If the two options are investigated, then the statistic with greatest probative value i. The procedure for DNA mixture interpretation using the CPI approach assumes that a laboratory has an established valid analytical or detection threshold AT , stochastic threshold ST , stutter filter values SF , and minimum peak height ratio s. One might be able to assume that the peak heights may be equivalent at every locus with very pristine un-degraded biological samples, but interpretation should be made on the resultant electropherogram [ 27 , 28 ].
Typically, across an entire DNA profile, there is a downward trend in peak heights such that longer length PCR amplicons, and therefore the alleles contained within, may exhibit shorter peak heights. DNA mixtures involve two or more donors. It is incumbent upon the DNA analyst to carefully assess and state the assumed number of contributors to a profile, even when using the CPI. An actual number of contributors, not a minimum number, is needed, as a different number of contributors for the same DNA mixture will result in more or less allele drop-out to explain the observed profile.
Consider, for example, a mixture profile with exactly 4 alleles at every locus, under the assumption of a two-person mixture there is no evidence of allele drop-out. However, if the assumption is that there are five contributors for the same mixture profile, then probability of allele drop-out is extremely high. Each donor may contribute 0, 1, or 2 alleles at each genetic marker locus tested with rare occurrences 3 alleles per locus. As the number of potential contributors increases, so does the uncertainty in accurately determining the true number of contributors [ 29 ].
For example, based on the total number of alleles observed across an entire STR profile, it can be extremely difficult, if not impossible, to distinguish a five-person from a six-person DNA mixture and in a number of cases even a three-person from a four-person mixture [ 29 ]. These guidelines do not describe in detail how to determine the number of contributors, as a minimum requirement, the number of alleles at each locus and their peak heights should be considered when assigning the number of contributors.
Because of the quantity and quality of the DNA being analysed, some loci may meet the determined number of contributors and some may not. Testing additional STR loci may reduce the uncertainty in estimating the potential number of contributors [ 29 ].
In addition, challenges arise when close biological relatives have contributed to a mixture or if the DNA is somewhat degraded. In some situations alleles may be missing i. Stutter, the inherent by-product of slippage during amplification of STRs, adds complexity to mixture interpretation. Typically, interpretation of whether a peak is solely stutter or stutter along with an allele from another contributor arises when a minor or trace contributor peak s is observed at a locus or other loci that is similar in height relative to the stutter of the major contributor alleles at the locus.
These peaks and their heights are used to help determine whether to qualify or disqualify the locus for use in the CPI calculation. Random variation in peak heights is an inherent property of current DNA typing methodologies. These random variations of peak heights within an individual STR profile or between replicate samples are known as stochastic variation.
As the quantity and quality of the input DNA decreases stochastic effects can increase. These effects manifest as variation in peak height between the two peaks at the same locus in a heterozygote or the variation of allele peak heights from the same donor at different loci across the degradation slope line. Such allele peak height variation arises from several factors:. Sampling of template from the extract for the aliquot used for the PCR [ 33 ],.
The greater stuttering and lower amplification efficiency of larger alleles or template accessibility during PCR , and. It is likely that most of the variation in allele peak heights results from the sampling of template [ 34 , 35 ] and quality of the sample, but variation during the PCR also contributes, especially with very low template DNA. If the template level is low in the DNA extract then relative variability in the peak heights can be large. This variability is empirically observed and is predicted [ 36 — 39 ].
Because of the strong linear relationship between template or, more correctly, effective template and allele peak height, peak height in the actual profile has been a reliable indicator of the presence of stochastic effects and, as such, has been a good indicator for establishing a stochastic threshold ST [ 40 , 41 ].
The ST is the peak height value s above which it is reasonable to assume that allele drop-out of a sister allele of a heterozygote has not occurred at a locus [ 40 , 41 ]. The ST must be determined empirically, based on validation data derived within the laboratory and specific to a given STR kit and analytical instrumentation. Although a binary approach, use of a ST has been deemed important to more formally assess potential allele drop-out. There are several ways in current use to assign a ST see the Appendix for discussion on setting a ST.
A formulaic derivation of the stochastic threshold is displayed in the Additional file 1. If a single allele is observed and its peak height is below the ST it is considered possible that a sister allele at that same locus may have dropped out. In contrast to single source samples, in DNA mixtures any given allele peak may actually represent a composite of allele peaks and depending on position can include stutter peaks. Because of the potential of allele sharing among different contributors to a DNA mixture and the accompanying additive effects in peak heights, a peak height above the ST does not necessarily assure one that a sister allele has not dropped out at that locus.
Analysis of the full profile is required to assist in the determination of potential allele drop-out. Laboratories typically apply a ST for interpretation using a peak height threshold determined based on validation experiments. If the same ST peak height is used across all loci in an entire DNA profile, for many cases involving low level or degraded samples, the loci at the low molecular weight end of the profile i.
STR allelic peak heights are approximately proportional to the effective i. Typically, across an entire DNA profile, there is a downward trend in peak heights such that longer sized PCR amplicons, and therefore the alleles contained within them, may exhibit shorter peak heights. Such general peak height behavior and locus-specific performance should be considered in DNA mixture interpretation. The phenomenon of allele drop-out was first documented in the early days of PCR-based typing [ 10 , 42 ].
Each STR allelic peak may be associated with one backward stutter peak and occasionally a lower signal forward stutter peak [ 17 , 41 — 44 ]. Therefore, analysts should be familiar with the nuances of each STR marker. In some situations it may be possible for the stutter peaks from one donor to exhibit a similar height to the allelic peaks from another donor. In such instances the potential allele peaks may not be distinguishable from stutter. Consider a case where it is ambiguous whether a peak is stutter or an allele.
In such an instance a contributor with an allele in this ambiguous position would not be excluded. The appropriate inclusion statistic for this locus then includes the allele probabilities for the ambiguous peak positions in the summation for the CPI calculation [ 13 ]. Subtraction of the stutter component may assist in determining the signal from the allelic component of that peak. It might be possible to determine that such peaks must be stutter by assuming a certain number of contributors, or a number of minor contributors.
For example, if it is reasonable to assume that there is one minor contributor, and two minor allelic peaks already have been identified, then other small peaks in stutter positions can be assumed to represent true stutter.
Forensic DNA profiling procedures are mainly based on high resolution and high throughput capillary electrophoresis separation and detection systems of PCR. DNA Electrophoresis Protocols for Forensic Genetics (Methods in Molecular Biology): Medicine & Health Science Books @ facfapesrioher.gq
If a single source profile may be deduced from the mixture, then do so. Approaches for calculating single-source statistical estimates of a profile probability can be found in the National Research Council Report [ 46 ].
The random match probability RMP describes the estimate of the probability that a randomly selected unrelated person would match the deduced single-source major or minor profile from the mixture. If a deduced profile is incomplete at any locus e. Often the 2p rule is applied for modified RMP calculations at those specific loci [ 45 , 46 ]. If no single-source profile could be deduced or there is some interest in interpreting irresolvable components of the mixture, the CPI approach can be invoked.
To formalize the interpretation the overriding principle P for use of loci in CPI calculations is:. P 1 : Any locus that has a reasonable probability of allele drop-out should be disqualified from use in calculation of the CPI statistic. All guidelines that follow are subservient to P 1. Failing to consider the potential of allele drop- out when there are no detectable peaks between the AT and the ST has allowed the often misguided concept to develop that if all observed peaks are above the ST, then the locus unequivocally can be used.
However, if a numerical estimation is sought then one could consider allele drop-out no higher than 0. With one exception the approach to DNA mixture interpretation should never trump P 1. The exception to P 1 termed modified or restricted CPI is an interpretation that can apply to a portion of a profile as opposed to the entire profile.
This scenario sometimes occurs where the mixture profile is comprised of multiple major contributors and minor or trace contributors where the majors can be resolved readily from the lesser contributing alleles for example, two major contributors and one minor contributor — see the section on a major cluster, R 4 [ 13 , 24 , 30 ]. A locus is included for use in a CPI calculation if allele drop-out is considered to be highly unlikely. Only qualified loci are used in the calculation of the CPI statistic Figs. A depiction of the TPOX locus in an assumed two person mixture.
If the overall profile supports the best assumption of a two-person mixture, then plausible genotype deconvolution should proceed considering a two-person contribution. If the contributors donated different amounts to the signal, then plausible genotype deconvolutions to explain the mixture are 8, 8 and 11,11 and 8,11 and 11, Hence, there is no reasonable expectation of allele drop-out, and the locus can be used in the CPI calculation.