Next Article in Journal
Hashimoto’s Thyroiditis and Graves’ Disease in Genetic Syndromes in Pediatric Age
Next Article in Special Issue
Animal Forensic Genetics
Previous Article in Journal
Spindle Cell Hemangioma and Atypically Localized Juxtaglomerular Cell Tumor in a Patient with Hereditary BRIP1 Mutation: A Case Report
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification

1
Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
2
Department of Biomedicine and Prevention, Section of Legal Medicine, Social Security and Forensic Toxicology, University of Rome Tor Vergata, 00133 Rome, Italy
3
Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
4
Austech, 00153 Rome, Italy
*
Author to whom correspondence should be addressed.
Genes 2021, 12(2), 221; https://doi.org/10.3390/genes12020221
Submission received: 11 December 2020 / Revised: 21 January 2021 / Accepted: 1 February 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Advances in Forensic Genetics)

Abstract

:
A custom plate of OpenArray™ technology was evaluated to test 60 single-nucleotide polymorphisms (SNPs) validated for the prediction of eye color, hair color, and skin pigmentation, and for personal identification. The SNPs were selected from already validated subsets (Hirisplex-s, Precision ID Identity SNP Panel, and ForenSeq DNA Signature Prep Kit). The concordance rate and call rate for every SNP were calculated by analyzing 314 sequenced DNA samples. The sensitivity of the assay was assessed by preparing a dilution series of 10.0, 5.0, 1.0, and 0.5 ng. The OpenArray™ platform obtained an average call rate of 96.9% and a concordance rate near 99.8%. Sensitivity testing performed on serial dilutions demonstrated that a sample with 0.5 ng of total input DNA can be correctly typed. The profiles of the 19 SNPs selected for human identification reached a random match probability (RMP) of, on average, 10−8. An analysis of 21 examples of biological evidence from 8 individuals, that generated single short tandem repeat profiles during the routine workflow, demonstrated the applicability of this technology in real cases. Seventeen samples were correctly typed, revealing a call rate higher than 90%. Accordingly, the phenotype prediction revealed the same accuracy described in the corresponding validation data. Despite the reduced discrimination power of this system compared to STR based kits, the OpenArray™ System can be used to exclude suspects and prioritize samples for downstream analyses, providing well-established information about the prediction of eye color, hair color, and skin pigmentation. More studies will be needed for further validation of this technology and to consider the opportunity to implement this custom array with more SNPs to obtain a lower RMP and to include markers for studies of ancestry and lineage.

1. Introduction

Short tandem repeats (STRs) are genetic variants widely used in forensic investigations for the identification of offenders and in cases concerning relationship testing [1,2]. However, the multiplexing capacity of STRs is relatively low because of the need for capillary electrophoresis and inability to accommodate large numbers of markers. In the genomic era, it is well known that STRs are not the only source of genetic variation in humans [3]. Single-nucleotide polymorphisms (SNPs) are the most common type of genetic variation among people and can provide additional information such as the inferred ancestry of a DNA sample and phenotypes (eye and hair color) [4]. From a technical point of view, SNPs can be used to support forensic DNA analyses because of an abundance of potential markers, amenability to automation, and potential reduction in required fragment length to only 60–80 bp [5,6,7].
Forensic analysis usually involves comparisons between genetic profiles extracted from biological samples collected from a crime scene, and objects and suspects which are thought to be associated to that crime. In the absence of suspects, the power of forensic DNA analysis has been enhanced through the development of DNA databases, allowing the identification of unknown offenders and serial offenders by linking different crimes [1,8,9]. However, in criminal cases without an STR profile match, police investigation can be aided by different kinds of genetic tests.
In this context, the analysis of SNPs for the study of phenotypic variability (forensic DNA phenotyping, FDP) and for the determination of biogeographical origin is particularly promising [1,10]. The main advantage of such analysis is to narrow down the number of potential crime scene trace donors to a smaller group of people who most likely have the externally visible characteristics and biogeographic ancestry that were inferred from the crime scene DNA [11].
Forensic DNA phenotyping is explicitly regulated and permitted by law in two EU member states (the Netherlands and Slovakia) and practiced in compliance with existing laws in six more (Poland, Czech Republic, Sweden, Hungary, Austria, and Spain) and the United Kingdom [11].
In the “omic era”, different approaches have been developed to test thousands of genetic variations for forensic purposes (phenotype, ancestry, and identification). Real-time PCR using TaqMan chemistry is a well-known technology routinely used in the workflow of forensic laboratories to quantify the DNA extracted from biological samples [12,13]. The OpenArray™ system (Thermo Fisher Scientific, Waltham, MA, USA) applies TaqMan chemistry for the simultaneous and massive study of samples and SNPs [14,15].
Here we report a pilot study aimed at evaluating the applicability of OpenArray™ technology (Thermo Fisher Scientific, Waltham, MA, USA) for the testing of SNPs for human identification and for inferring phenotypes. To this end, we selected the entire panel of 41 SNPs for eye, hair, and skin color prediction from the validated tool HIrisPlex-S and 19 SNPs for individual identification from the Precision ID Identity SNP Panel (Thermo Fisher Scientific, Waltham, MA, USA) and ForenSeq DNA Signature Prep Kit (Verogen Inc, San Diego, CA, USA). A total of 314 samples were typed to test the genotyping accuracy of OpenArray™ through comparison with resequencing data.
The sensitivity of the platform was assessed using four serial dilutions of DNA consisting of 10, 5, 1, and 0.5 ng [16,17,18].

2. Materials and Methods

2.1. Selection of SNPs

In this work we selected 41 SNPs for eye, hair, and skin color prediction from the HIrisPlex-S system and 19 SNPs from a universal individual identification SNPs panel validated for forensic purposes [16,17,18,19]. The SNPs for human identification were selected based on their chromosomal location (each SNP is mapped to a different chromosome to avoid linkage and linkage disequilibrium effects). These genetic variants were included in a customized panel of 60 SNPs able to provide both human identification and phenotyping information in the same analytical session. The TaqMan probe related to rs1805008 could not be developed. Therefore, rs11538871 was chosen because of its complete linkage disequilibrium (LD) with rs1805008 [20].
The selected SNPs are summarized in Table 1.

2.2. Ethical Committee Approval

These investigations were carried out following the rules of the Declaration of Helsinki of 1975, revised in 2013. According to Point 23 of this declaration, approval from the ethical committee of Policlinico Tor Vergata (protocol number: Prot. CE/PROG.177/20) was obtained.

2.3. Reference DNA Samples

A total of 314 reference DNA samples were used. In particular, 114 genomic DNA samples were obtained from the 1000 Genomes Project (Coriell Institute for Medical Research, Camden, NJ, USA) and 200 blood samples were obtained from the Laboratory of Genomic Medicine UILDM (Italian Muscular Dystrophy Association) Santa Lucia Foundation [21]. The 114 samples from 1000 Genomes were selected from a small town near Florence in the Tuscany region of Italy. The genotype data of genomic DNA samples from 1000 Genomes were extracted from the Ensembl genome browser [22]. An additional four volunteer donors were used for the sensitivity testing.

2.4. Biological Samples from Real Cases

We analyzed a total of 21 samples of biological evidence that had single STR profiles generated during the routine workflow and that were assigned to their reference perpetrator. This evidence was from different tissues (10 from saliva, 2 from semen, 7 from blood, 2 from urine) and was attributed to 8 different subjects from different tissues [23,24]. All samples were marked with a specific internal code, as shown in Table 2 [2]. The forensic samples were loaded in the same plate twice with the purpose of verifying the reproducibility of genotyping assignation.

2.5. DNA Purification and Quantification

In accordance with the quality standards of forensic laboratories that require the use of automatized instruments, the genomic DNA of 225 samples were extracted using the Maxwell®16 MDx Instrument (Promega, Madison, WI, USA) and the DNA IQ Casework Pro Kit for Maxwell®16 (Promega, Madison, WI, USA).
Each DNA sample was quantified using the Quantifiler®Trio DNA Quantification Kit (Thermo Fisher Scientific, Waltham, MA, USA).

2.6. OpenArray™ Technology

OpenArray™ Technology (Thermo Fisher Scientific, Waltham, MA, USA) is a high-throughput real-time PCR genotyping method that allows for rapid screening of several TaqMan assays in many samples [13]. This real-time method involves the use of an array composed of 3072 through-holes running on the QuantStudio 12K Flex Real Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) with an OpenArray™ block. The OpenArray™ system is composed of a specific plate (OpenArray™ plate) enabling 192 samples to be typed in a single run [14].
For each sample, 5 ng (volume of 3 μL) of extracted DNA from the reference samples and 3 μL of 2× TaqMan OpenArray™ Genotyping Master Mix were manually loaded into 384 well-plates according to the manufacturer’s instructions (Thermo Fisher Scientific, Waltham, MA, USA). A negative control was obtained by adding 3 μL of pure distilled water to the Master Mix. The QuantStudio 12K Flex OpenArray™ AccuFill System transferred the previously generated mix to the TaqMan OpenArray™ plate. The amplification was performed using the QuantStudio 12K Flex Real Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) instrument, and the results were analyzed using TaqMan Genotyper Software (Thermo Fisher Scientific, Waltham, MA, USA) [14]. For each SNP, the call rate and concordance rate were calculated as described in Section 2.7 [25].

2.7. Sensitivity

For sensitivity studies, genotyping results from four volunteer donors were assessed from serial dilutions of DNA consisting of 10 ng, 5 ng, 1 ng, and 0.5 ng. These four volunteer donors provided a total of one sample (buccal swab) each.

2.8. Resequencing Analysis

The genotyping accuracy was assessed through comparison with resequencing data. Resequencing data were available from the Ensembl genome browser for the 1000 Genomes Project samples and from an internal database for the Laboratory of Genomic Medicine UILDM Santa Lucia Foundation samples. The PCR sequence reactions were performed with BigDye Terminator v3.1 (Thermo Fisher Scientific, Waltham, MA, USA) and the purification with BigDyeXTerminator (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. The samples were run on ABI3130xl (Thermo Fisher Scientific, Waltham, MA, USA) and the obtained sequences were read by Sequencing Analysis software v5.2 (Thermo Fisher Scientific, Waltham, MA, USA) [13].

2.9. Statistical Analysis

At the end of the analytical assay, TaqMan Genotyper Software indicated the percentage of successful genotyping, reported as the call rate. The call rate is defined as the percentage of SNPs that were assigned a genotype call by the software out of the total number of SNPs typed. According to the software’s instructions, a successful call rate is more than 90%. The concordance rate is defined as the proportion of SNPs typed by OpenArray™ confirming the resequencing data. This value was determined by comparing the allele calls generated by OpenArray™ technology with the genotypes resulting from resequencing data [25]. Concordance is the percentage of SNPs with identical results using both OpenArray™ and resequencing.
For phenotypic characterization of biological forensic samples (n = 17), we constructed a statistical prediction model using HIrisPlex-S webtool systems, available at https://hirisplex.erasmusmc.nl/ (accessed on 20 January 2021). Each phenotype was predicted by applying established algorithms for the color of the eyes, hair, and skin, which returns a p-value (prediction probability) [19]. In terms of eye color, the highest p-value is assigned to the most probable phenotype [19,26]. For the interpretation of hair color, we considered the hair color prediction guide developed by Walsh S. et al. [23,27]. This guide examines categorical hair color probabilities in combination with light/dark hair color shade probabilities as obtained from genotype data [23,27]. For the interpretation of skin color, we used the guide developed by L. Chaitanya et al. [16]. This guide considers the highest p-value in combination with the second highest p-value [16,28]. For example, if in the pale category we obtained a p-value greater than 0.7, this phenotypic characteristic depends on the effect of the second highest category if this p-value is greater than 0.15. In particular, it will appear darker if the second most likely category is intermediate, whereas it will appear lighter if the second most likely category is pale [16].
Prior visual phenotyping was performed by two blind assessments (two independent individuals from our laboratory not involved in this study) to verify the validity of the software prediction.
In order to verify the discrimination power of 17 human identification SNPs (not including rs9786184 and rs2032652 on the Y chromosome), we determined the random match probability (RMP) [29]. The random match probability is the estimated frequency of a genetic profile in the reference population and is calculated using allele frequencies from that population group. Therefore, the RMP is a useful value for evaluating the discriminating power of a DNA profiling system, and it indicates the probability of obtaining a match between two distinct and unrelated individuals [29].

3. Results

3.1. Genotyping of the Control Sample and Concordance

The OpenArray™ System (Thermo Fisher Scientific, Waltham, MA, USA) successfully analyzed 100% of the reference DNA samples. The average call rate and the average concordance rate were 96.9 ± 1.72 and 99.8 ± 0.56, respectively [25]. The call rate was also calculated for each SNP. The average SNP call rate was 98.9% ± 1.67, with values from 91.3–100% (Table 1).

3.2. Sensitivity Study

The results of the sensitivity study are shown in Table 3. A call rate higher than or equal to 90% was obtained for all samples. The results obtained demonstrated that the system is able to correctly type DNA evidence in quantities starting from 0.5 ng.

3.3. Genotyping of Biological Evidence from Real Cases

A total of 21 single biological evidence samples from different tissues (10 from saliva, 2 from semen, 7 from blood, 2 from urine), previously assigned to their reference perpetrator, were analyzed using the OpenArray™ System. This evidence was attributed to eight different subjects [23,24] (Table 2). A total of 17 samples presented call rates higher than 90%, with only 4 samples showing call rates below 90% (Table 2). As expected, these 4 samples were characterized by an extremely low starting concentration (Table 2).
We typed two replicates of each sample to verify the genotype call reliability. We obtained concordant results between the two replicates for all samples.

3.3.1. Predicting Human Appearance from Forensic Samples

Once the correct allelic call was ascertained for all subjects, it was possible to carry out predictive analysis on the HIrisPlex-S System software [19].
The data reported in Supplementary Table S1 describe the outputs (p-values) obtained by the HIrisPlex-S System Software for each subject [19].
Table 2 reports the phenotype predictions given by the HIrisPlex-S System software [19].

3.3.2. RMP Calculation

Calculation of the random match probability (RMP) was performed. The calculation considered 17 SNPs located on the autosomal chromosomes. The frequencies relating to the European (EUR), African (AFR), and East Asian (EAS) populations of the 1000 Genomes Project, available on the Ensembl genome browser, were used [22].
Table 4 shows the RMP values obtained for each subject.
These values show that the discriminatory power of the analyzed SNPs is not sufficient to uniquely identify a subject in the world population but can be used to verify genetic compatibility with suspects to be confirmed by traditional STR analysis [30,31].

4. Discussion

To the best of our knowledge this is the first application of the OpenArray™ System in the forensic field. We designed a panel of 60 SNPs for phenotype estimation and personal identification [2]. SNPs for eye, hair, and skin color prediction were directly selected from the HIrisPlex-S system, while SNPs for human identification were selected from already validated subsets (the Precision ID Identity SNP Panel (Thermo Fisher Scientific, Waltham, MA, USA) and ForenSeq DNA Signature Prep Kit (Verogen Inc, San Diego, CA, USA) [16,17,18,19]. These SNPs were first tested on 314 reference samples, revealing an average call rate of 96.9% and a concordance rate near 99.8%.
Sensitivity testing performed on serial dilutions demonstrated that samples with 0.5 ng of total input DNA can be correctly typed.
Of 21 biological evidence samples from different tissues (10 from saliva, 2 from semen, 7 from blood, 2 from urine), a total of 17 were correctly typed using OpenArray™, revealing a call rate higher than 90%.
Regarding identification purposes, since most SNPs are biallelic, they are less informative for identity testing than STR analyses [32]. Thus, many more SNPs will be needed to achieve the same level of discrimination afforded by the commonly used STR loci [33]. However, our system is not intended to replace the present STR analysis for human identification. Despite the reduced discrimination power of this system compared to STR based kits, the OpenArray™ System can be used to exclude suspects, to prioritize samples for downstream analyses providing well-established information about the prediction of eye color, hair color, and skin pigmentation.
It should be used primarily as a tool for phenotype prediction, while identification should also be supported by traditional methods. A total of 17 SNPs for human identification were used in this study, yielding a mean RMP of approximately 10−8.
It is important to outline that our work was intended to provide a new tool, based on previously validated SNPs, for phenotype prediction and prioritizing samples for downstream analyses for human identification [16,17,18,19,34,35]. The HIrisPlex-S panel and the related software were previously validated elsewhere [16,19]. Thus, our work was aimed at verifying the ability of OpenArray™ to correctly type the selected SNPs.
The main strengths of the OpenArray™ System are the speed of the analysis (less than four hours against the current 24–48 h), the standardized interpretation of the results, the low cost for the analysis of a single sample (about 10 euros, in the region of 20% of the current average) and the opportunity to provide both a first evaluation of biological compatibility and phenotypic information. Moreover, TaqMan chemistry is a well-known technology routinely used in the workflow of forensic laboratories to quantify the DNA extracted from biological samples.
Current weaknesses are that it is necessary to analyze the samples with a second system based on traditional STRs to determine biological compatibility. Furthermore, the OpenArray™ System is not used in the workflow of forensic genetics laboratories, limiting its availability for routine use.

5. Conclusions

To the best of our knowledge, this study represents the first application of the OpenArray™ System as a genotyping tool in forensic genetics. In particular, the results obtained suggest that the OpenArray™ System is robust and reliable and is suitable for use in forensic genotyping. Herein, we developed a panel for phenotypic prediction and human identification. Although the 19 SNPs used provide useful information for personal identification, a greater number of SNPs is required for the reliable identification of a subject.
The quality of the results obtained from biological evidence also promotes the application of this system in forensic laboratories to assist investigators in resolving cases.
Finally: these data suggest the applicability of this system as a genotyping method in the forensic field. In particular, it could be suitable as a first line method to verify the presence of genetic compatibility between evidence and suspects and to achieve rapid investigative information also providing well-founded predictions of eye color, hair color, and skin pigmentation. More studies will be needed for further validation of this technology and to consider opportunities to implement this custom array with more SNPs to lower the RMP and to include markers for studies of ancestry and lineage.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4425/12/2/221/s1. Table S1. The output obtained by the HIrisPlex-S System Software for Subject A (SubjA), Subject B (SubjB), Subject C (SubjC), Subject D (SubjD), Subject E (SubjE), Subject F (SubjF), Subject G (SubjG), and Subject H (SubjH).

Author Contributions

Conceptualization, M.R. and E.G.; writing—original draft preparation, M.R., and E.G.; software and formal analysis, G.P., V.E., L.M., L.L., S.P., C.S., C.P., F.N. and V.C.; methodology and validation, G.P., V.E., L.M. and V.C.; data curation, M.R., G.P., V.E., L.M., and V.C.; supervision and project administration, E.G. All authors contributed to the revision of the manuscript and approved the final version. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Policlinico Tor Vergata (protocol number: Prot. CE/PROG.177/20).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data generated in the present study are included within the manuscript and its supplementary file.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kayser, M. Forensic DNA Phenotyping: Predicting human appearance from crime scene material for investigative purposes. Forensic Sci. Int. Genet. 2015, 18, 33–48. [Google Scholar] [CrossRef] [PubMed]
  2. Ragazzo, M.; Carboni, S.; Caputo, V.; Buttini, C.; Manzo, L.; Errichiello, V.; Puleri, G.; Giardina, E. Interpreting Mixture Profiles: Comparison between Precision ID GlobalFiler™ NGS STR Panel v2 and Traditional Methods. Genes 2020, 11, 591. [Google Scholar] [CrossRef] [PubMed]
  3. Cascella, R.; Strafella, C.; Caputo, V.; Errichiello, V.; Zampatti, S.; Milano, F.; Potenza, S.; Mauriello, S.; Novelli, G.; Ricci, F.; et al. Towards the application of precision medicine in Age-Related Macular Degeneration. Prog. Retin. Eye Res. 2018, 63, 132–146. [Google Scholar] [CrossRef] [PubMed]
  4. Giardina, E.; Spinella, A.; Novelli, G. Past, present and future of forensic DNA typing. Nanomedicine 2011, 6, 257–270. [Google Scholar] [CrossRef] [PubMed]
  5. Giardina, E.; Pietrangeli, I.; Martone, C.; Zampatti, S.; Marsala, P.; Gabriele, L.; Ricci, O.; Solla, G.; Asili, P.; Arcudi, G.; et al. Whole genome amplification and real-time PCR in forensic casework. BMC Genom. 2009, 10, 159. [Google Scholar] [CrossRef] [Green Version]
  6. Oldoni, F.; Podini, D. Forensic molecular biomarkers for mixture analysis. Forensic Sci. Int. Genet. 2019, 41, 107–119. [Google Scholar] [CrossRef]
  7. Schwark, T.; Meyer, P.; Harder, M.; Modrow, J.-H.; von Wurmb-Schwark, N. The SNPforID Assay as a Supplementary Method in Kinship and Trace Analysis. Transfus. Med. Hemother. 2012, 39, 187–193. [Google Scholar] [CrossRef] [Green Version]
  8. Machado, H.; Granja, R. DNA Databases and Big Data. In Forensic Genetics in the Governance of Crime; Palgrave Pivot: Singapore, 2020; pp. 57–70. [Google Scholar] [CrossRef] [Green Version]
  9. Amankwaa, A.O. Trends in forensic DNA database: Transnational exchange of DNA data. Forensic Sci. Res. 2019, 5, 8–14. [Google Scholar] [CrossRef] [Green Version]
  10. Butler, J.M.; Willis, S. Interpol review of forensic biology and forensic DNA typing 2016–2019. Forensic Sci. Int. Synerg. 2020, 2, 352–367. [Google Scholar] [CrossRef]
  11. Schneider, P.M.; Prainsack, B.; Kayser, M. The Use of Forensic DNA Phenotyping in Predicting Appearance and Biogeographic Ancestry. Dtsch. Arztebl. Int. 2019, 51–52, 873–880. [Google Scholar] [CrossRef]
  12. Butler, J.M. DNA Quantitation. In Advanced Topics in Forensic DNA Typing: Methodology; Butler, J.M., Ed.; Academic Press: Cambridge, MA, USA, 2012; pp. 49–67. [Google Scholar] [CrossRef]
  13. Cascella, R.; Strafella, C.; Ragazzo, M.; Manzo, L.; Costanza, G.; Bowes, J.; Hüffmeier, U.; Potenza, S.; Sangiuolo, F.; Reis, A.; et al. KIF3A and IL-4 are disease-specific biomarkers for psoriatic arthritis susceptibility. Oncotarget 2017, 8, 95401–95411. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Broccanello, C.; Gerace, L.; Stevanato, P. QuantStudio™ 12K Flex OpenArray® System as a Tool for High-Throughput Genotyping and Gene Expression Analysis. In Quantitative Real-Time PCR Methods in Molecular Biology; Biassoni, R., Raso, A., Eds.; Humana: New York, NY, USA, 2020; Volume 2065, pp. 199–208. [Google Scholar] [CrossRef]
  15. Pomeroy, R.; Duncan, G.; Sunar-Reeder, B.; Ortenberg, E.; Ketchum, M.; Wasiluk, H.; Reeder, D. A low-cost, high-throughput, automated single nucleotide polymorphism assay for forensic human DNA applications. Anal. Biochem. 2009, 395, 61–67. [Google Scholar] [CrossRef] [PubMed]
  16. Chaitanya, L.; Breslin, K.; Zuñiga, S.; Wirken, L.; Pośpiech, E.; Kukla-Bartoszek, M.; Sijen, T.; Knijff, P.; Liu, F.; Branicki, W.; et al. The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation. Forensic Sci. Int. Genet. 2018, 35, 123–135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Pakstis, A.J.; Speed, W.C.; Fang, R.; Hyland, F.C.; Furtado, M.R.; Kidd, J.R.; Kidd, K.K. SNPs for a universal individual identification panel. Hum. Genet. 2010, 127, 315–324. [Google Scholar] [CrossRef]
  18. Ochiai, E.; Minaguchi, K.; Nambiar, P.; Kakimoto, Y.; Satoh, F.; Nakatome, M.; Miyashita, K.; Osawa, M. Evaluation of Y chromosomal SNP haplogrouping in the HID-Ion AmpliSeq™ Identity Panel. Leg. Med. 2016, 22, 58–61. [Google Scholar] [CrossRef]
  19. HIrisPlex-S Eye. Hair and Skin Colour DNA Phenotyping Webtool. Available online: https://hirisplex.erasmusmc.nl/ (accessed on 28 September 2020).
  20. rs1805008 SNP. Available online: http://www.ensembl.org/Homo_sapiens/Variation/HighLD?db=core;r=16:89919236-89920236;v=rs1805008;vdb=variation;vf=22711199#373537_tablePanel (accessed on 30 November 2020).
  21. 1000 Genomes Project Consortium; Abecasis, G.R.; Altshuler, D.; Auton, A.; Brooks, L.D.; Durbin, R.M.; Gibbs, R.A.; Hurles, M.E.; McVean, G.A. A map of human genome variation from population-scale sequencing. Nature 2010, 467, 1061–1073. [Google Scholar] [CrossRef] [Green Version]
  22. Yates, A.D.; Achuthan, P.; Akanni, W.; Allen, J.; Allen, J.; Alvarez-Jarreta, J.; Amode, M.R.; Armean, I.M.; Azov, A.G.; Bennett, R.; et al. Ensembl 2020. Nucleic Acids Res. 2020, 48, D682–D688. [Google Scholar] [CrossRef]
  23. Walsh, S.; Chaitanya, L.; Clarisse, L.; Wirken, L.; Draus-Barini, J.; Kovatsi, L.; Maeda, H.; Ishikawa, T.; Sijen, T.; de Knijff, P.; et al. Developmental validation of the HIrisPlex system: DNA-based eye and hair colour prediction for forensic and anthropological usage. Forensic Sci. Int. Genet. 2014, 9, 150–161. [Google Scholar] [CrossRef]
  24. Cascella, R.; Stocchiy, L.; Strafella, C.; Mezzaroma, I.; Mannazzu, M.; Vullo, V.; Montella, F.; Parruti, G.; Borgiani, P.; Sangiuolo, F.C.; et al. Comparative analysis between saliva and buccal swabs as source of DNA: Lesson from HLA-B*57:01 testing. Pharmacogenomics 2015, 16, 1039–1046. [Google Scholar] [CrossRef] [Green Version]
  25. Ragazzo, M.; Melchiorri, S.; Manzo, L.; Errichiello, V.; Puleri, G.; Nicastro, F.; Giardina, E. Comparative Analysis of ANDE 6C Rapid DNA Analysis System and Traditional Methods. Genes 2020, 11, 582. [Google Scholar] [CrossRef]
  26. Walsh, S.; Liu, F.; Ballantyne, K.N.; van Oven, M.; Lao, O.; Kayser, M. IrisPlex: A sensitive DNA tool for accurate prediction of blue and brown eye colour in the absence of ancestry information. Forensic Sci. Int. Genet. 2011, 5, 170–180. [Google Scholar] [CrossRef] [PubMed]
  27. 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. 2013, 7, 98–115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Walsh, S.; Chaitanya, L.; Breslin, K.; Muralidharan, C.; Bronikowska, A.; Pospiech, E.; Koller, J.; Kovatsi, L.; Wollstein, A.; Branicki, W.; et al. Global skin colour prediction from DNA. Hum. Genet. 2017, 136, 847–863. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Butler, J.M. Statistical Interpretation Overview. In Advanced Topics in Forensic DNA Typing: Interpretation; Butler, J.M., Ed.; Academic Press: Cambridge, MA, USA, 2014; pp. 213–237. [Google Scholar] [CrossRef]
  30. Sanchez, J.J.; Phillips, C.; Børsting, C.; Balogh, K.; Bogus, M.; Fondevila, M.; Harrison, C.D.; Musgrave-Brown, E.; Salas, A.; Syndercombe-Court, D.; et al. A multiplex assay with 52 single nucleotide polymorphisms for human identification. Electrophoresis 2006, 27, 1713–1724. [Google Scholar] [CrossRef]
  31. Kidd, K.K.; Soundararajan, U.; Rajeevan, H.; Pakstis, A.J.; Moore, K.N.; Ropero-Miller, J.D. The redesigned Forensic Research/Reference on Genetics-knowledge base, FROG-kb. Forensic Sci. Int. Genet. 2018, 33, 33–37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Yang, J.; Lin, D.; Deng, C.; Li, Z.; Pu, Y.; Yu, Y.; Li, K.; Li, D.; Chen, P.; Chen, F. The advances in DNA mixture interpretation. Forensic Sci. Int. 2019, 301, 101–106. [Google Scholar] [CrossRef]
  33. King, J.L.; Churchill, J.D.; Novroski, N.M.M.; Zeng, X.; Warshauer, D.H.; Seah, L.H.; Budowle, B. Increasing the discrimination power of ancestry- and identity-informative SNP loci within the ForenSeq™ DNA Signature Prep Kit. Forensic Sci. Int. Genet. 2018, 36, 60–76. [Google Scholar] [CrossRef]
  34. Palencia-Madrid, L.; Xavier, C.; de la Puente, M.; Hohoff, C.; Phillips, C.; Kayser, M.; Parson, W. Evaluation of the VISAGE Basic Tool for Appearance and Ancestry Prediction Using PowerSeq Chemistry on the MiSeq FGx System. Genes 2020, 11, 708. [Google Scholar] [CrossRef]
  35. Breslin, K.; Wills, B.; Ralf, A.; Ventayol Garcia, M.; Kukla-Bartoszek, M.; Pospiech, E.; Freire-Aradas, A.; Xavier, C.; Ingold, S.; de La Puente, M.; et al. HIrisPlex-S system for eye, hair, and skin color prediction from DNA: Massively parallel sequencing solutions for two common forensically used platforms. Forensic Sci. Int. Genet. 2019, 43, 102152. [Google Scholar] [CrossRef]
Table 1. Single-nucleotide polymorphism (SNP) details. FDP: forensic DNA phenotyping; ID: SNPs for forensic identification [16,17,18,19].
Table 1. Single-nucleotide polymorphism (SNP) details. FDP: forensic DNA phenotyping; ID: SNPs for forensic identification [16,17,18,19].
Reference SNP NumberAssay NameCall Rate (%)Concordance Rate (%)FDP/ID
rs3114908C__27458355100%100%skin
rs1800414C___8866240_10100%100%skin
rs10756819C_____69643_1097.8%97.5%skin
rs2238289C__26683035_1097.8%97.5%skin
rs17128291C__34508041_10100%100%skin
rs6497292C__31479145_1097.8%100%skin
rs1129038C____489033_1097.8%100%skin
rs1667394C__26683195_1097.8%100%skin
rs1126809C_175675702_10100%100%skin
rs1470608C___8866144_1097.8%100%skin
rs1426654C___2908190_10100%100%skin
rs6119471C__30035872_10100%100%skin
rs1545397C__11751858_10100%100%skin
rs6059655C_189135912_10100%100%skin
rs12441727C___1289851_1097.8%100%skin
rs8051733C__43670381_20100%100%skin
rs3212355C__32390816_10100%100%hair
rs312262906/rs796296176C_339644346_10100%100%hair
rs11547464C__27541634_10100%100%hair
rs885479C___7519060_20100%100%hair
rs1805008 (rs11538871)C_175977481_10100%100%hair
rs1805005C___7519054_10100%100%hair
rs1805006C___7519055_20100%100%hair
rs1805007C___2033404_3097.8%100%hair
rs1805009C___1010501_30100%100%hair
rs201326893C_190249836_20100%100%hair
rs2228479C___2033405_2097.8%100%hair
rs1110400ANKCDY297.8%100%hair
rs28777C___2390574_10100%100%hair
rs12821256C__26988870_10100%100%hair
rs4959270C__29334795_1097.8%100%hair
rs1042602C___8362862_1095.7%100%hair
rs2402130C__27204528_20100%100%hair
rs2378249C__16003674_10100%100%hair
rs683C___3119206_1097.8%100%hair
rs16891982C___2842665_10100%100%eye
rs12203592C__31918199_10100%100%eye
rs1800407C___8866200_1097.8%97.5%eye
rs12913832C__30724404_1097.8%100%eye
rs12896399C___3244615_10100%100%eye
rs1393350C___9491300_10100%100%eye
rs10488710C___2450075_3097.8%100%ID
rs2920816C__16156638_1097.8%100%ID
rs6955448C__29220288_10100%100%ID
rs1058083C___1619935_1_95.7%97.4%ID
rs221956C____824925_10100%100%ID
rs13182883C___2556113_10100%100%ID
rs1821380C__11673733_10100%100%ID
rs6811238C__11245682_10100%100%ID
rs430046C___9603287_10100%100%ID
rs576261C____788229_1097.8%100%ID
rs10092491C___2049946_1095.7%100%ID
rs560681C___1006721_1_100%100%ID
rs1736442C___3285337_1_91.3%97.3%ID
rs4364205C__26449463_1095.7%100%ID
rs445251C___2997607_10100%100%ID
rs7041158C__29060279_1097.8%100%ID
rs13218440C___9371416_10100%100%ID
rs9786184C__29554891_10100%100%ID
rs2032652C___2259382_10100%100%ID
Table 2. Details of forensic biological samples. Subject A (SubjA), Subject B (SubjB), Subject C (SubjC), Subject D (SubjD), Subject E (SubjE), Subject F (SubjF), Subject G (SubjG), and Subject H (SubjH).
Table 2. Details of forensic biological samples. Subject A (SubjA), Subject B (SubjB), Subject C (SubjC), Subject D (SubjD), Subject E (SubjE), Subject F (SubjF), Subject G (SubjG), and Subject H (SubjH).
SampleSourceSubject[ng/μL]Call Rate (%)Predicted PhenotypeReal Phenotype
20–352SalivaSubjA9.48397.5blue eyes; brown hair/dark hair; pale skinconfirmed
20–367BloodSubjA1.26391.7
20–368BloodSubjB2.32293.3blue eyes; blond hair/light hair; intermediate skinconfirmed
20–369BloodSubjC6.60997.5brown eyes; brown hair/dark hair; dark skinconfirmed
20–361SalivaSubjD18.49395.8brown eyes; brown hair/dark hair; intermediate skinconfirmed
20-370BloodSubjD2.17894.2
20–364SalivaSubjE36.32796.7brown eyes; red hair; intermediate skinconfirmed
20–371BloodSubjE1.65795.0
20–144BloodSubjF0.07816.7blue eyes; blond hair/light hair; intermediate skinconfirmed
20–145SemenSubjF7.02292.5
20–146UrineSubjF0.0094.2
20–147SalivaSubjF5.53495.0
20–148BloodSubjG4.99496.7brown eyes; brown hair/light hair; intermediate skinconfirmed
20–149SemenSubjG0.64252.5
20–150UrineSubjG0.0120.8
20–151SalivaSubjG20.44899.2
20–065SalivaSubjH18.56297.5brown eyes; brown hair/dark hair; intermediate skinconfirmed
20–069SalivaSubjH19.64097.5
20–071SalivaSubjH13.43898.3
20–072SalivaSubjH12.51297.5
20–074SalivaSubjH10.60397.5
Table 3. The call rate and concordance rate obtained from the sensitivity study.
Table 3. The call rate and concordance rate obtained from the sensitivity study.
Sample Call Rate (%)Concordance Rate (%)
SAMPLE_1_10 ng100100
SAMPLE_1_5 ng100100
SAMPLE_1_1 ng 100100
SAMPLE_1_0.5 ng100100
SAMPLE_2_10 ng95100
SAMPLE_2_5 ng98.3100
SAMPLE_2_1 ng96.7100
SAMPLE_2_0.5 ng96.7100
SAMPLE_3_10 ng96.7100
SAMPLE_3_5 ng95100
SAMPLE_3_1 ng9098.21
SAMPLE_3_0.5 ng91.7100
SAMPLE_4_10 ng96.7100
SAMPLE_4_5 ng95100
SAMPLE_4_1 ng91.798.28
SAMPLE_4_0.5 ng95100
Table 4. The random match probability (RMP) values relating to the eight subjects. European (EUR), African (AFR), and East Asian (EAS).
Table 4. The random match probability (RMP) values relating to the eight subjects. European (EUR), African (AFR), and East Asian (EAS).
SubjectRMPEURRMPAFRRMPEAS
SubjA2.67 × 10−81.48 × 10−81.56 × 10−8
SubjB5.60 × 10−91.57 × 10−93.51 × 10−9
SubjC1.15 × 10−72.49 × 10−84.51 × 10−8
SubjD4.24 × 10−71.91 × 10−71.37 × 10−7
SubjE2.89 × 10−81.51 × 10−82.81 × 10−8
SubjF1.80 × 10−96.07 × 10−103.27 × 10−10
SubjG2.23 × 10−78.95 × 10−81.19 × 10−7
SubjH4.26 × 10−82.30 × 10−86.44 × 10−8
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ragazzo, M.; Puleri, G.; Errichiello, V.; Manzo, L.; Luzzi, L.; Potenza, S.; Strafella, C.; Peconi, C.; Nicastro, F.; Caputo, V.; et al. Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification. Genes 2021, 12, 221. https://doi.org/10.3390/genes12020221

AMA Style

Ragazzo M, Puleri G, Errichiello V, Manzo L, Luzzi L, Potenza S, Strafella C, Peconi C, Nicastro F, Caputo V, et al. Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification. Genes. 2021; 12(2):221. https://doi.org/10.3390/genes12020221

Chicago/Turabian Style

Ragazzo, Michele, Giulio Puleri, Valeria Errichiello, Laura Manzo, Laura Luzzi, Saverio Potenza, Claudia Strafella, Cristina Peconi, Fabio Nicastro, Valerio Caputo, and et al. 2021. "Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification" Genes 12, no. 2: 221. https://doi.org/10.3390/genes12020221

APA Style

Ragazzo, M., Puleri, G., Errichiello, V., Manzo, L., Luzzi, L., Potenza, S., Strafella, C., Peconi, C., Nicastro, F., Caputo, V., & Giardina, E. (2021). Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification. Genes, 12(2), 221. https://doi.org/10.3390/genes12020221

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop