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Bioinformatics Advance Access originally published online on July 6, 2009
Bioinformatics 2009 25(18):2376-2382; doi:10.1093/bioinformatics/btp418
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identification of distant family relationships

Øivind Skare 1,2, Nuala Sheehan 3,4 and Thore Egeland 5,6,*

1Norwegian Institute of Public Health, 0403 Oslo, 2Department of Public Health and Primary Health Care, University of Bergen, 5018 Bergen, 3Department of Health Sciences, 4Department of Genetics, University of Leicester, UK, 5Institute of Forensic Medicine, University of Oslo, 0027 Oslo and 6HOV, Oslo University College, Oslo, Norway

*To whom correspondence should be addressed.


  Abstract

Motivation: Family relationships can be estimated from DNA marker data. Applications arise in a large number of areas including evolution and conservation research, genealogical research in human, plant and animal populations, forensic problems and genetic mapping via linkage and association analyses. Traditionally, likelihood-based approaches to relationship estimation have used unlinked genetic markers. Due to the fact that some relationships cannot be distinguished from data at unlinked markers, and given the limited number of such markers available, there are considerable constraints on the type of identification problem that can be satisfactorily addressed with such approaches. The aim of this article is to explore the potential of linked autosomal single nucleotide polymorphism markers in this context. Throughout, we will view the problem of relationship estimation as one of pedigree identification rather than identity-by-descent, and thus focus on applications where determination of the exact relationship is important.

Results: We show that the increase in information obtained by exploiting large sets of linked markers substantially increases the number of problems that can be solved. Results are presented based on simulations as well as on real data.

Availability: The R library FEST is freely available from http://folk.uio.no/thoree/FEST.

Contact: thore.egeland{at}medisin.uio.no

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jeffrey Barrett

Received on March 26, 2009; revised on June 17, 2009; accepted on July 1, 2009

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