Joint Bayesian estimation of mutation location and age using linkage disequilibrium. Rannala, B. & Reeve, J., P. *Pac Symp Biocomput*, 2003. Paper abstract bibtex Associations between disease and marker alleles on chromosomes in populations can arise as a consequence of historical forces such as mutation, selection and genetic drift, and is referred to as "linkage disequilibrium" (LD). LD can be used to estimate the map position of a disease mutation relative to a set of linked markers, as well as to estimate other parameters of interest, such as mutation age. Parametric methods for estimating the location of a disease mutation using marker linkage disequilibrium in a sample of normal and affected individuals require a detailed knowledge of population demography, and in particular require users to specify the postulated age of a mutation and past population growth rates. A new Bayesian method is presented for jointly estimating the position of a disease mutation and its age. The method is illustrated using haplotype data for the cystic fibrosis deltaF508 mutation in Europe and the DTD mutation in Finland. It is shown that, for these datasets, the posterior probability distribution of disease mutation location is insensitive to the population growth rate when the model is averaged over possible mutation ages using a prior probability distribution for the mutation age based on the population frequency of the disease mutation. Fewer assumptions are therefore needed for parametric LD mapping.

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abstract = {Associations between disease and marker alleles on chromosomes in populations can arise as a consequence of historical forces such as mutation, selection and genetic drift, and is referred to as "linkage disequilibrium" (LD). LD can be used to estimate the map position of a disease mutation relative to a set of linked markers, as well as to estimate other parameters of interest, such as mutation age. Parametric methods for estimating the location of a disease mutation using marker linkage disequilibrium in a sample of normal and affected individuals require a detailed knowledge of population demography, and in particular require users to specify the postulated age of a mutation and past population growth rates. A new Bayesian method is presented for jointly estimating the position of a disease mutation and its age. The method is illustrated using haplotype data for the cystic fibrosis deltaF508 mutation in Europe and the DTD mutation in Finland. It is shown that, for these datasets, the posterior probability distribution of disease mutation location is insensitive to the population growth rate when the model is averaged over possible mutation ages using a prior probability distribution for the mutation age based on the population frequency of the disease mutation. Fewer assumptions are therefore needed for parametric LD mapping.},
bibtype = {article},
author = {Rannala, B and Reeve, J P},
journal = {Pac Symp Biocomput}
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