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Contact DPS. It looks like your browser does not have JavaScript enabled. Please turn on JavaScript and try again. The mitochondrial DNA team examines biological items of evidence from crime scenes to determine the mitochondrial DNA mtDNA sequence from samples such as hair, bones, and teeth.
Typically, these items contain low concentrations of degraded DNA, making them unsuitable for nuclear DNA examinations.
Current methods for evaluating mtDNA evidence rely directly on a database count of the observed mitogenome [ 2 , 3 ], and are affected by poor representativeness of the databases, and its limited informativeness when there are many rare mitotypes. Our approach can also make use of a database count of the haplotype, but this information is used to adjust an unconditional distribution and so is less sensitive to the database size and sampling scheme.
Limitations of our analysis include the range of demographic scenarios that we can consider, and the difficulty in assessing which demographic scenario is appropriate for any specific crime. Our assumption of neutrality is unlikely to be strictly accurate [ 19 ], nor our assumption of a generation time of 25 years, constant over generations.
We used two mutation rate schemes [ 13 , 14 ] based on phylogenetic estimates, as no pedigree-based mutation rates were available for the entire mitogenome. Some discrepancy has been noted between the two estimation methods [ 20 ], and the rate may have changed over time [ 21 ]. If contemporary pedigree-based mutation rates become available we could improve our mutation model, but that would not address mutation rate changes over time.
We have not here addressed the case of mixed mtDNA samples or heteroplasmy multiple mitogenomes arising from the same individual. While we have focussed our examples on human populations because of the important role of the mitogenome in human identification and relatedness testing, with appropriate modifications of the demographic model, mitolina and the methods described here can be used for non-human applications of mtDNA.
Examples include tracking the source of ivory [ 22 ], other areas of wildlife forensics [ 23 ] and inferences about the demographic histories of natural populations [ 24 ]. Our software may be useful for generating simulation data in approximate Bayesian computation and related methods, and the number of matching sequences may also provide a useful summary statistic for such methods.
We simulated the mitogenome as a binary sequence subject to neutral mutations, using the rates estimated by both Rieux et al. The values here are 25 times the per-year rates of [ 13 , 14 ], because we assume year generations. We simulated populations of mitogenomes under three demographic scenarios. Two constant-size Wright-Fisher populations [ 25 ], of 50K and K females per generation, were simulated for 1, generations.
All the females in any generation had the same distribution of offspring number no between-female variation in reproductive success. We assigned mitogenomes to the founders randomly with replacement from a US Caucasian database of mitogenomes distinct haplotypes, see Fig 1 [ 15 ], coding each site as 0 if it matched the rCRS reference sequence [ 8 ], and 1 otherwise.
Each mother-child transmission was subject to mutation, which changed a 0 to a 1, and vice versa. The mean whole-mitogenome mutation rate per generation was 0. Therefore, following one line of descent over 1, generations, the expected numbers of mutations to affect the mitogenome are The probabilities that there is any site affected by two mutations and so reverts to its original state during those 1, generations are 0.
We simulated five population under each of the three demographic scenarios. For each population simulation and both mutation models, we conducted five replicates of the sequence evolution process: assigning sequences to the founders and then mutations at each meiosis. Thus, for each mutation model and demographic scenario, 25 live populations of mitogenomes were created.
In each live population, a PoI person of interest was randomly drawn 10, times, and we recorded how many live individuals had the same mitogenome as the PoI. Following the methodology of [ 11 ], in addition to the unconditional distribution of the number of mitogenome matches between a PoI and another live individual, we use importance sampling reweighting to approximate the distribution conditional on observing the PoI mitogenome m times in a database of size n , assumed to have been chosen randomly in the population.
Software to perform these simulations is implemented in the open-source R packages mitolina [ 26 , 27 ], based on Rcpp [ 28 ], and malan [ 29 ], previously used for Y profile simulations [ 11 ].
Key quantiles of the distributions shown in Fig 2 for the mutation scheme of Rieux [ 14 ], and for the 1. Key quantiles of the distributions shown in Fig 2 for the mutation scheme of Rieux [ 14 ], and for the K constant demographic scenario. The distribution of the numbers of singletons, doubletons and distinct haplotypes in 2, random databases of sizes and obtained under our three demographic and two mutation models. The horizontal reference lines are from [ 15 , 16 ].
The whiskers are constructed as 1. Abstract Mitochondrial DNA mtDNA is useful to assist with identification of the source of a biological sample, or to confirm matrilineal relatedness. Author summary The maternally-inherited mitochondrial DNA mtDNA represents only a small fraction of the human genome, but mtDNA profiles are important in forensic science, for example when a biological evidence sample is degraded or when maternal relatedness is questioned.
Introduction Human mitochondrial DNA mtDNA has long been a useful tool to identify war casualties and victims of mass disasters, the sources of biological samples derived from crime scenes or to confirm matrilineal relatedness [ 1 — 3 ]. Results See Methods for details of our two mutation models, based on [ 13 ] and [ 14 ], and three demographic scenarios which we denote 1.
Download: PPT. Fig 2. Cumulative distributions of the number of matching individuals. Table 1. Estimated quantiles of the number of matching individuals. Table 2. Estimated quantiles of the number of matching individuals under the mutation scheme of [ 13 ]. Table 3. Estimated quantiles of the number of meioses between pairs of individuals with matching mitogenome.
Discussion Empirical mitogenome databases do not in practice represent random samples from a well-defined population, so that detailed comparisons with our simulation models are not meaningful. Methods Mitogenome mutation models We simulated the mitogenome as a binary sequence subject to neutral mutations, using the rates estimated by both Rieux et al. Table 4. Mutation rates per site and per 10 7 generations. Population simulations We simulated populations of mitogenomes under three demographic scenarios.
Supporting information. S1 Table. Approximate quantiles of the number of matching individuals. S2 Table. S3 Table. S4 Table. S5 Table. S1 Fig. Comparison of simulated with US and Iranian databases. References 1. Forensic applications of mitochondrial DNA.
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