Methodology article

Estimating genealogies from linked marker data: a Bayesian approach

Dario Gasbarra¹, Matti Pirinen¹, Mikko J Sillanpää^1* and Elja Arjas^1,2

• * Corresponding author: Mikko J Sillanpää mjs@rolf.helsinki.fi

Author Affiliations

¹ Department of Mathematics and Statistics, University of Helsinki, Finland

² National Public Health Institute (KTL), Helsinki, Finland

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BMC Bioinformatics 2007, 8:411 doi:10.1186/1471-2105-8-411

Published: 25 October 2007

Abstract

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.

Conclusion

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.