13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      How old is this mutation? - a study of three Ashkenazi Jewish founder mutations

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Several founder mutations leading to increased risk of cancer among Ashkenazi Jewish individuals have been identified, and some estimates of the age of the mutations have been published. A variety of different methods have been used previously to estimate the age of the mutations. Here three datasets containing genotype information near known founder mutations are reanalyzed in order to compare three approaches for estimating the age of a mutation. The methods are: (a) the single marker method used by Risch et al., (1995); (b) the intra-allelic coalescent model known as DMLE, and (c) the Goldgar method proposed in Neuhausen et al. (1996), and modified slightly by our group. The three mutations analyzed were MSH2*1906 G->C, APC*I1307K, and BRCA2*6174delT.

          Results

          All methods depend on accurate estimates of inter-marker recombination rates. The modified Goldgar method allows for marker mutation as well as recombination, but requires prior estimates of the possible haplotypes carrying the mutation for each individual. It does not incorporate population growth rates. The DMLE method simultaneously estimates the haplotypes with the mutation age, and builds in the population growth rate. The single marker estimates, however, are more sensitive to the recombination rates and are unstable. Mutation age estimates based on DMLE are 16.8 generations for MSH2 (95% credible interval (13, 23)), 106 generations for I1037K (86-129), and 90 generations for 6174delT (71-114).

          Conclusions

          For recent founder mutations where marker mutations are unlikely to have occurred, both DMLE and the Goldgar method can give good results. Caution is necessary for older mutations, especially if the effective population size may have remained small for a long period of time.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation.

          Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Estimating allele age.

            The age of an allele can be estimated both from genetic variation among different copies (intra-allelic variation) and from its frequency. Estimates based on intra-allelic variation follow from the exponential decay of linkage disequilibrium because of recombination and mutation. The confidence interval depends both on the uncertainty in recombination and mutation rates and on randomness of the genealogy of chromosomes that carry the allele (the intra-allelic genealogy). Several approximate methods to account for variation in the intra-allelic genealogy have been derived. Allele frequency alone also provides an estimate of age. Estimates based on frequency and on intra-allelic variability can be combined to provide a more accurate estimate or can be contrasted to show that an allele has been subject to natural selection. These methods have been applied to numerous cases, including alleles associated with cystic fibrosis, idiopathic torsion dystonia, and resistance to infection by HIV. We emphasize that estimates of allele age depend on assumptions about demographic history and natural selection.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities

                Bookmark

                Author and article information

                Journal
                BMC Genet
                BMC Genetics
                BioMed Central
                1471-2156
                2010
                14 May 2010
                : 11
                : 39
                Affiliations
                [1 ]Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
                [2 ]Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
                [3 ]General Medical Sciences (Oncology), Case Comprehensive Cancer Center, Cleveland, Ohio, USA
                [4 ]Department of Statistics and Actuarial Science, University of Western Ontario, London, Ontario, Canada
                [5 ]Department of Medical Genetics, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
                [6 ]Diagnostic Radiology, Massachusetts General Hospital, Boston, MA, USA
                [7 ]University of Michigan School of Public Health, Ann Arbor, Michigan, USA
                [8 ]Cancer Genetics, Departments of Oncology and Human Genetics, Gerald Bronfman Centre for Clinical Research in Oncology, McGill University, Montreal, QC, Canada
                [9 ]Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
                Article
                1471-2156-11-39
                10.1186/1471-2156-11-39
                2889843
                20470408
                756e4944-6347-4df0-9157-1cae829de907
                Copyright ©2010 Greenwood et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 November 2009
                : 14 May 2010
                Categories
                Research article

                Genetics
                Genetics

                Comments

                Comment on this article