- Trait-marker linkage disequilibrium
- Disequilibrium estimation
- Posterior probability of linkage
- Bayesian linkage analysis
The posterior probability of linkage (PPL) statistic has been developed as a method for the rigorous accumulation of evidence for or against linkage allowing for both intra- and inter-sample heterogeneity. To date, the method has assumed linkage equilibrium between alleles at the trait locus and the marker locus. We now generalize the PPL to allow for linkage disequilibrium (LD), by incorporating variable phase probabilities into the underlying linkage likelihood. This enables us to recover the marginal posterior density of the recombination fraction, integrating out nuisance parameters of the trait model, including the locus heterogeneity (admixture) parameter, as well as a vector of LD parameters. The marginal posterior density can then be updated across data subsets or new data as they become available, while allowing parameters of the trait model to vary between data sets. The method applies immediately to general pedigree structures and to markers with multiple alleles. In the case of SNPs, the likelihood is parameterized in terms of the standard single LD parameter D′; and it therefore affords a mechanism for estimation of D′ between the marker and the trait, again, without fixing the parameters of the trait model and allowing for updating across data sets. It is even possible to allow for a different associated allele in different populations, while accumulating information regarding the strength of LD. While a computationally efficient implementation for multi-allelic markers is still in progress, we have implemented a version of this new LD-PPL for SNPs and evaluated its performance in nuclear families. Our simulations show that LD-PPLs tend to be larger than PPLs (stronger evidence in favor of linkage/LD) with increased LD level, under a variety of generating models; while in the absence of linkage and LD, LD-PPLs tend to be smaller than PPLs (stronger evidence against linkage). The estimate of D′ also behaves well even in relatively small, heterogeneous samples.
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Dr. Veronica J. Vieland
Center for Statistical Genetics Research
2190 Westlawn, The University of Iowa
Iowa City, IA 52242 (USA)
Tel. +1 319 335 6722, Fax +1 319 353 3038, E-Mail firstname.lastname@example.org
Received: March 3, 2005
Accepted after revision: April 29, 2005
Published online: July 7, 2005
Number of Print Pages : 10
Number of Figures : 2, Number of Tables : 8, Number of References : 34
Human Heredity (International Journal of Human and Medical Genetics)
Vol. 59, No. 4, Year 2005 (Cover Date: Released August 2005)
Journal Editor: Devoto, M. (Wilmington, Del.)
ISSN: 0001–5652 (print), 1423–0062 (Online)
For additional information: http://www.karger.com/hhe
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