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Branicki, W. Predicting Physical Appearance from DNA- Towards Genomic Solutions. Encyclopedia. Available online: https://encyclopedia.pub/entry/18972 (accessed on 15 December 2025).
Branicki W. Predicting Physical Appearance from DNA- Towards Genomic Solutions. Encyclopedia. Available at: https://encyclopedia.pub/entry/18972. Accessed December 15, 2025.
Branicki, Wojciech. "Predicting Physical Appearance from DNA- Towards Genomic Solutions" Encyclopedia, https://encyclopedia.pub/entry/18972 (accessed December 15, 2025).
Branicki, W. (2022, January 28). Predicting Physical Appearance from DNA- Towards Genomic Solutions. In Encyclopedia. https://encyclopedia.pub/entry/18972
Branicki, Wojciech. "Predicting Physical Appearance from DNA- Towards Genomic Solutions." Encyclopedia. Web. 28 January, 2022.
Predicting Physical Appearance from DNA- Towards Genomic Solutions
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This entry is adapted from the peer-reviewed paper 10.3390/genes13010121

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The advances in understanding the genetics of various human phenotypes, the progress in high-throughput genotyping technologies and machine-learning methods allow the prediction of physical appearance traits. In addition, the evolution of approaches to genetic prediction of physical traits from building predictive models based on variables showing genetic association to variables that improve prediction performance is presented.

physical appearance DNA-based predictive tools machine learning whole-genome sequencing

1. Introduction

The information included in genomic data can be used to generate investigative leads that, when properly used, can speed up the process of human identification in forensic investigations. Such forensic DNA intelligence can use a variety of methods, including relatedness testing, the inference of ancestry, the prediction of physical phenotype, and age estimation [1][2][3][4]. As an inherently interdisciplinary science, forensic science today can benefit from the rapidly developing methods in the areas of genomics and machine learning, which is particularly beneficial for the further development of forensic DNA intelligence. Studies of human genome variation conducted today on an unprecedented scale are revealing how genes control phenotypes. This knowledge has fundamental meaning for understanding the genome–phenome relationship. Importantly, the growing knowledge of human genome variation allows for the development of algorithms that can more accurately predict phenotypes, providing more reliable investigative leads to help identify an unnamed perpetrator or victim and solve a case. It is worth noting that the DNA-based predictive tools developed in the forensic field are also useful in evolutionary anthropology

2. DNA-Based Predictive Tools for Forensic Applications

Several factors determine the accuracy of DNA-based predictive methods, including high heritability of a trait, the identification of appropriate predictors, and the selection of the best mathematical approach to model development. The forensic community very early recognised the investigative potential of extracting phenotypes from DNA data. The practical importance of a simple amelogenin genetic sex test [5], and also of the inference of biogeographical ancestry [6][7], made it clear that a description of the phenotypic characteristics of a person of undetermined identity can provide important investigative leads. The variation of the MC1R gene was soon proposed as an indicator of red hair colour [8], while the predictive potential of the OCA2 variation was proposed for the inference of eye colour [9]. The availability of GWAS data has made it possible to develop tools for predicting human appearance traits more effectively. The research carried out has made it possible to develop predictive tools with varying performances and practicalities of application for different physical characteristics.

2.1. Pigmentation Traits

In particular, the discovery of eye colour markers with large phenotypic effects has made it easy to develop pretty accurate genetic predictors of this trait. The best-known tool commonly used in the forensic field today is the IrisPlex predictive system, which includes both a genetic test for data acquisition and a mathematical algorithm for predicting the three categories of eye colour [10]. The algorithm was developed based on the systematic selection of markers made by Liu et al., who reported 24 variants from eight genes, enabling the prediction of blue and brown eye colour with a prediction accuracy expressed by an AUC of 0.91 and 0.93, respectively [11]. AUC, which stands for area under the ROC (receiver operating characteristic) curve, describes the general performance of the model in such a way that 1 means perfect classification and 0.5 means random assignment to the phenotype categories. For forensic purposes, the number of markers from the originally identified 24 was restricted to the six with the largest effect [10][11]. The six crucial predictors included HERC2 rs12913832, OCA2 rs1800407, SLC24A4 rs12896399, SLC45A2 rs16891982, TYR rs1393350, and IRF4 rs12203592. The original IrisPlex method implements a multinomial logistic regression algorithm and a simple single base extension method based on SNaPshot minisequencing, which allows the PCR amplification and genotyping of several SNPs in a multiplex reaction. Importantly, the products of primer extension are analysed using capillary electrophoresis platforms, which are commonly used in human identification testing laboratories. Other tools based on other mathematical solutions were soon developed but, essentially, each of these algorithms relied on exploiting information in the HERC2-OCA2 gene complex. In general, these works were limited to the development of predictive algorithms using various sets of samples and mathematical approaches, but did not present specific tools for the collection of genetic data [12][13][14][15][16][17]. Notably, IrisPlex and other forensic methods of eye colour prediction can accurately predict blue and brown iris colours, but have difficulty with the prediction of intermediate eye colours [3]. Moreover, in some populations, the effect of sex was noted on prediction results [18][19][20]. The IrisPlex tool for the genotyping and prediction of eye colour evolved to HIrisPlex [21] and finally to the HIrisPlex-S tool [22], which were developed based on the same strategy as IrisPlex. The algorithm for hair colour prediction implemented in HIrisPlex was developed based on the investigation of a Polish population sample, which enabled the selection of 22 crucial SNPs from 11 genes for hair colour. The study showed a high level of accuracy for red and black hair colour prediction (AUC ~ 0.9) and a lower prediction accuracy for blond and brown hair colour (AUC ~ 0.8) [23]. The skin colour predictor was proposed by Walsh et al. after a systematic study of skin colour candidate variants in a sample of 2025 individuals from 31 worldwide populations. The algorithm predicted skin colour with very high accuracy, with an AUC = 0.97 for light skin colour, 0.83 dark, and 0.96 for dark-black skin colour [24]. Notably, it has been demonstrated that the original SNaPshot protocol can be replaced by the targeted massive parallel sequencing (MPS) method [25], and the HIrisPlex-S method was also adopted in a tool combining pigmentation prediction capability with ancestry inference developed by the VISAGE consortium [26]. Other studies also investigated the possibility of hair and skin colour prediction in the forensic field [13][27][28][29][30]. The Snipper Application suite deserves more attention because it provides an online tool that allows the performance of predictive calculations based on data generated by any genotyping method. The tool was originally developed for the statistical interpretation of data in ancestry inference studies, but a number of new functionalities have subsequently been added to enable the prediction of pigmentation and even age [31]. A more complete prediction of pigmentation will be provided by the developed algorithms for freckle prediction [32][33]. It is worth noting that the use of extended DNA variant sets for prediction has begun to be explored, which may lead to the development of next-generation prediction tools. For example, the previously described association work of Hysi et al. was extended to predictive modelling. Hair colour prediction was compared in two independent cohorts using prediction models based on the 258 associated SNPs and the original HIrisPlex method, and these new models outperformed the previous HIrisPlex model [34]. Further development of pigmentation predictors may also require the use of sex information, and age will naturally be needed for the final interpretation of the data [34][18]. This issue is also addressed later in the article, as sex in particular can be important for predicting other appearance traits.

2.2. Hair Loss

Numerous association studies conducted for MPB raised questions about the predictive ability of the discovered genetic variants. In 2015, a compact regression model was developed based on analysis of five SNPs from five genomic regions (Xq12, 20p11, EBF1, TARDBP, and HDAC9), trained and validated on >600 samples from six European populations [35]. The model was shown to enable the prediction of hair loss in Europeans at an acceptable level, but only in two extreme phenotype categories, i.e., young men with significant alopecia vs. older men without symptoms of alopecia with AUC of 0.76. In the same study, Marcińska et al. also pointed to the potential role of allelic heterogeneity in determining scalp hair loss. Expanding the number of DNA variants in both crucial regions, i.e., Xq12 and 20p11, improved the accuracy of prediction, suggesting that there might be more functional variants in these loci. The extended 20-SNP regression model predicted hair loss with an AUC of 0.66 in all samples of all age categories and had the highest AUC value for the age category of ≥50 years old (AUC = 0.76; sensitivity = 67.7%; specificity = 90%), where the sensitivity refers to the ability of the model to correctly classify individuals with the particular phenotype (here baldness), while the specificity refers to the ability of the model to correctly classify individuals without this phenotype [35].
Liu et al. conducted a parallel study on the prediction of MPB in >2700 Europeans and developed a 14-SNP model that was found to predict early-onset MPB cases with a cross-validated AUC of 0.74 [36]. The accuracy of hair loss prediction status in elderly and middle-aged individuals was lower, with an AUC of 0.69–0.71. In 2017, Hagenaars and colleagues developed a polygenic predictor based on the genome-wide data generated for a large cohort of 40,000 individuals and showed that it can discriminate individuals with no signs of hair loss from those with severe baldness, with an AUC = 0.78, sensitivity = 0.74, and specificity = 0.69 [37].

2.3. Hair Shape and Other Hair Features

The first preliminary model for head hair shape was developed as a follow-up to the first GWA study conducted on hair characteristics [38], and included an analysis of three SNPs in three genes (TCHH, WNT10A, FRAS1), and was trained on data generated for 528 samples from Polish individuals [39]. The model was reported to predict straight hair with high accuracy but low specificity (cross-validated AUC = 0.622, sensitivity = 93.2%, specificity = 15.4%). The application of the model on an independent test set consisting of samples from six European populations and using a 65% probability threshold allowed for higher sensitivity (81.4%) and improved specificity (50.0%) of prediction, but at the same time with a very high rate of inconclusive results (66.9%). In 2018, a large-scale prediction study for hair shape prediction was conducted with more than 9600 samples used for predictor selection and model development and more than 2400 samples used for prediction model validation, collected from both European and non-European populations [40]. The binomial logistic regression model was developed to predict hair shape, defined as straight vs. non-straight, based on 32 informative SNPs from 26 loci. The model was reported to explain ~12% of hair shape variation and can predict straight vs. non-straight hair in European populations with an accuracy of AUC of 0.66, a sensitivity of 84.1% and a specificity of 34.2%. It was shown that the same set of SNP markers can predict hair shape with significantly different accuracies in Europeans and non-Europeans. For non-European samples, the AUC value was 0.79, sensitivity = 82.9%, and specificity = 49.8%. The higher prediction accuracy obtained for non-European populations compared to Europeans is due to the effect of the EDAR gene, which has a significant effect on the determination of straight hair in non-European populations, primarily East Asian. In addition to the binomial model, a multinomial logistic regression model was developed to allow for a higher resolution of hair shape prediction, considering three categories—straight, wavy and curly—based on an analysis of 33 SNP positions. There are few or no prediction studies of the remaining hair features. In 2016, Adhikari et al. predicted different hair traits using the GWAS data generated for Latin Americans and reported the highest accuracy of prediction for beard thickness and the lowest for hair greying, with ~18% and ~7% of the phenotypic variation explained by the associated SNPs, respectively [41]. Interestingly, for both of these traits, a large effect of age and sex on prediction was observed, explaining the additional ~11% and ~20% of the phenotypic variation, respectively, for beard thickness and greying. Age was found to be a main predictor of hair greying in a study conducted in 2020, explaining around 48% of the variation observed in hair greying in a cohort of 849 people from Poland [42]. A binary neural network model for greying vs. no greying prediction was developed in this study based on information relating to age, sex, and 10 SNPs selected using whole-exome sequencing data analysis (e.g., KIF1A rs59733750, SEMA4D rs45483393) and literature resources (IRF4 rs12203592, FGF5 rs7680591). The model achieved a high accuracy of prediction with a cross-validated AUC = 0.87 (sensitivity = 0.73; specificity = 0.85) but most of the prediction information was driven by age itself, while SNPs were found to explain merely ~7% of the variation in hair greying. As mentioned earlier, age is a very important factor in predicting hair loss. Sex and age were also shown to slightly improve the accuracy of prediction of hair shape [40].
This implies that there is a need to determine the sex and age of an individual from the analysed biological sample. Information on a person’s sex is usually available in criminal investigations due to the inclusion of marker for the amelogenin gene located on the X and Y chromosome in standard STR DNA profiling, as previously mentioned, whereas age can be estimated via epigenetic analysis [43].

2.4. Human Stature

Attempts at forensic human height prediction have not been particularly numerous and have been limited to the development of predictive algorithms that are not equipped with data collection tools. The reasons are related to the limitations of DNA analysis technology and stem from the need to analyse too many DNA variants. While the 5% heritability explained by the 54 DNA variants identified by the initial GWAS scans for human height was unlikely to predict the full range of human height, Aulchenko et al. tested whether it would allow the reliable prediction of extreme height. However, this turned out to be possible with only limited accuracy. Tall stature prediction was possible at AUC of 0.65, thus only moderately improving the accuracy resulting from a random hit (AUC = 0.5) [44]. Using the 180 height markers identified in the Lango Allen et al. paper improved the prediction of tall stature to AUC of 0.75 [45]. The study suggested the importance of allelic heterogeneity for the prediction of human stature. Further increasing the number of predictors to 697 reported in the paper by [46] enabled the prediction of tall stature with an AUC of 0.79 [47]. The possibilities of human height prediction have also been explored outside the forensic mainstream using a non-standard approach that has nevertheless yielded very promising results, enabling the prediction of the full range of human height at a good level of accuracy [48]. Based on the results obtained, the authors suggested changing the approach to phenotype prediction, pointing out the benefits of also including as predictors polymorphisms that do not show an association with a given trait, but only on the basis of the improved prediction accuracy obtained after their inclusion in the prediction model [49].

2.5. The Human Face

Drawing a forensic sketch based on the instructions of a witness in a criminal case is a tool that has been used for years to identify the perpetrator of a crime. People recognise each other through the high variability of facial features. Therefore, having a good understanding of the genetics of human facial variation and being able to predict this complex phenotype is a very exciting prospect for forensic DNA intelligence. The small amount of explained heritability for craniofacial traits does not bring good prospects for the prediction of human facial phenotypes. Nevertheless, attempts have been made to develop models that would allow the prediction of facial appearance. The proposed methods are based on the indirect prediction of facial phenotypes, with ancestry and sex prediction DNA data playing a key role in this regard. The method by Claes et al. implements a bootstrapped response-based imputation modelling that makes use of information on genomic ancestry and sex first to create a sketch called a base-face. At the second stage, the information in 24 SNPs associated with facial variation is used to improve the prediction outcome [50]. A similar strategy was proposed by Lippert et al., who used the whole genome sequencing data to gain information about the sex and ancestry proportions of the individual [51]. The data on genetic face predictors did not improve facial appearance predictions, but the study showed a positive effect on the prediction of age and body mass index. The genetic prediction of facial features was also explored by Qiao at el., who developed a quantitative model based on multiple SNP loci and tried to simulate 3D face models. The study suggests that epistasis is part of the genetic architecture of facial features and concludes that the model developed should be treated as an exploratory basis for future, more advanced predictive models [52].

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Subjects: Genetics & Heredity
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Wojciech Branicki
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