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Locally-efficient robust estimation of haplotype-disease association in family-based studies

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  • Andrew S. Allen
  • Glen A. Satten
  • Anastasios A. Tsiatis

Abstract

Modelling human genetic variation is critical to understanding the genetic basis of complex disease. The Human Genome Project has discovered millions of binary DNA sequence variants, called single nucleotide polymorphisms, and millions more may exist. As coding for proteins takes place along chromosomes, organisation of polymorphisms along each chromosome, the haplotype phase structure, may prove to be most important in discovering genetic variants associated with disease. As haplotype phase is often uncertain, procedures that model the distribution of parental haplotypes can, if this distribution is misspecified, lead to substantial bias in parameter estimates even when complete genotype information is available. Using a geometric approach to estimation in the presence of nuisance parameters, we address this problem and develop locally-efficient estimators of the effect of haplotypes on disease that are robust to incorrect estimates of haplotype frequencies. The methods are demonstrated with a simulation study of a case-parent design. Copyright 2005, Oxford University Press.

Suggested Citation

  • Andrew S. Allen & Glen A. Satten & Anastasios A. Tsiatis, 2005. "Locally-efficient robust estimation of haplotype-disease association in family-based studies," Biometrika, Biometrika Trust, vol. 92(3), pages 559-571, September.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:559-571
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    File URL: http://hdl.handle.net/10.1093/biomet/92.3.559
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    Cited by:

    1. Yuanjia Wang & Qiong Yang & Daniel Rabinowitz, 2011. "Unbiased and Locally Efficient Estimation of Genetic Effect on Quantitative Trait in the Presence of Population Admixture," Biometrics, The International Biometric Society, vol. 67(2), pages 331-343, June.
    2. Bo Zhang & Eric J. Tchetgen Tchetgen, 2022. "A semi‐parametric approach to model‐based sensitivity analysis in observational studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 668-691, December.
    3. Stijn Vansteelandt & Dawn L. DeMeo & Jessica Lasky-Su & Jordan W. Smoller & Amy J. Murphy & Matt McQueen & Kady Schneiter & Juan C. Celedon & Scott T. Weiss & Edwin K. Silverman & Christoph Lange, 2008. "Testing and Estimating Gene–Environment Interactions in Family-Based Association Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 458-467, June.

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