IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v68y2000i1p83-110.html
   My bibliography  Save this article

On the Application of Markov Chain Monte Carlo Methods to Genetic Analyses on Complex Pedigrees

Author

Listed:
  • N. A. Sheehan

Abstract

Markov chain Monte Carlo methods are frequently used in the analyses of genetic data on pedigrees for the estimation of probabilities and likelihoods which cannot be calculated by existing exact methods. In the case of discrete data, the underlying Markov chain may be reducible and care must be taken to ensure that reliable estimates are obtained. Potential reducibility thus has implications for the analysis of the mixed inheritance model, for example, where genetic variation is assumed to be due to one single locus of large effect and many loci each with a small effect. Similarly, reducibility arises in the detection of quantitative trait loci from incomplete discrete marker data. This paper aims to describe the estimation problem in terms of simple discrete genetic models and the single‐site Gibbs sampler. Reducibility of the Gibbs sampler is discussed and some current methods for circumventing the problem outlined. On se sert fréquemment des méthodes de chaines Markov Monte Carlo pour les analyses de données, gétiques sur des pedigress pour estimer les probabilités et les vraisemblances que ne peuvent pas étre calculées en se servant des méthodes exaxtes qui exstantes. Dans le cas de données discrétes, la chaine Markv sous‐jacente peut étre, réduite et il faut prendre soin de s' assurer que des estimations fiables sont obtenues. Le potentiel de réduction a done des implications pur I; analyse du modeéle mixte d' héritage. Par exemple dans le cas, oé'uon suppose que la variation est due á un locus avec grand effet et beaucoup de foyer avec un petit effet. Parallelement, la duction peut survenir dans la detection de trait quantitatif de foyer provenant de donnees incompletes de marqueur discret. Ce papier decrit le probleme d'estimation en termes de modeles ginttiques discrets simples et I'tchantillonneur Gibbs. La kduction de I'khantillonneur Gibbs est discutk et on esquisse certaines mCthodes existantes afin de contourner le probDme.

Suggested Citation

  • N. A. Sheehan, 2000. "On the Application of Markov Chain Monte Carlo Methods to Genetic Analyses on Complex Pedigrees," International Statistical Review, International Statistical Institute, vol. 68(1), pages 83-110, April.
  • Handle: RePEc:bla:istatr:v:68:y:2000:i:1:p:83-110
    DOI: 10.1111/j.1751-5823.2000.tb00389.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1751-5823.2000.tb00389.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1751-5823.2000.tb00389.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Anderson, Eric C. & Ng, Thomas C., 2016. "Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation," Theoretical Population Biology, Elsevier, vol. 107(C), pages 39-51.
    2. Sheehan, Nuala A. & Bartlett, Mark & Cussens, James, 2014. "Improved maximum likelihood reconstruction of complex multi-generational pedigrees," Theoretical Population Biology, Elsevier, vol. 97(C), pages 11-19.

    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:bla:istatr:v:68:y:2000:i:1:p:83-110. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.html .

    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.