Development of a forensic skin colour predictive test

Genetic landscape of forensic DNA phenotyping markers among Mediterranean populations

Forensic DNA Phenotyping can reveal the appearance of an unknown individual by predicting the External Visible Characteristics (EVC) from DNA obtained at the crime scene. Our aim is to characterize the genetic landscape of Human identification markers responsible for EVC among Mediterranean populations compared to other worldwide groups. We conducted an exhaustive search for genes involved in EVC variation. Then, variants located on these genes were extracted from public genotypic data of Mediterranean, American, African and East Asiatic populations. The genetic landscape of these Human identification markers, their allelic distribution and admixture analyses, were determined using plink, R and ADMIXTURE softwares. Our results showed that the Mediterranean populations appear close to the Mexican populations and distinguished from sub Saharan African populations living in the USA and from East Asiatic populations. We highlighted a total of 103454 common variants shared between the studied populations and among them, 25 common variants associated with EVC. Interestingly, genotype frequencies results showed that the rs17646946, rs13016869, rs977588, rs1805008 and rs2240751 variants located respectively in the TCHH, PRKCE, OCA2, MC1R and MFSD12 genes are significantly different between the Mediterranean and Asiatic populations. The genotype frequencies of the variants rs977589 and rs7179994 located in the OCA2 gene, and of rs12913832 and rs2240751 located respectively in HERC2 and MFSD12 genes are significantly different between the Mediterranean and American populations. Our work generates a large number of EVC variants that could be a valuable resource for future studies in the forensic field.

The face as folded object: Race and the problems with 'progress' in forensic DNA phenotyping

Forensic DNA phenotyping (FDP) encompasses a set of technologies aimed at predicting phenotypic characteristics from genotypes. Advocates of FDP present it as the future of forensics, with an ultimate goal of producing complete, individualised facial composites based on DNA. With a focus on individuals and promised advances in technology comes the assumption that modern methods are steadily moving away from racial science. Yet in the quantification of physical differences, FDP builds upon some nineteenth- and twentieth-century scientific practices that measured and categorised human variation in terms of race. In this article I complicate the linear temporal approach to scientific progress by building on the notion of the folded object. Drawing on ethnographic fieldwork conducted in various genetic laboratories, I show how nineteenth- and early twentieth-century anthropological measuring and data-collection practices and statistical averaging techniques are folded into the ordering of measurements of skin color data taken with a spectrophotometer, the analysis of facial shape based on computational landmarks and the collection of iris photographs. Attending to the historicity of FDP facial renderings, I bring into focus how race comes about as a consequence of temporal folds.

A genome-wide association scan reveals novel loci for facial traits of Koreans

DNA-based prediction of externally visible characteristics (EVC) with SNPs is one of the research areas of interest in the forensic field. Based on a previous study performing GWAS on facial traits in a Korean population, herein, we present results stemming from GWA analysis with KoreanChip and novel genetic loci satisfying genome-wide significant level. We discovered a total of 20 signals and 12 loci were found to have novel associations with facial traits, including six loci located in intergenic regions and six loci located at UBE2O, HECTD2, CCDC108, TPK1, FCN2, and FRMPD1. Additionally, we performed a polygenic score analysis for 33 distance-related traits in facial phenotyping and determined genetic relationships between facial traits and SNPs using the GCTA program. The results of the current study offer an understanding of how facial morphology is influenced by complex genetic structures and provide insights into forensic investigation and population genetics.

Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age

Forensic DNA Phenotyping (FDP) comprises the prediction of a person's externally visible characteristics regarding appearance, biogeographic ancestry and age from DNA of crime scene samples, to provide investigative leads to help find unknown perpetrators that cannot be identified with forensic STR-profiling. In recent years, FDP has advanced considerably in all of its three components, which we summarize in this review article. Appearance prediction from DNA has broadened beyond eye, hair and skin color to additionally comprise other traits such as eyebrow color, freckles, hair structure, hair loss in men, and tall stature. Biogeographic ancestry inference from DNA has progressed from continental ancestry to sub-continental ancestry detection and the resolving of co-ancestry patterns in genetically admixed individuals. Age estimation from DNA has widened beyond blood to more somatic tissues such as saliva and bones as well as new markers and tools for semen. Technological progress has allowed forensically suitable DNA technology with largely increased multiplex capacity for the simultaneous analysis of hundreds of DNA predictors with targeted massively parallel sequencing (MPS). Forensically validated MPS-based FDP tools for predicting from crime scene DNA i) several appearance traits, ii) multi-regional ancestry, iii) several appearance traits together with multi-regional ancestry, and iv) age from different tissue types, are already available. Despite recent advances that will likely increase the impact of FDP in criminal casework in the near future, moving reliable appearance, ancestry and age prediction from crime scene DNA to the level of detail and accuracy police investigators may desire, requires further intensified scientific research together with technical developments and forensic validations as well as the necessary funding.

Predicting Physical Appearance from DNA Data-Towards Genomic Solutions

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics.

Analysis of Skin Pigmentation and Genetic Ancestry in Three Subpopulations from Pakistan: Punjabi, Pashtun, and Baloch

Skin pigmentation is one of the most prominent and variable phenotypes in humans. We compared the alleles of 163 SNPs and indels from the Human Pigmentation (HuPi) AmpliSeq™ Custom panel, and biogeographic ancestry with the quantitative skin pigmentation levels on the upper arm, lower arm, and forehead of 299 Pakistani individuals from three subpopulations: Baloch, Pashtun, and Punjabi. The biogeographic ancestry of each individual was estimated using the Precision ID Ancestry Panel. All individuals were mainly of mixed South-Central Asian and European ancestry. However, the Baloch individuals also had an average proportion of Sub-Saharan African ancestry of approximately 10%, whereas it was <1% in the Punjabi and Pashtun individuals. The pairwise genetic distances between the Pashtun, Punjabi, and Baloch subpopulations based on the ancestry markers were statistically significantly different. Individuals from the Pashtun subpopulation had statistically significantly lower skin pigmentation than individuals from the Punjabi and Baloch subpopulations (p < 0.05). The proportions of European and Sub-Saharan African ancestry and five SNPs (rs1042602, rs10831496, rs1426654, rs16891982, and rs12913832) were statistically significantly associated with skin pigmentation at either the upper arm, lower arm or forehead in the Pakistani population after correction for multiple testing (p < 10⁻³). A model based on four of these SNPs (rs1426654, rs1042602, rs16891982, and rs12913832) explained 33% of the upper arm skin pigmentation. The four SNPs and the proportions of European and Sub-Saharan African ancestry explained 37% of the upper arm skin pigmentation. Our results indicate that the four likely causative SNPs, rs1426654, rs1042602, rs16891982, and rs12913832 located in SLC24A5, TYR, SLC45A2, and HERC2, respectively, are essential for skin color variation in the admixed Pakistani subpopulations.

The analysis of ancestry with small-scale forensic panels of genetic markers

In the last 10 years, forensic genetic analysis has been extended beyond identification tests that link a suspect to crime scene evidence using standard DNA profiling, to new supplementary tests that can provide information to investigators about a suspect in the absence of a database hit or eyewitness testimony. These tests now encompass the prediction of physical appearance, ancestry and age. In this review, we give a comprehensive overview of the full range of DNA-based ancestry inference tests designed to work with forensic contact traces, when the level of DNA is often very low or highly degraded. We outline recent developments in the design of ancestry-informative marker sets, forensic assays that use capillary electrophoresis or massively parallel sequencing, and the statistical analysis frameworks that examine the test profile and compares it to reference population variation. Three casework ancestry analysis examples are described which were successfully accomplished in the authors' laboratory, where the ancestry information obtained was critical to the outcome of the DNA analyses made.

Prediction of eye and hair pigmentation phenotypes using the HIrisPlex system in a Brazilian admixed population sample

Human pigmentation is a complex trait, probably involving more than 100 genes. Predicting phenotypes using SNPs present in those genes is important for forensic purpose. For this, the HIrisPlex tool was developed for eye and hair color prediction, with both models achieving high accuracy among Europeans. Its evaluation in admixed populations is important, since they present a higher frequency of intermediate phenotypes, and HIrisPlex has demonstrated limitations in such predictions; therefore, the performance of this tool may be impaired in such populations. Here, we evaluate the set of 24 markers from the HIrisPlex system in 328 individuals from Ribeirão Preto (SP) region, predicting eye and hair color and comparing the predictions with their real phenotypes. We used the HaloPlex Target Enrichment System and MiSeq Personal Sequencer platform for massively parallel sequencing. The prediction of eye and hair color was accomplished by the HIrisPlex online tool, using the default prediction settings. Ancestry was estimated using the SNPforID 34-plex to observe if and how an individual's ancestry background would affect predictions in this admixed sample. Our sample presented major European ancestry (70.5%), followed by African (21.1%) and Native American/East Asian (8.4%). HIrisPlex presented an overall sensitivity of 0.691 for hair color prediction, with sensitivities ranging from 0.547 to 0.782. The lowest sensitivity was observed for individuals with black hair, who present a reduced European contribution (48.4%). For eye color prediction, the overall sensitivity was 0.741, with sensitivities higher than 0.85 for blue and brown eyes, although it failed in predicting intermediate eye color. Such struggle in predicting this phenotype category is in accordance with what has been seen in previous studies involving HIrisPlex. Individuals with brown eye color are more admixed, with European ancestry decreasing to 62.6%; notwithstanding that, sensitivity for brown eyes was almost 100%. Overall sensitivity increases to 0.791 when a 0.7 threshold is set, though 12.5% of the individuals become undefined. When combining eye and hair prediction, hit rates between 51.3 and 68.9% were achieved. Despite the difficulties with intermediate phenotypes, we have shown that HIrisPlex results can be very helpful when interpreted with caution.

Prediction of eye, hair and skin colour in Latin Americans

Here we evaluate the accuracy of prediction for eye, hair and skin pigmentation in a dataset of > 6500 individuals from Mexico, Colombia, Peru, Chile and Brazil (including genome-wide SNP data and quantitative/categorical pigmentation phenotypes - the CANDELA dataset CAN). We evaluated accuracy in relation to different analytical methods and various phenotypic predictors. As expected from statistical principles, we observe that quantitative traits are more sensitive to changes in the prediction models than categorical traits. We find that Random Forest or Linear Regression are generally the best performing methods. We also compare the prediction accuracy of SNP sets defined in the CAN dataset (including 56, 101 and 120 SNPs for eye, hair and skin colour prediction, respectively) to the well-established HIrisPlex-S SNP set (including 6, 22 and 36 SNPs for eye, hair and skin colour prediction respectively). When training prediction models on the CAN data, we observe remarkably similar performances for HIrisPlex-S and the larger CAN SNP sets for the prediction of hair (categorical) and eye (both categorical and quantitative), while the CAN sets outperform HIrisPlex-S for quantitative, but not for categorical skin pigmentation prediction. The performance of HIrisPlex-S, when models are trained in a world-wide sample (although consisting of 80% Europeans, https://hirisplex.erasmusmc.nl), is lower relative to training in the CAN data (particularly for hair and skin colour). Altogether, our observations are consistent with common variation of eye and hair colour having a relatively simple genetic architecture, which is well captured by HIrisPlex-S, even in admixed Latin Americans (with partial European ancestry). By contrast, since skin pigmentation is a more polygenic trait, accuracy is more sensitive to prediction SNP set size, although here this effect was only apparent for a quantitative measure of skin pigmentation. Our results support the use of HIrisPlex-S in the prediction of categorical pigmentation traits for forensic purposes in Latin America, while illustrating the impact of training datasets on its accuracy.

The color of normal: How a Eurocentric focus erases pigmentation complexity

OBJECTIVES

Skin pigmentation is both a highly variable and highly visible human phenotypic trait. Investigations into the biology and origins of this variation have been the focus of research in the fields of dermatology, anthropology, and forensic science, among others. This manuscript explores how much of what we know about the biology, genetics, and evolutionary origins of pigmentation has been strongly influenced by investigations and applications that focus on lighter skin.

METHODS

I reviewed literature from the fields of dermatology, anthropology and evolutionary genetics, and forensic science to assess how perceptions of lighter skin as the "normal" state in humans can shape the ways that knowledge is gathered and applied in these fields.

RESULTS

This normalization of lighter skin has impacted common tools used in dermatology and shaped the framework of dermatological education. A strong Eurocentric bias has shaped our understanding of the genetic architecture of pigmentary traits, which influences the ways in we understand the evolutionary processes leading to modern pigmentation diversity. Finally, I discuss how these biases in pigmentation genetics work in combination with phenotypic systems that privilege predicting lighter pigmentation variation to impede accurate prediction of intermediate phenotypes, particularly in individuals with ancestry from multiple populations. This can lead to a disproportionate targeting of already over-policed populations with darker skin.

CONCLUSIONS

Potential changes to how we conceptualize clinical and basic pigmentation research may help to reduce existing health disparities and improve understanding of pigmentation genetic architecture and how this knowledge is applied in forensic contexts.

Assessment of the potential role of natural selection in type 2 diabetes and related traits across human continental ancestry groups: comparison of phenotypic with genotypic divergence

AIMS/HYPOTHESIS

Prevalence of type 2 diabetes differs among human ancestry groups, and many hypotheses invoke differential natural selection to account for these differences. We sought to assess the potential role of differential natural selection across major continental ancestry groups for diabetes and related traits, by comparison of genetic and phenotypic differences.

METHODS

This was a cross-sectional comparison among 734 individuals from an urban sample (none of whom was more closely related to another than third-degree relatives), including 83 African Americans, 523 American Indians and 128 European Americans. Participants were not recruited based on diabetes status or other traits. BMI was calculated, and diabetes was diagnosed by a 75 g oral glucose tolerance test. In those with normal glucose tolerance (n = 434), fasting insulin and 30 min post-load insulin, adjusted for 30 min glucose, were taken as measures of insulin resistance and secretion, respectively. Whole exome sequencing was performed, resulting in 97,388 common (minor allele frequency ≥ 5%) variants; the coancestry coefficient (F_ST) was calculated across all markers as a measure of genetic divergence among ancestry groups. The phenotypic divergence index (P_ST) was also calculated from the phenotypic differences and heritability (which was estimated from genetic relatedness calculated empirically across all markers in 761 American Indian participants prior to the exclusion of close relatives). Under evolutionary neutrality, the expectation is P_ST = F_ST, while for traits under differential selection P_ST is expected to be significantly greater than F_ST. A bootstrap procedure was used to test the hypothesis P_ST = F_ST. RESULTS: With adjustment for age and sex, prevalence of type 2 diabetes was 34.0% in American Indians, 12.4% in African Americans and 10.4% in European Americans (p = 2.9 × 10^-10 for difference among groups). Mean BMI was 36.3, 33.4 and 33.0 kg/m², respectively (p = 1.9 × 10^-7). Mean fasting insulin was 63.8, 48.4 and 45.2 pmol/l (p = 9.2 × 10^-5), while mean 30 min insulin was 559.8, 553.5 and 358.8 pmol/l, respectively (p = 5.7 × 10^-8). F_ST across all markers was 0.130, while P_ST for liability to diabetes, adjusted for age and sex, was 0.149 (p = 0.35 for difference with F_ST). P_ST was 0.094 for BMI (p = 0.54), 0.095 for fasting insulin (p = 0.54) and 0.216 (p = 0.18) for 30 min insulin. For type 2 diabetes and BMI, the maximum divergence between populations was observed between American Indians and European Americans (P_ST-MAX = 0.22, p = 0.37, and P_ST-MAX = 0.14, p = 0.61), which suggests that a relatively modest 22% or 14% of the genetic variance, respectively, can potentially be explained by differential selection (assuming the absence of neutral drift).

CONCLUSIONS/INTERPRETATION

These analyses suggest that while type 2 diabetes and related traits differ significantly among continental ancestry groups, the differences are consistent with neutral expectations based on heritability and genetic distances. While these analyses do not exclude a modest role for natural selection, they do not support the hypothesis that differential natural selection is necessary to explain the phenotypic differences among these ancestry groups. Graphical abstract.

Evaluation of the Ion AmpliSeq™ PhenoTrivium Panel: MPS-Based Assay for Ancestry and Phenotype Predictions Challenged by Casework Samples

As the field of forensic DNA analysis has started to transition from genetics to genomics, new methods to aid in crime scene investigations have arisen. The development of informative single nucleotide polymorphism (SNP) markers has led the forensic community to question if DNA can be a reliable "eye-witness" and whether the data it provides can shed light on unknown perpetrators. We have developed an assay called the Ion AmpliSeq™ PhenoTrivium Panel, which combines three groups of markers: 41 phenotype- and 163 ancestry-informative autosomal SNPs together with 120 lineage-specific Y-SNPs. Here, we report the results of testing the assay's sensitivity and the predictions obtained for known reference samples. Moreover, we present the outcome of a blind study performed on real casework samples in order to understand the value and reliability of the information that would be provided to police investigators. Furthermore, we evaluated the accuracy of admixture prediction in Converge™ Software. The results show the panel to be a robust and sensitive assay which can be used to analyze casework samples. We conclude that the combination of the obtained predictions of phenotype, biogeographical ancestry, and male lineage can serve as a potential lead in challenging police investigations such as cold cases or cases with no suspect.

Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits

The prediction of appearance traits by use of solely genetic information has become an established approach and a number of statistical prediction models have already been developed for this purpose. However, given limited knowledge on appearance genetics, currently available models are incomplete and do not include all causal genetic variants as predictors. Therefore such prediction models may benefit from the inclusion of additional information that acts as a proxy for this unknown genetic background. Use of priors, possibly informed by trait category prevalence values in biogeographic ancestry groups, in a Bayesian framework may thus improve the prediction accuracy of previously predicted externally visible characteristics, but has not been investigated as of yet. In this study, we assessed the impact of using trait prevalence-informed priors on the prediction performance in Bayesian models for eye, hair and skin color as well as hair structure and freckles in comparison to the respective prior-free models. Those prior-free models were either similarly defined either very close to the already established ones by using a reduced predictive marker set. However, these differences in the number of the predictive markers should not affect significantly our main outcomes. We observed that such priors often had a strong effect on the prediction performance, but to varying degrees between different traits and also different trait categories, with some categories barely showing an effect. While we found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, our analyses also showed that misspecification of those prior values often severely diminished the accuracy compared to the respective prior-free approach. This emphasizes the importance of accurate specification of prevalence-informed priors in Bayesian prediction modeling of appearance traits. However, the existing literature knowledge on spatial prevalence is sparse for most appearance traits, including those investigated here. Due to the limitations in appearance trait prevalence knowledge, our results render the use of trait prevalence-informed priors in DNA-based appearance trait prediction currently infeasible.

The impact of correlations between pigmentation phenotypes and underlying genotypes on genetic prediction of pigmentation traits

Predicting appearance phenotypes from genotypes is relevant for various areas of human genetic research and applications such as genetic epidemiology, human history, anthropology, and particularly in forensics. Many appearance phenotypes, and thus their underlying genotypes, are highly correlated, with pigmentation traits serving as primary examples. However, all available genetic prediction models, including those for pigmentation traits currently used in forensic DNA phenotyping, ignore phenotype correlations. Here, we investigated the impact of appearance phenotype correlations on genetic appearance prediction in the exemplary case of three pigmentation traits. We used data for categorical eye, hair and skin colour as well as 41 DNA markers utilized in the recently established HIrisPlex-S system from 762 individuals with complete phenotype and genotype information. Based on these data, we performed genetic prediction modelling of eye, hair and skin colour via three different strategies, namely the established approach of predicting phenotypes solely based on genotypes while not considering phenotype correlations, and two novel approaches that considered phenotype correlations, either incorporating truly observed correlated phenotypes or DNA-predicted correlated phenotypes in addition to the DNA predictors. We found that using truly observed correlated pigmentation phenotypes as additional predictors increased the DNA-based prediction accuracies for almost all eye, hair and skin colour categories, with the largest increase for intermediate eye colour, brown hair colour, dark to black skin colour, and particularly for dark skin colour. Outcomes of dedicated computer simulations suggest that this prediction accuracy increase is due to the additional genetic information that is implicitly provided by the truly observed correlated pigmentation phenotypes used, yet not covered by the DNA predictors applied. In contrast, considering DNA-predicted correlated pigmentation phenotypes as additional predictors did not improve the performance of the genetic prediction of eye, hair and skin colour, which was in line with the results from our computer simulations. Hence, in practical applications of DNA-based appearance prediction where no phenotype knowledge is available, such as in forensic DNA phenotyping, it is not advised to use DNA-predicted correlated phenotypes as predictors in addition to the DNA predictors. In the very least, this is not recommended for the pigmentation traits and the established pigmentation DNA predictors tested here.

Research Techniques Made Simple: Cutaneous Colorimetry: A Reliable Technique for Objective Skin Color Measurement

Skin color evaluation contributes to assessment of an individual's cutaneous phenotype. Skin color changes provide important clues to disease progression or treatment response. Skin color is also a predictor of skin cancer risk. Melanin pigment, blood flow, skin thickness, and photoaging contribute to skin color. Melanin, hemoglobin, bilirubin, and carotene are the primary chromophores of skin color. Their concentrations vary depending on the individual's phenotype, anatomic location, external insults of chemical irritants and UVR, and physiological changes. The evaluation and perception of skin color are often subjective. Objective quantification of skin color can be achieved with colorimetric devices such as tristimulus colorimeters. These devices compute the intensity of light reflected from skin and correlate with pigmentation and erythema. Cutaneous color and color changes can be quantified under color organization systems, such as the CIELAB color space, which is standardized by the Commission Internationale de l'Eclairage (CIE). The CIELAB expresses color's lightness, red/green intensity, and yellow/blue intensity, as L*, a*, and b* values, respectively. Additionally, skin color's full spectral characteristics and cutaneous physiology can be measured with spectrophotometers. This article outlines basic principles of the CIELAB color system and how to optimally use colorimetric devices as a skin research tool.

Insights on hair, skin and eye color of ancient and contemporary Native Americans

Over the past few years, tools capable of predicting pigmentation phenotypes have been developed aiming to contribute for criminal and anthropological investigations. In this study, we used eight genetic systems to infer eye, hair, and skin color of ancient and contemporary Native Americans. To achieve this goal, we retrieved 61 SNPs from 42 samples available in free online repositories of DNA sequences. We performed pigmentation predictions using two freely available tools, HIrisPlex-S and Snipper, in addition to two other published models. This workflow made possible to predict all three phenotypes with at least one tool for 29 out of the 42 samples. Considering these 29 individuals, predictions for eye, hair, and skin color were obtained with HIrisPlex-S for 27, 28 and 27 individuals, respectively, while 24, 25 and 25 individuals had such predictions with Snipper. In general, ancient and contemporary Native Americans were predicted to have intermediate/brown eyes, black hair, and intermediate/darker skin pigmentation.

The Use of Forensic DNA Phenotyping in Predicting Appearance and Biogeographic Ancestry

BACKGROUND

Persons whose identifying DNA profile (STR profile) is not yet known to the ingvestigating authorities cannot be identified by standard forensic DNA analysis (STR profiling) as it is now practiced. In view of the current public debate, particularly in Germany, on the legalization of so-called forensic DNA phenotyping, we present its scientific basis, societal aspects, and forensic applications and describe the analytic techniques that are now available.

METHODS

This review is based on pertinent publications that were retrieved by a selective search in PubMed and in public media, and on the authors' own research.

RESULTS

Forensically validated DNA test systems are available for the categorization of eye, hair, and skin color and the inference of continental biogeographic ancestry. As for statistical measures of test accuracy, the AUC (area under the curve) values lie in the range 0.74-0.99 for eye color, 0.64-0.94 for hair color, and 0.72-0.99 for skin color, depending on the predictive model and color category used.The corre- sponding positive predictive values (PPV) are lower. Empirical social-scientific research on forensic DNA phenotyping has shown that preserving privacy and protecting against discrimination are major ethical and regulatory considerations.

CONCLUSION

All three methods of forensic DNA phenotyping-the predition of exter- nally visible characteristics, biogeographic ancestry, and the estimation of age from crime scene DNA-require a proper regulatory framework and should be used in conjunction with each other. Before forensic DNA phenotyping can be implemented in forensic practice, steps must be taken to minimize the risks of violation of privacy scrimination and to ensure that these methods are used transpar- ently and proportionately.

Skin pigmentation and genetic variants in an admixed Brazilian population of primarily European ancestry

Although many genes have been shown to be associated with human pigmentary traits and forensic prediction assays exist (e.g. HIrisPlex-S), the genetic knowledge about skin colour remains incomplete. The highly admixed Brazilian population is an interesting study population for investigation of the complex genotype-phenotype architecture of human skin colour because of its large variation. Here, we compared variants in 22 pigmentary genes with quantitative skin pigmentation levels on the buttock, arm, and forehead areas of 266 genetically admixed Brazilian individuals. The genetic ancestry of each individual was estimated by typing 46 AIM-InDels. The mean proportion of genetic ancestry was 68.8% European, 20.8% Sub-Saharan African, and 10.4% Native American. A high correlation (adjusted R² = 0.65, p < 0.05) was observed between nine SNPs and quantitative skin pigmentation using multiple linear regression analysis. The correlations were notably smaller between skin pigmentation and biogeographic ancestry (adjusted R² = 0.45, p < 0.05), or markers in the leading forensic skin colour prediction system, the HIrisPlex-S (adjusted R² = 0.54, p < 0.05). Four of the nine SNPs, OCA2 rs1448484 (rank 2), APBA2 rs4424881 (rank 4), MFSD12 rs10424065 (rank 8), and TYRP1 1408799 (rank 9) were not investigated as part of the HIrisPlex-S selection process, and therefore not included in the HIrisPlex-S model. Our results indicate that these SNPs account for a substantial part of the skin colour variation in individuals of admixed ancestry. Hence, we suggest that these SNPs are considered when developing future skin colour prediction models.

Differentiation of Hispanic biogeographic ancestry with 80 ancestry informative markers

Ancestry informative single nucleotide polymorphisms (SNPs) can identify biogeographic ancestry (BGA); however, population substructure and relatively recent admixture can make differentiation difficult in heterogeneous Hispanic populations. Utilizing unrelated individuals from the Genomic Origins and Admixture in Latinos dataset (GOAL, n = 160), we designed an 80 SNP panel (Setser80) that accurately depicts BGA through STRUCTURE and PCA. We compared our Setser80 to the Seldin and Kidd panels via resampling simulations, which models data based on allele frequencies. We incorporated Admixed American 1000 Genomes populations (1000 G, n = 347), into a combined populations dataset to determine robustness. Using multinomial logistic regression (MLR), we compared the 3 panels on the combined dataset and found overall MLR classification accuracies: 93.2% Setser80, 87.9% Seldin panel, 71.4% Kidd panel. Naïve Bayesian classification had similar results on the combined dataset: 91.5% Setser80, 84.7% Seldin panel, 71.1% Kidd panel. Although Peru and Mexico were absent from panel design, we achieved high classification accuracy on the combined populations for Peru (MLR = 100%, naïve Bayes = 98%), and Mexico (MLR = 90%, naïve Bayes = 83.4%) as evidence of the portability of the Setser80. Our results indicate the Setser80 SNP panel can reliably classify BGA for individuals of presumed Hispanic origin.

Meta-analysis of GWA studies provides new insights on the genetic architecture of skin pigmentation in recently admixed populations

BACKGROUND

Association studies in recently admixed populations are extremely useful to identify the genetic architecture of pigmentation, due to their high genotypic and phenotypic variation. However, to date only four Genome-Wide Association Studies (GWAS) have been carried out in these populations.

RESULTS

We present a GWAS of skin pigmentation in an admixed sample from Cuba (N = 762). Additionally, we conducted a meta-analysis including the Cuban sample, and admixed samples from Cape Verde, Puerto Rico and African-Americans from San Francisco. This meta-analysis is one of the largest efforts so far to characterize the genetic basis of skin pigmentation in admixed populations (N = 2,104). We identified five genome-wide significant regions in the meta-analysis, and explored if the markers observed in these regions are associated with the expression of relevant pigmentary genes in human melanocyte cultures. In three of the regions identified in the meta-analysis (SLC24A5, SLC45A2, and GRM5/TYR), the association seems to be driven by non-synonymous variants (rs1426654, rs16891982, and rs1042602, respectively). The rs16891982 polymorphism is strongly associated with the expression of the SLC45A2 gene. In the GRM5/TYR region, in addition to the rs1042602 non-synonymous SNP located on the TYR gene, variants located in the nearby GRM5 gene have an independent effect on pigmentation, possibly through regulation of gene expression of the TYR gene. We also replicated an association recently described near the MFSD12 gene on chromosome 19 (lead variant rs112332856). Additionally, our analyses support the presence of multiple signals in the OCA2/HERC2/APBA2 region on chromosome 15. A clear causal candidate is the HERC2 intronic variant rs12913832, which has a profound influence on OCA2 expression. This variant has pleiotropic effects on eye, hair, and skin pigmentation. However, conditional and haplotype-based analyses indicate the presence of other variants with independent effects on melanin levels in OCA2 and APBA2. Finally, a follow-up of genome-wide signals identified in a recent GWAS for tanning response indicates that there is a substantial overlap in the genetic factors influencing skin pigmentation and tanning response.

CONCLUSIONS

Our meta-analysis of skin pigmentation GWAS in recently admixed populations provides new insights about the genetic architecture of this complex trait.

The Genetics of Human Skin and Hair Pigmentation

Human skin and hair color are visible traits that can vary dramatically within and across ethnic populations. The genetic makeup of these traits-including polymorphisms in the enzymes and signaling proteins involved in melanogenesis, and the vital role of ion transport mechanisms operating during the maturation and distribution of the melanosome-has provided new insights into the regulation of pigmentation. A large number of novel loci involved in the process have been recently discovered through four large-scale genome-wide association studies in Europeans, two large genetic studies of skin color in Africans, one study in Latin Americans, and functional testing in animal models. The responsible polymorphisms within these pigmentation genes appear at different population frequencies, can be used as ancestry-informative markers, and provide insight into the evolutionary selective forces that have acted to create this human diversity.

Prediction of skin color, tanning and freckling from DNA in Polish population: linear regression, random forest and neural network approaches

Predicting phenotypes from DNA has recently become extensively studied field in forensic research and is referred to as Forensic DNA Phenotyping. Systems based on single nucleotide polymorphisms for accurate prediction of iris, hair and skin color in global population, independent of bio-geographical ancestry, have recently been introduced. Here, we analyzed 14 SNPs for distinct skin pigmentation traits in a homogeneous cohort of 222 Polish subjects. We compared three different algorithms: General Linear Model based on logistic regression, Random Forest and Neural Network in 18 developed prediction models. We demonstrate Random Forest to be the most accurate algorithm for 3- and 4-category estimations (total of 58.3% correct calls for skin color prediction, 47.2% for tanning prediction, 50% for freckling prediction). Binomial Logistic Regression was the best approach in 2-category estimations (total of 69.4% correct calls, AUC = 0.673 for tanning prediction; total of 52.8% correct calls, AUC = 0.537 for freckling prediction). Our study confirms the association of rs12913832 (HERC2) with all three skin pigmentation traits, but also variants associated solely with certain pigmentation traits, namely rs6058017 and rs4911414 (ASIP) with skin sensitivity to sun and tanning abilities, rs12203592 (IRF4) with freckling and rs4778241 and rs4778138 (OCA2) with skin color and tanning. Finally, we assessed significant differences in allele frequencies in comparison with CEU data and our study provides a starting point for the development of prediction models for homogeneous populations with less internal differentiation than in the global predictive testing.

Shades of complexity: New perspectives on the evolution and genetic architecture of human skin

Like many highly variable human traits, more than a dozen genes are known to contribute to the full range of skin color. However, the historical bias in favor of genetic studies in European and European-derived populations has blinded us to the magnitude of pigmentation's complexity. As deliberate efforts are being made to better characterize diverse global populations and new sequencing technologies, better measurement tools, functional assessments, predictive modeling, and ancient DNA analyses become more widely accessible, we are beginning to appreciate how limited our understanding of the genetic bases of human skin color have been. Novel variants in genes not previously linked to pigmentation have been identified and evidence is mounting that there are hundreds more variants yet to be found. Even for genes that have been exhaustively characterized in European populations like MC1R, OCA2, and SLC24A5, research in previously understudied groups is leading to a new appreciation of the degree to which genetic diversity, epistatic interactions, pleiotropy, admixture, global and local adaptation, and cultural practices operate in population-specific ways to shape the genetic architecture of skin color. Furthermore, we are coming to terms with how factors like tanning response and barrier function may also have influenced selection on skin throughout human history. By examining how our knowledge of pigmentation genetics has shifted in the last decade, we can better appreciate how far we have come in understanding human diversity and the still long road ahead for understanding many complex human traits.

An overview of the latest developments in facial imaging

Facial imaging is a term used to describe methods that use facial images to assist or facilitate human identification. This pertains to two craniofacial identification procedures that use skulls and faces-facial approximation and photographic superimposition-as well as face-only methods for age progression/regression, the construction of facial graphics from eyewitness memory (including composites and artistic sketches), facial depiction, face mapping and newly emerging methods of molecular photofitting. Given the breadth of these facial imaging techniques, it is not surprising that a broad array of subject-matter experts participate in and/or contribute to the formulation and implementation of these methods (including forensic odontologists, forensic artists, police officers, electrical engineers, anatomists, geneticists, medical image specialists, psychologists, computer graphic programmers and software developers). As they are concerned with the physical characteristics of humans, each of these facial imaging areas also falls in the domain of physical anthropology, although not all of them have been traditionally regarded as such. This too offers useful opportunities to adapt established methods in one domain to others more traditionally held to be disciplines within physical anthropology (e.g. facial approximation, craniofacial superimposition and face photo-comparison). It is important to note that most facial imaging methods are not currently used for identification but serve to assist authorities in narrowing or directing investigations such that other, more potent, methods of identification can be used (e.g. DNA). Few, if any, facial imaging approaches can be considered honed end-stage scientific methods, with major opportunities for physical anthropologists to make meaningful contributions. Some facial imaging methods have considerably stronger scientific underpinnings than others (e.g. facial approximation versus face mapping), some currently lie entirely within the artistic sphere (facial depiction), and yet others are so aspirational that realistic capacity to obtain their aims has strongly been questioned despite highly advanced technical approaches (molecular photofitting). All this makes for a broad-ranging, dynamic and energetic field that is in a constant state of flux. This manuscript provides a theoretical snapshot of the purposes of these methods, the state of science as it pertains to them, and their latest research developments.

Off-target phenotypes in forensic DNA phenotyping and biogeographic ancestry inference: A resource

With recent advances in DNA sequencing technologies it has become feasible and cost effective to genotype larger marker sets for forensic purposes. Two technologies that make use of the larger marker sets have come into focus in forensic research and applications; inference of biogeographic ancestry (BGA) and forensic DNA phenotyping (FDP). These methods hold the promise to reveal information about a yet unknown perpetrator from a DNA sample. In contrast, DNA-profiling, that is a standard practice in case work, relies on matching DNA-profiles between crime scene material and suspects on a database of DNA-profiles. Markers for DNA-profiling were developed under the premise to reveal as little additional information about the human source of the profile as possible, the rationale being that personal privacy rights have to be balanced against the public interest in solving a crime. The same argument holds for markers used in BGA and FDP; these markers might also reveal information on off-target phenotypes (OTPs), that go beyond BGA and the phenotypes targeted in FDP. In particular, health related OTPs might shift the balance between privacy protection and public interest. However, to our knowledge, there is currently no convenient resource available to incorporate knowledge on OTPs in BGA and FDP assay design and application. In order to provide such a resource, we performed a systematic search for OTPs associated with a comprehensive set of markers (1766 SNPs) used or suggested to be used for BGA inference and FDP. In this set, we identified a relatively small number of 27 SNPs (1.53%) that convey information on diverse health related OTPs such as cancer risk, induced asthma, or risk of alcoholism. Some of these SNPs are commonly used for FDP and BGA across different marker sets. We conclude that the effects of SNP markers used in FDP and BGA on OTPs are currently limited, with few exceptions that should be considered in a balanced decision on assay design and application.

Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.

Identification of individuals by trait prediction using whole-genome sequencing data

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

Global skin colour prediction from DNA

Human skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin colour is limited. Here, we investigate the skin colour predictive value of 77 single-nucleotide polymorphisms (SNPs) from 37 genetic loci previously associated with human pigmentation using 2025 individuals from 31 global populations. We identified a minimal set of 36 highly informative skin colour predictive SNPs and developed a statistical prediction model capable of skin colour prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) ± standard deviation were 0.97 ± 0.02 for Light, 0.83 ± 0.11 for Dark, and 0.96 ± 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 ± 0.05 for Very Pale, 0.72 ± 0.03 for Pale, 0.73 ± 0.03 for Intermediate, 0.87±0.1 for Dark, and 0.97 ± 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that our model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; allow skin colour predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics.

Identification of a novel locus associated with skin colour in African-admixed populations

Skin pigmentation is a complex trait that varies largely among populations. Most genome-wide association studies of this trait have been performed in Europeans and Asians. We aimed to uncover genes influencing skin colour in African-admixed individuals. We performed a genome-wide association study of melanin levels in 285 Hispanic/Latino individuals from Puerto Rico, analyzing 14 million genetic variants. A total of 82 variants with p-value ≤1 × 10⁻⁵ were followed up in 373 African Americans. Fourteen single nucleotide polymorphisms were replicated, of which nine were associated with skin colour at genome-wide significance in a meta-analysis across the two studies. These results validated the association of two previously known skin pigmentation genes, SLC24A5 (minimum p = 2.62 × 10⁻¹⁴, rs1426654) and SLC45A2 (minimum p = 9.71 × 10⁻¹⁰, rs16891982), and revealed the intergenic region of BEND7 and PRPF18 as a novel locus associated with this trait (minimum p = 4.58 × 10⁻⁹, rs6602666). The most significant variant within this region is common among African-descent populations but not among Europeans or Native Americans. Our findings support the advantages of analyzing African-admixed populations to discover new genes influencing skin pigmentation.

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system

Individual age estimation has the potential to provide key information that could enhance and extend DNA intelligence tools. Following predictive tests for externally visible characteristics developed in recent years, prediction of age could guide police investigations and improve the assessment of age-related phenotype expression patterns such as hair colour changes and early onset of male pattern baldness. DNA methylation at CpG positions has emerged as the most promising DNA tests to ascertain the individual age of the donor of a biological contact trace. Although different methodologies are available to detect DNA methylation, EpiTYPER technology (Agena Bioscience, formerly Sequenom) provides useful characteristics that can be applied as a discovery tool in localized regions of the genome. In our study, a total of twenty-two candidate genomic regions, selected from the assessment of publically available data from the Illumina HumanMethylation 450 BeadChip, had a total of 177 CpG sites with informative methylation patterns that were subsequently investigated in detail. From the methylation analyses made, a novel age prediction model based on a multivariate quantile regression analysis was built using the seven highest age-correlated loci of ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132 and chr16:85395429. The detected methylation levels in these loci provide a median absolute age prediction error of ±3.07years and a percentage of prediction error relative to the age of 6.3%. We report the predictive performance of the developed model using cross validation of a carefully age-graded training set of 725 European individuals and a test set of 52 monozygotic twin pairs. The multivariate quantile regression age predictor, using the CpG sites selected in this study, has been placed in the open-access Snipper forensic classification website.

Association of genetic variants with skin pigmentation phenotype among populations of west Maharashtra, India

OBJECTIVES

South Asians exhibit extensive variation in skin melanin index (MI) which is observed across the broader region of South Asia as well as within restricted geographic regions. However, the genetic variants associated with variation in the skin pigmentation phenotype are poorly understood in these populations. The present study examines the association between MI measures and genetic variants from 5 candidate pigmentation genes among 533 individuals representing 6 populations of West Maharashtra.

METHODS

Associations between five single nucleotide polymorphisms (SNPs) known to play a role in pigmentation (rs1426654-SLC24A5, rs1042602-TYR, rs16891982-SLC45A2, rs6058017-ASIP, and rs642742-KITLG) and MI measures were tested using standard one-way analysis of variance (ANOVA) within each population. Multiple linear regression was used to test the effects of these SNPs in the full West Maharashtra sample using sex, age, and population or social group as covariates.

RESULTS

rs1426654 showed significant association with MI in all six study populations (P < 0.01). Association tests using sex, age, and population as covariates showed rs1426654 and rs1042602 to be significantly (P < 0.01) associated with lighter skin pigmentation in West Maharashtra as a whole. By contrast, when social group was added as a covariate instead of population, rs1426654, rs1042602, and rs16891982 were significantly (P < 0.01) associated with lighter skin pigmentation.

CONCLUSIONS

Only rs1426654 is significantly associated with MI in each individual population; however, rs1426654, rs1042602, and rs16891982 are significantly associated with pigmentation in the broader West Maharashtra region after controlling for population and social group, with rs1426654 (SLC24A5) explaining the majority of the observed variation. Am. J. Hum. Biol. 28:610-618, 2016. © 2016 Wiley Periodicals, Inc.

Exploration of SNP variants affecting hair colour prediction in Europeans

DNA profiling is a key tool for forensic analysis; however, current methods identify a suspect either by direct comparison or from DNA database searches. In cases with unidentified suspects, prediction of visible physical traits e.g. pigmentation or hair distribution of the DNA donors can provide important probative information. This study aimed to explore single nucleotide polymorphism (SNP) variants for their effect on hair colour prediction. A discovery panel of 63 SNPs consisting of already established hair colour markers from the HIrisPlex hair colour phenotyping assay as well as additional markers for which associations to human pigmentation traits were previously identified was used to develop multiplex assays based on SNaPshot single-base extension technology. A genotyping study was performed on a range of European populations (n = 605). Hair colour phenotyping was accomplished by matching donor's hair to a graded colour category system of reference shades and photography. Since multiple SNPs in combination contribute in varying degrees to hair colour predictability in Europeans, we aimed to compile a compact marker set that could provide a reliable hair colour inference from the fewest SNPs. The predictive approach developed uses a naïve Bayes classifier to provide hair colour assignment probabilities for the SNP profiles of the key SNPs and was embedded into the Snipper online SNP classifier ( http://mathgene.usc.es/snipper/ ). Results indicate that red, blond, brown and black hair colours are predictable with informative probabilities in a high proportion of cases. Our study resulted in the identification of 12 most strongly associated SNPs to hair pigmentation variation in six genes.

Evaluation of DNA variants associated with androgenetic alopecia and their potential to predict male pattern baldness

Androgenetic alopecia, known in men as male pattern baldness (MPB), is a very conspicuous condition that is particularly frequent among European men and thus contributes markedly to variation in physical appearance traits amongst Europeans. Recent studies have revealed multiple genes and polymorphisms to be associated with susceptibility to MPB. In this study, 50 candidate SNPs for androgenetic alopecia were analyzed in order to verify their potential to predict MPB. Significant associations were confirmed for 29 SNPs from chromosomes X, 1, 5, 7, 18 and 20. A simple 5-SNP prediction model and an extended 20-SNP model were developed based on a discovery panel of 305 males from various European populations fitting one of two distinct phenotype categories. The first category consisted of men below 50 years of age with significant baldness and the second; men aged 50 years or older lacking baldness. The simple model comprised the five best predictors: rs5919324 near AR, rs1998076 in the 20p11 region, rs929626 in EBF1, rs12565727 in TARDBP and rs756853 in HDAC9. The extended prediction model added 15 SNPs from five genomic regions that improved overall prevalence-adjusted predictive accuracy measured by area under the receiver characteristic operating curve (AUC). Both models were evaluated for predictive accuracy using a test set of 300 males reflecting the general European population. Applying a 65% probability threshold, high prediction sensitivity of 87.1% but low specificity of 42.4% was obtained in men aged <50 years. In men aged ≥50, prediction sensitivity was slightly lower at 67.7% while specificity reached 90%. Overall, the AUC=0.761 calculated for men at or above 50 years of age indicates these SNPs offer considerable potential for the application of genetic tests to predict MPB patterns, adding a highly informative predictive system to the emerging field of forensic analysis of externally visible characteristics.

SLC24A5 and ASIP as phenotypic predictors in Brazilian population for forensic purposes

Pigmentation is a variable and complex trait in humans and it is determined by the interaction of environmental factors, age, disease, hormones, exposure to ultraviolet radiation and genetic factors, including pigmentation genes. Many polymorphisms of these genes have been associated with phenotypic diversity of skin, eyes and hair color in homogeneous populations. Phenotype prediction from biological samples using genetic information has benefited forensic area in some countries, leading some criminal investigations. Herein, we evaluated the association between polymorphisms in the genes SLC24A5 (rs1426654) and ASIP (rs6058017) with skin, eyes and hair colors, in 483 healthy individuals from Brazilian population for attainable use in forensic practice. The volunteers answered a questionnaire where they self-reported their skin, eye and hair colors. The polymorphic homozygous genotype of rs1426654∗A and rs6058017∗A in SLC24A5 and ASIP respectively, showed strongest association with fairer skin (OR 47.8; CI 14.1-161.6 and OR 8.6; CI 2.5-29.8); SLC24A5 alone showed associations with blue eyes (OR 20.7; CI 1.2-346.3) and blond hair (OR 26.6; CI 1.5-460.9). Our data showed that polymorphic genotypes (AA), in both genes, are correlated with characteristics of light pigmentation, while the ancestral genotype (GG) is related to darker traits, corroborating with previous studies in European and African populations. These associations show that specific molecular information of an individual may be useful to access some phenotypic features in an attempt to help forensic investigations, not only on crime scene samples but also in cases of face reconstructions in unknown bodies.

Criteria for selecting microhaplotypes: mixture detection and deconvolution

Background

DNA sequencing is likely to become a standard typing method in forensics in the near future. We define a microhaplotype to be a locus with two or more single nucleotide polymorphisms (SNPs) that occur within a short segment of DNA (e.g., 200 bp) that can be covered by a single sequence run and collectively define a multiallelic locus. Microhaplotypes can be highly informative for many forensic questions, including detection of mixtures of two or more sources in a DNA sample, a common problem in forensic practice.

Results

When all alleles are equally frequent, the probability of detecting three or more alleles in a mixture is at maximum. The classical population genetics concept of effective number of alleles at a locus, termed A_e, converts the unequal allele frequencies at a locus into a value that is equivalent to some number of equally frequent alleles, allowing microhaplotype loci to be ranked. The expectations for the ability to qualitatively detect mixtures are given for different integer values of A_e, and the cumulative probabilities of detecting mixtures based on testing multiple microhaps are shown to exceed 95% with as few as five loci with average A_e values of even slightly greater than 3.0.

Conclusions

Microhaplotypes with A_e values of >3 will be exceedingly useful in ordinary forensic practice. Based on our studies, 3-SNP microhaplotypes will sometimes meet this criterion, but 4-SNP microhaplotypes can even exceed this criterion and have values >4.

Electronic supplementary material

The online version of this article (doi:10.1186/s13323-014-0018-3) contains supplementary material, which is available to authorized users.