Forensic SNP genotyping with SNaPshot: technical considerations for the development and optimization of multiplexed SNP assays.
TABLE OF CONTENTS INTRODUCTION Forensic SNP Analysis Is a Specialized and Complementary Technique to Mainstream DNA Profiling An Overview of the SNaPshot Technique I. DEVELOPING A CUSTOM SNP ASSAY A. PCR Primer Design B. SBE Primer Design C. In-Silico Testing of Primer Designs II. GUIDELINES FOR THE INTERPRETATION OF SNAPSHOT PROFILES A. Extreme Allele Imbalance and Allele Dropout B. Nonspecific Electrophoresis Signals III. INTERPRETATION OF MULTIPLE-SOURCE DNA MIXTURES IV. CONCLUDING REMARKS REFERENCES ABOUT THE AUTHORS
Forensic SNP Analysis Is a Specialized and Complementary Technique to Mainstream DNA Profiling
Single-nucleotide polymorphism (SNP) genotyping has a relatively long history in the forensic genetics field [1-5,7,11,12,15,16,19-23,27,28,32,33,35,36,39,40,43-53,55,57,59-61,72,75]. SNPs underlie most of the protein polymorphisms originally analyzed in forensic identification systems prior to the use of DNA polymorphisms. However, with the advent of short tandem repeat (STR)-based tests as the system of choice for forensic DNA analysis, SNPs were only relevant when a nucleotide variant altered the target sequence of a PCR primer and a linked allele in the target STR dropped out, causing a null allele. More recently, standalone SNP assays have been suggested as supplementary tests to STR analysis [2,23,34,43,59,61] that enable amplification of very short PCR fragments from highly degraded DNA samples [4,15,16,19,20,27,55,61,75], or provide tests for the prediction of biogeographical ancestry [11,39,49,51,52] and externally visible characteristics [10,24,36,37,41,54,56,69]. SNPs also provide complimentary data to STRs for the analysis of complex pedigrees, particularly when such tests seek to analyze distant family relationships or when the pedigree is deficient [2,23,33,43,59]. One particular benefit of SNPs in kinship analyses is their mutational stability, with mutation rates three orders of magnitude lower than those of STRs, giving a much lower probability of a genotype incompatibility due to mutation that appears as an exclusion.
Despite the simplicity of the binary variation of SNPs and their evident benefits to forensic analysis, SNP genotyping has yet to be widely adopted as a routine forensic identification tool. So far, few laboratories have incorporated SNP genotyping into routine analyses, although a considerable number are actively developing SNP sets for specific forensic purposes. One reason for the slow uptake has been a lack of suitable SNP genotyping systems and dedicated software for SNP data analysis. Length polymorphisms such as STRs or Indels can be readily genotyped by multiplex PCR using fluorescent dyes linked to the PCR primers, followed by capillary electrophoresis (CE) on validated automatic sequencers, originally developed for sequencing. The CE signal data is analyzed using well-established software that has undergone refinement and optimization for more than 20 years. In a well-balanced multiplexed PCR, both the intra- and interlocus electrophoretic signal balance is reproducible and this simplifies data analysis as well as aiding the interpretation of mixtures of multiple DNA donors [22,23,39,40,50,75]. This straightforward PCR-CE strategy can easily be implemented in all modern forensic laboratories using thermal cyclers and CE detectors. In contrast, SNP genotyping methods are more complex and require more DNAprocessing steps than STR analysis . All SNP genotyping methods start with a PCR enrichment step followed by one or more cleanup steps, then the SNP detection reaction [26,36,65].
An Overview of the SNaPshot Technique
The most widely used SNP detection system uses Life Technologies' SNaPshot kit (ABI PRISM[R] SNaPshot[TM] Multiplex Kit, herein SNaPshot) [38,63], which applies a single-base extension (SBE) reaction and detection of the resulting dye-labeled SBE products by CE. The SBE reaction consists of the hybridization of oligonucleotide primers designed to bind to the sequence immediately upstream of the target SNP and the extension of this primer with a dideoxynucleotide (ddNTP). Thus, only the single base that is complementary to the target SNP position is added to the primer. To recognize the variant alleles present at the SNPsite, only the specific ddNTPs added to the primer require identification. In the case of the SNaPshot SBE reaction, this is achieved by use ofa specific fluorescent dye linked to each of the four ddNTPs. The SNaPshot primer extension products are separated with an automated CE sequencer that detects fluorescence at timed intervals to record the electrophoretic mobility of the fragment and its fluorescent signal's wavelength. This technology allows considerable multiplexing capacity, which is ensured by applying mobility-modifying nonhuman DNA sequences to allow simultaneous individualization of multiple primers extended in parallel in a combined extension reaction. The SNaPshot process begins with PCR amplification to enrich the genomic regions carrying each SNP with primers flanking the target site. PCR is followed with an enzymatic cleanup of the PCR products to eliminate both the remaining PCR primers (with Exonuclease I) and unincorporated dNTPs (with shrimp alkaline phosphatase, SAP), preventing interference with the subsequent extension reaction. The cleaned-up PCR products are extended with a third primer that binds next to the SNP and usually carries a 5'-mass-modifying sequence. The SBE products are then cleansed of unused dye-linked ddNTPs (with SAP) as these can interfere with the CE detection of extension products with the same dye labels as unused 3'terminating nucleotides. Finally, the cleansed SBE products are loaded onto the sequencer for separation and detection. Agraphic overview of the SNaPshot process is shown in Figure 1.
The first forensic SNP test used a SNaPshot-type reaction, originally termed minisequencing (a name for this technique still often used), that examined variants in the MC1R gene underlying the red hair phenotype, which was specifically designed to analyze forensic DNA as a complementary test for STR profiling . SNaPshot has been the preferred method for forensic SNP analysis due to its relative simplicity, robustness, sensitivity, and high multiplexing capacity. Furthermore, the separation of the PCR amplification reaction from the subsequent SNP detection reaction results in much-improved performance when analyzing highly degraded DNA due to the small size of the PCR amplicons [4,15,16,19,20,27,55,61,75]. With STRs, it is necessary to amplify each marker within the multiplex with carefully controlled fragment mobilities, whether by mobility-modifying elements embedded in the primers or by actual amplicon length. This approach keeps the signals from each marker properly separated in the CE profile, but limits how many STRs can be typed within any one size range. Commercial STR kit suppliers have overcome this limitation by expanding the number of discrete dye wavelengths used in order to increase the number of labels from four to five (adding an orange dye) and recently to six (adding purple). In very degraded forensic samples, most of the DNA is shorter than 150 basepairs (bp), but only a proportion of STRs can be amplified below that size limit [25,40]. SNP genotyping is much less restricted by size limits, as the SBE reaction can be performed on very short amplicons, often with near-identical lengths. In theory, all fragments in a SNP assay can have the same amplicon length and can all be much shorter than 100 bp (assuming SNPs have a theoretical lower limit of 41 bp from a single base site and two 20mer primers). However, analysis of SNaPshot CE data can be challenging because the SBE products from each allele are detected with dyes that have quite different spectral characteristics. The signal strengths of fluorophore emissions from the four SNaPshot dyes are much less balanced than those used for STR primers, so the more varied SNaPshot peak heights directly reflect this characteristic. Furthermore, small peaks from spurious PCR products or PCR primers that are adventitiously extended with the labeled terminator bases of SNaPshot are often detected and these products may be misinterpreted as true alleles [6,40,58].
The above limitations can be overcome with the sufficient experience and proper knowledge of SNaPshot's capabilities and behavior [58,59]. In order to support adoption of SBE tests for forensic SNP genotyping, we have compiled this technical review. Starting with a brief description of the technique, we detail a set of guidelines for optimizing SNaPshot assay designs and for making the most reliable SNP profile interpretations. We pay particular attention to commonly seen PCR product or signal artifacts and cover the recommended ways to reduce their occurrence.
The dideoxynucleotide (ddNTP) terminator SBE chemistry of SNaPshot was originally developed by Molecular Tool and known commercially as Genetic Bit Analysis . Later, the company was acquired by Orchid Bioscience and the technology became more widely adopted. The SNaPshot kit was developed by Applied Biosystems to exploit dye-linked oligonucleotide detection technology using automated sequencers. Forensic SNP genotyping with SNaPshot provides a range of advantages: (a) a high multiplexing capacity that has surpassed the developer's original suggested reaction limits (e.g., there is an optimized SNaPshot PCR and SBE one-tube reaction simultaneously analyzing 34 SNPs), (b) a high level of robustness, sensitivity, interlaboratory genotyping concordance, and reproducibility, shown by extensive tests indicating that SNaPshot generates complete profiles even in highly degraded samples, (c) a relatively simple genotyping protocol consisting of PCR and SBE reactions, each with short and efficient cleanup steps. Although more complex than STR genotyping, SNaPshot  is much simpler than most other SNP typing strategies [26,36,65]. The few SNP-genotyping systems relevant to forensic analysis that are easier to perform, notably Taqman SNP genotyping, are hampered by the constraint of singleplex PCR that does not make best use of limited DNA extract. In addition, the interrun and interlaboratory precision of SNaPshot indicates peak mobilities vary by as little as [+ or -] 0.1 bp. Overall, the SNaPshot system provides the best balance of optimum characteristics relevant to every aspect of SNP analysis applied to forensic casework.
Although emerging mass-genotyping technologies based on next-generation sequencing (NGS) are gaining traction as a more extensive form of forensic SNP testing, small SNaPshot-based assays will continue to contribute to forensic casework while NGS matures and becomes simpler to run as a routine genotyping approach. NGS provides much more data per assay than CE-based tests and SNaPshot assays amplify a smaller proportion of markers compared to the PCR scales possible with NGS. However, SNaPshot provides several advantages that will continue to be important in forensic analysis for the next few years. First, the near universal use of CE sequencers in forensics and the expertise in their management and daily use to analyze contact trace-level DNA ensures that laboratories already have a validated and optimized detection system for running SNaPshot tests. Second, the incorporation of SNPs into small-scale multiplexes is a much more straightforward process compared to the construction of PCR reactions numbering more than a hundred markers as in the typical NGS assay. This allows SNaPshot to offer affordable and flexible SNP testing so that optimization and the assessment of the value of any one SNP or set of SNPs for forensic purposes is much easier than using NGS from the start. Since typical NGS SNP-typing methods can successfully merge smaller-scale multiplex PCRs, previously optimized with SNaPshot , the two-stage optimization of SNP multiplexes uses SNaPshot typing as the testing ground for new SNPs, multiplex-reaction optimization, validation, and genotype data analysis in the most cost-effective way.
After a multiplexed PCR, the exonuclease and SAP purification stage digests unused, single-stranded primers and unincorporated nucleotides, respectively. The multiplexed SBE reaction then proceeds, consisting of the addition of a ddNTP to a primer designed to anneal up to the position immediately adjacent to the SNP site. A mix of the four dye-linked ddNTPs provides the complementary nucleotide extending the SNP bases present, then blocks further extension. The analysis of multiple SBE products generated from SNaPshot relies on mobility-modifying nonhuman [24,49] oligonucleotide "pigtail" sequences placed at the 5' end of the extension primer sequence. These mobility-modifying sequences allow reasonably precise positioning of the extension products in the size fractionation of the CE run. Pigtails therefore give a readily adaptable way to differentiate SNP products with identical alleles. Likewise, SNP pairs with mutually exclusive alleles (e.g., an AC SNP paired with a GT SNP) can be combined to run to the same electrophoretic position.
I. DEVELOPING A CUSTOM SNP ASSAY
A. PCR Primer Design
Designing PCR primers for SNaPshot assays largely follows identical principles to those used for other forensic multiplexes. A range of web-based primer design utilities have been published that all share common features that need consideration for designing multiplexes with optimum amplification performance.
The design begins with candidate sets of selected SNPs of interest. If possible, the pool of candidates should be larger than those intended for the final assay since some SNPs may not be reach a suitable level of optimization. The flanking sequence of each marker should be obtained from online databases  such as NCBI dbSNP or 1000 Genomes [59,62]. The sequence characteristics of the SNP flanking region is the key factor that affects each SNP's ability to be multiplexed. Usually short homopolymeric tracts, high GC content (an upper limit of 70% GC), and low nucleotide variability [59,58] all characterize low-quality flanking sequence. Particular attention must be paid to flanking-sequence polymorphisms close to the targeted SNP site since these can interfere with primer binding during the initial PCR or consequent extension reactions [17,18,34]. Both dbSNP and 1000 Genomes provide detailed sequence maps with variants marked in order to locate any polymorphisms within the flanking region where primers will bind. Sequences with the variant allele of a flanking SNP will have a different primer-melting temperature and will be underrepresented in the PCR products. The substitution of a purine for a pyrimidine has the most severe effect on the melting temperature. Commonly, a flanking SNP variant is linked to a particular allele in the target SNP leading to systematic underrepresentation or even full dropout of that allele in populations where the flanking SNP variant is seen and the detected genotypes of the targeted SNP then depart from Hardy Weinberg equilibrium if left unaccounted for. At this stage, knowledge about the presence of such flanking SNPs is important, as preemptive measures can be factored in; notably use of degenerate primer designs (a mixture of primers specific to all target-sequence configurations to account for the presence of sequence variants) [58,59].
Although several freely accessible online primer design tools are available, we will concentrate on the most widely used, which is Primer3 . Four key parameters dictate the design of primers and the PCR reaction in which they are used [9,14,67]:
* Amplified fragment length: the amplicons should have similar sizes to minimize PCR bias, where shorter-than-average DNA fragments are more efficiently synthesized than those of above-average length [4,15,16,19,20,27,55,61,75]. For forensic purposes, where short DNA fragments tend to remain intact in larger proportions after degradative processes, DNA lengths should ideally be between 60 and 100 bp to ensure efficient PCR amplification from the degraded DNA typical of challenging forensic samples.
* Melting temperature: melting temperature (Tm) must be equilibrated for all primers in the multiplex reaction as far as possible, since all SNPs are amplified with one annealing temperature. As each SNP's flanking sequences differ, the purine-pyrimidine ratio influences the Tm of any one primer design and the length of the primer becomes the main way to adjust the Tm so that it is as close as possible to the overall value. For the majority of primer designs established in the author's laboratory, a common Tm value is 62[degrees]C, and consequently primer designs are developed to match this value as nearly as possible. The addition or removal of a pyrimidine creates a Tm variation of 2[degrees]C less than a purine modification in the same sequence . Although achieving a very close match to an overall melting temperature is impossible for many primer designs, variation of [+ or -] 2[degrees]C does not markedly influence the PCR performance of those primers in both PCR and extension reactions [52,58,59]. A maximum Tm difference between the forward and reverse PCR primers of each SNP can be as high as 5[degrees]C without undue loss of amplification efficiency.
* Primer length: an optimum primer length range is from 18 to 27 bases. Depending on the sequence's %GC (i.e., ratio of purines and pyrimidines), the primer length is adjusted to obtain a suitable Tm. Short primer sequences bring a higher risk of nonspecific hybridization to short homologous sequence tracts. Similarly, nonspecific binding is more likely if short homopolymeric tracts occur in the primer sequences and these also increase the opportunity of primer hairpin formation (described later).
* GC content: to keep control of the above parameters it is recommended to aim for a maximum range of 40-60% GC in the primer sequences.
Applying these four parameters, Primer3 provides a primary selection of primer designs, followed by an accompanying list of secondary designs. The primary selection may not produce the best design, so all output from the software is worth retaining when scrutinizing the primer design.
Additional issues regarding the selection of SNPs for forensic typing purposes and multiplex development have been raised and applied with success by Ken Kidd and his collaborators in a range of studies over the years [29,30,41,43]. A range of SNP markers carefully tailored to forensic applications has been developed via the approaches adopted by Kidd et al. [29,30,41,43].
For human identification, a set of maximum heterozygosity/lowest fixation index ([F.sub.st]) SNPs have been developed. A high heterozygosity ensures that the most informative random-match probability values are obtained in any given case [29,43]. Minimal [F.sub.st] values ensure low allele frequency variation among populations to minimize the differences in random-match probability estimates among population; moving toward what has been termed a "universal" forensic identification SNP set, signifying the set is applicable to all populations.
This selection strategy consists of a series of sequential steps . First, candidate polymorphisms are identified from the genome by making use of depth of data present available. Second, candidates are screened in a number of populations to select those with the best statistical performance. Third, the selected SNPs are genotyped in an expanded range of populations and the best markers combined into a manageable multiplexed assay. In each iteration of the selection process, SNPs are ranked by average heterozygosity and [F.sub.st]. The [F.sub.st] cutoff values were set to 0.02  to provide the largest proportion of low-[F.sub.st] SNPs in all the tested populations. However, the [F.sub.st] threshold could be increased slightly and still yield SNPs with low [F.sub.st] values in the largest population set tested. Additionally, once a SNP is selected in any of the steps, no other marker is considered within a distance of 1 Mb from that marker, thus avoiding statistical issues with linkage disequilibrium .
SNPs to predict biogeographic origin have also been developed by Kidd's group with similarly successful results [30,41]. In contrast to the SNP selection method for identification purposes, ancestry-informative SNPs need to show large allele frequency differences among the broadest possible set of populations [30,41]. A limitation of this selection process is that population origins outside the already studied groups for a given SNP assay cannot be predicted ; therefore, the broadest possible population test group is necessary. The selection process again involves the collection of a large number of candidate SNPs from genomic databases and estimating pairwise [F.sub.st] values for each population comparison . This ensures a selection of markers that provides the highest possible predictive power for all populations included in the test while maintaining a manageable number of markers and balancing the information obtained for each population .
B. SBE Primer Design
Because SBE primers can only be positioned immediately next to the SNP site, there is little need for primer design software. The only requirement is to use the necessary bases to obtain a Tm as close to the general value as possible. When choosing an SBE primer design that uses the opposite direction to the reference sequence or that of the SNP database used to obtain data, the opposite nucleotides are produced by the SNaPshot reaction (e.g., an A/G SNP will be visualized as a T/C). Particular care is required for the symmetrical SNPs that have either A/T or C/G allele combinations, since the reported bases can be opposite to those of the reference sequence and this effect is difficult to detect, e.g., a G allele on the reference strand will be reported as a C when the opposite strand is extended in SNaPshot. A notable example is rs16891982, which is a C/G SNP forming a component of several forensic tests, including the HI/IrisPlex eye color tests and SNP for ID's 34-plex ancestry test [52,56,74]. The inference of blue eye color and European ancestry is strongly influenced by the accurate detection of the C and G alleles in this SNP, which is dependent on the strand direction chosen for extension in the SNaPshot reaction used for this SNP.
Although the Tm must be equilibrated for SBE primers as much as possible by length adjustments, the primer length also sets its electrophoretic mobility and to separate each extension product appropriately, mobility-modifying extra sequence tails are required. A size window of 4 bp is recommended to separate the extension products from each paired set of SNPs to reach an optimum level of differentiation and analyze as many SNPs as possible [58,59]. Although smaller separation intervals can be used, this risks electrophoretic overlap between two sets of SNPs and peaks may be too close to be unequivocally identified as the products from a particular SNP. Note that since most SNPs are biallelic, the pairing of the extension primer lengths of SNPs with complementary alleles (e.g., an A/C SNP with a G/T SNP) is routine, provided the extended strands are matched accordingly. However, triallelic SNPs do occur and have been used in forensic tests [50,52,75], although these loci require a single electrophoretic window with a bigger mobility interval, as three different bases are more likely to show a wider range of mobilities than two.
To adjust electrophoretic mobility a 5' sequence tail (often termed pigtails), is added to the SBE primer comprising nonhuman DNA with the appropriate length. We have applied poly-CT tails with success, but a single, universally applied bacterial-specific sequence provides the most robust mobility-modifying base sequence as this is generally free from interactions within the SBE primer pool. A widely used sequence in a range of forensic SNaPshot tests has been: 5' TGAACTAGGTGCCACGTCGTGAAAGTCTGACAA 3' [49,52,56,59]. Longer tails can include additional segments of the same sequence. The first 3-4 bases at the 3' end of the mobility-modifying sequence should also be checked for any adventitious complimentarily to the bases upstream of the 5' primer end as these will change its Tm. Tails as long as 95 bp have been linked to 22-bp primers without the nonspecific sequences interfering with primer binding, and it can be assumed that longer tails could be applied if necessary [49,52,56,59]. The problems associated with accurate synthesis of oligonucleotides with sequences longer than 100 bp then becomes a factor in ensuring primers are sufficiently specific, i.e., the synthesized sequences are free from closely matching but different sequences.
It should be noted that the mobility of the extension product also depends on the base added in the SBE reaction, [49,52,56,59]. The addition of the G ddNTP terminator generally creates a mobility modification matching the size of one base, as shown in Figure 2, but adding an A leads to a mobility change equivalent to two bases, C creates a three-base change to mobility, and T four. Unusually, an exception to this rule is consistently seen in A/C SNPs, when the C allele peak appears in a position one base shorter than the A allele [17,58,59]. Molecular differences between each combination of base and linked fluorescent reporter are responsible for this mobility differentiation among ddNTP-extended primers. Regardless of the mobility modification introduced by the incorporation of a labeled ddNTP, each fragment's mobility is always consistent and reproducible. Furthermore, as a general rule the expected and observed mobility differences are reduced in the lowest fragment-size range of about 20 bp upwards.
C. In-Silico Testing of Primer Designs
There are a number of situations that can negatively affect primer binding, leading to amplification failure or unexpected extension of nonspecific primer interactions. Therefore, a series of checks are important to perform before ordering the synthesis of primers.
The first check necessary is to test the nucleotide alignments of primer sequences applying the NCBI-BLAST (Basic Local Alignment Search Tool) utility . BLAST scrutinizes each primer sequence in comparison to the human genome and should indicate a single binding site for any one design with a fully matched tract of nucleotides in the human genome forming the binding site. Partial homology to some regions of the genome other than the targeted binding site are indicated by BLAST, but these should be only small segments of matching sequence if the region around the SNP is free from low-complexity sequence (i.e., sequence that is recognized as repetitive or segmentally duplicated genomic segments able to bind a primer in multiple genomic positions leading to nonspecific products and severely reduced amplification efficiency).
The second important check to make is to assess the possibility of hairpin-type secondary structure formation, which if possible, will adversely affect the efficiency of the primer in large-scale PCR or extension reactions. Hairpin formation in an SBE primer or its tail particularly affects the extension efficiency and therefore relative peak height of allelic products from any SBE primer where a degree of hairpin formation occurs. The task of checking for hairpin structure potential can be made using the free online tool Oligonucleotide Properties Calculator . Note that AutoDimer software (see below) can also check for hairpin structures in the primer designs it checks for dimers.
The third step is to check for the presence of clustering polymorphisms, which ensures the primer Tm is not adversely affected. Both SNPs and insertion-deletion polymorphisms (indels) may be found close to the target SNP site and should be avoided when their variation is common in any population. Indels tend to lead to complete allele or SNP dropout as the primer will not bind at all to the strand with the deletion allele present, while the impact of clustering SNPs depends on their proximity to the 3' end of the primer where the initial base pairing has the strongest effect on the Tm [17,58], as shown in Figure 3 Clustering SNPs can be identified using the SNPchecktool, (https://secure.ngrl.org.uk/SNPCheck/snpcheck.htm). The SNPcheck tool scrutinizes primer sequences for the presence of the polymorphisms currently listed in dbSNP, 1000 Genomes [62,64], and ESP (Exome Sequencing Project) human variant databases . Direct links to these databases are provided for the polymorphisms identified, so population frequencies and the variant's validation status (how reliably the variation has been characterized at the SNP site) can both be checked. When there is no possibility to avoid a polymorphism in the primer sequence with a common variant frequency, it is possible, and common practice, to synthesize degenerate primer pairs with each of the two bases complimentary to the alleles of the clustering SNP.
The fourth check required is to assess the probability of primer-dimer formation. When large numbers of primers are combined at a relatively high concentration, there are increased probabilities for some level of primer-dimer formation. A highly interactive primer design can easily upset the balanced amplification of many of the primer pairs in the multiplexed PCR. In extension primer pools it is particularly important to avoid 3' interactions since these will produce spurious extension products of the same size as those intended in the reaction, which can often be indistinguishable from those extended from the target SNP. Such reduced specificity in the extension reaction leads to a lower efficiency and will adversely affect the peak heights for the SNPs detected with the interacting SBE primers. Addition of ammonium sulfate at a concentration of 0.2 mM in the primer mix will destabilize the weakest primer-dimer interactions [58,59], whereas the strongest primer-dimer sequences in the primer of a SNP will require removal of the SNP from the multiplex. AutoDimer software  checks for primer-dimer and hairpin formation among large pools of oligonuclotides. AutoDimer allows definition of the minimum length of hybridization reported, allows modification of the salt concentration in the reaction mix, and indicates the optimum annealing temperature applicable to avoid the stronger interactions.
Although the above checks seem exhaustive, when carefully applied to the primer-design process they markedly increase the chances of the multiplex working optimally. However, multiplex PCR and extension reactions are complex environments and checks alone do not guarantee that primer interactions will not occur to some extent.
1. Postsynthesis Oligonucleotide Quality Control
The guidelines for primer design and in-silico evaluation presented here, although highly useful tools, can only aid the design process. Once the primers are synthesized, a new set of manufacturing-related factors appears. A number of issues may arise at this stage, including deficient yields of a particular primer proportion (i.e., often shown as shorter mass tags than desired); less primer concentration than expected; and molecular damage during transportation. Therefore, it is strongly recommended to perform a primer-quality test when they are received from the manufacturer. A singleplex amplification and genotyping reaction should be conducted to help identify synthesis issues. These tests can gauge parameters such as electrophoretic mobility of the detected signals, nontemplate peak formation, stutter-like artifacts, and overall signal strength of the specific extension product formed. Although uncommon, contamination of the integrity of primer stocks should also be tested by running control PCRs lacking template DNA.
An additional test that is highly recommended is the quantification of manufactured oligonucleotide concentrations. Given that no genomic DNA should be present in the primer stock dilutions, wavelength DNA absorption quantification methods are recommended for this task. Slight variation in individual primer production can give rise to inconsistencies in multiplex optimization and interlaboratory reproduction of electrophoretic patterns. Careful quantification of the primer stocks is of primary importance in order to achieve a standardized assay that provides results that can be reproduced in different laboratories.
2. SNaPshot Assay Validation Guidelines
As with any genotyping assay targeted for forensic casework, independently of the SNPs analyzed, tailored SNaPshot multiplexes should be carefully validated to ensure that results are reliable, consistent, and presentable in court.
Several factors must be considered for the developed assay to be suitably validated. Although most of the data needed for the validation process can be acquired with the same batch of samples (e.g., a serial dilution of commercial control DNAs), a set of controlled in-house samples should also be studied (degraded DNA samples, blood, saliva, artificially constructed DNA mixtures). Replicated analyses in different laboratories may also be performed to complete more comprehensive validation of a test's reproducibility .
Reproducibility and Precision. It is important that an assay can provide robust results that can be replicated both internally and externally. External validation can run replicated analyses of serial dilutions of commercially available control DNAs in universal use in conventional STR profiling kits. Reproducibility can then be evaluated by comparing peak positions to those of the mean of all runs and genotyping precision by comparison to reference data for kit-based control DNA such as the data maintained at the National Institute of Standards and Technology (Gaithersburg, MD). Note that it has already been experimentally established  that the peak positioning of any SNaPshot assay is precise if electrophoretic conditions are maintained, and therefore the standard deviation of these tests should not exceed values less than 10% of a base in the fragment-size estimate. Given the precise nature of SNaPshot electrophoretic separation, it is of key importance to establish the precise mobility of each allele peak, as this is critical for the identification of artifact signals in the SNaPshot profile.
Heterozygote Balance. This parameter gauges the balance of each SNP's peak pairs in heterozygotes, and because imbalance signals between different extended bases is an inherent characteristic of the SBE chemistry, is a critical value to measure in the markers of a forensic assay. Knowledge of the expected heterozygote peak height ratios ensures the most accurate allele calls and profile interpretation, but also provides the identification and interpretation of mixed DNA in SNaPshot profiles.
Detection Limit. Dilution series analysis of commercially available DNA controls is applied to measure this parameter. The lower limit of DNA concentration where no profile or largely incomplete profile is observed and the rate of allele dropout in successive dilutions is recorded, as well as the extent to which observed heterozygote peak height ratios are different from expected values.
Casework Sample Analyses. Several replicates of nonprobative or artificially made casework samples can be genotyping to test the ability of the SNaPshot assay to provide consistent and concordant results. Special attention should be given to degraded DNA samples, since the analysis of degraded DNA is one of the principal applications of forensic SNP genotyping. A combination of degraded DNA, low-level samples, and both degraded and low-level extracts were analyzed for the validation of forensic SNaPshot assays developed by the SNP for ID Consortium with successful results .
Recording of Artifact Signals in the Profile. It is important to take careful note of nonspecific electrophoretic signals, since proportions will be permanent characteristics of the profile of the assay being assessed and exhibit a fixed electrophoretic mobility. Awareness and knowledge of such nonallelic profile artifacts is an important factor in the correct identification of the allelic peaks in any one SNaPshot assay.
II. GUIDELINES FOR THE INTERPRETATION OF SNAPSHOT PROFILES
The main obstacle to the implementation of SNaPshot into routine forensic analysis is the complexity of profile interpretation and the degree of expertise required when analyzing profiles where peak heights are imbalanced and can therefore be incorrectly identified as true extension products rather than artifacts [58,59].
The four possible SNP alleles are reported using fluorochromes: G = blue DR110, A = green DR6G, C = yellow TAMRA, T = red ROX . Since these allele-reporter dyes have different emission levels, their signal strengths--measured as relative fluorescence units (RFUs)--vary considerably, even when the quantity of extended DNA is identical. For this reason, it is important to differentiate an artifact signal from an imbalanced heterozygote formed from two signals of contrasting strengths [58,59].
In this section we review three commonly encountered challenges when dealing with SNaPshot profiles: (1) extreme allele imbalance, (2) very low allele signals, and (3) nonspecific peaks or allele drop in. These three factors are also often seen in STR genotyping, but they can be accentuated in SNaPshot by the inherent peak imbalances created by the dye signals produced from the SBE reaction.
A. Extreme Allele Imbalance and Allele Dropout
When the overall peak height level is low in the SNaPshot profile, the signal imbalances inherent in the fluorochromes used tend to be more accentuated, while the lowest signals can fall below the analytical threshold --i.e., where allele peaks have similar RFU values to the baseline signals that include a significant range of nonspecific peaks or signal activity unrelated to the detection of DNA fragments in the capillary. Therefore, from a practical point of view, allele imbalance and allele dropout represent extreme examples of a continuum of signal imbalance commonly observed in SNaPshot profiles (examples shown in Figures 4A-4C).
The task of identifying peaks as allelic products becomes more straightforward when applying a simple rule of pre-set signal ratios. For the same DNA strand, a G base extension would report twice the signal strength of an A base extension, which would be four times stronger than C or T base extensions. Therefore, an A base extension is twice as strong as those of C or T extension products, which led to C/T SNPs being initially analyzed as forensic markers to reduce the complexities of peak interpretation . The ratio for G:A:C:T extension-product signals equating to 4:2:1:1 in standardized conditions [58,59] is shown in Figure 2. The observed ratios can depart from the expected values if the extension-reaction kinetics are strongly influenced by primer imbalances as outlined in sections 3.1 and 3.2. However, the expected ratios can act as a suitable starting point for the interpretation of the observed relative peak heights, e.g., a G peak with a lower RFU value than a T peak in the same SNP would indicate an artifact in the profile.
When the analyzed DNA is degraded or low-level, the resulting profiles often show much lower overall signal heights, so more flexibility is necessary in applying the relative signal ratios rule. Degraded and low-level DNA often produce stochastic amplification effects where one allele or SNP is disproportionately amplified and this can create peak height imbalances that add to variation in signal ratios. In such cases, allele dropout and extreme signal imbalance is much more likely to occur, and must be kept in mind when approaching SNaPshot profile interpretation. The occurrence of such stochastic effects tends to be more common in those SNPs that already underperform in amplification or extension, so departures from the expected fluorochrome peak-height ratios can be predicted to some extent from the analyses made when initially developing the multiplex with normal DNA samples.
A useful initial test of any new SNaPshot multiplex is to genotype a range of DNA samples to capture as many heterozygotes as possible, as shown in Figure 5. This process allows the calculation of each heterozygote RFU ratio and its comparison to the expected signal ratio. The standard signal ratio rule can then be specifically redefined to the observed assay profile patterns in normal DNA. The observed heterozygote peak-height ratios tend to match well with expected values, do not vary markedly between runs with different samples, and more importantly, are observed to be constant among laboratories following identical SNaPshot protocols .
A consistent effect when running a wide range of heterozygotes for different SNPs when developing new SNaPshot assays is that the relative peak-height ratios are also dependent on the combination of alleles in any one SNP. We have observed that the signals in C/T SNPs closely match the expected 1:1 ratio with almost no variation. In fact, departures from the expected 1:1 ratio in C/T SNPs are rare enough to lead to more detailed studies of the effect of uncharted clustering SNPs linked to one of the alleles influencing the extension of one allele compared to the other in such SNPs . A/C and A/T SNPs are also similar in their allele ratios to those of C/T SNPs. Almost all A/C and A/T SNPs match an expected 2:1 ratio. It is in A/G SNPs that more signal-ratio variation occurs and this reaches the highest level of peak-height-ratio variation in G/C and G/T SNPs . The more extreme the signal ratios are observed to be between a strong G peak and the weaker C and T peaks when analyzing normal DNA, the more likely it is that these alleles are lost when analyzing challenging DNA with SNaPshot. It should be kept in mind that any A/G SNP can be analyzed as a C/T allele combination by extending the opposite DNA strand direction and this can be a useful strategy to secure a more balanced peak pattern for the selected SNP.
When peak-height ratios show minor but consistent departure from the expected values in the initial analyses made with normal DNA, our experience has indicated that clustering polymorphisms in primer binding sequences that had not been detected by variant database searches made during the primer design phase [17,18,58]. Due to the low mutation rate of both SNPs and indels, there is more likely to be direct linkage between both the clustering variant and a SNP allele. Consequently, only one allele of the SNP will be affected and the imbalance will follow the allele frequency of the clustering variant in the population such that a proportion of samples will exhibit different peak patterns compared to the bulk of profiles obtained for the analyzed SNP [17,18,34]. When finding peak imbalances due to clustering SNPs, the signal ratios can usually be efficiently rebalanced by introducing degenerate primers containing both complimentary bases to the variant site in the primer sequence , although the signal strength for that SNP can be reduced [17,18].
In summary, although peak imbalances in SNaPshot profiles can be perceived to be a major limitation, experience and the application of a set of simple interpretation rules can overcome the difficulties of profile analysis with this SNP genotyping system. In most cases the 4:2:1:1 ratio for G:A:C:T signals matches the observed peak heights sufficiently well and departures from the expected rule can be anticipated by observing the behavior of each SNP when analyzing normal DNA. While the analysis of challenging DNA will exaggerate the signal imbalances to the point where already low signals fall below the analytical threshold RFU level and may not be identified as allelic products, the common approach adopted for STR analysis can be a prudent step where related analysis can produce a more secure identification of a heterozygote genotype where one of the peaks is disproportionately low but present in the profile. Awareness of clustering polymorphisms in close proximity to the selected SNPs is a critical screening step during primer design. However, rare, often population-specific, variants cannot be reliably detected in a large number of cases and detailed scrutiny of profile patterns in a range of samples becomes a key step before primer designs are reviewed or a SNP peak pattern is recorded as imbalanced beyond the standard 4:2:1:1 rule.
B. Nonspecific Electrophoresis Signals
The presence of nonspecific peak activity in SNaPshot profiles is the most common artifact. Although frequent, nonspecific peaks that do not correspond to the signals of allelic products are generally at a much lower level of activity and are therefore easier signals to discount from a profile.
A nonspecific signal can occur at a fixed position close to those of extension-product peaks, but those falling outside these positions, in random locations in the profile, can be readily excluded. Nonspecific peaks in SNaPshot profiles can be classified into four signal categories: (1) electrophoretic artifacts, (2) adenylation peaks from the amplification reaction, (3) stutter peaks, and (4) adventitious extensions of primer dimers.
Electrophoretic artifacts include common issues linked to the fractionation of fluorescence-labeled extension products. Forensic STR genotyping commonly displays such artifacts and SNPgenotyping electrophoresis produces similar extra peaks within the SNaPshot profile. Fluorescence cross-signal pullup can occur (Figure 4D), caused by above-average signal levels for an allele peak that, due to excessive activity, increases the detected signals in the wavelengths of the other fluorochromes to create a set of peaks in identical positions to the main signal from the true extension product. The patterns of such peaks are quite characteristic and are generally easily identified as artifacts with a characteristic "sharper" peak shape, lacking a broad base. However, in some rare cases the pullup peak may disguise or overlay an allele-specific signal denoting an extension peak in a very close position in the profile. Such cases are rare, since there are different mobilities for extension products depending on the base added to the extension primer. Therefore, a pullup peak caused by a G base extended primer should fall outside the expected position of an A base extension. Although this is true for alleles of the same SNP, two peaks may come close to each other from two SNPs combined in the same electrophoretic position. When two very closely positioned peaks overlay each other and one can be pulled up by a very active signal, dilution of the SBE products before a repeated capillary electrophoresis run will reduce or eliminate the signal pullups.
Dye artifacts or "blobs" (resulting from fluorochrome molecules becoming unattached from the ddNTP terminators, Figure 4G) and salt peaks are also common electrophoretic artifacts [8,9], easily identified by their characteristic peak patterns. Whereas pullup peaks are thin and sharp signals, blobs are broad-based, rounded peaks. They usually appear in several profiles at the same time in different positions between runs but with the same pattern among samples from the same run. There is a high probability of blobs interfering with several of the fast mobility (shortest) extension-product positions, often completely masking their peaks or those of the size standard used to estimate each product's size. Dye artifacts caused by the fluorescent dyes breaking away from their ddNTP linkage [8,9] to varying degrees become accumulated in the capillaries of the detector array. Therefore, an electrophoretic run using formamide only and without SNaPshot products, can act to flush the affected capillaries and should reduce the problem. Accumulation of urea crystals in the capillary array creates a similar effect, although the resulting signals tend to be sharper in profile and can be identified from this characteristic pattern .
The adenylation of extension products (Figure 4F) will only affect the electrophoretic mobility of peaks longer than 60 bp in size, and this only in the A base extension products. Adenylation peaks are detected as multiple extra peaks at fixed positions that are longer than the 60-bp position. They are produced by the addition of adenosine bases due to the 5' adenylation function of the DNA polymerase used by the PCR, and this occurs when the polymerase activity is transferred to the extension reaction and interferes with the single-base termination process. Some adenylation peaks can fall in the same position as the A base extension products of the longer SNPs, interfering with the correct interpretation of the profile . Since it is relatively straightforward to identify the origin of adenylation peaks and their position is always fixed, it is easy to discount them when reading the profile and thus compensate for their effect. Since 5' adenylation is a secondary function of the polymerase used in the PCR, these peaks will only appear when the amplification efficiency creates excessive activity. Dilution of the PCR products, followed by a second extension reaction, should reduce the problem when adenylation peaks are too high and coincide with allele-specific peak positions.
Although stutter activity is always assumed to be a purely STR-associated phenomenon, stutter peaks do occur in SNP profiles (Figure 4E). In comparison to STR stutter activity, when the polymerase can cause slippage error during PCR, SNP stutter peaks are produced by errors during the extension-primer synthesis that lead to a small proportion of oligonucleotides with lengths shorter by single-base increments. Therefore, when the extension-reaction efficiency is high, a very small proportion of extension primer with one or more bases less is detected as very low peaks associated with the allelic peak but one base shorter. Since these always appear linked to a strong allele peak and are always in the same lane, SNP stutter peaks do not interfere with the interpretation of profiles at any level of peak activity. When ordering above-average-length extension primers, it is prudent to ensure the synthesized oligonucleotides are HPLC purified to reduce the incidence of stutter.
Although the bulk of primer-dimer interactions can be excluded at the primer design stage, these artifacts represent a source of extra peaks that are often in an unexpected position where no SNP extension product is set to appear (Figure 4J). When primer-dimer peaks do occur, their position can be duly noted and should be present to varying levels of signal activity in a fixed but nonspecific place in the SNaPshot profile. The main problem presented by primer-dimer formation and consequent extension is when the product peak occupies the mobility window close to a SNP with the same base extension (Figure 4I).
Depending on the kinetics of the dimer formation and its extension, these extra peaks may only appear as strong signals in negative controls, when there is no target DNA to compete with the dimers for primer binding and their extension into labeled products. On many occasions, the peak may be small when target DNA is present and the SBE primer preferentially binds to the target DNA obtained from the PCR. In these cases, the primer-dimer peak may be present or not, depending on the overall amplification efficiency of the extension reaction to the amplified DNA, and can be easily identified. Strong primer-primer interactions can compete with specific extension primer binding, creating a strong nonspecific peak and also negatively impacting the rest of the multiplex efficiency. In such circumstances, it is advisable to genotype a reference sample with individual primer-pair sets, grouping the SNP in which the nonspecific peak position appears with each of the remaining primers of the multiplex, in order to identify the problem primer designs. Once the problem primers are identified it is advisable to redesign one of them or reject the
SNP from the multiplex altogether. In those rare cases in which the nonspecific peak is due to a more complex interaction than a dimmer, it is possible to redesign the mobility-modifying tail of the SNP next to the nonspecific peak and simply reposition that SNP away from a potentially interfering signal. In order to keep track of non-specific peaks, it is important to recognize when a signal in a known peak position does not comply with the expected peak-height ratio detailed above. In such circumstances the peak can be discounted, as shown in the example of a G nonspecific peak considerably lower in signal strength than that of a T base extension peak (Figure 2).
Nonspecific signals of any description can be safely discounted when they appear close to the known extension products but not in their exact position, with even a small difference in mobility enough to lead to their identification and exclusion from consideration. In nearly all cases, SNPs in forensic tests produce signals that are extremely stable in their electrophoretic mobility, both between runs and between laboratories, although minute individual differences in mobility can be expected due to the capillary electrophoresis detector used. An experiment performed with a hundred electrophoretic runs of one sample for several SNP assays  shows that on average, the mobility of a specific signal and therefore its size estimate does not vary between multiple runs more than 10% of a single base and as little as 1% of a base, as shown in Figure 6. With such stable mobility characteristics for SNaPshot extension products, it is relatively safe to exclude peaks falling outside the expected mobility positions of the true extension peaks, even when this amounts to a fraction of a single-base difference in size. We recommend running a reference sample with known genotypes when using a forensic SNaPshot assay for the first time on a new CE detector to evaluate any mobility variation expected between detectors.
At this stage it is important to emphasize that the choice of electrophoresis polymer is a key parameter influencing SNaPshot extension-product mobilities. We tend to use POP4 as the first-choice polymer for forensic SNaPshot tests; POP6 is also suitable, although it will modify the mobility of the extension products across the whole profile, with a stronger effect on the first quarter of the profile. We strongly advise avoiding the use of POP7 polymer, as its capacity to separate extension products lower than 40 bp is impaired in comparison to the other two polymers in common use.
A custom set of panels and bins would be a useful tool to facilitate SNaPshot profile interpretation. Unfortunately, the latest versions of AB Genemapper analysis software used to analyze SNaPshot data do not support a dedicated SNP-typing utility. Instead, in the author's laboratory, we have opted to develop panel and bin systems for each SNaPshot assay used for forensic analysis applying the STR-oriented built-in panel manager of Genemapper. We treat each allele of any given SNP as an independent one-allele STR marker. Although automated quality control issues such as heterozygote balance and mixture interpretation are lost, fine-scale positioning of the electrophoretic peaks and easy signal identification is maintained, making it significantly easier to call SNP alleles. In order to construct custom panel and bin sets, we strongly recommend use of singleplex amplification peak positioning data, which can be refined after a properly optimized multiplex assay is achieved.
The full range of interpretation challenges that SNaPshot profiles present is outlined in Figure 4. To conclude this section; nonspecific electrophoresis signals have less impact on profile interpretation compared to allele peak imbalance and dropout from very low signals. The very stable intra- and interlaboratory mobilities observed for SNaPshot products helps to identify the true extension-product peak positions with confidence since these hardly vary in their mobilities. We recommend genotyping standard samples with known genotype (e.g., the 9947A STR kit control DNA or NIST standard reference material DNAs). Running control samples provides the most secure system for defining allele peak positions that also help to accommodate interlaboratory mobility shifts when using a SNaPshot assay on a electrophoresis detector for the first time.
III. INTERPRETATION OF MULTIPLE-SOURCE DNA MIXTURES
Mixture interpretation in routine DNA profiling of any description represents the principal challenge to the forensic practitioner [22,23,50]. It is quite common in criminal casework DNA analysis to find that the crime-scene sample or most commonly, swab samples taken from the victim of a sexual assault, contain material from more than one individual. In such cases, it is difficult to identify the individual STR profile components detected from all contributors to the mixed-DNA sample. As different people can share an allele, peaks representing the STR alleles detected in any one marker can be in common among donors and consequently difficult to differentiate, particularly in the least polymorphic STRs.
Since the polymorphism of biallelic SNPs is limited to only two alleles, the probability is high in any one SNP that contributors to mixtures share identical genotypes. From calculations we have made with SNPs and indels selected for maximum human identification discrimination power and heterozygosity, it was found that for biallelic loci, only about 10% on average of sample pairings in a population do not share alleles. In the pairings, 40% of SNPs had the same allele and the remaining 60% comprised a heterozygote hiding a homozygote genotype at the same position and therefore as a single signal, as shown in Figure 7.
Therefore, for biallelic SNPs with the maximum level of heterozygosity (i.e., loci with 0.5:0.5 allele frequencies), in 40% of DNA mixture cases it would be impossible to detect any mixture effect, and in 90% of cases it would be challenging to identify any mixture components as they would share the same peak positions. In those 50% of cases, the allele peak imbalance may be sufficiently affected to allow the mixture to be detected, since multiple-base extension of the same allele will contribute to the signal strength and the peak heights will consequently reflect the contributor ratios to a large extent. However, the SNaPshot signal imbalance detailed in previous sections makes exact mixture-contributor genotype inference very difficult.
Given the inherent signal imbalances in SNaPshot, it will generally be more straightforward to identify C/T, A/C, and A/T SNP variation in mixed DNA than is possible in A/G, G/T, and C/G SNPs. Therefore, deviations of C, T, and A allele extension product peaks that are markedly different from the expected ratios with G signals can be interpreted as mixed-DNA indicators to some extent.
In biallelic SNPs only 10% of the genotypes in any one marker would be opposite homozygotes in mixed DNA, and this applies to the most polymorphic approaching 0.5:0.5 allele ratios. In such cases, mixtures can be readily detected and to a large extent the mixture ratio can be estimated within the framework of relative peak heights established for the genotypes in normal DNA samples. Heterozygote-homozygote combinations are much less reliable points of reference for inferring mixtures inasmuch as peaks overlay each other and the direct relationship between input DNA and observed peak heights is not so well preserved when extension products coincide between mixture contributors.
One simple solution to the analysis of mixtures with SNPs designed to work more efficiently with degraded DNA is to analyze triallelic SNPs [23,50,75]. Although triallelic SNPs are much less frequent than biallelic SNPs, they have been found to be present in the genome in sufficient numbers to be able to build mixture-analysis panels that are informative for mixture analysis of forensic samples but preserve the short-amplicon advantages that SNP loci offer compared to STR analysis. Although appearing to offer little benefit, triallelic SNPs provide the significant advantage of allowing the detection of multiple-source DNA mixtures from the analysis of SNaPshot profiles. For this reason, we have begun the optimization of a multiple high-heterozygosity triallelic SNPs SNaPshot assay, in order to provide a SNP-based solution to such challenging casework.
In multiple SNaPshot SNPgenotyping interlaboratory exercises [10,15,39,57,60], with large numbers of participants running SNaPshot in forensic analysis for the first time, we found the technique to be easily adopted in forensic laboratories. One factor that has aided first-time users is the application of a set of profile interpretation guidelines. These guidelines provide a clear framework with which to identify and distinguish allelic products observed in the SNaPshot assay from nonspecific signals, and are particularly useful when DNA quality is poor.
Although no set of guidelines can replace experience, we expect that the review of SNaPshot limitations and troubleshooting steps outlined here will allow the inexperienced forensic DNA analyst to approach forensic SNP genotyping using CE with confidence and some knowledge of the potential pitfalls.
Recently the interest of the forensic community in SNP typing has become focused on sequence-based NGS technologies. However, a degree of experience in SNaPshot-based SNP typing provides the forensic analyst new to SNP analysis a firm ground on which to build up expertise in dealing with challenging casework. Use of SNP genotyping for casework analysis involves the complexities of detecting DNA mixtures with SNPs, handling biallelic genotype data, and assessing the increased sensitivity to degraded DNAthat SNPs offer, as well as the establishment of large-scale multiplexes designed to amplify 30 or more SNPs. The latter development process in particular is a very useful pre-amble to the much larger PCR multiplexes offered by NGS. In fact, several preexisting SNaPshot multiplexes can be placed directly into the PCR capture stage of NGS without modification and with good-quality sequence data obtained from such procedures .
For these reasons, SNaPshot will continue to be a useful technology of forensic SNP genotyping while NGS systems become increasingly adopted. SNaPshot will also continue to provide the most practical way to assess the multiplexing adaptability of SNP loci in small-scale PCR multiplex assays, before these SNP sets are expanded to much larger marker assays in NGS. Therefore, SNaPshot will continue to have a key role to play in the further development of police-intelligence assays such as ancestry-informative and external-visible-characteristics SNP tests [37,54].
Much of the research work of MF, CB, and CP that contributed to this review was funded by the EUROFORGEN Node of Excellence Consortium (Grant Agreement No. 285487).
[1.] Ayres KL: The expected performance of single nucleotide polymorphism loci in paternity testing; Forensic Sci Int Genet 154:167; 2005.
[2.] Bersting C, Morling N: Mutations and/or close relatives? Six casework examples where 49 autosomal SNPs were used as supplementary markers; Forensic Sci Int Genet 5:236; 2011.
[3.] Bersting C, Rockenbauer E, Morling N: Validation of a single nucleotide polymorphism (SNP) typing assay with 49 SNPs for forensic genetic testing in a laboratory accredited according to the ISO 17025 standard; Forensic Sci Int Genet 4:34; 2009.
[4.] Bersting C, Smidt Mogensen H, Morling N: Forensic genetic SNP typing of low-template DNA and highly degraded DNA from crime case samples; Forensic Sci Int Genet 7:345; 2013.
[5.] Bersting C, Tomas C, Morling N: SNP typing of the reference materials SRM 2391b 1-10, K562, XY1, XX74, and 007 with the SNP for ID multiplex; Forensic Sci Int Genet 5:e81; 2011.
[6.] Brion M, Sanchez JJ, Balogh K, Thacker C, Blanco-Verea A, Berstling C, Stradmann-Bellinghausen B, Bogus M, Syndercombe-Court D, Schneider PM, et al.: Introduction of a single nucleodite polymorphism-based "Major Y-chromosome haplogroup typing kit" suitable for predicting the geographical origin of male lineages; Electrophoresis 26:4411; 2005.
[7.] Budowle B, van Daal A: Forensically relevant SNP classes; Biotechniques 44:603; 2008.
[8.] Butler JM: Fundamentals of Forensic DNA Typing; Academic Press: Burlington, MA; 2010.
[9.] Butler JM, ShenY, McCord BR: The development of reduced size STR amplicons as tools for analysis of degraded DNA; J Forensic Sci 48:1054; 2003.
[10.] Chaitanya L, Walsh S, Andersen JD, Ansell R, Ballantyne K, Ballard D, Banemann R, Bauer CM, Bento AM, Brisighelli F, et al.: Collaborative EDNAP exercise on the IrisPlex system for DNA based prediction of human eye colour; Forensic Sci Int Genet 11:241; 2014.
[11.] Daca-Roszak P, Pfeifer A, Zebracka-Gala J, Jarzab B, Witt M, Zictkiewicz E: EurEAs_Gplex--A new SNaPshot assay for continental population discrimination and gender identification; Forensic Sci Int Genet 20:89; 2016.
[12.] Daniel R, Santos C, Phillips, C, Fondevila M, van Oorschot RA, Carracedo A, Lareu MV, McNevin D: A SNaPshot of next generation sequencing for forensic SNP analysis; Forensic Sci Int Genet 14:50; 2015.
[13.] Devlin B, Risch N: A comparison of linkage disequilibrium measures for fine-scale mapping; Genomics 29:311; 1995.
[14.] Dieffenbach CW, Lowe TMJ and Dveksler GS: General concepts for PCR primer design; PCR Methods and Applications 3:S30; 1993.
[15.] Dixon LA, Dobbins AE, Pulker HK, Butler JM, Vallone PM, Coble MD, Parson W, Berger B, Grubwieser P, Mogensen HS, et al.: Analysis of artificially degraded DNA using STRs and SNPs--Results of a collaborative European (EDNAP) exercise; Forensic Sci Int 164:33; 2006.
[16.] Fondevila M, Phillips C, Naveran N, Fernandez L, Cerezo M, Salas A, Carracedo A, Lareu MV: Case report: Identification of skeletal remains using short-amplicon marker analysis of severely degraded DNA extracted from a decomposed and charred femur; Forensic Sci Int Genet 2:212; 2008.
[17.] Fondevila M, Phillips C, Santos C, Freire Aradas A, Vallone PM, Butler JM, Lareu MV, Carracedo A: Revision of the SNPforI, 34-plex forensic ancestry test: Assay enhancements, standard reference sample genotypes and extended population studies; Forensic Sci Int Genet 7:63; 2013.
[18.] Fondevila M, Phillips C, Santos C, Pereira R, Gusmao L, Carracedo A, Butler JM, Lareu MV, Vallone PM: Forensic performance of two insertion-deletion marker assays; Int J Legal Med 126:725; 2012.
[19.] Freire-Aradas A, Fondevila M, Kriegel AK, Phillips C, Gill P, Prieto L, Schneider PM, Carracedo A, Lareu MV: A new SNP assay for identification of highly degraded human DNA; Forensic Sci Int Genet 6:341; 2012.
[20.] Gilbert MT, Sanchez JJ, Haselkorn T, Jewell LD, Lucas SB, Van Marck E, Bersting C, Morling N, Worobey M: Multiplex PCR with minisequencing as an effective high-throughput SNP typing method for formalin-fixed tissue; Electrophoresis 28:2361; 2007.
[21.] Gill P: Application of low copy number DNA profiling; Croat Med J 42:229; 2001.
[22.] Gill P, Brenner CH, Buckleton JS, Carracedo A, Krawczak M, Mayr WR, Morling N, Prinz M. Schneider PR, Weir BS: DNA commission of the International Society of Forensic Genetics: Recommendations on the interpretation of mixtures; Forensic Sci Int 160:90; 2006.
[23.] Gill P, Curran J, Neumann C, Kirkham A, Clayton T, Whitaker J, Lambert J: Interpretation of complex DNA profiles using empirical models and a method to measure their robustness; Forensic Sci Int Genet 2:91; 2008.
[24.] Grimes EA, Noake PJ, Dixon L, Urquhart A: Sequence polymorphism in the human melanocortin 1 receptor gene as an indicator of the red hair phenotype; Forensic Sci Int 122:124; 2001.
[25.] Hill CR, Kline MC, Coble MD, Butler JM: Characterization of 26 miniSTR loci for improved analysis of degraded DNA samples; J Forensic Sci 53:73; 2008.
[26.] Hu G: DNApolymerase-catalyzed addition of non-templated extra nucleotides to the 3' end of a DNA fragment; DNA Cell Biol 12:763; 1993.
[27.] Hughes-Stamm SR, Ashton KJ, van Daal A: Assessment of DNA degradation and the genotyping success of highly degraded samples; Int J Legal Med 125:341; 2011.
[28.] Jeffreys AJ, Wilson V, Thein SL: Individual-specific fingerprints of human DNA; Nature 316:76; 1985.
[29.] Kidd KK, Pakstis AJ, Speed WC, Grigorenko EL, Kajuna SL, Karoma NJ, Kungulilo S, Kim JJ, Lu RB, Odunsi A, et al.: Developing a SNP panel for forensic identification of individuals; Forensic Science Int 164:20; 2006.
[30.] Kidd KK, Speed WC, Pakstis AJ, Furtado MR, Fang R, Madbouly A, Maiers M, Middha M, Friedlaender FR, Kidd JR, et al. Progress toward an efficient panel of SNPs for ancestry inference; Forensic Sci Int Genet 10:23; 2014.
[31.] Kibbe WA: OligoCalc:An online oligonucleotide properties calculator; Nucleic Acids Res 35(S2):W43; 2007.
[32.] Krawcza M: Informativity assessment for biallelic single nucleotide polymorphisms; Electrophoresis 20:676; 1999.
[33.] Lareu MV, Garcia-Magarinos M, Phillips C, Quintela I, Carracedo A, Salas A: Analysis of a claimed distant relationship in a deficient pedigree using high density SNP data; Forensic Sci Int Genet 6:350; 2012.
[34.] LaRue BL, Ge J, King JL, Budowle B: A validation study of the Qiagen Investigator DIPplex[R] kit, an INDEL-based assay for human identification; Int J Legal Med 126:533; 2012.
[35.] Lou C, Cong B, Li S, Fu L, Zhang X, Feng T, Su S, Ma C, Yu F, Ye J, Pei L: A SNaPshot assay for genotyping 44 individual identification single nucleotide polymorphisms; Electrophoresis 32:368; 2011.
[36.] Magnuson VL, Ally DS, Nylund SJ, Karanjawala ZE, Rayman JB, Knapp JI, Lowe AL, Ghosh S, Collins FS: Substrate nucleotide-determined non-templated addition of adenine by Taq DNA polymerase: Implications for PCR-based genotyping and cloning; Biotechniques 21:700; 1996.
[37.] Marcinska M, Pospiech E, Abidi S, Andersen JD, van den Berge M, Carracedo A, Eduardoff M, Marczakiewicz-Lustig A, Morling N, Sijen T, et al.: Evaluation of DNA variants associated with androgenetic alopecia and their potential to predict male pattern baldness; PLoS One 10:e0127852; 2015.
[38.] Morley JM, Bark JE, Evans CE, Perry JG, Hewitt CA, Tully G: Validation of mitochondrial DNA minisequencing for forensic casework; Int J Legal Med 112:241; 1999.
[39.] Mullaney JM, Mills RE, Pittard WS, Devine SE. Small insertions and deletions (INDELs) in human genomes; Hum Mol Genet 19: R131; 2010.
[40.] Musgrave-Brown E, Ballard D, Balogh K, Bender K, Berger B, Bogus M, Bersting C, Brion M, Fondevila M, Harrison C, et al.: Forensic validation of the SNPforID 52plex assay; Forensic Sci Int Genet 1:186; 2007.
[41.] Nievergelt CM, Maihofer AX, Shekhtman T, Libiger O, Wang X, Kidd KK, Kidd JR: Inference of human continental origin and admixture proportions using a highly discriminative ancestry informative 41-SNP panel; Investig Genet 4:13; 2013.
[42.] Nikiforov TT, Rendle RB, Goelet P, Rogers YH, Kotewicz ML, Anderson S, Trainor GL, Knapp MR: Genetic bit analysis: A solid phase method for typing single nucleotide polymorphisms; Nucleic Acids Res 22:4167; 1994.
[43.] Pakstis AJ, Speed WC, Kidd JR, Kidd K: Candidate SNPs for a universal individual identification panel; Hum Genet 121:305; 2007.
[44.] Parson W, Fendt L, Ballard D, Bersting C, Brinkmann B, Carracedo A, Carvalho M, Cobble MD, Real FC, Desmyter S, et al.: Identification of West Eurasian mitochondrial haplogroups by mtDNA SNP screening: Results of the 2006-2007 EDNAP collaborative exercise; Forensic Sci Int Genet 2:61; 2008.
[45.] Parsons TJ, Huel R, Davoren J, Katzmarzyk C, Milos A, Seimanovic L, Coble MD, Rizvic A: Application of novel "mini-amplicon" STR multiplexes to high volume casework on degraded skeletal remains; Forensic Sci Int Genet 1:175; 2007.
[46.] Pereira R, Phillips C, Alves C, Amorim A, Carracedo A, Gusmao L: A new multiplex for human identification using insertion/deletion polymorphisms; Electrophoresis 30:3682; 2009.
[47.] Pestoni C, Lareu MV, Rodriguez MS, Munoz I, Barros F, Carracedo A: The use of the STRs HUMTH01, HUMVWA31/A, HUMF13A1, HUMFES/FPS, HUMLPL in forensic application: validation studies and population data for Galicia (NW Spain); Int J Legal Med 107:283; 1995.
[48.] Phillips C: Using online databases for developing SNP markers of forensic interest; Methods Mol Biol 297:83; 2005.
[49.] Phillips C, Fondevila M, Garcia-Magarinos M, Rodriguez A, Salas A, Carracedo A, Lareu MV: Resolving relationship tests that show ambiguous STR results using autosomal SNPs as supplementary markers; Forensic Sci Int Genet 2:198; 2008.
[50.] Phillips C, Freire Aradas A, Kriegel AK, Fondevila M, et al.: Eurasiaplex: A forensic SNP assay for differentiating European and South Asian ancestries; Forensic Sci Int Genet 7:359; 2013.
[51.] Phillips C, Lareu MV, Salas A, Carracedo A: Nonbinary single-nucleotide polymorphism markers; International Congress Series 1261:27; 2004.
[52.] Phillips C, Parson W, Lundsberg B, Santos C, Freire-Aradas A, Torres M, EduardoffM, Bersting C, Johansen P, Fondevila M: Building a forensic ancestry panel from the ground up: The EUROFORGEN Global AIM-SNP set; Forensic Sci Int Genet 11:13; 2014.
[53.] Phillips C, Salas A, Sanchez JJ, Fondevila M, Gomez-Tato A, Alvarez-Dios J, Calaza M, de Cal MC, Ballard D, Lareu MV: Inferring ancestral origin using a single multiplex assay of ancestry-informative marker SNPs; Forensic Sci Int Genet 1:273; 2007.
[54.] Pospiech E, Karlowska-Pik J, Marcinska M, Abidi S, Andersen JD, van den Berge M, Carracedo A, Eduardoff M, Freire-Aradas A, Morling N, et al.: Evaluation of the predictive capacity of DNA variants associated with straight hair in Europeans; Forensic Sci Int Genet 19:28; 2015.
[55.] Romanini C, Catelli ML, Borosky A, Pereira R, Romero M, Salado Puerto M, Phillips C, Fondevila M, Freire A, Santos C: Typing short amplicon binary polymorphisms: supplementary SNP and Indel genetic information in the analysis of highly degraded skeletal remains; Forensic Sci Int Genet 6:469; 2012.
[56.] Ruiz Y, Phillips C, Gomez-Tato A, Alvarez-Dios J, de Cal MC, Cruz R, Maronas O, Sochtig J, Fondevila M, Rodriguez-Cid MJ, et al.: Further development of forensic eye color predictive tests; Forensic Sci Int Genet 7:28; 2013.
[57.] Sanchez JJ, Bersting C, Balogh K, Berger B, Bogus M, Butler JM, Carracedo A, Syndercombe Court D, Dixon LA, Filipovic B, et al.: Forensic typing of autosomal SNPs with a 29 SNP-multiplex--Results of a collaborative EDNAP exercise; Forensic Sci Int Genet 2:176; 2008.
[58.] Sanchez JJ, Endicott P: Developing multiplexed SNP assays with special reference to degraded DNA templates; Nat Protoc 1:1370; 2006.
[59.] Sanchez JJ, Phillips C, Bersting C, Balogh K, Bogus M, Fondevila M, Harrison CD, Musgrave-Brown E, Salas A, Syndercombe-Court D, et al.: A multiplex assay with 52 single nucleotide polymorphisms for human identification; Electrophoresis 27:1713; 2006.
[60.] Santos C, Fondevila M, Ballard D, Banemann R, Bento AM, Bersting C, Branicki W, Brisighelli F, Burrington M, Capal T, et al.: Forensic ancestry analysis with two capillary electrophoresis ancestry informative marker (AIM) panels: Results of a collaborative EDNAP exercise; Forensic Sci Int Genet 19:56; 2015.
[61.] Senge T, Madea B, Junge A, Rothschild MA, Schneider PM: STRs, mini STRs and SNPs--Acomparative study for typing degraded DNA; Legal Med 13:68; 2011.
[62.] Sherry ST, Ward M, Sirotkin K: dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation; Genome Res 9: 667; 1999.
[63.] Sokolov BP: Primer extension technique for the detection of single nucleotides in genomic DNA; Nucleic Acids Res 77:701; 1990.
[64.] Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Hsi-Yang Fritz M, et al.: An integrated map of structural variation in 2,504 human genomes; Nature 526:75; 2015.
[65.] Syvanen AK: Accessing genetic variation: Genotyping single nucleotide polymorphisms; Nat Rev Genet 2:930; 2001.
[66.] Tatusova TA, Madden TL: BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences; FEMS Microbiol Lett 174:247; 1999.
[67.] Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, McGee S, Do R, Liu X, Jun G, et al.: Evolution and functional impact of rare coding variation from deep sequencing of human exomes; Science 337:64; 2012.
[68.] Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, Leunissen JA: Primer3Plus, an enhanced web interface to Primer3; Nucleic Acids Res 35; 2007.
[69.] Urquhart A, Kimpton CP, Downes TJ, Gill P: Variation in short tandem repeat sequences--A survey of twelve microsatellite loci for use as forensic identification markers; Int J Legal Med 107:13; 1994.
[70.] User Bulletin: Applied Biosystems 3730/3730xl DNA Analyzers and 3130/3130xl Genetic Analyzers; July 2005; http://tools.li/etechnologies.com/content/s/s/manuals/ cms_042107.pd/ (accessed Noverber 4, 2016).
[71.] Vallone PM, Butler JM: AutoDimer: A screening tool for primer-dimer and hairpin structures; Biotechniques 37:226; 2004.
[72.] Vallone PM, Decker AE, Butler JM: Allele frequencies for 70 autosomal SNP loci with US Caucasian, African-American, and Hispanic samples; Forensic Sci Int 149:279; 2005.
[73.] von Ahsen N, Wittwer CT, Schutz E: Monovalent and divalent salt correction algorithms for Tm prediction--recommendations for Primer3 usage; Brief Bioinform 12:514; 2011.
[74.] Walsh S, Liu F, Wollstein A, Kovatsi L, Ralf A, Kosiniak-Kamysz A, Branicki W, Kayser M: The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA; Forensic Sci Int Genet 7:98; 2013.
[75.] Westen AA, Matai AS, Laros JFJ, Meiland HC, Jasper M, de Leeuw WJF, de KnijffP, Sijen T: Tri-allelic SNP markers enable analysis of mixed and degraded DNA samples; Forensic Sci Int Genet 3:233; 2009.
Manuel Fondevila obtained his B.S. degree in biology at the University of Santiago de Compostela (Santiago de Compostela, Spain) in 2001. In the same year he started his thesis research work in the Forensic Genetics Unit, Institute of Forensic Sciences at the same university, obtaining his Ph.D. in 2009. In 2010 he achieved a Barrie de la Maza fund grant and from then on worked as a post-doc researcher at the US National Institute of Standards and Technology (NIST), on the forensic research team of John Butler until 2012. In that year he returned to the Institute of Forensic Sciences, University of Santiago de Compostela, where he has been conducting research up to the present time.
Claus Barsting received his M.Sc. (1994) and Ph.D. (1999) degrees in molecular biology from Odense University (Odense, Denmark). Dr. Bersting is currently the manager of the research group at the Forensic Genetics Section, University of Copenhagen, Denmark). Upon completion of his Ph.D. degree, Dr. Bersting continued with the same line of research as a postdoctoral fellow at Albany Medical College (Albany, NY). In 2001, he was employed as a forensic geneticist at the Forensic Genetics Section, University of Copenhagen. He was trained as a reporting officer in paternity and immigration casework. His research activities earned him positions as an assistant professor in 2004 and a senior advisor in 2010. In 2007, he validated and implemented the SNPforID HID assay for relationship casework according to the ISO 17025 standard. He became the manager of the SnP laboratory and continued to have this function until 2012 where he became the daily manager of the research group. The main focus of Dr. Bersting's current research is to explore the use of next-generation sequencing methods in forensic genetics.
Christopher Phillips studied genetics at Birmingham University (Birmingham, UK) between 1974 and 1977 and in 1978 obtained his M.Sc. degree in applied genetics at the same institute. Mr. Phillips is currently a researcher in the Forensic Genetics Unit of the University of Santiago de Compostela. Mr. Phillips started his forensic genetics career in 1979 at the Biochemistry Division of the Metropolitan Police Forensic Science Laboratory (London, UK). He then moved to the Forensic Haematology Department, Barts Health NHS Trust (London, UK) and the London School of Medicine and Dentistry (London, UK) and worked there until 2001. Since 2001 he has been a full-time researcher in the Forensic Genetics Unit of the University of Santiago de Compostela. Mr. Phillips's Research interests include SNP analysis applied to medical, population, and forensic genetics, the development of novel forensic polymorphisms, and building open-access online genomics search tools for the genetics and forensic communities.
Maria de la Puente studied biology, concentrating on biotechnology and molecular biology, at the University of Santiago de Compostela between 2006 and 2011. She completed her M.Sc. degree in biomedical investigation at the same university on 2012. She is now completing her Ph.D. research on the forensic applications of new genomic technologies at the Forensic Genetics Unit of the University of Santiago de Compostela, supported by funding awarded by the Xunta de Galicia as part of the Plan Galego de Investigacion, Innovacion e Crecemento 2011-2015.
Carla Santos studied biology at the University of Aveiro (Aveiro, Portugal). Since 2008 she has been continuously conducting research at the Forensic Genetics Unit of the University of Santiago de Compostela focusing on binary markers, particularly the development of SNP assays dedicated to forensic ancestry analysis. She recently finished her Ph.D. in forensic sciences and pathology at the University of Santiago de Compostela.
The EUROFORGEN-NoE Consortium (European Forensic Genetics Network of Excellence) consists of 16 participating institutions based in nine different European Union member states. The activities of this network of excellence are centered on the mobilization of the synergies of the major relevant European research groups to investigate new technologies and methods for the application of forensic genetics in the context of security-relevant issues and the justice system. The EUROFORGEN-NoE consortium is formed by renowned researchers from all over Europe, each one with a strong scientific background in the field of forensic genetics research and its application in casework.
Angel Carracedo received his degree in medicine from the University of Santiago de Compostela. Dr. Carracedo is professor of legal medicine at the University of Santiago de Compostela, director of the Galician Foundation of Genomic Medicine, and director of the Spanish National Genotyping Centre. Dr. Carracedo was formerly director of the Institute of Legal Medicine at Santiago and president of the International Society of Forensic Genetics (ISFG). Dr. Carracedo's research lines include the genetics of complex traits with different applications ranging from forensic to clinical genetics.
Niels Morling received his M.D. degree from the University of Copenhagen (Denmark) in 1975. Specializing in clinical immunology, Dr. Morling is professor of forensic genetics at the Universities of Copenhagen and Tromso, (Tromso, Norway). He is the director of the Department of Forensic Medicine at the University of Copenhagen. Dr. Morling is the past president of the International Society of Forensic Genetics and chairman of the European DNA Profiling Group. His research includes immuno, forensic, and clinical genetics as well as biostatistics and advanced technologies in forensic genetics.
Maria Victoria Lareu received her M.D. degree from the University of Santiago de Compostela in 1984. Dr. Lareu is professor of legal medicine at the University of Santiago de Compostela and the director of the Institute of Legal Medicine at Santiago, directing the forensic science services provided by the Forensic Genetics Unit. Dr. Lareu is the principal investigator of many research projects undertaken in the department and directed the Ph.D. studies of the following coauthors of this article: Manuel Fondevila, Maria de la Puente, Carla Santos, and Christopher Phillips.
M. Fondevila (1) *, C. Barsting (2), C. Phillips (1), M. de la Puente (1), C. Santos (1), EUROFORGEN-NoE Consortium (3), A. Carracedo (1), N. Morling (2), M. V. Lareu (1)
(1) Forensic Genetics Unit, Department of Legal Medicine University of Santiago de Compostela Santiago de Compostela, Galicia Spain
(2) Section of Forensic Genetics, Department of Forensic Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
(3) The European Forensic Genetics Network of Excellence http://www.euroforgen.eu/
* Corresponding author: Dr. Manuel Fondevila, Forensic Genetics Unit, Department of Legal Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain; + 34 881 812 316 (voice); mafondevila@,hotmail.com.
Caption: Figure 1: Outline of the SNaPshot process steps and depiction of the single-base extension reaction (dye-linked terminating dideoxynucleotides shown as circles with their relevant colors).
Caption: Figure 3: Two examples of clustering SNPs in primer-binding sites. Example A shows the effect of an unlinked clustering variant on the A (green) allele of the target SNP. The extreme allele imbalance shown will only be observed in a fraction of the individuals carrying the variant allele in the clustering SNP, while the rest will show an A/G heterozygote imbalance. Example B shows a clustering SNP variant in full linkage with the A allele of the target SNP. In all profiles the peak patterns will show an underamplification of the A allele of the SNP, and the consequent peak imbalance is a consistent effect.
Caption: Figure 4: A series of examples of SNaPshot profile interpretation challenges: (A) allele imbalance; (B) extreme allele imbalance; (C) allele dropout; (D) signal pullups; (E) stutter peaks; (F) amplicon adenylation peaks; (G) dye-artifacts; (H) interlaboratory peak mobility variation; (I) primer-dimer peaks coinciding with specific allele product peaks; (J) primer-dimer peaks out of expected peak-position size ranges.
Caption: Figure 5: Signal data for 100 heterozygote samples  for each SNP in the 34plex forensic AIM-SNP assay developed by Phillips et al. . In most SNPs the heterozygote peak-height ratio is maintained within an expected range of values (shaded areas of each plot). In a small proportion of SNPs with outlier peak-height ratio values, data indicates a link to the genotype of the SNP.
Caption: Figure 6: Observed average variance in the electrophoretic mobility of extension products of 1,000 samples generated for 34 SNPs in a SNaPshot multiplex [17,52], using the same assay shown analyzed in Figure 4. Data clearly indicate considerable stability of peak mobility, suggesting within-laboratory mobility variation of less than 10% of a bp value and less than 1% bp for the most stable mobilities.
Figure 2: Relative electrophoresis signal strength. The expected peak-height ratio for a hypothetical tetranucleotide SNP would be 4:2:1:1, as shown for each of the G:A:C:T extensions. In the same situation, each of the nucleotides creates an electrophoretic mobility modification of 1, 2, 3, and 4 bp, adding those nucleotides respectively. Analysis of degraded or low-level challenging DNA samples may affect the signal-ratio rule in unpredictable ways. Ideal conditions Vs Challenging conditions G A C T G A C T Bps +1 4 1,8 +2 2 6 +3 1 2,5 +4 1 0,5 cacgtcgtgaaagtctgacaaAGTCCGTAGTAACGTATC 39 + SNP Note: Table made from bar graph. Figure 7. Calculation of the probability of two random individuals from the same population having opposite homozygote genotypes, identical genotypes, or a mixture of homozygote-heterozygote genotypes in biallelic SNPs with maximum heterozygosity. Probabilities indicate random pairings lead to identical genotypes or homozygote-heterozygote mixed-peak patterns in the majority of cases. Example A shows the genotyping of a DNA mixture with SNaPshot. The three SNPs' peak patterns shown are imbalanced due to mixed signals, although the imbalance is within the expected ratios observed in single-source samples. Example B shows several SNPs analyzed in mixed DNA. The SNPs shown are strongly imbalanced and markedly outside the expected peak-height ratio, strongly indicating mixed DNA. Example C shows the same DNA mixture used for biallelic SNP analysis in examples A and B but detected with a triallelic SNP. Using SNaPshot analysis of this type of SNP provides an unequivocal indication of a mixture, with both contributors' genotype discernible from the signals detected (AA and CT). Example D shows two SNPs that both share identical genotypes in the mixed-DNA components leading to an absence of any indication of a mixture in the SNaPshot profile. DIPplex U.S. Cauc U.S. Asian Opposite homozygotes Mean 0.120298 0.086734 stdDev 0.007091 0.0380883 Equal genotype Mean 0.380312 0.454749 stdDev 0.008620 0.106862 Homozygote/Heterozygote Mean 0.499389 0.458516 stdDev 0.001576 0.070849 DIPplex U.S. Hisp U.S. Af-Am Opposite homozygotes Mean 0.113791 0.091555 stdDev 0.013674 0.027206 Equal genotype Mean 0.388822 0.428191 stdDev 0.019766 0.061062 Homozygote/Heterozygote Mean 0.496912 0.477114 stdDev 0.006153 0.033534
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Single-nucleotide polymorphism|
|Author:||Fondevila, M.; Borsting, C.; Phillips, C.; de la Puente, M.; Santos, C.; Carracedo, A.; Morling, N.;|
|Publication:||Forensic Science Review|
|Date:||Jan 1, 2017|
|Previous Article:||Drugs in hair. Part I. Metabolisms of major drug classes.|
|Next Article:||Greenish-blue gastric content: literature review and case report on acute copper sulfate poisoning.|