Mtdna sampling can identify

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. The databases tend to represent the general major population groups of the potential contributors of evidence. The relevance and representativeness of these databases should be considered for forensic applications.

Pair wise comparison of haplo types and genetic diversity has been used to assess the relevance and representativeness of these databases [22]. Currently, a fast and highly accurate mass spectrometer-based process for detecting the presence of a particular nucleic acid in a biological sample for diagnostic purposes is also being attracted to many scientists [23]. Mass spectrometry provides detailed information about the molecules being analyzed, including high mass accuracy.

It is also a process that can be easily automated. Direct sequencing has several advantages; it is faster and produces more discrete results.

The sequencing can be performed on formalin fixed tissues, on blood which was exposed to normal temperature, on cadaveric tissue and on hair samples. In compare to any other technique attempted in forensic laboratory, the multiplex mini sequencing of mtDNA provided high success rates [24].

Heteroplasmy is a problem for forensic investigators since a sample from a crime scene can differ from a sample from a suspect by one base pair and this difference may be interpreted as sufficient evidence to eliminate that individual as the suspect. Hair samples from a single individual can contain heteroplasmic mutations at vastly different concentrations and even the root and shaft of a single hair can differ.

The detection methods currently available to molecular biologists cannot detect low levels of heteroplasmy. Furthermore, if present, length heteroplasmy will adversely affect sequencing runs by resulting in an out-of-frame sequence that cannot be interpreted [25,26]. Rapidly developing biotechnologies offer an almost perfect tool for law enforcement agencies. The advantage of molecular genetics typing over any other methodology is in continuation and should not be considered with uncertainty.

Heteroplasmy at more than one site may occur but at very lower frequency. The fact that heteroplasmy occurs more often than originally observed and the mechanism and rate of heteroplasmy are not well defined is often raised in acceptability challenges in an attempt to exclude mtDNA evidence. But with careful evaluation, one can avoid erroneous interpretations.

The forensic lab increasingly will be concerned about amplification competition between authentic DNA, contaminating modern DNA, and the occasional damaged postmortem DNA template. What is clear is that the small amplicon approach is highly successful at capturing degraded but abundant mtDNA from challenging samples and should be part of the testing repertoire for all missing persons and mass disaster programs.

With the aid of all modern technologies, it is possible to analyze mtDNA for forensic laboratory with careful consideration of sample collection. Nature Cell Biology Virkler K, Lednev IK Analysis of body fluids for forensic purposes: From laboratory testing to non-destructive rapid confirmatory identification at a crime scene. For Sci Int : Nature Forensic Science Communications 5 2 : Foren Sci Int 5 2 : Crime Lab Digest 20 4 : Forensic Science Communications 1 2 : Fast and accurate short read alignment with Burrows-Wheeler transform.

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Tamura, K. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Thomas, P. Coding single-nucleotide polymorphisms associated with complex vs. Toncheva, D. Ancient mitochondrial DNA pathogenic variants putatively associated with mitochondrial disease. Vai, S. A genetic perspective on longobard-era migrations. They are often drawn from a much larger population than is relevant to a specific crime scenario, and sometimes from a number of different administrative regions such as states.

However, broad-brush comparisons are useful, because while the databases are not scientific in their design, the resulting deviations from population values may not be very large.

Most of our simulated databases of size show less haplotype diversity than this database, but those under the 1. The simulated databases of size under the 1. Boxplots show the distribution of the number of distinct haplotypes arising from 2, random databases of sizes and obtained under our three demographic and two mutation models.

The horizontal reference lines show the numbers of distinct haplotypes in US [ 15 ] and Iranian [ 16 ] databases of those sizes. See S1 Fig for distributions of the numbers of singletons and doubletons and details on how the boxplots were constructed. These lower levels of diversity may be appropriate in some forensic contexts, and can be analysed with our methods using a smaller population size than the examples presented here.

For both mutation schemes, Fig 2 black curves, which are the same in each row shows the cumulative distribution of the number of mitogenomes in the live population matching that of the PoI person of interest. The distributions see Table 1 for quantiles are similar for the 1. Black lines show unconditional distributions. Coloured lines show the distributions conditional on m matching mitogenomes in a reference database of size n , for up to five values of m see legend for colour codes and three values of n one per row.

Quantiles of the distributions shown in the middle column are given in Table 2 and S3 Table for the mutation models of [ 13 ] and [ 14 ], respectively. See text for references to additional tables for the other demographic scenarios. Key quantiles of the unconditional distributions black curves of Fig 2.

These distributions are altered by conditioning on an observation of m matches in a randomly-sampled database of size n Fig 2 , coloured curves. For the largest database we now see a clear difference between the two constant-size populations. However, 0. Estimated quantiles for the solid curves in the middle column of Fig 2 are given in Table 2. Corresponding quantiles for the Rieux mutation scheme [ 14 ] are given in S3 Table 1. Distributions shown in Fig 2 , middle column. The number of meioses separating individuals with matching mitogenomes ranges up to a few hundred, and is almost never larger than Fig 3.

This is close to unrelated for most practical purposes, but random pairs of individuals are very unlikely to be related within 1, meioses, and so pairs with matching mitogenomes are much more closely related than average pairs of individuals.

Key quantiles for the distributions of matching pairs are given in Table 3. As a guide for comparison, a coalescent theory approximation [ 18 ] for the mean numbers of meioses separating a random pair are K and K for our small and large constant-size populations, respectively.

The dotted lines correspond to random pairs of individuals, the solid and dashed lines are for pairs observed to have matching mitogenomes. See Table 3 for quantiles. Quantiles of the distributions shown in Fig 3 solid and dashed curves. 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.

However, we have verified here that the haplotype diversity generated by our simulation models is broadly comparable with that observed in two real databases from large populations. In our related paper on Y profile matching [ 11 ], we showed that because of the high mutation rates of contemporary Y profiles, the numbers of males with Y profile matching a PoI person of interest are low, typically up to a few tens, and that this number is little affected by population size or growth.

Moreover the clusters of matching males are related within a few tens of meioses and so are unlikely to be randomly distributed in the population relevant to a typical crime scene.

We argued that it was therefore not appropriate to report a match probability a special case of the likelihood ratio to measure the weight of evidence, even though likelihood ratios are central to the evaluation of autosomal DNA profiles. In the present paper we have shown that the situation for mtDNA evidence is intermediate between Y and autosomal profiles. Because the whole-mitogenome mutation rate is an order of magnitude smaller than the mutation rate for contemporary Y profiles, the number of individuals matching a PoI is correspondingly larger, and varies more with demography.

The unconditional distribution Table 1 is very similar for the two constant-size populations that differ in size by a factor of four, but for the growing population the median number of matches is about twice as big.

As for the case of Y profiles, our simulation-based approach can easily take into account information from a frequency database, although this requires the assumption that the database is a random sample from the population, which is rarely the case in practice. The mitolina software that we have presented here can be used to inform the evaluation of the weight of mtDNA evidence in forensic applications, similar to our recommended approach to presenting Y-profile evidence: simulation models are used to obtain an estimate of the number of individuals sharing the evidence sample mitogenome, with conditioning on a database frequency if available.

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.