The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Genomewide Association of Quantitative 

Pigmentary Traits in Admixed US 

Populations 

Author(s): Heather L. Norton 

Document Number: 252861 

Date Received: April 2019 

Award Number: 2013-DN-BX-K011 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
Report Title: Genomewide Association of quantitative pigmentary traits in 

admixed US populations: Final Report 

Award Number: 2013-DN-BX-K011 

Author(s): Heather L. Norton 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

i
Abstract 

Pigmentation of the skin, hair, and eyes shows remarkable levels of variation across 

human populations. Identifying the genetic loci that underlie this variation has potential 

applications in the field of forensic science, as this may make it possible to determine 

the probability of certain phenotypic characteristics (e.g. skin, hair, and eye color) using 

an unidentified sample of DNA. Such information could potentially be useful as a way to 

direct police investigations by limiting suspect pools in the absence of other intelligence. 

These predictions may also help to facilitate the identification of missing persons from 

skeletonized human remains and remains from mass disaster sites. To date most of the 

research in this field has focused on identifying genetic loci responsible for variation in 

hair and iris pigmentation in populations of European ancestry. However, far less is 

known about phenotypic variation and associated genetic loci in non-European and 

particularly in admixed populations. A thorough understanding of both phenotypic and 

genetic variation in these populations will be essential in order to determine if prediction 

of pigmentary phenotype from DNA is possible, particularly across populations of 

diverse origin. 

The goals of this project were to address this problem by characterizing 

phenotypic and genetic variation in a diverse set of populations and identifying 

associations between genotypes and quantitatively measured pigmentary phenotype. 

Pigmentation measurements and DNA were collected from individuals from Cincinnati, 

OH who self-identified as African American or Hispanic. We supplemented our sample 

to include two previously collected admixed samples from outside of the United States 

(Mexico and Brazil) as well as a previously collected sample of individuals who selfreported as being of European, East Asian, or South Asian ancestry from Toronto, 

Ontario, Canada. Skin, hair, and iris phenotype were assessed using quantitative 

methods (reflectance spectroscopy and high resolution iris imaging). Dense genotype 

data was obtained via either the Illumina MEGA or Affymetrix Axiom Array. After 

phasing, untyped markers are imputed using the 1000 Genomes Panel populations as a 

reference. Population stratification, which if uncorrected can lead to spurious 

associations, is assessed using EIGENSOFT. Tests for associations between 

quantitative pigmentary phenotype and genotype are carried out, using sex and 

significant ancestry components as covariates. 

Our results show that although significant differences in skin pigmentation 

phenotype exist between our population samples, there is also extensive variation 

within these samples, particularly in admixed populations. While hair pigmentation is 

less variable outside of European populations, we demonstrate within-population 

heterogeneity in our Hispanic sample and variation between Hispanics and other 

groups. Finally, we observe significant differences in quantitatively assessed iris 

pigmentation (measured in CIELab color space) among these populations, as well as 

substantial variation within population samples, including within admixed groups. This 

highlights the ways that broad categorical phenotyping terms (e.g. “brown”) can mask 

phenotypically relevant variation. 

In a small genotype-association study using European samples we confirm 

previously reported associations between SLC45A2, IRF4, and HERC2 and skin 

pigmentation and between IRF4, SLC24A4, SCL45A2, and OCA2/HERC2 and hair 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

ii
pigmentation. In a GWAS of East Asian skin and iris pigmentation we confirm the 

importance of the OCA2 locus in shaping pigmentation phenotype in East Asian 

populations. We also demonstrate that markers traditionally associated with 

pigmentation variation in European populations may be unhelpful in predicting 

pigmentation variation in this group. Looking ahead to the completion of the genetic 

analyses of our admixed samples, we predict that newly discovered markers in 

populations of African descent may play a significant role in shaping the genetic 

architecture of pigmentation in some of these groups. This suggests that the prediction 

of pigmentary traits from genetic data, particularly in admixed or non-European 

populations may present a significant challenge. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

iii
 

 

 

 

 

 

 

 

 

 

Table of Contents 

Title Page ................................................................................................................ i 

Abstract ................................................................................................................... ii 

Table of Contents .................................................................................................... iv 

Executive Summary ................................................................................................ 1 

Introduction.............................................................................................................. 6 

Methods................................................................................................................... 10 

Results..................................................................................................................... 14 

Conclusions............................................................................................................. 25 

References .............................................................................................................. 30 

Dissemination of Research Findings....................................................................... 34 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

iv
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

v
 

 

Executive Summary 

Problem. Pigmentation of the skin, hair, and eyes are easily visible phenotypic traits 

that vary extensively within and between human populations. As a complex trait, 

pigmentation is controlled by multiple genetic loci of varying effect sizes. The ability to 

use genetic data to predict the pigmentary phenotype of an unknown individual has 

potential applications in the field of forensic science. While this information cannot be 

used in the same manner that a traditional 13-marker STR profile may be used (e.g. to 

match a suspect to a crime scene sample or to establish/exclude paternity), it may still 

provide useful information that can be used to guide an investigation, particularly in 

early stages or when other evidence is limited. For example, such information may help 

to direct early stages of criminal investigations in the absence of other intelligence (such 

as reliable eye-witness accounts), or may be used to help facilitate the identification of 

unknown individuals from skeletonized remains or mass disaster sites. 

The foundation for our current understanding of pigmentation genotypephenotype relationships has largely been based on studies of populations of European 

ancestry. Specifically, past work has focused on genes that regulate skin, hair, or iris 

variation within European populations or that can explain mean differences between 

European and non-European individuals. This strong body of work has led to the 

identification of loci such as SLC24A5, which can explain a large portion of variation 

observed between populations of European and African ancestry, and the 

HERC2/OCA2 complex, which plays an important role in determining blue vs. brown 

eye color phenotype in European populations. Expanding on such discoveries, efforts 

have been made to develop panels of genetic markers that can be used to predict skin, 

hair, and iris pigmentation. 

While some of these panels have been forensically validated, there remains 

some question about their utility when applied in non-European or admixed populations. 

Concerns with the portability of such tests to other populations groups rests on two 

primary issues. The first is that many of these studies utilize a categorical system to 

describe variation in pigmentation phenotype. While such systems have the advantage 

of being relatively simple and easy to assess, they often mask the true extent of 

variation in pigmentation phenotype, which is better described as a quantitative, rather 

than categorical, trait. The second disadvantage is that these panels have been 

developed largely (but not exclusively) using loci known to play significant roles in 

regulating European pigmentation variation. This raises the question of whether or not 

such loci will be relevant in broader and more diverse population samples, and in 

particular in populations of admixed origin—such broad applicability is an important 

component of any marker panel to be used in the US. For example recent research 

exploring the genetic architecture of pigmentation in African populations suggests that 

alleles not common in European populations may play an important role in regulating 

pigmentation in populations there and in other regions. A second research report, 

focusing on the genetics of pigmentation in Latin American populations identified 

several novel loci associated with skin and iris pigmentation, including a locus 

(MFSD12) associated with lighter skin color that is not associated with European 

ancestry. Such loci would certainly impact pigmentation prediction in admixed 

populations common in the US. Finally, there is also evidence that the effects of certain 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

1
 

 

 

loci on pigmentation phenotype may vary between populations, suggesting that as-yet 

unknown loci may modulate the effects of key pigmentary predictor SNPs in different 

genomic backgrounds. Taken together these reports suggest that more thorough 

investigations using more diverse populations and quantitative phenotyping methods 

are going to be necessary to identify panels of markers that may be used to predict 

pigmentation phenotype in diverse population settings. 

Purpose. This study set out to address these issues by pursuing two primary goals. 

The first was to use quantitative methods to assess variation in skin, hair, and iris 

pigmentation in populations of diverse origin, particularly in admixed populations 

(individuals who self-identify as African American or Hispanic in the US). This is crucial 

for understanding not just patterns of variation between, but also within, populations. 

Such variation can be masked when individuals are placed into broad pigmentation 

phenotype categories (e.g “blue”, “brown”, “green” for eye color), making it more difficult 

to identify loci that have small to moderate effects on phenotype. The second goal was 

to identify genetic loci associated with this quantitatively assessed variation using dense 

genotype arrays that to include variants of moderate to low frequency in non-European 

populations. 

Research Design. The original goal of this study was to focus on a large sample of 

admixed individuals (500 self-identified African Americans and 500 self-identified 

Hispanics) from Cincinnati, OH. Due to lower-than expected participation (259 African 

Americans, 90 Hispanics) we augmented these admixed samples with 101 individuals 

from Palenque Mexico and ~480 Brazilians from Sao Paolo. We also incorporated 

previously collected data from 583 European, 425 East Asian, and 314 South Asian 

individuals from Toronto, Ontario, Canada. In addition to obtaining quantitative 

phenotypic measurements and a saliva sample to be used for DNA analyses, 

participants also completed a questionnaire providing basic information about their 

ancestry and self-reported descriptions of their skin, hair, and iris pigmentation. 

Constitutive skin pigmentation was measured in the inner upper arm using a 

DSM II ColorMeter (Cortex, Technology, Hadsund, Denmark) following established 

protocols. Pigmentation values were recorded as Melanin Index (MI) and in CIELAB 

coordinates. Higher MI values correspond to darker skin colors. The CIELAB color 

system describes color using three dimensions: L* (measuring brightness), a* 

(measuring the intensity of green/red reflected light), and b* (measuring the intensity of 

blue/yellow reflected light). The DSM II was also used to record hair pigmentation, with 

measurements taken at the crown of the head. Three measurements were taken at 

each site with mean skin and hair values calculated for each individual. Iris pigmentation 

was assessed using high-resolution digital images of the iris using a Miles Research 

Professional Iris camera under controlled lighting conditions. Iris images were analyzed 

using a custom computer program developed in the lab of the co-PI 

(http://iris.davidcha.ca). Briefly, the pupil and sclera were cropped from the image and a 

wedge was extracted from the iris. Average L*, a*, and b* values were calculated for 

pixels within the wedge to provide quantitative measurements of iris pigmentation. 

2mL of saliva were collected from participants and DNA was extracted following 

standard protocols. DNA samples from the African American, Hispanic, European, East 

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2
Asian, and South Asian samples were genotyped on the Illumina Infinium Multi-Ethnic 

Array, which includes 1.7 million markers targeting common variants across diverse 

population groups. Genotyping of the Brazilian samples using this array is currently in 

progress. The Mexican samples were genotyped on the Axiom BioBank array. All 

genotype data went through standard QC procedures. Untyped markers are imputed 

using the 1000 Genomes populations as reference panels. Because population 

structure can lead to false-positive associations, population structure is assessed using 

the program EIGENSOFT, and significant ancestry components are used as covariates 

in association analyses. 

Findings. Our quantitative measurements of skin pigmentation reveal significant 

variation within and between populations in MI. As expected, Europeans exhibit the 

lowest and least variable MI values (mean = 35.3, range: 28.6-58.5), with African 

Americans (mean = 63.1, range: 33.4-104.7) and South Asians (mean = 47.5, range: 

32.0-68.7) exhibit the highest and most variable values. Mean skin MI was significantly 

different among the populations (F = 1044, p < 2 x 10-16), however the MI distributions of 

all populations overlapped. In other words, certain ranges of MI are common to all 

populations examined here. A pairwise t-test (with Bonferroni correction) comparing skin 

MI among pairs of populations revealed that all pairs of populations differed significantly 

from each other, with the exception of the Hispanic-East Asian (mean Hispanic MI = 

39.2, mean East Asian MI = 37.9) and Mexican-South Asian (mean Mexican MI = 49.0, 

mean South Asian MI = 47.5) comparisons. As expected, admixed populations exhibited 

a wide range of MI values. MANOVA comparing the skin CIELab values confirmed that 

all populations differed from each other in CIELab space as well (p < 0.0001). 

In contrast to skin pigmentation, Europeans had the lowest hair MI values and 

highest CIELab values, while also exhibiting the widest range of variation. This confirms 

visual assessments and the wide range of self-reported hair colors recorded by 

European participants in their participant questionnaires. Populations were significantly 

different from each other in both MI (p < 2 x 10-16) and CIELab (p < 2 x 10-16) 

coordinates, although as with skin color all populations overlapped in hair MI and 

CIELab values. Pairwise comparisons showed that the European sample was 

significantly different from all other populations, as was the Hispanic sample (which had 

the second lowest mean hair MI value: 149.8). 

Iris pigmentation (as measured in CIELab space) was compared among 

populations. The admixed African American and Hispanic population samples tended to 

exhibit darker eye colors compared to European individuals, with lower mean L*, a*, and 

b* values. However, these values were highly variable in both admixed samples. While 

the African American and Hispanic irises primarily occupy color space associated with 

dark and light brown irises in the European, East Asian, and South Asian samples, they 

also included individuals with lighter and intermediate eye colors, including one blueeyed individual. 

As genetic analyses in the admixed populations are still currently underway no 

conclusions can be drawn about specific associations between particular loci and 

quantitative pigmentation phenotypes. However, we have completed a preliminary 

investigation to identify associations between nine candidate SNPs and skin and hair 

pigmentation in the European sample as well as a full genome-wide association study to 

identify loci associated with skin and iris variation in the East Asian sample. 

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3
We confirmed the association of three alleles (rs16891982 in SLC45A2, 

rs12203592 in IRF4, and rs12913832 in HERC2) with lower MI values in the European 

sample. We also confirmed the association of four alleles (rs12203592 in IRF4, 

rs12896399 in SLC24A4, rs7495174 in OCA2, and rs12913832 in HERC2) with 

variation in hair L*, a*, and b* values. Interestingly, we determined that the derived T 

allele at rs12203592 is associated with lighter skin color but darker hair color in this 

population, indicating that this SNP has different effects on pigmentation phenotype 

depending on the tissue in which it is expressed. 

In a GWAS of skin and iris pigmentation in the East Asian sample no 

associations reaching genome-wide significance were identified for skin pigmentation 

(measured as MI), despite having reasonable power to do so. However, one suggestive 

signal at rs2373391 (located in the ZNF804B locus on chromosome 7) was confirmed in 

a replication sample, identifying a new locus to pursue. A single SNP in the OCA2 gene, 

rs76930569 was significantly associated with two dimensions of eye color (a*, and b*). 

The association is likely driven by LD between this SNP and the nonsynonymous OCA2 

polymorphism rs1800414, previously associated with skin pigmentation variation in East 

Asian populations. 

Conclusions. Although genomic analyses of the admixed samples is still underway, 

multiple conclusions can be drawn from the work completed to date. First, our use of 

quantitative, rather than categorical, measurements of skin, hair, and iris pigmentation 

was crucial for the accurate characterization of both between and within population 

variation. While we observed significant differences in both skin and hair pigmentation 

between study populations, we also noted that distributions of MI and CIELab values 

overlapped for all groups, including those with the most discordant values (e.g. skin MI 

in African Americans and Europeans). As we expected, we observed significant 

heterogeneity in skin, hair, and iris pigmentation in our samples, and particularly within 

the admixed and South Asian population samples. This variation likely highlights both 

underlying population substructure and differences in admixture proportions as well as 

potentially novel combinations of pigmentation alleles. For example, the South Asian 

sample here includes individuals who identify as being from Bangladesh, Pakistan, India 

(itself highly structured), and Sri Lanka, and the Hispanic sample includes individuals 

claiming ancestry from Mexico, the Caribbean, and other parts of Central and South 

America. This highlights the importance of being able to identify and control for 

population substructure in subsequent GWAS analyses, as failure to do so may result in 

spurious associations that reflect differences in ancestry proportions rather than true 

associations. 

Our use of quantitative methods to characterize pigmentation traits also supports 

previous arguments that pigmentation should be considered as a continuous, rather 

than categorical, phenotype, both to accurately report within population variation as well 

as to increase power to identify loci of small to moderate effect. This is particularly true 

for eye color, as we were able to distinguish between different shades of brown eyes 

(dark vs. light) in CIELab space. This ultimately became important in the identification of 

loci that have subtle, but important effects on iris pigmentation, as seen in the GWAS of 

skin and iris pigmentation in the East Asian sample. 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

4
 

Finally, despite having good power (> 0.8) to identify loci explaining 12.5% of 

variation in skin pigmentation phenotype in the East Asian sample, we identified no 

candidates reaching genome-wide significance. This may be due to a number of factors, 

including small sample size, the possibility that loci with important roles in East Asian 

pigmentation have become fixed (much like the derived allele at rs1426654 in 

Europeans), or because pigmentation variation in East Asian populations is regulated 

by many loci of small effect, which may not be common in European populations. 

Recent studies in African populations have identified alleles impacting pigmentation 

phenotype that are uncommon in European populations. Taken together, these results 

suggest that many of the loci currently identified as good predictors of pigmentary traits 

may underperform in pigment prediction in non-European populations, suggesting that 

more in-depth investigations into such populations will be key to developing a better 

understanding of the genetic architecture of pigmentation outside of Europe. 

Implications for Policy and Practice. In this study we have prioritized the use of 

quantitative measurements of pigmentation traits over simpler categorical descriptions. 

We believe, as others have shown, that treating pigmentation as a quantitative trait 

improves the ability to identify genetic loci with moderate effects on phenotype, and that 

such quantitative methods should be used in GWA studies when possible. We also 

stress that treatment of pigmentation phenotypes as continuous, rather than discrete, 

traits may help to counter perceptions that individuals belonging to a particular socially 

defined racial or ethnic group can be distinguished on the basis of pigmentary 

phenotype. While we observed significant differences among the groups in this study, 

we also observe notable overlap in the distribution of skin, hair, and eye color values, 

indicating these groups can be placed into discrete pigmentation categories that neatly 

correspond to social/ethnic identities (which may themselves be fluid in different 

contexts). The range of variation that we observed in skin, hair, and eye color within 

each of these groups also argues against ideas that such groups are homogeneous 

with respect to pigmentary traits. This fact should be emphasized any time that FDP 

methods are employed, to prevent investigators from making possibly erroneous links 

between pigmentation phenotype and race or ethnicity. 

While we advocate for the treatment of pigmentation as a continuous trait, we 

note that translating this information in practical ways to investigators more familiar with 

categorical treatments of pigmentation (especially for skin and iris color) presents 

substantial challenges. As a first step in understanding how such predictions would be 

used by investigators, it will be important to develop a better understanding of how 

numerical estimates of pigmentation variation correlate with commonly used categorical 

terms for relevant phenotypes. Training investigators into the highly variable nature of 

pigmentation phenotypes is recommended. 

Finally, while some efforts have been made to demonstrate that current 

pigmentation prediction panels are applicable to a broad range of populations, we 

believe that this warrants further investigation. Our work and that of others suggests that 

the genetic architecture underlying pigmentation variation in European populations 

(relatively few loci of large effect) may not translate well to non-European populations, 

where many loci (some that may not be common in Europeans) with smaller effects 

may contribute to within population variation. Completion of the genomic analyses of our 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

5
 

 

admixed samples will certainly contribute to this body of knowledge, and we are eager 

to report those results. For now we recommend that existing pigmentation prediction 

panels be used with caution in US settings until it can be better determined if loci 

included in those panels influence pigmentation phenotype in the same way across a 

broader sample of populations. 

Introduction 

Over the last two decades there have been several important advances in our 

understanding of the genetic basis of non-pathological variation in human skin, hair, and 

iris pigmentation. The identification of loci that impact these externally visible 

characteristics (EVCs) presents new opportunities for the development and 

improvement of forensic DNA phenotyping (FDP) techniques. These techniques could 

be useful in guiding police investigations by limiting suspect pools when additional 

intelligence is limited (Kayser and de Knijff 2011; Kayser and Schneider 2009; Keating 

et al. 2013; Walsh et al. 2012) and may also help in the identifying of missing persons 

and disaster victims. Until recently the bulk of research in this area focused on 

populations of European ancestry. While this led to the identification of loci that may 

play significant roles in variation of hair and iris phenotype within such populations, 

large gaps in our understanding of pigmentation in non-European and admixed 

populations remained. These gaps limit the application of FDP techniques to the 

general US public, which includes individuals with ancestry from many different parts of 

the world or who may self-identify as belonging to an admixed population (e.g. African 

Americans or Hispanics). Understanding the extent of variation in pigmentary 

phenotypes between and within different populations and the genetic loci that underlie 

that variation are crucial steps in understanding if it possible to develop a forensic panel 

that could be used to predict pigmentary phenotypes across an increasingly diverse US 

population. This project sets out to address these gaps by quantitatively assessing skin, 

hair, and iris pigmentation in multiple populations, including US individuals who selfidentify as belonging to one of two admixed populations (African American or Hispanic). 

Using dense genome-wide genotype data our second goal is to identify genetic loci 

associated with the observed phenotypic variation. A subset of loci identified here may 

ultimately be used to develop panels of markers that can develop predictions of 

pigmentary phenotype. 

Skin, hair, and iris pigmentation are primarily determined by the quantity, type, 

and distribution of the biopolymer melanin. Melanin production takes place in 

melanocyte cells, found in the basal layer of the epidermis, hair bulb, and iris. It is 

produced in cellular organelles known as melanosomes, which are then transported to 

keratinocyte cells. In the presence of cysteine, alkali-soluble red-yellow pheomelanin is 

produced, while the absence of cysteine results in the production of brown-black 

eumelanin. Pigmentation phenotype is determined by the ratio of pheomelanin to 

eumelanin, total melanin content, and the distribution of melanosomes. 

Pigmentation phenotype has been characterized a number of ways. In many 

studies skin pigmentation has been described in terms of Fitzpatrick Skin Type 

Classification System (Fitzpatrick 1988), and hair and iris color have been commonly 

described using descriptive terms (e.g. “blue”, “green”, “brown”) (Frudakis et al. 2007; 

Frudakis et al. 2003; Han et al. 2008; Sulem et al. 2008; Sulem et al. 2007). Such 

characterization methods have the advantages of being relatively simple (no special 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

6
 

 

 

equipment is required to collect these data) and because they can be collected 

relatively easily (often through self-report). However, a disadvantage of these simple 

descriptive phenotypic categories is that they may mask the continuous nature of 

pigmentary traits (both within and between different populations). In addition, the 

Fitzpatrick Skin Type Classification System, while relatively simple to use, was originally 

derived to assess skin melanoma risk in light-skinned populations (Fitzpatrick 1988), 

and not as a tool to accurately describe skin pigmentation variation. While it has been 

adapted to include categories for darker skin types, some studies suggest that the 

wording of questions in the assessment may be culturally biased and/or not useful in 

characterizing pigmentation variation outside of lightly pigmented, (largely European) 

populations (Eilers et al. 2013; Pichon et al. 2010). These issues point to a need for a 

quantitative way to assess skin, hair, and iris pigmentation, both to accurately describe 

the extent of variation in these phenotypes but to also facilitate the discovery of genetic 

loci that influence this variation. 

While reflectometers have been used to quantitatively assess skin pigmentation 

since the 1960’s and 1970’s (Parra 2007), it was only with the more recent development 

of narrow-band reflectometers that it became possible to directly assess chromophores 

of primary relevance to skin pigmentation phenotype (i.e., melanin). For example, 

narrow-band reflectometers, such as the Dermaspectrometer (Cortex Technologies, 

Hadsund, Denmark), utilize the differential absorbance properties of the two primary 

pigments of the skin—hemoglobin and melanin—at long (red) and short (green) 

wavelengths (Diffey et al. 1984). By calculating differences of these absorbance 

spectra the Dermaspectrometer yields an estimate of melanin content known as the M 

Index (MI). One advantage to using MI as a metric is that it is easy to interpret: higher 

MI values correspond to darker pigmentation. Other quantitative assessments of color 

have also been applied to skin pigmentation studies, most notably the CIELAB system, 

which summarizes color differences across three dimensions pertaining to brightness 

(L*), and the intensities of red/green (a*) and blue/yellow (b*) light. While this system is 

less intuitive than MI, it provides a more nuanced understanding of color appearance, 

and may be more suitable for assessments of hair and iris pigmentation. Assessments 

of skin pigmentation using both types of systems have been used in studies of 

pigmentation (in both European and non-European populations) for the past fifteen 

years (Akey et al. 2001; Andrade et al. 2017; Beleza et al. 2013; Bonilla et al. 2005; 

Bonilla et al. 2004a; Bonilla et al. 2004b; Crawford et al. 2017; Edwards et al. 2010; 

Hernandez-Pacheco et al. 2017; Jonnalagadda et al. 2016; Martin et al. 2017; Norton et 

al. 2016; Norton et al. 2006; Shriver and Parra 2000; Shriver et al. 2003; Stokowski et 

al. 2007) and more recently instruments have been developed that can take 

measurements in multiple color systems (e.g. the DSMII). 

While reflectometers have recently been used to assess hair pigmentation 

variation , the majority of studies have relied on broad descriptive terms (e.g. “red”, 

“brown”, “blond”, etc.) to classify hair color, either through self-assessment or via rating 

by a trained grader. With a few exceptions (Norton et al. 2016; Norton et al. 2006) there 

is also a pronounced bias towards the study hair pigmentation variation in populations 

of European ancestry, where there is the broadest distribution of phenotypic variation. 

Similarly, iris pigmentation has also typically been described in broad categorical terms, 

often self-reported by study participants . Significant improvements in the objective and 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

7
quantitative assessment of iris pigmentation accompanied the development of highresolution photographic tools to capture iris images (Edwards et al. 2016; Edwards et al. 

2011; Liu et al. 2010). Studies using these methods reported extensive variation in iris 

pigmentation that transcended the traditional categories of “blue”, “green”, “brown”, as 

well as identified subtle shifts in iris pigmentation across CIELab space in populations 

commonly thought to be relatively homogeneous in iris pigmentation (Beleza et al. 

2013; Edwards et al. 2016; Edwards et al. 2011). Finally, Liu et al. (Edwards et al. 2016; 

Edwards et al. 2011; Liu et al. 2010) demonstrated that genome-wide association 

studies of iris pigmentation had improved power when using quantitative, rather than 

categorical, iris characterizations. 

Traditional STR profiling methods are most useful when evidentiary DNA can be 

compared to a sample from a known individual or profile in a forensic database. While 

such methods can be used to exclude a suspect from investigation, the use of DNA to 

predict EVCs has a different application. Genetic predictions of EVCs may help to 

support or refute eyewitness accounts of a suspect. This can have a potentially 

significant impact on the early stages of an investigation, as such accounts can be 

highly varied and sometimes erroneous (Spinney 2008; Wells et al. 2000). Genetic 

prediction of EVCs may also be useful in reconstructing the appearance of a deceased 

individual for whom many physical characteristics cannot be determined—this could 

include skeletonized individuals or fragmentary remains (such as those found at mass 

disaster sites). While these information cannot be used to make a conclusive 

identification, they may be helpful in narrowing the pool of potential candidate 

individuals. 

To that end forensic panels have been developed to predict iris, hair, and skin 

pigmentation. While early attempts to predict hair color focused largely on the 

associations of the MC1R gene with red hair color (Grimes et al. 2001), more recently 

attempts have been made to develop tests more appropriate to populations in which red 

hair is not common. While these panels have been successful in distinguishing between 

qualitative hair colors in European populations (Branicki et al. 2011; Keating et al. 2013; 

Walsh et al. 2013), their utility in admixed populations remains unknown, due in part to 

poor understanding of the extent of variation in hair pigmentation phenotype in these 

groups. Panels developed to predict iris color have built on early work identifying SNPs 

in the HERC2/OCA2 complex as being associated with blue vs. brown eye color 

phenotype (Eiberg et al. 2008; Frudakis et al. 2007; Frudakis et al. 2003; Kayser et al. 

2008; Sturm et al. 2008). SNPs from these studies have been included in forensically 

validated assays (e.g. IrisPlex and HIrisPlex) with the ability to predict blue eye color 

with an accuracy of 90% (Walsh et al. 2011a; Walsh et al. 2011b; Walsh et al. 2013; 

Walsh et al. 2012). 

A key question regarding such panels is their applicability in non-European 

populations. This is particularly challenging given the limited number of studies 

exploring variation in hair or iris phenotype in non-European and/or admixed 

populations. Attempts to support the use of these panels globally have sometimes 

focused on the genotype distribution of panel SNPs across the HGDP-CEPH 

populations (Walsh et al. 2011b). For example, when six loci from the Irisplex test were 

genotyped in these populations they rarely predicted blue eye color in populations 

outside of Europe, the Middle East, and Western Europe. However, because the 

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8
 

 

HGDP-CEPH populations do not have any associated phenotypic information it is 

difficult to determine how well such panels may predict intermediate eye colors in global 

populations, or how sensitive they may (or may not) be to more subtle shifts in iris 

pigmentation. As a specific example, Edwards et al. (Edwards et al. 2016) identified 

several South Asian individuals homozygous for derived alleles at the SNP rs12913832 

(the polymorphism in the HERC2/OCA2 complex often associated with blue vs. brown 

eye color) whose eyes were intermediate (but not blue) in color. These authors suggest 

that other loci may modify the effects of rs12913832 in European and South Asian 

populations, leading to its different phenotypic effects in each population. While forensic 

SNP panels rely on multi-locus genotypes for phenotype prediction, the differing effects 

of rs12913832 in Europeans and South Asians suggests that some caution may be 

necessary before broadly applying panels developed with Europeans to other groups. 

On a more positive note, attempts to correlate genotype with iris phenotype in admixed 

populations from Brazil have met with some success, demonstrating that the 

HERC2/OCA2 complex plays a role in determining broad differences in eye color in at 

least some admixed populations (Andrade et al. 2017; Freire-Aradas et al. 2014). 

Because hair and eye color can potentially be temporarily modified (through the 

use of bleach, hair dye or colored contacts), the ability to develop a forensic panel that 

can predict skin pigmentation is also of great interest. In 2010 Valenzuela et al. 

identified a panel of 5 SNPs that could explain ~76% of variation in total hair melanin, 

~45% of variation in skin pigmentation, and ~75% of variation in iris pigmentation in an 

ethnically diverse sample from Arizona (Valenzuela et al. 2010). While this study was 

significant for not exclusively focusing on individuals of European ancestry, failure to 

control potential effects of population structure in the sample may have confounded 

their results. Subsequently Maroñas et al. used African, European, and admixed 

individuals to develop a Bayesian classifier model based on a panel of 10 markers that 

could be used to predict skin phenotype into one of three categories: white, 

intermediate, or black (Maronas et al. 2014). While this study was notable for 

specifically focusing on admixed samples, its relatively small sample size and reliance 

on SNPs with a strong European ascertainment bias are potential limitations. 

More recently Walsh et al. set out to develop a panel of markers that could be 

used to predict skin color, with the explicit goal of being able to predict variation within 

and between continents (Walsh et al. 2017). To that end they used a large sample of 

individuals, primarily sampled from across Europe, but also including US individuals of 

diverse backgrounds. Individuals were placed into 5-tiered (very pale, pale, 

intermediate, dark, and black) and 3-tiered (light, dark, dark black) skin classification 

categories. As with previous iris studies, unphenotyped individuals from the HGDPCEPH populations were also included. The authors developed a multiple-linear 

regression model using 36 SNPs to predict skin pigmentation according to the 5-tiered 

and 3-tired models. Their system outperformed that of Maroñas, and allowed them to 

distinguish between pigmentation variation within continents (primarily relevant to 

variation within Europeans) as well as between continents. However, the use of the 

unphenotyped HDGP-CEPH populations as stand-ins for those with a “dark black” 

pigmentation phenotype raises issues similar to those discussed above for the 

validation of the IrisPlex panel. Quantitative studies of pigmentation variation in both 

African and Melanesian populations indicate that skin pigmentation phenotype can be 

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9
 

 

quite variable in these regions (Crawford et al. 2017; Martin et al. 2017; Norton et al. 

2006), and that not all individuals are good models for the “dark “black” skin category 

used here. As such, it’s difficult to know how well this panel may predict variation in 

skin pigmentation, particularly across darker skin tones. 

More recent studies of pigmentation variation in admixed and African population 

suggest that models predicting skin and iris pigmentation may need to be modified, due 

to the discovery of alleles affecting pigmentation variation in African populations that are 

rare or absent from European-derived populations. For example, in their study of 

pigmentation variation in KhoeSan populations of Africa, Martin and colleagues 

identified both classic (SLC24A5 and TYRP1) and novel (SMARCA2/VLDLR and 

SNX13) loci influencing pigmentation in these groups (Martin et al. 2017). Their work 

suggests that pigmentation variation in these, and likely other African populations, is 

regulated by a large number (> 50) of loci with relatively minor effects—this contrasts 

with current models of understanding (based on studies of European populations) that 

suggest pigmentation is regulated by relatively few genes of major effect. Their findings 

are bolstered by a larger study of African pigmentary diversity conducted by Crawford et 

al. (Crawford et al. 2017), who also identify novel loci (MFSD12, DDB1, TMEM138) in 

addition to others (SLC24A5, OCA2, HERC2) influencing pigmentation phenotype 

across the continent. In a study of admixed Puerto Rican and African American 

individuals in the US, Hernandez-Pacheco, et al. identify associations between the loci 

BEND7 and PRFP18 and quantitative estimates of skin color (Hernandez-Pacheco et 

al. 2017). A recent GWAS study investigating skin and iris pigmentation in a sample of ~ 

6,000 Latin Americans identified several novel loci associated with pigmentary traits. 

These include a mutation in the MFSD12 gene that is associated with lighter skin color. 

This mutation is thought to be related to East Asian/Native American (rather than 

European) ancestry (Adhikari et al. 2019). Taken together, these results suggest that 

pigmentary architecture, particularly in non-European and admixed populations, may be 

more complex than previously appreciated. The development of broadly applicable 

forensic panels that could be used to predict different pigmentary phenotypes should 

therefore include the use of diverse populations with associated quantitative phenotypic 

data. 

Given the desired goal of developing predictive models of pigmentary phenotypes 

across a range of populations, particularly those relevant in a US context, this project 

set out to conduct GWA studies using populations of diverse origins, including two US 

admixed populations (African Americans and Hispanics). Two advantages to including 

admixed populations in pigmentation GWAS include the anticipated wide range of 

phenotypic variation observed in these groups and the ability to utilize admixture 

mapping approaches to identify relevant genetic loci. 

Methods 

Participant Recruitment and Consent Process. The original goal of this study was to 

recruit a total of 1000 admixed individuals from the Cincinnati, OH region: 500 selfidentified African American individuals and 500 self-identified Hispanic individuals. It 

should be noted that while we specifically advertised for participants that self-identified 

as “Hispanic”, a small number (seven) of participants reported their ancestry as 

“Hispanic/Latino”. Further review of questionnaire responses indicated that three of 

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10
 

these individuals also included at least one parent/grandparent who was Brazilian (e.g. 

Latino). We have included these individuals into our broader “US Hispanic” sample. 

Participants in this broad “US Hispanic” sample report ancestry from a range of 

countries, including Puerto Rico Mexico, Columbia, Cuba, and Venezuela among 

others. Participants were recruited through the University of Cincinnati community, and 

later more broadly through the city of Cincinnati, via recruitment flyers and course 

announcements. Recruitment materials were also translated into Spanish in an attempt 

to boost US Hispanic enrollment numbers. Recruitment of both African American and 

US Hispanic individuals proved to be far more difficult than expected, due we believe in 

part to external events that occurred in the Cincinnati community (as noted in bi-annual 

reports). All individuals recruited to the project were provided with an informed consent 

document, which was reviewed with study personnel. This document, as well as study 

procedures, were approved by the University of Cincinnati’s IRB (UC IRB #2013-7610). 

A Change of Scope (submitted in July 2014) specified the inclusion of an additional 

~1500 individuals of European, East Asian, and South Asian descent. Phenotypic data 

from these individuals had already been collected by the co-PI, and DNA samples were 

available to utilize in genome-wide association testing for pigmentary traits. 

In an effort to boost the size of our admixed sample, we reached out to other 

researchers with similar datasets. This resulted in the inclusion of data from 101 

Mexican individuals from the city of Palenque collected by Dr. Abigail Bigham at the 

University of Michigan. Phenotypic data for these individuals included skin and hair 

reflectance measurements and DNA genotype data (see below). We also were able to 

extend our collaboration to include ~480 admixed individuals collected by Dr. Celso 

Texiera Mendes Junior of the University of Sao Paolo, Brazil. Phenotypic data from 

these individuals includes skin reflectance, categorical iris assessment, and categorical 

descriptions of hair color. DNA samples from these Brazilian individuals are also 

available and are in the process of being genotyped on the Illumina MEGA Array. Due 

to delays in the approval for the inclusion of these data, and in the final purchase of 

genotyping kits to be used on these samples genotyping is still in process. Because of 

the different nature of data collected from each group, as well as the different stages of 

genomic analyses, summary tables have been provided at the end of this section to 

denote the types of phenotypic data available for each sample and the stage of 

genotypic analyses. In subsequent discussion, tables, and figures, the “US Hispanic” 

sample refers to individuals sampled in Cincinnati, OH who self-identified as being 

either Hispanic or Latino. “Mexican” refers to the Palenque sample collected by Dr. 

Bigham, while “Brazilian” refers to the samples collected by Dr. Texiera Mendes. 

Questionnaire data. US (African American and US Hispanic) and Canadian (European, 

East Asian, and South Asian) participants completed a brief questionnaire prior to 

phenotypic data collection. This provided information about their self-reported ancestry, 

place of birth and first language of parents and grandparents. The questionnaire also 

asked a series of questions about pigmentation and skin sensitivity to sun, allowing us 

to place individuals into one of the six Fitzpatrick skin type categories. 

Phenotypic measurement. Constitutive skin pigmentation (pigmentation in unexposed 

areas of the skin) was measured in the inner upper arm using a DSM II ColorMeter 

(Cortex Technology, Hadsund, Denmark). The DSM II ColorMeter measures skin 

reflectance by shining a light on the surface of the skin and measuring the amount of 

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11
 

 

light that is reflected back at different wavelengths, providing a non-invasive and 

quantitative method to investigate skin color. The probe of the DSM II was placed gently 

against the surface of the skin for ~ 5-7 seconds to allow the light emitted by the DSM II 

diode to shine on the skin and for reflectance measurements to be obtained. 

Reflectance data was recorded as Melanoma (M) and Erythema (E) index, CIELab 

values (L*, a*, and b*), and RGB values. Three measurements from the arm were 

taken, and averaged together to yield mean skin reflectance values. Analyses focus on 

the M, L*, a*, and b* measurements. 

Hair pigmentation was quantitatively assessed in all subjects using the DSM II. 

The probe of the DSM II was placed against hair at the crown for 5-7 seconds to allow 

the light emitted by the DSM II diode to shine on the hair and for reflectance 

measurements to be taken. As with skin, three measurements were taken and 

averaged together to produce mean MI and L*, a*, and b* values. Hair that was dyed or 

graying was not measured. We also asked for optional hair samples from participants to 

allow for quantitative assessments of melanin content by Dr. Kazu Wakamatus at the 

Fujita University School of Health Sciences. The majority of our participants declined to 

provide a hair sample. 

A photograph of each subject’s iris was taken with the Miles Research 

Professional Iris camera (Miles Research, California, USA). This camera consists of a 

Fujifilm Finepix S3 Pro 12-megapixel DSLR mounted on a Nikkor 105mm macro lens. A 

coaxial biometric illuminator delivers a constant and uniform source of light to each iris 

at 5500K (D55 illuminant). In order to ensure that iridial structures, such as Wolfflin 

nodules, could be accurately characterized (even in dark irides), a second photograph 

was taken using 12-Megapixel Fujifilm Finepix S3Pro SLR camera equipped with a 105 

mm macro lens, illuminator kit, and infrared filter. These modifications were necessary 

because certain structures, such Wolfflin nodules, cannot be visualized in brown eyes 

because they rest below the melanin layer. Infrared photography is unique because it is 

primarily sensitive to light with a wavelength of 700-900 nanometers. The pigment layer 

does not absorb or reflect light in this range, which means that it is effectively 

transparent to infrared light. In contrast, the collagen fibers and connective tissues 

underneath the melanin layer do appear to absorb and reflect radiation in that range. 

This means that photographs taken using infrared technology will be able to pick up 

information about the structures, such as Wolfflin nodules, that lie beneath the melanin 

layer. 

DNA collection and extraction. 2mL of saliva were collected using the OraGene OG-500 

DNA Collection kit. DNA extraction followed recommended procedures recommended 

by the manufacturer, with an additional cleanup step to increase yield and purity. DNA 

samples were quantified via Qubit assay. 500ng of total DNA were shipped to the 

Clinical Genomics Centre at Mt. Sinai Hospital, Toronto, CA for genotyping. 

DNA genotyping. The European, East Asian, South Asian, African American, and US 

Hispanic samples were genotyped on the Illumina Infinium Multi-Ethnic Array at the 

Clinical Genomics Centre (Mount Sinai Hospital, Toronto, Ontario, Canada). This array 

includes ~1.7 million markers and was designed to target common genome variants 

across diverse population groups. Basic QC steps were carried out using the program 

Genome Studio, following recommendations by Illumina (resulting in ~ 1.4 million 

markers remaining). Additional QC steps included: removal of samples with missing call 

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12
 

 

rates < 0.9, removal of samples with sex discrepancies, removal of samples that were 

outliers for heterozygosity, and removal of related individuals, removal of markers with 

genotype call rate 0.95, removal of markers with Hardy Weinberg p values < 10-6 , 

removal of indels, removal of markers with MAF < 0.01, removal of markers not found 

on 1000 Genomes reference panel or that show other discrepancies regarding 

chromosome, position, or alleles. The Mexican samples from Palenque were 

genotyped using the Axiom Biobanking array and underwent standard QC procedures. 

The Brazilian samples are still in the process of being genotyped for the Illumina MEGA 

array. QC of those data will follow what is outlined above. 

Statistical analyses/methods. 

Phenotypic analyses. The three skin M index values of the right upper inner arm were 

averaged to produce a mean skin M value for each individual. The same process was 

followed for CIELab measurements to produce mean L*, a*, and b* values for each 

individual. Pigmentation values were compared among population samples, based on 

self-reported ancestry. The mean and standard deviation of M and CIELab values were 

calculated for each population sample. Mean differences among populations for M index 

and CIELab values were calculated using ANOVA and MANOVA, respectively. M and 

CIELab values were transformed as needed to meet assumptions of normality prior to 

analyses. 

Hair M and CIELab values were analyzed in the same manner as skin values. 

When data was available, these values were also compared to self-reported 

assessments of five different categories of hair color (light blond/red, blond, dark 

blond/light brown, dark brown, and black). Comparisons of M and CIELab values among 

these groups were conducted using ANOVA. M and CIELab values are transformed as 

needed to meet assumptions of normality prior to analyses. 

Iris pigmentation and iridial structures were scored using a custom program 

developed in the laboratory of the Co-PI (http://iris.davidcha.ca) and described in 

Edwards et al. 2016 (Edwards et al. 2016). Briefly, this program was used to crop the 

pupil and sclera from iris images and then extract a wedge from the iris itself. The 

program estimated color scores in CIELab color space from pupillary and ciliary zones, 

providing mean estimates of L*, a*, and b* for each eye. Mean L*, a*, and b* values 

were calculated for each population and compared using MANOVA. These values can 

also be compared to the categorical terms used by participants to describe their eye 

color (these data are obtained from questionnaire answers). These comparisons have 

not yet been made, but will be completed as part of the broader investigation into 

genetic variants associated with iris pigmentation. CIELab values were transformed as 

needed to meet assumptions of normality prior to analyses. 

Genomic analyses. Genotypes are phased using the program SHAPEIT2 

(http://www.shapit.fr/), using the 1000 Genomes populations as reference haplotypes. 

Genotype data will be pruned for loci showing short-range and long-range linkage 

disequilibrium (r2 > 0.2) or genomic complexity. The program EIGENSOFT will be used 

to perform a Principal Component Analysis (PCA) to identify population substructure in 

each of our samples. Statistically significant PC’s will be included as covariates in 

subsequent association analyses. This is especially important in the admixed population 

samples, as PC scores are highly correlated with admixture proportions of major 

parental groups involved in the admixture process (Parra et al. 2011). The program 

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13
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SNPTEST (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html) is 

used to test for associations between genotype and phenotype, conditioning on 

covariates of sex and significant PC components. In genomic regions where multiple 

significant signals are detected, SNPTEST can also test for associations while 

conditioning upon the most significant SNP. This will help to distinguish between signals 

due to linkage with that SNP or independent signals in the region. In admixed samples 

admixture mapping methods may also be utilized to identify SNPs with significant 

effects on pigmentary traits. 

Population Skin Hair Iris 

African 

American 

Quantitative (M, L*, a*, 

b*), Fitzpatrick selfassessment 

Quantitative (M, L*, a*, 

b*), categorical selfreport 

Quantitative (L*,a*,b*), 

categorical self-report 

US Hispanic Quantitative (M, L*, a*, 

b*), Fitzpatrick selfassessment 

Quantitative (M, L*, a*, 

b*), categorical selfreport 

Quantitative (L*,a*,b*), 

categorical self-report 

Mexican Quantitative (M, L*, a*, 

b*) 

Quantitative (M, L*, a*, 

b*) 

NA 

Brazilian Quantitative (L*, a*, b*) Categorical self-report Categorical self-report 

European Quantitative (M, L*, a*, 

b*), Fitzpatrick selfassessment 

Quantitative (M, L*, a*, 

b*), categorical selfreport 

Quantitative (L*,a*,b*), 

categorical self-report 

East Asian Quantitative (M, L*, a*, 

b*), Fitzpatrick selfassessment 

Quantitative (M, L*, a*, 

b*), categorical selfreport 

Quantitative (L*,a*,b*), 

categorical self-report 

South Asian Quantitative (M, L*, a*, 

b*), Fitzpatrick selfassessment 

Quantitative (M, L*, a*, 

b*), categorical selfreport 

Quantitative (L*,a*,b*), 

categorical self-report 

Table 1: Phenotypic data available from each population sample. 

Population 

Sample 

DNA 

collected 

DNA 

extracted 

Genotyping 

complete 

QC Phasing Population 

Stratification 

Association 

testing 

African 

American X 

X X X In 

process 

Pending Pending 

US 

Hispanic 

X X X X In 

process 

Pending Pending 

Mexican X X X X In 

process 

Pending Pending 

Brazilian X X In process Pending Pending Pending Pending 

European X X X X X X In process 

East Asian X X X X X X complete 

South 

Asian 

X X X X X X In process 

Table 2: State of genomic analysis for population samples. 

Results 

Skin pigmentation. The mean and standard deviation of MI and CIELab values were 

calculated for all populations (Table 3). As expected, Europeans exhibit the lowest MI 

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14
 

 

 

(and highest L* values), while African Americans and South Asians exhibit the highest 

MI (and lowest L* values). It is also notable that skin pigmentation is the least variable 

in the European sample, but shows a much wider range of variation in the admixed 

(African American, US Hispanic, Mexican) and South Asian samples (Figure 1). Mean 

skin MI is significantly different among all six populations (F = 1044, df = 5, p < 2 x 10-

16). A pairwise t-test (with Bonferroni correction) comparing skin MI means among all 

pairs of populations indicates that all populations are significantly different from each 

other (p < 0.00001) with the exception of the US Hispanic-East Asian comparison (p = 

0.51) and the Mexican-South Asian comparison (p = 0.10.). This suggests that these 

differences are not driven by the darker skin color of the African American sample 

alone. A MANOVA comparing CIELab skin values confirmed that all six populations are 

significantly different from each other in CIELab space as well. 

Population N MI (S.D.) L* (S.D.) a* (S.D) b* (S.D) 

African 

American 

259 63.1 (12.5) 17.24 (6.5) 12.7 (2.1) 13.63 (4.0) 

US Hispanic 90 39.2 (5.2) 33.3 (5.8) 13.5 (1.8) 17.12 (1.7) 

Mexican 101 49.0 (4.2) 24.8 (3.8) 15.0 (1.6) 15.2 (1.0) 

European 583 35.3 (2.8) 38.0 (3.2) 12.4 (2.0) 14.0 (1.9) 

East Asian 425 37.9 (2.9) 35.2 (3.3) 12.8 (1.8) 15.8 (1.7) 

South Asian 314 47.5 (6.1) 26.8 (4.7) 13.6 (1.3) 15.2 (1.5) 

Table 3: Mean (and standard deviation) for skin MI and CIELab values for the study population samples. 

Hair. The mean and standard deviations for hair MI and CIELab values can be found in 

table 4. Europeans are once again notable for their lower MI values. In contrast to skin 

MI, Europeans display the widest variation in hair MI (Figure 2). Europeans also exhibit 

the highest and most variable values for CIELab measurements (Figures 2-4), most 

likely reflecting the greater diversity of hair colors, including lighter hair, in European 

populations (this was evident in MI and CIELab values as well as through self-report 

data obtained from questionnaires). Mean MI differed significantly among the 

populations (F = 193.9, df = 5, p < 2x10-16). A pairwise t-test (with Bonferroni correction) 

comparing hair MI means among all pairs of populations indicates that this is driven 

largely by differences between Europeans and other populations (all comparisons 

significant at p < 2x10-16) and between the US Hispanic sample and other populations 

(p < 0.001). Differences between US Hispanics and other groups may be explained by 

the lighter hair colors of this group (Table 4, Figure 2). A MANOVA of hair CIELab 

values confirmed that the populations are significantly different from each other across 

all the L*, a*, and b* dimensions of CIELab space (p < 2 x 10-16). 

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15
 

 

 

 

 

 

 

 

Skin M by population 

40 60 80 100

AFAM EAS EUR HIS MEX SAS

Figure 1: Distribution 

of skin MI values (yaxis) by population. 

AFAM = African 

American; EAS = East 

Asian; EUR = 

European; HIS = US 

Hispanic; MEX = 

Mexican, SAS = South 

Asian 

Hair color is often reported (in witness statements, drivers licenses, etc.) 

categorically, rather than in quantitative terms. We evaluated the relationship between 

self-reported hair color and the quantitative L*, a*, and b* values in the European data 

sample (the sample with the most variation in hair color). Using information from the 

participant questionnaires, we divided hair color into five categories: light blond or red, 

blond, dark blond or light brown, dark brown, or black. Distributions of L*, a*, and b* 

values for these five categories are show in Figures 5-7. Significant differences in all 

three values were observed across the three categories (p < 0.001). Higher values of 

L*, a*, and b* are more common in lighter hair colors. Darker hair categories showed a 

narrower range of CIELAB values than lighter colors, with those in the light blond/red 

hair category exhibiting the widest distribution. 

Population N M (S.D.) L* (S.D.) a* (S.D) b* (S.D) 

African 

American 

259 167.1 (12.5) 1.32 (6.50) 0.20 (2.12) 0.82 (4.00) 

US Hispanic 90 149.8 (17.3) 2.06 (1.2) 0.49 (0.99) 1.35 (1.21) 

Mexican 101 163.7 (16.7) 1.53 (0.60) 0.11 (0.38) 0.83 (0.40) 

European 583 129.4 (24.4) 3.522 

(2.870) 

1.584 

(1.752) 

2.424 

(2.322) 

East Asian 425 162.8 (18.9) 1.559 

(0.788) 

0.143 

(0.229) 

0.747 

(0.511) 

South Asian 314 164.9 (20.0) 1.558 

(1.176) 

0.231 

(0.547) 

0.766 

(0.682) 

Table 4: Mean (and standard deviation) for hair MI and CIELab values for the study population samples. 

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16
 

 

 

Hair L* by Population 

Figure 2: Distribution 

of hair L*(y axis) 

values by population. 

Afr American East Asian European Hispanic Mexican South Asian 

Hair a* by Population 

Figure 3: Distribution 

of hair a* values (yaxis) by population. 

Afr American East Asian European Hispanic Mexican South Asian 

−2 0 2 4 6 8 10 0 5 10 15 20 

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17
 

 

 

 

 

 

 

Hair b* by Population 

0 5 10 15 

Figure 4: Distribution 

of hair b* values (yaxis) by population. 

Afr American East Asian European Hispanic Mexican South Asian 

Figure 5: Distribution 

of L* values across 

each of five selfdescribed 

hair color categories in 

Europeans: 0: Light 

blond/red; 

1: blond; 2: light 

brown/dark blond; 3: 

dark brown; 4: black. 

18

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Figure 6: Distribution 

of a* values across 

each of five selfdescribed 

hair color categories in 

Europeans: 0: Light 

blond/red; 

1: blond; 2: light 

brown/dark blond; 3: 

dark brown; 4: black. 

Figure 7: Distribution 

of b* values across 

each of five selfdescribed 

hair color categories in 

Europeans: 0: Light 

blond/red; 

1: blond; 2: light 

brown/dark blond; 3: 

dark brown; 4: black. 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

19
 

 

 

 

 

 

Iris pigmentation. Summaries of variation in iris pigmentation for the European, East 

Asian, and South Asian population samples can be found in Edwards (Edwards et al. 

2016). Briefly, that study demonstrated eyes that were self-described as “blue” tended 

to exhibit high L* values and negative a* and b* values. At the opposite end of the 

spectrum, self-reported brown eyes exhibit low L* and high a* and b* values. The 

authors were also able to draw some distinction between light and dark brown eyes, 

with the latter typically having lower values for L*, a*, and b*. Mean and ranges of L*, a*, 

and b* variation for the African American and US Hispanic population samples are 

shown in Table 5. Compared to the European population samples reported by Edwards 

and colleagues., both the African American and US Hispanic samples tend to have 

darker eye colors (evidenced by lower mean values of L*). However, values in both 

admixed populations are highly variable. Figure 8 plots the distribution of these iris 

values across CIELab color space for the African American and US Hispanic samples. 

These plots show a range of variation in iris pigmentation in both groups, with most eye 

colors ranging from dark to light brown. It is notable, though that some individuals from 

both the African American and US Hispanic samples exhibit relatively light eye colors 

(including one US Hispanic individual with CIELab values indicative of blue eye color. 

Population L* (Min-Max) a* (Min-Max) b* (Min-Max) 

African American 8.54 (2.30-44.19) 3.37 (-2.22-17.25) 2.26 (-2.23-21.92) 

US Hispanic 18.46 (4.366-76.43) 6.06 (-3.25-17.95) 6.45 (-11.70-20.68) 

Table 5: Mean (and minimum-maximum) values of iris L*, a*, and b* values for the African American and 

US Hispanic population samples. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

20
 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

0 5 10 15

L

−10 −5 0 5 10 15 20 0 20 40 60

0 5 10 15

A

B

0 20 40 60 −10 −5 0 5 10 15 20

Figure 8: Scatterplot matrix of CIELab iris values for African American and US Hispanic individuals. The 

color of each point corresponds to iris color in CIELab space. African American individuals are indicated 

by circles, US Hispanic individuals by triangles. 

Genetic associations. Because analyses of genomic data in the admixed populations 

are still ongoing, reports of associations at this time are limited. However, in a 

preliminary analysis a small number of SNPs previously associated with pigmentation 

phenotype were genotyped in the European sample and reported in Norton et al. 2016 

(Norton et al. 2016). One of these (rs1426654 in the gene SLC24A5) was excluded from 

further analyses because the allele frequency of the ancestral G allele (associated with 

darker skin color) was < 1%. Associations between the remaining eight SNPs and skin 

MI and hair CIELab values were tested for using an additive model (Tables 6 and 7). 

The derived alleles at three of these SNPs (rs16891982, rs12203592, and rs12913832) 

were significantly associated with lighter skin color in this European sample. Derived 

alleles in five SNPs were significantly associated with lighter hair pigmentation (higher 

L*, a*, or b* values): rs16891982, rs1291832, rs7495174, rs4778138, and rs12896399. 

The derived allele (T) at rs12203592 was associated with darker skin color. Tests that 

included rs12913832 (shown to have a strong effect on hair color) as a covariate 

indicated that significant associations between rs7495174 and rs4778138 (also located 

21
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

in the OCA2/HERC2) region were likely driven by linkage disequilibrium with 

rs12913832. 

Skin M 

Gene Polymorphism Effect allele BETA p-value 

SLC45A2 rs16891982 G -0.0431 1.21E-07 

IRF4 rs12203592 T -0.0286 2.71E-07 

TYR rs1393350 A -0.0104 0.0415 

SLC24A4 rs12896399 T -0.0005 0.9086 

OCA2 rs1800407 A -0.0047 0.6258 

OCA2 rs4778138 A -0.0126 0.0328 

OCA2 rs7495174 A -0.0207 0.0091 

HERC2 rs12913832 G -0.0178 9.95E-05 

Table 6: Linear regression coefficients and p-values for each of the 8 SNPs in the European sample for 

skin M index. Significant results (after a Bonferroni correction) are highlighted in bold. 

Gene Polymorphism Effect allele BETA p-value 

Hair L* 

SLC45A2 rs16891982 G 0.2270 0.0075 

IRF4 rs12203592 T -0.1929 0.0007 

TYR rs1393350 A 0.0441 0.3973 

SLC24A4 rs12896399 T 0.1246 0.0046 

OCA2 rs1800407 A 0.0972 0.3024 

OCA2 rs4778138 A 0.1297 0.0295 

OCA2 rs7495174 A 0.2410 0.0024 

HERC2 rs12913832 G 0.2280 7.21E-07 

Hair a* 

SLC45A2 rs16891982 G 0.2522 0.0056 

IRF4 rs12203592 T -0.2454 5.55E-05 

TYR rs1393350 A 0.0062 0.9121 

SLC24A4 rs12896399 T 0.1632 0.0005 

OCA2 rs1800407 A 0.0882 0.3833 

OCA2 rs4778138 A 0.2088 0.0011 

OCA2 rs7495174 A 0.2750 0.0013 

HERC2 rs12913832 G 0.3271 2.08E-11 

Hair b* 

SLC45A2 rs16891982 G 0.1575 0.0050 

IRF4 rs12203592 T -0.1426 0.0050 

TYR rs1393350 A 0.0073 0.8312 

SLC24A4 rs12896399 T 0.0923 0.0015 

OCA2 rs1800407 A 0.0610 0.3270 

OCA2 rs4778138 A 0.0987 0.0123 

OCA2 rs7495174 A 0.1658 0.0016 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

22
 

 

 

 

 

 

 

 

 

HERC2 rs12913832 G 0.1583 1.90E-07 

Table 7: Linear regression coefficients and p-values for each of the 8 SNPs in the European sample for 

hair CIELab values. Significant results (after a Bonferroni correction) are highlighted in bold. 

A GWAS of skin and iris pigmentation variation in the East Asian population 

sample has been completed—full results can be read in Rawofi et al. (Rawofi et al. 

2017). An important feature of this work (also emphasized in Edwards et al. 2016), is 

the distribution of L*, a*, and b* iris values. While eye color in this population would 

traditionally be classified as “brown”, there is notable variation across CIELAB space 

(indicating variation in shades of brown) demonstrating that the use of broad categories 

to describe iris phenotype may mask important and relevant phenotypic variation. 

No associations reaching genome-wide significance (p = 5x10-8) were observed 

for skin M index (Figure 9). Association tests between five common markers (MAF > 

1%) with the suggestive p-values (< 10-5) were carried out in two replication samples. 

An association between one of these, rs2373391 (located in the ZNF804B gene on 

chromosome 7), was replicated in one of these cohorts, with the A allele being 

significantly associated with lighter skin color. A single SNP, rs76930569 in the OCA2 

region, was significantly associated with two dimensions of iris color (Figure 10). This 

association is likely driven by LD between this SNP and rs1800414, a nonsynonymous 

polymorphism previously associated with skin pigmentation. This SNP was estimated to 

explain 11.9%, 10.4%, and 6% of variation in iris b*, a*, and L* values in this sample. 

Figure 9: Manhattan 

plot reporting 

association results for 

skin pigmentation in 

East Asian sample 

(from Rawofi et al. 

2017) 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

23
 

 

Figure 10: Manhattan plot reporting associations for iris pigmentation (L*, a*, and b*), as well as for delta 

(the difference the ciliary and pupillary regions of the iris). From Rawofi et al. 2017. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

24
 

 

Conclusions 

Discussion. As analyses of genomic data from the admixed populations in this study is 

still ongoing this discussion will focus largely on the phenotyping data collected here. 

One of the key features of this study compared to others is the use of quantitative 

methods to assess skin, hair and iris color. This was a specific and deliberate choice on 

the part of the PI and co-PI for a number of reasons. First, our experience with these 

methods in previous studies has emphasized to us the continuous nature of these 

pigmentary traits, and the degree to which phenotypic variation can be masked under 

simple categorical labels. In addition, our previous work also highlighted to us that the 

use of categorical labels to describe these phenotypes led to the mistaken belief that 

there was little variation in traits such as hair and iris color outside of European 

populations. Second, previous work examining the genetic basis of iris pigmentation 

(Liu et al. 2010) has shown that the use of quantitative metrics to assess iris 

pigmentation resulted in improved power to detect associated genetic loci. 

Skin pigmentation. We observe significant variation in skin pigmentation (measured as 

MI or with CIELab values) between the populations included here. Pairwise 

comparisons among populations indicate that these differences are not driven by one or 

two outlier populations. However, we also observed that the distributions of quantitative 

skin pigmentation measurements overlapped for all six populations (Figure 1), including 

the two populations with the most discordant mean skin MI values (Europeans and 

African Americans). Despite the additional effort and technical equipment required to 

obtain quantitative pigmentation estimates, we feel that the benefits (better 

understanding of within and between population, improved power to detect genetic loci0 

outweigh these difficulties. 

As expected, we observed significant variation in skin pigmentation in the 

admixed population samples, consistent with studies of other admixed populations 

(Andrade et al. 2017; Beleza et al. 2013; Bonilla et al. 2004a; Bonilla et al. 2004b; 

Hernandez-Pacheco et al. 2017; Martin et al. 2017; Shriver et al. 2003). This is 

important, as it reflects not only variation in ancestry proportions as well as possible 

differences in the genetic architecture of skin pigmentation in these populations due to 

distinct combinations of pigmentary alleles (Hernandez-Pacheco et al. 2017). This 

variation also highlights the fact that our admixed populations are drawn from 

heterogeneous groups. For example, the individuals in the US Hispanic sample report 

ancestry from Puerto Rico, Mexico, Columbia, Cuba, and Venezuela as well as 

generally “Latino”. The variation in this sample, as well as variation when compared to 

the Mexican sample from Palenque is a strong reminder that social labels such as race 

or ethnicity or geopolitical affiliations do not always map neatly onto phenotype. A 

similar conclusion can be drawn from the South Asian sample, which consisted of 

participants claiming ancestry from Bangladesh, India, Pakistan, and Sri Lanka, and 

also showed a wide range of variation in skin M index. This heterogeneity also 

emphasizes the importance of being able to control for population substructure in the 

GWAS analyses, as failure to do so may result in false-positive associations related to 

differences in ancestry proportions. 

We observed the greatest variation in hair pigmentation in the European sample, 

where hair color was generally lighter (higher values of L*, a*, and b*) and more variable 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

25
 

than in other populations. However, differences in hair color between other populations 

was also observed, partly due to the wide range of variation observed in the US 

Hispanic sample. Perhaps surprisingly, differences in L*, a*, and b* values were also 

observed between the East and South Asian samples, where hair would typically be 

classified as brown or black. These differences were largely attributed to higher a* 

values in the South Asian sample. Notably no significant differences in MI were 

observed between these two groups, suggesting that CIELAB space may allow for more 

nuanced phenotyping of hair color than MI or categorical labels. This also suggests that 

it may be possible to identify loci with subtle effects on hair color variation (particularly in 

the a* component) across populations. 

We compared quantitative estimates of hair color in the European sample to selfreported hair color in order to assess whether such self-categorization could accurately 

capture phenotypic variation. Our results (Figures 5, 6, and 7) demonstrate why using 

self-reported categories in association studies is likely to be a suboptimal strategy. The 

broad distribution of CIELAB values within each category (particularly among lighter hair 

colors) suggests that a substantial amount of nuanced phenotypic information is lost 

when only self-reported categories are used. It is also clear that there is overlap in 

CIELAB values across categories, meaning that two individuals who self-report different 

hair colors may have similar hair reflectance values. This may impact the effects of 

downstream association analyses. 

Our quantitative characterization of iris pigmentation confirmed previous reports 

demonstrating that eye color better fits a continuous, rather than categorical distribution 

(Beleza et al. 2013; Edwards et al. 2016; Edwards et al. 2011; Liu et al. 2010). Details 

of the distribution of L*, a*, and b* values for the European, East, and South Asian 

samples can be found in Edwards et al. 2016 and a preliminary comparison of a subset 

of those and the African American and US Hispanic samples is reported in Norton et al. 

2016a. One of the key conclusions drawn from the admixed populations we examined is 

that while eye color in many of these individuals would classically be categorized as 

“brown” there is substantial variation in CIELab space. The African American and US 

Hispanic samples observed here exhibit greater variation in L* and a* values than the 

East and South Asian samples, although variation in the b* component is roughly 

similar. Several of these admixed individuals exhibit values consistent with intermediate 

iris color phenotypes, and one US Hispanic individual exhibits blue eye color (Figure 8). 

Once again, this highlights the wide range of variation present in admixed populations 

and cautions against assuming simplistic correlations between pigmentary phenotypes 

and social categories such as race or ethnicity. 

Our preliminary analyses testing for associations between nine known 

pigmentary alleles and variation in the European sample confirmed previously identified 

associations with skin color for three SNPs located in the genes SLC45A2, IRF4, and 

HERC2. We also confirmed the association of four genes (HERC2, IRF4, SLC24A4, 

and SLC45A2). We noted that the derived T allele at the rs12203592 polymorphism was 

associated with lighter skin but darker hair color in our European sample (Norton et al. 

2016). This suggests that the quantitative phenotyping methods that we used facilitated 

the detection of alleles having different effects in different tissues. These more subtle 

effects may be masked in non-European populations, especially when these complex 

phenotypes are considered in only categorical terms. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

26
 

 

 

Edwards et al. (2016) tested for associations between iris L*, a*, and b* values 

and 14 pigmentation-related SNPs in the European, East Asian, and South Asian 

samples included in this study. They confirmed the association of five of those SNPs (all 

of which are presently on the IrisPlex forensic panel) with iris pigmentation in the 

European sample. They found that the rs12913892 polymorphism in HERC2 played an 

important role in modulating iris color in the South Asian sample, primarily through the 

modulation of brown eye color (dark vs. light). While having two copies of the derived 

allele at this locus is associated with a decrease in b* values in Europeans, no such 

decrease was observed in the South Asian sample. The authors postulate that 

rs12913892 may impact iris pigmentation differently in European and South Asian 

populations because of its interactions with other (as yet unknown) loci that are 

common in European, but not South Asian populations. These results suggest that the 

genetic architecture of iris pigmentation may vary among populations, a critical factor 

when considering the development and broad application of marker panels to predict iris 

phenotype across diverse populations. 

A GWAS of skin and iris pigmentation in the East Asian sample is complete 

(Rawofi et al. 2017). This study did not identify any SNPs explaining variation in skin 

pigmentation variation that reached genome-wide significance, despite having good 

power (>0.8) to detect loci explaining ~12.5% of variation in skin pigmentation 

phenotype. This suggests that there are no variants segregating in the sample that have 

large effects on skin pigmentation, indicating that the genetic architecture of skin 

pigmentation variation in East Asians may be regulated by many markers of relatively 

small effect. One SNP in the gene ZNF804B reached suggestive significance, and that 

will merit follow-up in future studies. Notably, the derived A allele at this SNP 

(rs2373391), which is associated with lighter skin pigmentation, is polymorphic in 

admixed populations of the 1000 Genomes Panel (with frequencies ranging from 0.234-

0.429), suggesting that we should be able to evaluate its effects on pigmentation in our 

admixed samples. Finally, by using quantitative phenotyping methods this study was 

able to identify a variant, rs1800414 in the OCA2 gene that influenced iris pigmentation. 

However, this SNP could only explain ~10% of variation in observed L*, a*, and b* 

values, leaving a substantial amount of variation unexplained. 

Implications for policy and practice. Here we have emphasized the use of quantitative 

methods to characterize pigmentation variation, and have highlighted the importance of 

such methods in the identification of genes that can explain moderate effects on 

phenotype, particularly in non-European populations. We also believe that presenting 

these phenotypes as continuous, rather than discrete, traits is critical to prevent 

individuals from incorrectly perceiving socially-defined racial or ethnic groups as being 

homogeneous for a given phenotype and/or from being biologically distinct from other 

such groups. In other words, we caution strongly against interpreting a phenotypic 

prediction of skin, hair, or iris color as evidence of a particular socially-constructed 

identity. Supporting this, we point to the overlap in skin, hair, and iris MI and CIELab 

values observed across populations in this study. We also observed a wide range of 

variation in pigmentation phenotypes in our study populations, even for phenotypes 

(e.g. iris color) believed to be generally homogeneous. 

If FDP methods can be refined such that predictions can be made about 

pigmentation phenotypes (e.g., skin pigmentation falls within a certain range of MI 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

27
 

 

 

 

values) it is currently unclear how to translate this information so that it is useful for 

investigators. While it is generally easy to understand that higher MI values correlate 

with darker skin colors, it is less clear how easily an individual will be able to understand 

what a predicted MI value of 40-55 means in terms of physical appearance. One 

possible way forward to is to assemble suites of photographs of skin representing 

different MI ranges to be used as a training tool. It is unclear how well the human eye 

can discriminate among these different ranges, however. In addition, given the fact that 

hair and eye color are commonly reported in categorical terms by eye witnesses and on 

official documents (e.g. drivers’ licenses) it may be useful to better understand how 

those descriptive categories correlate with quantitative pigmentation assessments. 

Several studies have published panels of markers that may be used to predict 

skin, hair, and/or iris pigmentation with various degrees of accuracy (Ruiz et al. 2013; 

Walsh et al. 2017; Walsh et al. 2011b; Walsh et al. 2013). While many of these were 

developed first in European populations, there is some evidence that these panels may 

be useful in predicting phenotype in more diverse population samples, including 

admixed populations (Andrade et al. 2017; Freire-Aradas et al. 2014), although 

prediction rates for “intermediate” phenotypes seem to lag behind prediction rates for 

phenotypes at the extremes of distributions (e.g. prediction success is higher for 

blue/brown eye color vs. intermediate eye color). In some cases prediction models were 

derived (or tested) using unphenotyped samples, and were deemed successful when 

their predictions met the authors’ assumptions of phenotype in those populations. Our 

work here and others investigations into pigmentation variation, particularly in African 

populations, suggests that these may be faulty assumptions, masking substantial 

pigmentation variation in these populations. 

Of particular concern regarding the development of broadly applicable panels of 

markers to be used in FDP is that loci commonly included on such panels may not 

accurately predict phenotype with suitable accuracy in certain individuals. Edwards 

(Edwards et al. 2016) work noting the different expression of rs1291382 in European 

and South Asian populations provides one such example where this concern seems 

justified. There is also growing evidence that the genetic architecture of pigmentation 

variation may differ substantially across populations. Much of our knowledge of 

pigmentation genetics comes from studies in European populations, where it seems like 

variation may be controlled by relatively small numbers of loci of large effect. The East 

Asian pigmentation GWAS reported by Rawofi and colleagues suggests that this may 

not be the case in East Asian populations (Rawofi et al. 2017). In addition, recent work 

identifying novel loci with small but significant effects on pigmentation in African and 

admixed populations (Crawford et al. 2017; Hernandez-Pacheco et al. 2017; Martin et 

al. 2017) all suggest that accurate phenotypic prediction may be substantially more 

challenging than anticipated. It may be that while loci underlying the genetic architecture 

of pigmentation across diverse samples can be identified, the number of loci required 

for accurate prediction may not be suitable in forensic settings. 

Future research. The completion of the genotypic analyses in our admixed samples will 

clearly help to improve our understanding of the genetic architecture of pigmentation 

variation in admixed populations, and may facilitate the discovery of loci that can be 

used to predict pigmentation across a broader array of populations. Replication studies 

in other admixed populations of significant and suggestive signals will be carried out. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

28
As noted above, it is currently unclear how quantitative ranges of pigmentation 

values can be translated into information that can be used productively by investigators 

on the ground. It is also unclear how providing information about pigmentation 

phenotype might influence an individual’s perception of the race or ethnicity of an 

unknown individual. Investigations exploring how well quantitative measures of 

pigmentation map onto commonly used hair and eye color categories (as we did for hair 

color in the European sample) may be useful here, as would investigations into how well 

people are able to discriminate among different MI ranges. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

29
	 	

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33
Dissemination of research findings 

Publications 

2017 Rawofi L, Edwards M, Krithika S, Le P, Cha D, Yang Z, Ma Y, Wang J, Su 

B, Jin L, Norton HL, Parra EJ. Genome-wide association study of 

pigmentary traits (skin and iris color) in individuals of East Asian ancestry. 

Peer J 5e2951. doi: 10.7717/peerj.3951 

2016 Norton HL, Edwards M, Krithika S, Johnson M, Werren EA, Parra E. 

Quantitative assessment of skin, hair, and iris variation in a diverse 

sample of individuals and associated genetic variation. American Journal 

of Physical Anthropology.160:570-581. doi: 10.1002/ajpa.22861 

Presentations 

2018 Norton HL, Fist L, Parra EJ. Associations of Fitzpatrick Skin Type with skin 

M index in diverse populations. Poster presented at the American 

Association of Physical Anthropology meeting in Austin, TX, April 11-14, 

2018. 

2017 Lona-Durazo F, Edwards M, Krithika S, Le P, Norton HL, Parra EJ. New 

regions underlying pigmentation of skin, hair and iris using quantitative 

phenotypes in individuals of European ancestry. Poster presented at the 

Society for Molecular Biology and Evolution (SMBE) meeting 2017, Austin, 

TX July 2-6 2017. 

2017 Rawofi L, Edwards M, Krithika S, Murray N, Norton HL, Parra EJ. 

Genetics of pigmentation in East Asia: The role of OCA2 polymorphisms. 

Poster presentation at the 86th annual meeting of the American 

Association of Physical Anthropologists, New Orleans, LA. April 19-22, 

2017 

2016 Norton HL, Edwards M, Parra EJ. Quantitative characterization of 

pigmentation phenotype and associated genetic variation. Paper 

presented at the International Union of Anthropological and Ethnological 

Sciences Inter-Congress. Dubrovnik, Croatia, May 4-8, 2016. 

2015 Norton HL, Edwards M, Werren E, Parra EJ. Skin, hair, and iris 

pigmentation: quantifying phenotype and identifying genetic loci 

associated with variation in diverse populations. Poster presented in 

invited symposium (“Thinking Anthropologically About Genetics”)of the 

84th annual meeting of the American Association of Physical 

Anthropologists, St. Louis, MO. March 25-March 28, 2015. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

34