A1 Refereed original research article in a scientific journal
Estimating genealogies from unlinked marker data:: A Bayesian approach
Authors: Gasbarra, Dario; Pirinen, Matti; Sillanpaa, Mikko J.; Salmela, Elina; Arjas, Elja
Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
Publishing place: SAN DIEGO
Publication year: 2007
Journal:Theoretical Population Biology
Journal name in sourceTHEORETICAL POPULATION BIOLOGY
Journal acronym: THEOR POPUL BIOL
Volume: 72
Issue: 3
First page : 305
Last page: 322
Number of pages: 18
ISSN: 0040-5809
eISSN: 1096-0325
DOI: https://doi.org/10.1016/j.tpb.2007.06.004
Abstract
An issue often encountered in statistical genetics is whether, or to what extent, it is possible to estimate the degree to which individuals sampled from a background population are related to each other, on the basis of the available genotype data and some information on the demography of the population. In this article, we consider this question using explicit modelling of the pedigrees and gene flows at unlinked marker loci, but then restricting ourselves to a relatively recent history of the population, that is, considering the genealogy at most some tens of generations backwards in time. As a computational tool we use a Markov chain Monte Carlo numerical integration on the state space of genealogies of the sampled individuals. As illustrations of the method, we consider the question of relatedness at the level of genes/genomes (IBD estimation), using both simulated and real data. (C) 2007 Elsevier Inc. All rights reserved.
An issue often encountered in statistical genetics is whether, or to what extent, it is possible to estimate the degree to which individuals sampled from a background population are related to each other, on the basis of the available genotype data and some information on the demography of the population. In this article, we consider this question using explicit modelling of the pedigrees and gene flows at unlinked marker loci, but then restricting ourselves to a relatively recent history of the population, that is, considering the genealogy at most some tens of generations backwards in time. As a computational tool we use a Markov chain Monte Carlo numerical integration on the state space of genealogies of the sampled individuals. As illustrations of the method, we consider the question of relatedness at the level of genes/genomes (IBD estimation), using both simulated and real data. (C) 2007 Elsevier Inc. All rights reserved.
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