The first evidence of a possible correlation between genetic variants and eye colour dates back to the early 2000s, when it was first suggested that the OCA2 gene was responsible for a great deal of normal eye-colour variation [76, 77, 78]. The following year, Duffy et al. [71] confirmed this assertion, finding a strong association between blue versus non-blue eye colour and three OCA2 SNPs (rs7495174, rs6497268, and rs11855019), with the TGT/TGT genotype explaining the 0.905 of total light eye colour (blue or green). A few years later, Branicki et al. [72] and Andersen et al. [62] explored in greater depth the contribution of the OCA2 gene to determining human eye colour. Significant association was found for eight new OCA2 SNPs (rs17566952, rs11638265, AY392134, rs1800411, rs1900758, rs1800404, rs1800407, and rs749846). Among these, the strongest association resulted between rs1800407 and intermediate (green/hazel) iris colour. Some years later, Andersen et al. [73] also identified two further important OCA2 variants (rs74653330 and rs121918166).

In order to evaluate the possible involvement of other genes in human eye-colour variation in addition to OCA2, Kayser et al. [59] performed GWAS (Genome Wide Association Studies) and linkage analysis with people of European descent. They showed that the HERC2 gene, located in the same region of OCA2 (15q13.1), is a new important determinant of human iris colour and identified up to 15 relevant loci in this region, among which rs916977 emerged as the most influential variant (the T allele, which represents the ancestral state of the marker, being predictive for brown iris colour, the C allele for blue iris colour), followed by rs1667394 [59]. In the same year, Eiberg et al. [63] identified the linkage disequilibrium (LD) of two tightly linked loci rs12913832 and rs1129038 with good predictive power for blue and brown eye colour. LD between alleles at two loci has been defined in many ways, but all definitions depend on the difference between the frequency of gametes carrying the pair of two alleles at two loci and the product of the frequencies of these alleles [79]. With regard to rs12913832, although located within the HERC2 region, it is part of a regulatory element upstream from OCA2 exerting an inhibitory effect on OCA2 itself. The decreased expression of OCA2, particularly within iris melanocytes, causes the blue eye-colour phenotype [47, 63]. Furthermore, Pośpiech et al. [24] found multiple epistatic interactions among genes affecting pigmentation phenotype, particularly eye colour.

A turning point came with the development by Walsh et al. and Liu et al. [40, 41, 42] of the first highly sensitive multiple genotyping assay for the prediction of blue/brown eye colour, named IrisPlex. It consists of the six SNPs currently identified as major eye-colour predictors in Europeans:rs12913832 (HERC2), rs1800407 (OCA2), rs12896399 (SLC24A4), rs16891982 (SLC45A2/MATP), rs1393350 (TYR), and rs12203592 (IRF4)—albeit two subsequent publications did question the usefulness of rs12203592 since they found very little or no predictive power linked to this locus [43, 56]. The model was based on initial genotype and phenotype data from a single European population (the Dutch) but its reliability for accurate eye-colour prediction was also demonstrated for individuals from several countries across Europe (including Norway, Estonia, the UK, France, Spain, Italy, and Greece), showing extremely high predictive power (0.96 AUC—Area Under Curve) for both blue and brown eye colour [41]. The prediction accuracy for blue/brown eye colour decreased when IrisPlex was applied to a more biogeographically diverse sample, including individuals of Asian, African, and South American descent [23].

Moreover, the intermediate eye colour could not be predicted by IrisPlex with as high accuracy as with blue and brown eyes, resulting in a higher rate of misclassified or unclassified predictions [40, 41, 42, 57]. Improved detection of intermediate phenotype (correct classification rate of 26.1%) was observed using a different eye-colour prediction model (Snipper5+1) elaborated by Freire-Aradas et al. [44] and based on five IrisPlex SNPs (rs1800407, rs12896399, rs16891982, rs1393350, and rs12203592) plus rs1129038 counted in combination with IrisPlex rs12913832 (HERC2 haplotype). Among these, particularly noteworthy is the effect on green eye-colour determination made by OCA2rs1800407 when considered in haplotype with rs12913832 [47]. Likewise, the interaction between rs12913832 and TYRP1rs1408799 also allowed for a slight increase in the accuracy of green-colour predictivity [47].

Ruiz et al. [43] confirmed the remarkable effect, as already described in other studies, of rs12913832, rs1129038, rs11636232, rs1289399, rs1800407, rs16891982, and rs1393350 and proposed to add three additional SNPs (rs1129038, rs1667394, and rs7183877 from HERC2) into the six-SNP panel of IrisPlex, aiming at increasing its discriminative power in predicting Europeans’ iris colour, especially for intermediate phenotypes (green and hazel) [43, 45].

Among all the SNPs discovered, HERC2rs12913832 was found to be the most strongly associated with iris colour, although HERC2rs1129038 and OCA2rs1800407 have been reported to be of predominant importance as well [42, 46, 47, 52, 62, 64, 65, 66, 67]. Indeed, many studies have observed that most eye-colour variation (ranging from 68.8% to 74.8% depending on the study) could be explained by rs12913832, while additional SNPs, although found to be associated with eye colour, allowed for only a slight improvement in predictive ability (75.6–76%) [46, 62]. Similarly, the five additional SNPs within the IrisPlex also turned out to produce a small predictive value to that determined by rs12913832 [42].

However, the selection of the best SNPs for EVC determination seems to be highly population-dependent, suggesting that panels for eye-colour prediction should be adjusted for different geographical regions: for instance, a relevant effect on eye colour was observed for rs1408799 in the Polish population, and for rs916977 in the Czech population, while rs1800416 was found to be significantly associated with eye colour only in the Brazilian population [24, 60, 70].

Although developed based on a European database, IrisPlex’s usability has also been tested in individuals outside of Europe. For example, Rahat et al. [80] showed the reliability of IrisPlex for brown and blue colour prediction in Pakistan’s population, but also pointed out the need for the inclusion of more SNPs in the model to increase prediction accuracy especially for intermediate colour. Al-Rashedi et al. [81] came to similar conclusions with the Iraqi population. Some other studies have demonstrated instead the existence of a significant bias linked to the geographical origins of the population to which it was applied, especially in cases of admixed populations or intermediate eye colour [56, 57, 82]. Yun et al. [56] observed a higher proportion of uncertain prediction, besides some inconsistences, when IrisPlex was applied in the Eurasian population, which presents an admixed genetic structure (European and Asian), except for East Asian populations (such as Koreans and Chinese), in which predictions were always consistent for brown eye colour. Bulbul et al. [57] also observed a worse performance of IrisPlex on the Turkish population compared with European results. Dembinski et al. [82] obtained only a moderate predictive power with the IrisPlex essay in a North American sample (US population), which is highly admixed compared to the European population.

To overcome this limit, further models were later developed for prediction of eye colour in other populations. Allwood et al. [52] developed a predictive model for New Zealanders. They first evaluated the effect of preselected SNPs upon eye-colour phenotype in a collection of dichotomous tree models (i.e., blue vs. non-blue, brown vs. non-brown, and intermediate vs. non-intermediate) and, finally, in a multiple-response tree considering all eye colours, i.e., blue vs. brown vs. intermediate. The SNPs chosen for use in the different models were: rs1129038 (HERC2), rs1393350 (TYR), and rs12896399 (SLC24A4) for blue vs. non-blue; rs1129038, rs1800407 (OCA2), rs12913832 (HERC2), and rs1393350 for brown vs. non-brown; rs1800407, rs916977, rs1393350, and rs4778138 (OCA2) for intermediate vs. non-intermediate; and rs1129038, rs1800407, and rs1393350 for the final model. It is worth noting that part of the SNPs employed in the binary response models (except for rs1129038, rs12896399, rs916977, rs4778138), and all SNPs composing the multiple response model, are also included in the IrisPlex system. Both models performed well though with differences for each specific eye-colour group, (i.e., considerably better for brown and blue than for intermediate eye colour). The “all-eye-colour” model predicted blue colour with an accuracy level of 89%, brown colour at 94%, and intermediate eye colour at only 46%, with an overall accuracy of 79% (obviously conditioned by the latter rate). As for IrisPlex, and in similar proportion, most prediction errors were generated by intermediate eye colour. Similarly, Alghamdi et al. [61] proposed an eye-colour prediction model for Saudi individuals containing five SNPs (rs12913832, rs7170852, rs7183877, rs7495174, and rs916977) that showed a high accuracy for both brown and intermediate eye colours (no participant was categorized as blue iris). Gettings et al. [69] developed a 50-SNP assay to predict eye phenotype among European-Americans, which was able to accurately predict eye colour in 61% of individuals tested.

Another discrepancy factor in eye-colour prediction of the Irisplex was seen to be gender. Martinez-Cadenas et al. [83] noticed that, given a specific IrisPlex genetic profile, males had lighter eye colours than predicted by genotype, while females tended to have darker eye colours, suggesting the possible existence of an unidentified gender-related component contributing to human eye-colour variation.

It is worth noting that, although developed on a European (Dutch) database, IrisPlex was also found to be a little less predictive in some European subpopulations (Italians, Spanish, and Portuguese) than in others (Germans and Dutch) [45, 84, 85]. It has been hypothesized that this result may reflect a greater degree of genomic admixing in the Southern European populations, just as reported by Dembinski et al. [82] in the US population. Another study highlighted that, in contrast to Northern European populations, not all six IrisPlex SNPs had significant association with eye colours in individuals from Mediterranean Europe, and this could be traced back to a lower frequency of blue iris colour in these populations, classically presenting a darker pigmentation phenotype [83].

Human hair colour depends on the combined amount of two types of melanin, eumelanin (dark pigment) and pheomelanin (light pigment). The first studies on hair-colour predictability were based on the search for genetic variations in people with a far-away biogeographical origin (individuals of Asian, African, Australian, and Caucasian descent), or among individuals from the same geographic region but presenting multi-coloured phenotypes (e.g., the Brazilian population). Among genes showing significant association with pigmentation variation, polymorphisms in ASIP, MATP (then renamed SLC45A2), SLC24A5, and MC1R—all implicated in the melanin biosynthesis—showed significant association with hair colour [34]. For instance, specific allelic polymorphisms in SLC45A2rs16891982, SLC452rs26722 and ASIPrs369378152 were found at a higher frequency in non-Caucasian dark-haired populations, such as African-Americans and Asians, as well as in dark-haired people of Caucasian descent [34]. Moreover, Pośpiech et al. [24] identified epistatic interactions between MC1R variants and both the HERC2 gene and rs731223 in VDR (Vitamin D Receptor), having an advantageous impact on red versus non-red hair colour prediction.

The publication of Valenzuela et al. [46] represented an important step forward, since they indicated four SNPs, sited in as many genes, as major genetic contributors to hair pigmentation: rs1426654 (SLC24A5), rs16891982 (SLC45A2), rs12913832 (HERC2), and rs1805007 (MC1R). Specifically, two of them (SLC24A5rs1426654 and SLC45A2rs16891982) showed the strongest association with both total hair-melanin amount and eumelanin/pheomelanin ratio, while HERC2rs12913832 turned out to be the third most significant contributor to total hair melanin and MC1R rs1805007 the third major contributor to eumelanin/pheomelanin ratio [46]. However, as for eye colour, some studies suggested that the strongest association with pigmentation traits, including hair, was with SNPs from the OCA2-HERC2region [70, 73, 86].

New acquaintances were used to integrate IrisPlex with the 22 most predictive SNPs currently identified for hair-colour determination, creating a system for simultaneous eye and hair-colour prediction named HIrisPlex. Among the 22 SNPs involved in hair-colour determination, four variants were already included in IrisPlex (being predictive for eye colour as well), while 18 were of new introduction, for a total of 24 SNPs (including one InDel) making up the model: SLC45A2rs28777, KITLGrs12821256, EXOC2rs4959270, TYRrs1042602, SLC24A4rs2402130, ASIP/PIGUrs2378249, and TYRP1rs683, 10 SNPs from MC1R (rs11547464, rs885479, rs1805008, rs1805005, rs1805006, rs1805007, and rs1805009, Y152OCH (later renamed as rs201326893), rs2228479 and rs1110400), and one InDel N29insA (also denotes as rs86inA and rs312262906). As its forerunner, HIrisPlex was initially developed using DNA samples collected from 1551 European subjects living in Poland (n = 1093), the Republic of Ireland (n = 339) and Greece (n = 119). The hair-colour prediction component of the HIrisPlex tool applied to individuals from different parts of Europe yielded prediction accuracies of 69.5% for blond hair colour, 78.5% for brown, 80% for red and 87.5% for black independently from bio-geographic ancestry. To verify its use outside of Europe, Walsh et al. [37] performed HIrisPlex analysis on worldwide DNA samples from the HGDP-CEPH panel relative to 952 individuals from 51 populations. Although with minor accuracy, it was demonstrated to provide satisfactory eye/hair colour prediction in non-European individuals as well [87]. Likewise, it was revealed to be a suitable and sufficiently robust predictive system for human skeletal remains [88].

Söchtig et al. [38] indicated a subset of 12 SNPs as the best hair-colour prediction markers (OCA2rs7495174 and rs4778138; TPCN2 rs35264875; HERC2 rs1129038, rs12931267, rs12913832, and rs28777; MC1R-R rs11547464, rs1805006, rs1805007, rs1805008, and rs1805009), only seven of which were also included in HIrisPlex. Moreover, they indicated HERC2 rs1129038 as the strongest predictor for blond hair, HERC2 rs12913832 the strongest predictor for black hair, while TPCN2 rs35264875 and HERC2 rs12931267 as key markers for brown hair colour. However, the predictive performance of the 12 SNPs set in the European population remained lower than with HIrisPlex [38].

In an effort to explain the higher rate of inaccuracy observed in HIrisPlex’s blonde-hair prediction, Kukla-Bartoszek et al. [89] focused on age-dependent hair-colour darkening, i.e., some individuals with blonde hair colour in early childhood may experience a hair-colour darkening during advanced childhood or adolescence. They observed that the number of incorrect blond hair-colour predictions given by HIrisPlex was significantly higher in adult individuals with brown hair who were blond in early childhood (2–3 years old), compared to those who had always had brown hair (only one third of individuals who experienced hair-colour darkening from childhood to adulthood were correctly predicted by HIrisPlex) [89]. Still in regard to age-related hair-colour changes, Pośpiech et al. [90] investigated the genetics underlying the hair-greying process but concluded that most predictive power was given by age alone, while genetic variants had only a small impact on hair-greying variation (10%). However, their age- and sex-based model made up of 13 selected SNPs attains a fairly accurate prediction rate for greying vs. non-greying, with AUC equalled 0.873 [90].

Finally, with regard to red-hair-colour prediction, Keating et al. [23] observed that by removing four MC1R SNPs from the 22 HIrisPlex DNA variants, there was more red hair missed (nearly 60% compared to the 14% of HIrisPlex). This result confirmed the important role of the MC1R gene in red hair determination, as previously reported [23, 66].

As for other pigmentation traits previously argued, the first genes to be associated with skin colour were those encoding for proteins which are involved in the melanin production within melanocytes, e.g., MC1R (encoding for a membrane receptor whose activation by $\alpha$-MSH ($\alpha$-Melanocyte-Stimulating Hormone) stimulates eumelanin while inhibiting pheomelanin production), SLC45A2/MATP (which regulates the introduction of substrates for melanin production in the melanocytes and whose mutation causes albinism), ASIP (which inhibits MC1R activation acting as an antagonist of $\alpha$-MSH), SLC24A5, and OCA2 [35, 36, 91]. The first studies aiming at identifying DNA variants responsible for skin-colour variation were based on sample populations of varying ancestry/distant biogeographical origins (e.g., Africans versus Northern Europeans), thus characterized by clear differences in skin pigmentation and, hypothetically, underlying genetic differences [36]. As might be expected, some of these pigmentation-related SNPs were also observed to be ancestry-informative (AIMs) [35, 36, 92]. It is also worth noting that in the study by Castel et al. [93], that considered 14 autosomal SNPs grouping participants into four different phenotypes according to their declared hair, eye and skin colour, 30% of Type IV participants were incorrectly assigned to Type II, possibly due to overlap in dark hair and eye colour between these phenotypes. The authors concluded that this result indicated that the autosomal SNPs selected may have stronger affiliations with these traits rather than with skin colour [93].

Significant associations with skin colour were found for MC1R gene variants, including rs1805006, rs1805007, rs1805008, rs1805009, rs11547464, rs201326893, N29insA (InDel), and rs3212345. It has been hypothesized that rs3212345: C$>$T is associated with light skin, red hair, and poor tanning ability, while the rs3212346: G$>$A is associated with dark skin, black hair, and strong tanning ability [53, 74]. In the HERC2 gene, significant loci are rs1636232, rs1133496, and rs2238289, with the TCT haplotype showing association with light skin colour (as well as eye and hair colour), whereas the CTC haplotype was correlated with dark traits [70]. Similarly, the CGG haplotype resulting from OCA2rs1800416, rs1800407, and rs1800404 has proved to be a good predictor for dark features; other OCA2 loci showing association with skin type are rs7170989, rs4778138, rs1375164, rs1805007, and rs1805008 [36, 66, 70]. Furthermore, haplotype results are not always in line with all respective allelic associations, but findings suggest that haplotype analysis, rather than single SNPs, provides a more accurate prediction [70]. Moreover, rs16891982 and rs26722 within SLC45A2 have been linked with dark skin in the Caucasian population [24, 34, 35, 36]. Other significant associations were found for rs1426654 in SLC24A5 (which plays a key role in light-skin determination in people of European and South Asian descent) and rs1042602 in TYR [24, 35, 36, 53, 54].

Maroñas et al. [39] described the 10 best predictive SNPs linked to skin colour in European and non-European individuals, among which are: SLC45A2rs16891982 and SLC24A5rs1426654 (the two most important markers, the former for intermediate, the latter for black and white skin colour); ASIP rs60580017 (the second major important marker for classifying black skin and fourth for classifying intermediate skin); TYRP1rs1408799 (for distinguishing intermediate skin); OCA2rs1448484 (important contributor to black versus white); SLC45A2rs13289 (olive skin colour in Europeans); KILTGrs10777129, TPCN2rs3829241, and SLC24A4rs2402130 (not previously described). Remarkably, the first two SNPs described above account for most of the classification success (respectively, 77.6% for intermediate, 87.6% for black, and 95.7% for white), the remaining eight SNPs enhancing classification success by only a few percentage points (2–3%) each.

However, as happened for eye colour, biogeographical divergences were observed here too: in the Indian population, in contrast to Maroñas’ results, the nine major contributors to skin pigmentation (overall explaining the 31% variance) were found to be OCA2rs1800404 and rs1375164, SLC24A5rs2924566, rs4775730, rs1426654, MYEF2rs11070627, CTXN2rs12913316, DUTrs11637235, CEP152rs8041414 [39, 58]. In the Polish population, HERC2 rs12913832 seemed to be the strongest variant for skin phenotype [68].

In the footsteps of HIrisPlex, a combined tool for simultaneous prediction of eye, hair, and skin colour named HIrisPlex-S was introduced, where skin colour prediction was based on a set of 36 SNPs (of which 19 had also been included in the previous model, plus 17 novel markers): SLC24A5rs1426654, IRF4rs12203592, MC1Rrs1805007, rs1805008, rs11547464, rs885479, rs228479, rs1805006, rs1110400 and rs3212355, OCA2rs1800414, rs1800407, rs12441727, rs1470608, and rs1545397, SLC45A2rs16891982 and rs28777, HERC2rs1667394, rs2238289, rs1129038, rs12913832, and rs6497292, TYRrs1042602, rs1126809 and rs1393350, RALYrs6059655, DEF8rs8051733, PIGUrs2378249, ASIPrs6119471, SLC24A4rs2402130, rs17128291, rs12896399, TYRP1rs683, KITLGrs12821256, ANKRD11rs3114908, and BNC2rs10756819. The model has proved capable of skin-colour prediction on a global scale with prediction accuracies of 0.74 for very pale, 0.72 for pale, 0.73 for intermediate, 0.87 for dark, and 0.97 for dark black [49]. More recently, another tool named VISAGE BT A&A (PSeq) for contemporary eye, hair, and skin-colour prediction was developed by Palencia-Madrid et al. [16], consisting of 41 phenotype SNPs plus 115 markers for biogeographical ancestry inference (three overlapping with the EVCs’ SNP set) for a total of 153 markers.

Freckles, including both lentigines and ephelides, consist of brown or reddish spots of the skin that can show up on different body areas (face, neck, arms, shoulders, back, and legs) predominantly in individuals with fair skin and light hair (therefore, people of European descent). Their presence, especially on exposed areas such as the face, represents a particular, and easily visible, phenotypic characteristic. Although different studies, as seen above, have focused on skin pigmentation, only four of them have investigated freckles as a separate trait.

Cao et al. [75] investigated the association between genetic variation on melanocortin-1-receptor (MC1R) gene and the presence of freckles in 225 Chinese subjects. Although MC1R was indicated as a major genetic determinant of freckle phenotype, no statistical difference was observed in individuals with freckles compared to controls, at least for the four SNPs tested (rs33832559, rs2228478, rs2228479, rs885479).

Conversely, Zaorska et al. [68] demonstrated a strong association between freckling and a different genetic locus, rs12203592 in IRF4, in 222 Polish individuals. No association was observed with MC1R gene (rs1805007) in this case either.

Based on genetic predictors previously correlated with human pigmentation, Kukla-Bartoszek et al. [94] developed a predictive model for freckle presence, divided into three categories, non-, medium-, and heavily-freckled, and obtained a moderate accuracy (respectively, AUC = 0.75, 0.66 and 0.79).

The model proposed by Hernando et al. [50] in 2018 for freckle prediction considered five genetic determinants: R variants of the MC1R gene (rs1805006, rs11547464, rs1805007, rs1110400, rs1805008 and rs1805009, defined as “R” alleles for their strong association with the red hair colour phenotype in population), IRF4rs12203592, r variants of the MC1R gene (rs1805005, rs2228479 and rs885479, defined as “r” alleles for their lower association with the red hair colour phenotype), ASIPrs4911442 and BNC2rs2153271). It leads to a cross-validated prediction accuracy of up to 74.13% [50].

On the sidelines of studies focused on the prediction of hair colour, different authors investigated scalp hair morphology, which is known to be heritable, in terms of shape and degree of the curl [25, 26, 27]. The first study evaluated the predictive capacity of six SNPs (rs17646946, rs11803731, rs4845418, rs12130862, rs1268789, and rs7349332) in 670 Europeans, identifying three of them (rs11803731) as the most informative. Among these, rs11803731 on TCHH (gene of the trichohyalin, a structural protein of the hair follicle) showed the strongest effect on hair morphology, particularly on straight hair (and, to a lesser degree, also on wavy and curly hair). Weaker correlation with straight hair was also found for rs7349332 (WNT10A) and rs1268789 (FRAS1). Together, these three SNPs explained about 8% of total hair-shape variability, with the highest predictive ability for the (rare) TTGGGG genotype (present in only 4.5% of individuals, giving $>$80% probability of straight hair) [25]. In their second study, EDAR (ectodysplasin A receptor) rs3827760 was evaluated as the best predictor of straight hair in non-Europeans (East Asians), also with a lower effect on curly and wavy hair and confirmed a moderate effect of TCCH on non-Europeans as well. Moreover, further SNPs were found to be related to hair shape in both European and non-European individuals (e.g., rs1268789 in FRAS1, rs80293268 in ERRFI1/SLC45A1, and rs310642 in PTK6), and in non-Europeans only (e.g., rs2219783 in GR4 and rs11150606 in PRSS53). Finally, they developed a new model for hair-shape prediction (considering phenotype straight vs. non-straight), based on 32 SNPs, which achieves a better prediction accuracy than previous models, although with a significant difference between Europeans (AUC = 0.66) and non-Europeans (AUC = 0.789) [26]. In this regard, it is noted than people of European descent are characterized by higher variability in hair morphology than other ethnicities such as Asians (prevalence of straight hair) and Africans (prevalence of curly hair). Among other genes reported in the literature as being involved in hair morphology, there are GATA3, OFCC1, LCE3E, PEX14, PADI3, TGFA, LGR4, HOXC13, and KRTAP [95, 96, 97, 98, 99, 100].

Male pattern baldness (MPB), also named androgenetic alopecia (AGA), is a common form of hair loss in adult men, characterized by a receding hairline and/or a hair loss on the top or front of the head, thus determining a significant alteration in a person’s physical appearance. This condition is affected by both male sex hormones (androgens) and genetic predisposition, hence the name androgenetic.

Marcińska et al. [29] confirmed 29 SNPs’ role in MBP determination in European people of different ages: rs12565727 (chr1), rs929626 in EBF1, rs756853 in HDAC9, 8 SNPs on chromosome 20 (rs61374441, rs19980761, rs201571, rs6047844, rs913063, rs1160312, rs6113491, and rs2180439), rs10502861 in SLC12A2, and 17 SNPs on Xq12 (rs4827379, rs1385699, rs1352015rs1041668, rs1397631, rs5919324, rs6625150,rs12558842, rs6625163, rs2497938, rs2497911, rs2497935, rs962458, rs6152, rs12396249, rs4827545, and rs7885198). Among them, 2 SNPs, rs5919324 near AR/EDAR2 genes (chrX) and rs1998076 (chr20), showed the strongest association, followed by three other SNPs: rs929626 in EBF1, rs12565727 in TARDBP and rs756853 in HDAC9.

On this basis, they created a predictive model for MPB made up of 20 SNPs, which showed higher specificity (90%) but lower sensitivity (67.7%) in the population of men over 50 years old as compared to men under 50 years old (sensitivity of 87.1% and specificity od 42.4%), and a better predictive power for early-onset MBP (AUC = 0.761) rather than late-onset MPB (AUC = 0.657) [29]. Li and collaborators [101] conduct a large-scale meta-analysis of seven genome-wide association studies for early-onset AGA in 12,806 individuals of European ancestry demonstrating unexpected association between early-onset AGA Parkinson’s disease, and decreased fertility.

Liu et al. [102] built another predictive model including 14 SNPs and achieved a similar accuracy value for predicting early-onset MPB (AUC = 0.74). However, in 2017, Hagenaars et al. [103] studied genetic variants in a cohort of 52,000 English men, finding over 250 genetic loci to be associated with severe hair loss, and developed a prediction algorithm for identifying those at greatest risk of hair loss (AUC = 0.78).

Facial morphology is probably the most discernible physical trait, whose accurate prediction would have highly relevant implications in forensic applications. Nevertheless, given that it is affected by a large number of genes, most of which are still unknown, it remains a daunting challenge to predict an individual’s facial appearance. Moreover, although a strong genetic effect, many other factors, such as age, sex, and environment, may play a relevant role in its determination.

Given its extreme complexity and variability, all studies that have tried to predict facial morphology from genetic information have broken down the human face into basic bi-dimensional phenotypes, each consisting of a Euclidean distance (e.g., eye distance, nose width, facial height) between anatomical landmarks located on the facial surface (such as palpebral commissures, alae nasi, oral commissures, ear lobules, etc.). In this way, each facial feature could be considered separately from other facial traits and analysed in an easier way.

Boehringer et al. [48] investigated whether genetic loci involved in the pathogenesis of cleft lip and cleft palate (a birth defect determining a pathologic facial trait) were also correlated to variation in normal facial morphology, specifically in nose width and/or bizygomatic distance, studying the effect of 11 SNPs in 3026 European individuals (from Germany and the Netherlands). They found a statistically significant association between rs1258763 (near the GREM1 gene) and nose width (but only in the German cohort, and stronger in males than females), and between rs987525 and bizygomatic distance (but only in the Dutch cohort). These markers were able to predict, respectively, ca. 2% of nose width variation (rs1258763) and 0.57% of bizygomatic distance variation (rs987525) [48].

In 2014, Paternoster et al. [30] identified an association between rs7559271, which is an intron of PAX3, and nasion position in a population of adolescents. Furthermore, common variants in this gene are also associated with prominence and vertical position of the nasion [30].

Claes et al. [104] investigated the effect of 24 SNPs showing a significant effect on normal-range facial morphology, spread over 20 genes: POLR1D, CTNND2, SEMA3E, SLC35D1, FGFR1, WNT3, LRP6, SATB2, EVC2, RAI1, ADAMTS2, ASPH, DNMT3B, RELN, UFD1L, ROR2, FGFR2, FBN1, GDF5, and COL11A1. They first reconstructed a ‘base-face’ using sex and ancestry information, both estimated from the same DNA sample through analysis of, respectively, amelogenin and AIMs. Then, they overlapped the effects of the 24 SNPs onto the ‘base-face’ to obtain the final predictive model. Although facial morphology turned out to be mainly affected by sex and ancestry, the SNPs’ effect could significantly increase the distinctiveness of facial prediction [22, 104].

Jin et al. [32] investigated the genetic association between four SNPs selected from facial-shape-associated genes (rs2277404 and rs7316271 on ABCC9, rs12635264 and rs1717652 on MASP1) and eyelid morphology (single vs. double eyelid) in a cohort of 96 Chinese individuals. Only one SNP, rs2277404 in ABCC9, demonstrated significant association with difference in the Chinese-eyelid phenotype [32]. Shaffer et al. [105] demonstrated that MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1 play roles in craniofacial development or in syndromes affecting the face. Li et al. [28] tested the effect of 125 facial-shape-associated SNPs on facial features in a European-Asian admixed population of 612 individuals. Eight SNPs showed a significant association with one or more facial traits (EDARrs3827760, LYPLAL1rs5781117, PRDM16rs4648379, PAX3rs7559271, DKK1rs1194708, TNFSF12rs80067372, CACNA2D3rs56063440, and SUPT3Hrs227833) and explained 6.47% of the facial variation, adjusted for sex, age, and BMI. For example, rs3827760 on EDAR (a gene involved in the development of ectodermal-derived tissues, including skin) showed an association with incisor-teeth shovelling, earlobe size and attachment, ear protrusion, and ear-helix rolling. All of the eight SNPs had a different allele frequency between Europeans and Asians, and four of them (rs4648379,rs3827760, rs7559271 and rs1194708) showed an inverse allele frequency in the two groups [28]. Li L. et al. [33] focused on two specific facial traits: the epicanthal fold (a skin fold covering the inner angle of the eye, which is typical of people of Asian descent but can also be present with lower frequency in other populations) and palpebral fissure height and width. They observed, in a Chinese cohort, a significant association between rs2074612 and palpebral fissure appearance, while no correlation was found for epicanthal fold [33].

Fagertun et al. [106] evaluated the genetic association between facial traits in an Icelandic population of 1266 individuals and a large number of SNPs selected from a genome-wide association analysis, instead of using a few markers previously chosen from candidate genes. Even with this innovative method, they could explain only 4.4–9.6% of facial shape. In particular, they observed that six facial features were predictable with statistically significant accuracy from genetic information, with some gender differences: face width (for both sexes); mouth width (only in men); lip fullness and, to a lesser degree, eye distance, eye size, and eyebrow width (only in women) [106].

In 2019, Mbadiwe et al. [107] proposed, through a literature search, a panel composed of 6,816 SNPs associated with the human face and called it FaceSNPs. This panel is available upon request. Moreover, they also identified chromosomes that promise better performance in genotype-to-phenotype prediction of human face characteristics (so, for example, chromosomes 10, 17, 1 and 5) [107].

Liu M. et al. [31] have also worked with the genetic basis of facial morphology in a Chinese population, confirming a significant association with 12 reported SNPs which are together responsible for up to 3.89% of age- and BMI-adjusted variance (EX41rs17479393, PAX3rs974448, RAB7A/ACAD9rs2977562, DCHS2rs9995821 and rs2045323, C5orf50rs6555969, SUPT3H/RUNX2rs1852985, MSRArs11782517, EYA1rs10504499, GSCrs2224309, DICER1rs7161418 and DHX35rs2206437) [31].

Recently, White and collaborators identified 203 genome-wide significant signals associated with multivariate normal-range facial morphology. Among which 53 genome-wide significant peaks are located in region with no previously known role in facial development or disease. Moreover, they demonstrated interaction between variants at different loci affecting similar aspects of facial shape variation, identifying gene sets that work in concert to build human faces [108].

High myopia is a condition characterized by a highly negative refractive error ($>$–6 to –8 dioptres) in the context of eye elongation (26–26.5 mm) [109]. Although its pathogenesis is not completely understood, actual knowledge suggests that high myopia is a complex trait whose occurrence is affected by multiple genes [55]. Visible characteristics of high myopia includes both myopic exophthalmos (i.e., protrusion of the eyeball due to an increase of ocular axial length) and the need to wear glasses. Nevertheless, little research focused on high myopia as an external visible characteristic and only three SNPs have been identified so far. A meta-analysis by Tang et al. [51] showed a relationship with rs644242 on the PAX6 gene, encoding a protein essential for eye development, in people of Asian ancestry; while Xie et al. [55] found a statistically significant correlation in the Chinese Han population for rs8004825 and rs12976445, both located in micro-RNA sequences (small RNA sequences involved in the regulation of gene expression). However, starting from these preliminary results on correlation, both papers conclude that, given the polygenic nature of the condition, to first correlate and then predict high myopia with high accuracy, more genetic markers in more samples should be investigated.

Amer et al. [110] investigated several (sixteen) SNPs in the cytochrome b gene of mitochondrial DNA to test their association with obesity in 66 Saudi Arabian individuals. Contrary to a previous study, they found only a weak relation with two non-synonymous mutations (corresponding to nucleotide substitutions, A15043G and C15677A respectively, in two obese males and two obese females), concluding that further research is needed to provide evidence for the possibility of applying cyt-b gene in obesity diagnosis [111].

Adult height has demonstrated itself to be a highly hereditable trait, whose variability is affected by hundreds to thousands of contributing genes [112]. In 2014, Liu et al. [113] introduced a model for DNA predictability of tall stature in Europeans consisting of a subset of 180 height-associated SNPs previously identified in other studies. In 2017, Marouli et al. [114] identified 32 rare and 51 low-frequency coding variants associated with adult height. In 2019, the model was updated and expanded by the same authors with the addition of newly discovered polymorphisms, for a total amount of 689 SNPs, in order to increase the model’s prediction accuracy (AUC = 0.79, compared to the previous 0.75) [112]. Subsequently, Jing et al. [115] investigated the predictive power of the same SNPs, originally identified for European individuals, in the Uyghurs, a population presenting an admixture of European and East-Asian genetic traits. On the one hand, the study confirmed a moderate genetic correlation between Uyghurs and Europeans, since some European height-associated SNPs also showed significant correlation in the Uyghurs. However, on the other hand, the study emphasises a substantial difference in terms of genetics of human stature (allele frequencies, allele effect sizes, and allele effect directions) between the two different populations [115].

It should be noted that different methods have been used to evaluate and classify pigmentation trait phenotype (iris, hair and skin colour). In some studies, colour assignments were made in a subjective manner, through direct observation and visual identification [26, 45, 84]. Some studies made use of specific tools and equipment, such as professional cameras with top-quality lens and flash systems, but also colourimeters, spectrometers and spectrophotometers, usually ensuring standardized lighting and distance-to-subject conditions for each subject, so as to obtain a more objective and quantitative classification [39, 42, 43, 44, 46, 53, 57, 58, 61, 62, 66, 67, 73, 75, 84, 85, 92]. Sometimes trait colour was determined in both manners, subjectively and objectively. In some studies, participants were scrutinized directly by one or more investigator(s) or volunteer(s), who were not specialists but who had been specially trained for the task [39, 44, 46, 62, 66, 70, 83, 85]. However, especially for eye and skin colour, one of the main approaches consisted of seeking advice from specialists in the sector, usually ophthalmologists and dermatologists, but also anthropologists with experience in phenotyping [24, 26, 38, 61, 72, 73, 75, 87, 90]. In a few cases, phenotype data were collected from participants themselves, through self-declaration or self-administered questionnaires [60, 64, 69, 92, 93] or were not known and just inferred from ethnic background [53, 65]. Occasionally, the method used was not indicated [41, 60, 65, 74, 76]. In one study, no pigmentation data were available for any of the participants [56].