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Estimating genealogies from linked marker data: a Bayesian approach.

Gasbarra D, Pirinen M, Sillanpää MJ, Arjas E - BMC Bioinformatics (2007)

Bottom Line: The main contribution of our work is the development of sampling algorithms in the resulting vast state space with highly dependent variables.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki, Finland. dag@rni.helsinki.fi

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.

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Pedigree of the first example. 439 individuals and 10 generations of which the youngest one consisted of the children of 13 nuclear families. Squares denote males, circles denote females. Reprinted from [10].
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Figure 1: Pedigree of the first example. 439 individuals and 10 generations of which the youngest one consisted of the children of 13 nuclear families. Squares denote males, circles denote females. Reprinted from [10].

Mentions: We considered a simulated pedigree that extended for 10 generations and contained 439 individuals (Figure 1). This pedigree was also used by Gasbarra et al. [10], as their Example III, and the details of the simulation procedure are given there.


Estimating genealogies from linked marker data: a Bayesian approach.

Gasbarra D, Pirinen M, Sillanpää MJ, Arjas E - BMC Bioinformatics (2007)

Pedigree of the first example. 439 individuals and 10 generations of which the youngest one consisted of the children of 13 nuclear families. Squares denote males, circles denote females. Reprinted from [10].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2233650&req=5

Figure 1: Pedigree of the first example. 439 individuals and 10 generations of which the youngest one consisted of the children of 13 nuclear families. Squares denote males, circles denote females. Reprinted from [10].
Mentions: We considered a simulated pedigree that extended for 10 generations and contained 439 individuals (Figure 1). This pedigree was also used by Gasbarra et al. [10], as their Example III, and the details of the simulation procedure are given there.

Bottom Line: The main contribution of our work is the development of sampling algorithms in the resulting vast state space with highly dependent variables.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki, Finland. dag@rni.helsinki.fi

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.

Show MeSH