Skip Navigation


Biostatistics Advance Access originally published online on December 14, 2005
Biostatistics 2006 7(3):374-386; doi:10.1093/biostatistics/kxj012
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
7/3/374    most recent
kxj012v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Armstrong, N. J.
Right arrow Articles by Speed, T. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Armstrong, N. J.
Right arrow Articles by Speed, T. P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Incorporating interference into linkage analysis for experimental crosses

Nicola J. Armstrong*

Division of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands n.armstrong{at}nki.nl

Mary Sara McPeek

Department of Statistics, University of Chicago, Chicago, IL 60637, USA

Terence P. Speed

Department of Statistics, University of California at Berkeley, Berkeley, CA 94720, USA and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

* To whom correspondence should be addressed.

The phenomenon of interference in genetic recombination is well-known and studied in a wide variety of organisms. Multilocus linkage analysis, which makes use of recombination patterns among all genetic markers simultaneously, is routinely used with data on humans and experimental organisms to build genetic maps. It is also used to try to determine the genes involved in traits of interest, such as common diseases. Most linkage analyses performed today ignore the occurrence of genetical interference. We present an extension to the Lander–Green algorithm for experimental crosses (backcross and intercross) to incorporate crossover interference according to the {chi}2 model. Simulation results show the impact of using this model on the accuracy of estimated genetic maps.

Keywords: Crossover interference; Hidden Markov model; Linkage analysis


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.