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Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes

Author

Listed:
  • French Benjamin

    (University of Pennsylvania)

  • Lumley Thomas

    (University of Auckland)

  • Cappola Thomas P.

    (University of Pennsylvania)

  • Mitra Nandita

    (University of Pennsylvania)

Abstract

The general availability of reliable and affordable genotyping technology has enabled genetic association studies to move beyond small case-control studies to large prospective studies. For prospective studies, genetic information can be integrated into the analysis via haplotypes, with focus on their association with a censored survival outcome. We develop non-iterative, regression-based methods to estimate associations between common haplotypes and a censored survival outcome in large cohort studies. Our non-iterative methods—weighted estimation and weighted haplotype combination—are both based on the Cox regression model, but differ in how the imputed haplotypes are integrated into the model. Our approaches enable haplotype imputation to be performed once as a simple data-processing step, and thus avoid implementation based on sophisticated algorithms that iterate between haplotype imputation and risk estimation. We show that non-iterative weighted estimation and weighted haplotype combination provide valid tests for genetic associations and reliable estimates of moderate associations between common haplotypes and a censored survival outcome, and are straightforward to implement in standard statistical software. We apply the methods to an analysis of HSPB7-CLCNKA haplotypes and risk of adverse outcomes in a prospective cohort study of outpatients with chronic heart failure.

Suggested Citation

  • French Benjamin & Lumley Thomas & Cappola Thomas P. & Mitra Nandita, 2012. "Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-24, February.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:3:n:4
    DOI: 10.1515/1544-6115.1764
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    References listed on IDEAS

    as
    1. Thomas H. Scheike & Torben Martinussen & Jeremy D. Silver, 2010. "Estimating Haplotype Effects for Survival Data," Biometrics, The International Biometric Society, vol. 66(3), pages 705-715, September.
    2. Venkatraman E. S & Mitra Nandita & Begg Colin B, 2004. "A Method for Evaluating the Impact of Individual Haplotypes on Disease Incidence in Molecular Epidemiology Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-22, October.
    3. Jinbo Chen & Nilanjan Chatterjee, 2006. "Haplotype-Based Association Analysis in Cohort and Nested Case–Control Studies," Biometrics, The International Biometric Society, vol. 62(1), pages 28-35, March.
    4. Lin, D.Y. & Zeng, D., 2006. "Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 89-104, March.
    Full references (including those not matched with items on IDEAS)

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