IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v91y2004i1p219-225.html
   My bibliography  Save this article

Estimating genetic association parameters from family data

Author

Listed:
  • Alice S. Whittemore

Abstract

We consider the problem of estimating a parameter theta, reflecting association between a disease and genotypes of a genetic polymorphism, using nuclear family data. In many applications, some parental genotypes are missing, and the distribution of these genotypes is unknown. Since misspecification of this distribution can bias estimators for theta, we consider estimating functions that are unbiased, regardless of how the distribution is specified. We call the resulting estimators parental-genotype-robust. Rabinowitz (2002) has proposed a constrained optimisation method for obtaining locally optimal unbiased tests of the null hypothesis of no association. We use a similar method to derive estimating functions that yield parental-genotype-robust estimators with minimum variance in the class of all such estimators. We extend the estimating functions to obtain parental-genotype-robust estimators when theta is a vector of unknown parameters, and show that the estimating functions enjoy a certain optimality property. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Alice S. Whittemore, 2004. "Estimating genetic association parameters from family data," Biometrika, Biometrika Trust, vol. 91(1), pages 219-225, March.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:1:p:219-225
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:biomet:v:91:y:2004:i:1:p:219-225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.