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A general model for likelihood computations of genetic marker data accounting for linkage, linkage disequilibrium, and mutations

Journal article
Authors D. Kling
A. Tillmar
T. Egeland
Petter Mostad
Published in International Journal of Legal Medicine
Volume 129
Issue 5
Pages 943-954
ISSN 0937-9827
Publication year 2015
Published at Department of Mathematical Sciences, Mathematical Statistics
Pages 943-954
Language en
Keywords FamLinkX, Lander-Green, Likelihood computations, X chromosome, Markov-chain, Linkage, Mutation
Subject categories Forensic Science


Several applications necessitate an unbiased determination of relatedness, be it in linkage or association studies or in a forensic setting. An appropriate model to compute the joint probability of some genetic data for a set of persons given some hypothesis about the pedigree structure is then required. The increasing number of markers available through high-density SNP microarray typing and NGS technologies intensifies the demand, where using a large number of markers may lead to biased results due to strong dependencies between closely located loci, both within pedigrees (linkage) and in the population (allelic association or linkage disequilibrium (LD)). We present a new general model, based on a Markov chain for inheritance patterns and another Markov chain for founder allele patterns, the latter allowing us to account for LD. We also demonstrate a specific implementation for X chromosomal markers that allows for computation of likelihoods based on hypotheses of alleged relationships and genetic marker data. The algorithm can simultaneously account for linkage, LD, and mutations. We demonstrate its feasibility using simulated examples. The algorithm is implemented in the software FamLinkX, providing a user-friendly GUI for Windows systems (FamLinkX, as well as further usage instructions, is freely available at Our software provides the necessary means to solve cases where no previous implementation exists. In addition, the software has the possibility to perform simulations in order to further study the impact of linkage and LD on computed likelihoods for an arbitrary set of markers.

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