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Asymptotic powers for matched trend tests and robust matched trend tests in case-control genetic association studies

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  • Zang, Yong
  • Fung, Wing Kam
  • Zheng, Gang

Abstract

The matched trend test (MTT), developed using a conditional logistic regression, has been proposed to test for association in matched case-control studies to control the bias of known confounding effects and reduce the potential impact of population stratification. The MTT requires a known genetic model. When the genetic model is unknown, a Monte Carlo robust test, MAX, has been proposed for the analysis of matched case-control studies. The MAX statistic takes the maximum of three MTTs optimal for three common genetic models. We derive the asymptotic power for MTTs and robust tests. In particular, we derive the asymptotic p-value for MAX. Using these analytical results, we conduct simulation studies to compare the performance of MAX and the two-degree-of-freedom Chi-square test for matched case-control studies, where the latter is implemented in most computing software. Our simulation results show that MAX is always asymptotically more powerful than the two-degree-of-freedom Chi-square test under common genetic models. Our results provide guidelines for the analysis of genetic association using matched case-control data. An illustration of our results to a real matched pair case-control etiologic study of sarcoidosis is given.

Suggested Citation

  • Zang, Yong & Fung, Wing Kam & Zheng, Gang, 2010. "Asymptotic powers for matched trend tests and robust matched trend tests in case-control genetic association studies," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 65-77, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:65-77
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    1. Jinbo Chen & Carmen Rodriguez, 2007. "Conditional Likelihood Methods for Haplotype-Based Association Analysis Using Matched Case–Control Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1099-1107, December.
    2. Robert Sladek & Ghislain Rocheleau & Johan Rung & Christian Dina & Lishuang Shen & David Serre & Philippe Boutin & Daniel Vincent & Alexandre Belisle & Samy Hadjadj & Beverley Balkau & Barbara Heude &, 2007. "A genome-wide association study identifies novel risk loci for type 2 diabetes," Nature, Nature, vol. 445(7130), pages 881-885, February.
    3. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
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    1. Zang, Yong & Fung, Wing Kam & Cao, Sha & Ng, Hon Keung Tony & Zhang, Chi, 2019. "Robust tests for gene–environment interaction in case-control and case-only designs," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 79-92.

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