Estimating genealogies from linked marker data: a Bayesian approach.
BACKGROUND
Answers to several fundamental questions in statistical genetics would ideally require knowledge of the ancestral pedigree and of the gene flow therein. A few examples of such questions are haplotype estimation, relatedness and relationship estimation, gene mapping by combining pedigree and linkage disequilibrium information, and estimation of population structure.


RESULTS
We present a probabilistic method for genealogy reconstruction. Starting with a group of genotyped individuals from some population isolate, we explore the state space of their possible ancestral histories under our Bayesian model by using Markov chain Monte Carlo (MCMC) sampling techniques. The main contribution of our work is the development of sampling algorithms in the resulting vast state space with highly dependent variables. The main drawback is the computational complexity that limits the time horizon within which explicit reconstructions can be carried out in practice.


CONCLUSIONS
The estimates for IBD (identity-by-descent) and haplotype distributions are tested in several settings using simulated data. The results appear to be promising for a further development of the method.
Gasbarra
Dario
D
Department of Mathematics and Statistics, University of Helsinki, Finland. dag@rni.helsinki.fi
Pirinen
Matti
M
Sillanpää
Mikko J
MJ
Arjas
Elja
E
D000465:Algorithms; D000483:Alleles; D001499:Bayes Theorem; D002874:Chromosome Mapping; D003627:Data Interpretation, Statistical; D005260:Female; D018703:Founder Effect; D005787:Gene Frequency; D008040:Genetic Linkage; D005819:Genetic Markers; D005828:Genetics, Population / Q000379:methods*; D006239:Haplotypes; D006801:Humans; D008297:Male; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010375:Pedigree; D040641:Quantitative Trait Loci / Q000235:genetics*
D005819:Genetic Markers