Estimating genealogies from unlinked marker data: a Bayesian approach.
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.
Department of Mathematics and Statistics, University of Helsinki, P.O.Box 68, FIN-00014, Finland. Dario.Gasbarra@rni.helsinki.fi
Dario
D
Gasbarra
Matti
M
Pirinen
Mikko J
MJ
Sillanpää
Elina
E
Salmela
Elja
E
Arjas
D000465:Algorithms; D000483:Alleles; D001499:Bayes Theorem; D005789:Genealogy and Heraldry; D005819:Genetic Markers; D005828:Genetics, Population; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D005810:Multigene Family; D010375:Pedigree
D005819:Genetic Markers