

  • Zihuai He & Min Zhang & Seunggeun Lee & Jennifer A. Smith & Xiuqing Guo & Walter Palmas & Sharon L. R. Kardia & Ana V. Diez Roux & Bhramar Mukherjee, 2015. "Set‐based tests for genetic association in longitudinal studies," Biometrics, The International Biometric Society, vol. 71(3), pages 606-615, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:606-615
    DOI: 10.1111/biom.12310
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    References listed on IDEAS

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    1. Jung-Ying Tzeng & Daowen Zhang & Sheng-Mao Chang & Duncan C. Thomas & Marie Davidian, 2009. "Gene-Trait Similarity Regression for Multimarker-Based Association Analysis," Biometrics, The International Biometric Society, vol. 65(3), pages 822-832, September.
    2. Zihuai He & Min Zhang & Xiaowei Zhan & Qing Lu, 2014. "Modeling and testing for joint association using a genetic random field model," Biometrics, The International Biometric Society, vol. 70(3), pages 471-479, September.
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    Cited by:

    1. Zihuai He & Min Zhang & Seunggeun Lee & Jennifer A. Smith & Sharon L. R. Kardia & V. Diez Roux & Bhramar Mukherjee, 2017. "Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 966-978, July.

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