Using Object Oriented Bayesian Networks to Model Linkage, Linkage Disequilibrium and Mutations between STR Markers
Journal article, 2012

In a number of applications there is a need to determine the most likely pedigree for a group of persons based on genetic markers. Adequate models are needed to reach this goal. The markers used to perform the statistical calculations can be linked and there may also be linkage disequilibrium (LD) in the population. The purpose of this paper is to present a graphical Bayesian Network framework to deal with such data. Potential LD is normally ignored and it is important to verify that the resulting calculations are not biased. Even if linkage does not influence results for regular paternity cases, it may have substantial impact on likelihood ratios involving other, more extended pedigrees. Models for LD influence likelihoods for all pedigrees to some degree and an initial estimate of the impact of ignoring LD and/or linkage is desirable, going beyond mere rules of thumb based on marker distance. Furthermore, we show how one can readily include a mutation model in the Bayesian Network; extending other programs or formulas to include such models may require considerable amounts of work and will in many case not be practical. As an example, we consider the two STR markers vWa and D12S391. We estimate probabilities for population haplotypes to account for LD using a method based on data from trios, while an estimate for the degree of linkage is taken from the literature. The results show that accounting for haplotype frequencies is unnecessary in most cases for this specific pair of markers. When doing calculations on regular paternity cases, the markers can be considered statistically independent. In more complex cases of disputed relatedness, for instance cases involving siblings or so-called deficient cases, or when small differences in the LR matter, independence should not be assumed. (The networks are freely available at http://arken.umb.no/~dakl/BayesianNetwor​ks.)

Author

Daniel Kling

Norwegian Institute of Public Health

Norwegian University of Life Sciences

Thore Egeland

Norwegian University of Life Sciences

Norwegian Institute of Public Health

Petter Mostad

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

PLoS ONE

1932-6203 (ISSN)

Vol. 7 9 artikel nr e43873- e43873

Roots

Basic sciences

Areas of Advance

Life Science Engineering

Subject Categories

Probability Theory and Statistics

Genetics

DOI

10.1371/journal.pone.0043873

More information

Latest update

5/2/2018 1