A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Estimating genealogies from unlinked marker data:: A Bayesian approach
Tekijät: Gasbarra, Dario; Pirinen, Matti; Sillanpaa, Mikko J.; Salmela, Elina; Arjas, Elja
Kustantaja: ACADEMIC PRESS INC ELSEVIER SCIENCE
Kustannuspaikka: SAN DIEGO
Julkaisuvuosi: 2007
Lehti:Theoretical Population Biology
Tietokannassa oleva lehden nimiTHEORETICAL POPULATION BIOLOGY
Lehden akronyymi: THEOR POPUL BIOL
Vuosikerta: 72
Numero: 3
Aloitussivu: 305
Lopetussivu: 322
Sivujen määrä: 18
ISSN: 0040-5809
eISSN: 1096-0325
DOI: https://doi.org/10.1016/j.tpb.2007.06.004
Tiivistelmä
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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