The variation in skin colour of modern humans is a product of thousands of years of natural selection. All human ancestry can be traced back to African populations, which were dark-skinned to protect them from the intense UV rays of the sun.

Over time, humans spread to other parts of the world, and people in the northern latitudes with lower UV developed lighter skin through natural selection. This was likely driven by a need for vitamin D, which requires UV rays for production.

Separate genetic mechanisms were involved in the evolution of lighter skin in each of the two main branches of human migration: the European branch (which includes peoples on the Indian subcontinent and Europe) and the East Asian branch (which includes East Asia and the Americas).

A variant of the gene SLC24A5 is the primary contributor to lighter skin colour in the European branch, but a corresponding variant driving light skin colour evolution in the East Asian branch remains to be identified.

One obstacle to finding such variants is the high prevalence of European ancestry in most people groups, which makes it difficult to separate the influence of European genes from those of other populations. To overcome this issue, Ang et al. studied a population that had a high proportion of Native American and African ancestors, but a relatively small proportion of European ancestors, the Kalinago people. The Kalinago live on the island of Dominica, one of the last Caribbean islands to be colonised by Europeans.

Ang et al. were able to collect hundreds of skin pigmentation measurements and DNA samples of the Kalinago, to trace the effect of Native American ancestry on skin colour. Genetic analysis confirmed their oral history records of primarily Native American (55%) – one of the highest of any Caribbean population studied to date – compared with African (32%) and European (12%) ancestries.

Native American ancestry had the highest effect on pigmentation and reduced it by more than 20 melanin units, while the European mutations in the genes SLC24A5 and SLC45A2 and an African gene variant for albinism only contributed 5, 4 and 8 melanin units, respectively. However, none of the so far published gene candidates responsible for skin lightening in Native Americans caused a detectable effect. Therefore, the gene responsible for lighter skin in Native Americans/East Asians has yet to be identified.

The work of Ang et al. represents an important step in deciphering the genetic basis of lighter skin colour in Native Americans or East Asians. A better understanding of the genetics of skin pigmentation may help to identify why, for example, East Asians are less susceptible to melanoma than Europeans, despite both having a lighter skin colour. It may also further acceptance of how variations in human skin tones are the result of human migration, random genetic variation, and natural selection for pigmentation in different solar environments.

Human skin pigmentation is a polygenic trait that is influenced by health and environment (Barsh, 2003). Lighter skin is most common in populations adapted to northern latitudes characterized by lower UV incidence than equatorial latitudes (Jablonski and Chaplin, 2000). Selection for lighter skin, biochemically driven by a solar UV-dependent photoactivation step in the formation of vitamin D (Engelsen, 2010; Hanel and Carlberg, 2020; Holick, 1981; Loomis, 1967) is regarded as the most likely basis for a convergent evolution of lighter skin color in European and East Asian/Native American populations (Lamason et al., 2005; Norton et al., 2007). The hypopigmentation polymorphisms of greatest significance in Europeans have two key characteristics: large effect size and near fixation. For example, the A111T allele in SLC24A5 (Lamason et al., 2005) explains at least 25% of the difference in skin color between people of African vs. European genetic ancestry, and is nearly fixed in European populations. No equivalent polymorphism in Native Americans or East Asians has been found to date.

Native Americans share common genetic ancestry with East Asians (Derenko et al., 2010; Tamm et al., 2007), diverging before ~15 kya (Gravel et al., 2013; Moreno-Mayar et al., 2018; Reich et al., 2012), but the extent to which these populations share pigmentation variants remains to be determined. The derived alleles of rs2333857 and rs6917661 near OPRM1, and rs12668421 and rs11238349 in EGFR are near fixation in some Native American populations, but all also have a high frequency in Europeans (Quillen et al., 2012), and none reach genome-wide significance in Adhikari et al., 2019. However, the latter found a significant association for the Y182H variant of MSFD12 with skin color, but its frequencies were only 0.27 and 0.17 in Native Americans and East Asians, respectively, suggesting that it can explain only a small portion of the difference between Native American and/or East Asians and African skin color. Thus, the genetic basis for lighter skin pigmentation specific to Native American and East Asian populations, whose African alleles would be expected to be ancestral, remains to be found.

The shared genetic ancestry of East Asians and Native Americans suggests the likelihood that some light skin color alleles are shared between these populations. This is particularly the case for any variants that achieved fixation in their common ancestors. For Native American populations migrating from Beringia to the Tropics, selection for darker skin color also appears likely (Jablonski and Chaplin, 2000; Quillen et al., 2019). This would have increased the frequency of novel dark skin variants, if any, and would have decreased the frequency of light skin variants that had not achieved fixation. Hypopigmenting alleles are associated with the European admixture characteristic of many current Native American populations (Brown et al., 2017; Gravel et al., 2013; Keith et al., 2021; Klimentidis et al., 2009; Reich et al., 2012). Since the European hypopigmenting alleles may mask the effects of East Asian and Native American alleles, we searched for an admixed Native American population with high African, but low European admixture.

Prior to European contact, the Caribbean islands were inhabited by populations who migrated from the northern coast of South America (Benn-Torres et al., 2008; Harvey et al., 1969; Honychurch, 2012; Island Caribs, 2016; Benn Torres et al., 2015). During the Colonial period, large numbers of Africans were introduced into the Caribbean as slave labor (Honychurch, 2012; Benn Torres et al., 2013). As a consequence of African and European admixture and high mortality among the indigenous populations, Native American genetic ancestry now contributes only a minor portion (<15%) of the genetic ancestry of most Caribbean islanders (Auton et al., 2015; Benn Torres et al., 2015). The islands of Dominica and St. Vincent were the last colonized by Europeans in the late 1700s (Honychurch, 2012; Honychurch, 1998; Rogoziński, 2000). In 1903, the British granted 15 km² (3700 acres) on the eastern coast of Dominica as a reservation for the Kalinago, who were then called ‘Carib.’ When Dominica gained Independence in 1978, legal rights and a degree of protection from assimilation were gained by the inhabitants of the Carib Reserve (Honychurch, 2012) (redesignated Kalinago Territory in 2015). Oral history and beliefs among the Kalinago, numbering about 3000 living within the Territory, 2021; Figure 1—figure supplement 2 are consistent with the primarily Native American and African genetic ancestry, assessed and confirmed genetically here.

Early in our genetic and phenotypic survey of the Kalinago, we noted an albino individual, and upon further investigation, we learned of two others residing in the Territory. We set out to identify the mutant albinism allele to avoid single albino allele effects that would potentially mask Native American hypopigmentation alleles. Oculocutaneous albinism (OCA) is a recessive trait characterized by visual system abnormalities and hypopigmentation of skin, hair, and eyes (Gargiulo et al., 2011; Grønskov et al., 2007; Grønskov et al., 2014; Hong et al., 2006; Vogel et al., 2008) that is caused by mutations in any of a number of autosomal pigmentation genes (Carrasco et al., 2009; Edwards et al., 2010; Gao et al., 2017; Grønskov et al., 2013; Kausar et al., 2013; King et al., 2003; Spritz et al., 1995; Stevens et al., 1997; Stevens et al., 1995; Vogel et al., 2008; Woolf, 2005; Yi et al., 2003). The incidence of albinism is ~1:20,000 in populations of European descent, but much higher in some populations, including many in sub-Saharan Africa (1:5000)(Greaves, 2014). Here, we report on the genetic ancestry of a population sample representing 15% of the Kalinago population of Dominica, the identification of the new albinism allele in that population, and measurement of the hypopigmenting effects of the responsible albinism allele, the European SLC24A5^A111T and SLC45A2^L374 alleles. Native American genetic ancestry alone caused a measurable effect on pigmentation. In contrast, alleles identified in past studies of Native American skin color caused no significant effect on skin color.

Our search for a population admixed for Native American/African ancestries with minimal European admixture led us to the ‘Carib’ population in the Commonwealth of Dominica. Observations from an initial trip to Dominica suggested wide variation in Kalinago skin color. Pursuit of the genetic studies described here required learning about oral and written histories, detailed discussion with community leadership, IRB approval from Ross University (until Hurricane Maria in 2017, the largest medical school in Dominica) and the Department of Health of the Commonwealth of Dominica, and relationship-building with three administrations of the Kalinago Council over 15 years.

Kalinago genetic ancestry

The earliest western mention of the Kalinago (originally as ‘Caribs’) was in Christopher Columbus’s journal dated November 26, 1492 (Honychurch, 2012). Little is known about the detailed cultural and genetic similarities and differences between them and other Caribbean pre-contact groups such as the Taino. African admixture in the present Kalinago population derived from the African slave trade; despite inquiry across community, governmental, and historical sources, we were unable to find documentation of specific regions of origin in Africa or well-defined contributions from other groups. The population’s linguistics are uninformative, as they speak, in addition to English, the same French-based Antillean Creole spoken on the neighboring islands of Guadeloupe and Martinique.

To study Kalinago population structure, we analyzed an aggregate of our Kalinago SNP genotype data and HGDP data (Li et al., 2008) using ADMIXTURE (Figure 1 and Figure 1—figure supplement 1) as described in Materials and methods. At K=3, the ADMIXTURE result confirmed the three major clusters, corresponding roughly to Africans (black cluster), European/Middle Easterners/Central and South Asians (yellow cluster), and East Asians/Native Americans (green cluster). At K=4 and higher, the red component that predominates Native Americans separates the Kalinago from the East Asians (green cluster). Consistent with prior work (Li et al., 2008), a purple cluster (Oceanians) appears at K=5 and a brown cluster (Central and South Asians) appears at K=6; both are minor sources of genetic ancestry in our Kalinago sample (average <1%) (Appendix 1—table 2).

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At K=4 to K=6, the Kalinago show on average 55% Native American, 32% African, and 11–12% European genetic ancestry. Estimates from a two-stage admixture analysis are similar, as are results from local genetic ancestry analysis (see Materials and methods) (Appendix 1—table 3), leading to estimates of 54–56% Native American, 31–33% African, and 11–13% European genetic ancestry. The individual with the least admixture has approximately 94% Native American and 6% African genetic ancestry. The results of the principal component (PC) analysis (PCA) (Figure 2—figure supplement 1) were consistent with ADMIXTURE analysis. The first two PCs suggest that most Kalinago individuals show admixture between Native American and African genetic ancestry, with a smaller but highly variable European contribution apparent in the displacement in PC2 (Figure 2—figure supplement 1). A smaller number of Kalinago individuals with substantial East Asian genetic ancestry exhibit displacement in PC3 (Figure 2—figure supplement 1).

Our analysis of Kalinago genetic ancestry revealed considerably more Native American and less European genetic ancestry than the Caribbean samples of Benn Torres et al., 2013, and the admixed populations from the 1000 Genomes Project (1KGP) (Auton et al., 2015; Figure 2). Some Western Hemisphere Native Americans reported in Reich et al., 2012, have varying proportions of European but very little African admixture (Figure 2B). Overall, the Kalinago have more Native American and less European genetic ancestry than any other Caribbean population.

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The 55% Native American genetic ancestry calculated from autosomal genotype in the Kalinago is greater than the reported 13% in Puerto Rico (Gravel et al., 2013), 10–15% for Tainos across the Caribbean (Schroeder et al., 2018), and 8% for Cubans (Marcheco-Teruel et al., 2014). This is also considerably higher than the reported 6% Native American genetic ancestry found in Bwa Mawego, a horticultural population that resides south of the Kalinago Territory (Keith et al., 2021). However, this result is lower than the 67% Native American genetic ancestry reported by Crawford et al., 2021, for an independently collected Kalinago samples based on the mtDNA haplotype analysis. This difference suggests a paternal bias in combined European and/or African admixture. Since our Illumina SNP-chip genotyping does not yield reliable identification of mtDNA haplotypes, we are currently unable to compare maternal to autosomal genetic ancestry proportions for our sample. Samples genotyped using 105 genetic ancestry informative markers from Jamaica and the Lesser Antilles (Benn Torres et al., 2015) yielded an average of 7.7% Native American genetic ancestry (range 5.6%–16.2%), with the highest value from a population in Dominica sampled outside the Kalinago reservation. Relevant to the potential mapping of Native American light skin color alleles, the Kalinago population has among the lowest European genetic ancestry (12%) compared to other reported Caribbean Native Americans in St. Kitts (8.2%), Barbados (11.5%), and Puerto Rico (71%) (Benn Torres et al., 2013). Contributing to the high percentage of Native American genetic ancestry in the Kalinago is their segregation within the 3700 acre Kalinago Territory in Dominica granted by the British in 1903, and the Kalinago tradition that women marrying non-Kalinago are required to leave the Territory; non-Kalinago spouses of Kalinago men are allowed to move to the Territory (KCA, KCC, Personal Communication with Kalinago Council, 2014). These factors help to explain why samples collected outside the Kalinago Territory (Benn Torres et al., 2013) show lower fractional Native American genetic ancestry.

During our fieldwork, it was noted that members of the Kalinago community characterized themselves and others in terms of perceived genealogical ancestry as ‘Black,’ ‘Kalinago,’ or ‘Mixed.’ Compared to individuals self-identified as ‘Mixed,’ those self-identified as ‘Kalinago’ have on average more Native American genetic ancestry (67% vs 51%), less European genetic ancestry (10% vs 14%), and less African genetic ancestry (23% vs 34%) (Figure 2—figure supplement 2). Thus, these folk categories based on phenotype are reflected in some underlying differences in genetic ancestry.

An OCA2 albinism allele in the Kalinago

OCA is a genetically determined condition characterized by nystagmus, reduced visual acuity, foveal hypoplasia, and strabismus as well as hypopigmentation of the skin, hair, and eye (Dessinioti et al., 2009; van Geel et al., 2013). The three sampled albino individuals had pale skin (MI 20.7, 22.4, and 23.8 vs. 29–80 for non-albino individuals), showed nystagmus, and reported photophobia and high susceptibility to sunburn. In contrast to the brown irides and black hair of most Kalinago, including their parents, the albino individuals had blonde hair and gray irides with varying amounts of green and blue.

To identify the albinism variant in the Kalinago, we first determined that none of the albino individuals carried any of 28 mutations previously found in African or Native American albino individuals (Carrasco et al., 2009; King et al., 2003; Stevens et al., 1997; Yi et al., 2003), including a 2.7 kb exon 7 deletion in OCA2 found at high frequency in some African populations. Whole exome sequencing of one albino individual and one parent (obligate carrier) revealed polymorphisms homozygous in the albino individuals and heterozygous in the parent, an initial approach that assumes that the albino individual was not a compound heterozygote. We identified 12 variant alleles in 7 OCA genes (or genomic regions) that met these criteria (summarized in Appendix 1—table 4). None were nonsense or splice site variants. Five of the twelve variants were intronic, one was synonymous, one was located in 5’UTR, and three were in the 3’UTR (Appendix 1—table 4). Two missense variants were found in OCA2: SNP rs1800401 (c.913C>T or p.Arg305Trp in exon 9), R305W, and multi-nucleotide polymorphism rs797044784 in exon 8 (c.819_822delCTGGinsGGTC; p.Asn273_Trp274delinsLysVal), NW273KV.

Among 458 Kalinago OCA2 genotypes, 26 carried NW273KV and 60 carried R305W (Table 1). Only NW273KV homozygotes were albino individual. We know that the allele responsible for albinism was NW273KV because neither of the two individuals, homozygous for R305W but not NW273KV, was albino individual. In further support of this conclusion is that one individual who was homozygous for R305W and homozygous ancestral for NW273 had an MI of 72, among the darkest in the entire population. R305W is notably present with frequency >0.10 in some African, South Asian, and European populations (Auton et al., 2015), predicting a Hardy-Weinberg frequency of homozygotes above 1%. This is far greater than the observed frequency of individuals with albinism and therefore inconsistent with the idea that this is not a variant responsible for albinism. The fact that R305W scores incorrectly as pathogenic using SIFT, Polyphen 2.0, and PANTHER that R305W (Kamaraj and Purohit, 2014) suggests a need for refinement of these methods. The universal association of R305W with the NW273KV haplotype indicates that the founder haplotype of the NW273KV albinism mutation carried the silent R305W variant.

Table 1

Allele/genotype		NW273KV genotype
		Homozygous ancestral*	Heterozygous	Homozygous derived	Total
R305W genotype	Homozygous ancestral	398	0	0	398
	Heterozygous	33	22	0	55
	Homozygous derived	1	1	3†	5
	Total	432	23	3†	458

1. *

   Ancestral = reference allele and derived = alternate allele for both variants.

2. †

   Albino phenotype. Notably, none of the other genotypic categories are albino individuals.

To identify the origin of the albino allele, albino individuals and carriers were analyzed for regions exhibiting homozygosity, and identity-by-descent and local genetic ancestry was estimated (see Materials and methods). All three albino individuals share a homozygous segment of ~1.7 Mb that encompasses several genes in addition to OCA2 (Figure 3). The albino haplotype defined by homozygosity in individuals 2 and 3 extends ~11 Mb; comparison to local genetic ancestry shows that this haplotype is clearly of African origin.

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The Kalinago albino individuals are the only reported individuals where the albinism was caused by homozygosity for the NW273KV allele of OCA2. Two reported albino individuals of African-American/Dutch descent were compound heterozygotes for the OCA2 mutation, with one allele being the NW273KV variant chromosome (Garrison et al., 2004; Lee et al., 1994). Conservation of the NW sequence among vertebrates and its inclusion in a potential N-linked glycosylation site (Rinchik et al., 1993) that is eliminated by the mutation supports the variant’s pathogenicity. The NW273KV frequency in our sample (0.03) translates into a Hardy-Weinberg albinism frequency (p²=0.0009) of ~1 per 1000, as observed (3 in a population of about 3000). Examination of publicly available data reveals three OCA2^NW273KV heterozygotes in the 1000 Genome Project, a pair of siblings from Barbados (ACB) and one individual from Sierra Leone (MSL). The three 1KGP individuals share a haplotype of ~1.5 Mb, of which ~1.0 Mb matches the albino haplotype in the Kalinago. The phasing for the OCA2^NW273KV variant in the public data is inconsistent, with the variant assigned to the wrong chromosome for the ACB siblings.

Genetic contributions to Kalinago skin color variation

One motivation for undertaking this work was to characterize genetic contributions to skin pigmentation in a population with primarily Native American and African genetic ancestry, so that we could focus on the effect of Native American hypopigmenting alleles without interference from European alleles. The Kalinago population described here comprises the only population we are aware of that fits this genetic ancestry profile. To control for the effects of the major European pigmentation loci, all Kalinago samples were genotyped for SLC24A5^A111T and SLC45A2^L374F. The phenotypic effects of these variants and OCA2^NW273KV are shown in Figure 4. Each variant decreases melanin pigmentation, with homozygotes being lighter than heterozygotes. The greatest effect is seen in the OCA2^NW273KV homozygotes (the albino individuals), as previously noted. The frequencies of the derived alleles of SLC24A5^A111T and SLC45A2^L374F in the Kalinago sample are 0.14 and 0.06, respectively.

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The markedly higher frequency of SLC24A5^A111T compared to SLC45A2^L374F is not explained solely by European admixture, given that most Europeans are nearly fixed for both alleles (Soejima and Koda, 2007). This deviation can be explained by the involvement of source populations that carry the SLC24A5^A111T variant but not SLC45A2^L374F. Although some sub-Saharan West African populations (the likeliest source of AFR genetic ancestry in the Kalinago) have negligible SLC24A5^A111T frequencies, moderate frequencies are found in the Mende of Sierra Leone (MSL, allele frequency = 0.08) (Micheletti et al., 2020; Auton et al., 2015), while some West African populations such as Hausa and Mandinka who have allele frequencies of 0.11 and 0.15, respectively (Cheung et al., 2000; Rajeevan et al., 2012). Such African individuals carrying the SLC24A5^A111T allele could potentially cause the observed frequencies by founder effect. In addition, the region of chromosome 5 containing SLC45A2 exhibits low European genetic ancestry (6.5%) that is consistent with low observed SLC45A2^L374F frequency.

In order to investigate the potential effect of the SLC25A5^A111T allele on the albinism phenotype, we also compared other pigmentation phenotypes such as the hair and eye colors for all albino individuals and carriers. One of the three Kalinago albino individuals was also heterozygous for SLC24A5^A111T, but neither skin nor hair color for this individual was lighter than that of the other two albino individuals, who were homozygous for the ancestral allele at SLC24A5^A111; this observation is consistent with epistasis of OCA2 hypopigmentation over that of SLC24A5^A111T. Nine sampled non-albino individuals had combinations of hair that was reddish, yellowish, or blonde (n=6), skin with MI <30 (n=3), and gray, blue, green, or hazel irides (n=2); among these, six were heterozygous and one homozygous for SLC24A5^A111T, and three were heterozygous for the albino variant. A precise understanding of the phenotypic effects of the combinations of these and other hypopigmenting alleles will require further study.

The strong dependence of pigmentation on Native American genetic ancestry is clarified by focusing on individuals lacking the hypopigmenting alleles SLC24A5^A111T, SLC45A2^L374F, and OCA2^NW273KV (Figure 5). Although positive deviations from the best fit are apparent at both high and low Native American genetic ancestry, the trend toward lighter pigmentation as Native American genetic ancestry increases is clear. The net difference between African and Native American contributions to pigmentation appears likely to be bounded by the magnitudes of the slope vs NAM genetic ancestry (24 units) and the slope vs AFR genetic ancestry (29 units, not shown). The difference in melanin index value is expected to be explained by genetic variants that are highly differentiated between African and Native American populations.

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To further investigate the contributions of genetic variation to skin color, we performed association analyses using an additive model for melanin index, conditioning on sex, genetic ancestry (using 10 PCs), and genotypes for SLC24A5^A111T, SLC45A2^L374F, and OCA2^NW273KV. Assuming likely epistasis of albinism alleles over other hypopigmenting alleles, these analyses omitted the three albino individuals. Employing a linear regression model, we found that sex and all three genotyped polymorphisms were statistically significant (Table 2 and Figure 2—figure supplement 2). However, only SLC24A5^A111T reaches genome-wide significance. PC1, which strongly correlated with Native American vs African genetic ancestry, exhibits the lowest p-value. Effect sizes were about –6 units (per allele) for SLC24A5^A111T, –4 units for SLC45A2^L374F, and –8 units for the first OCA2^NW273KV allele.

Table 2

Covariate	Effect size (MI)	p-Value
rs1426654 (SLC24A5A111T)	–5.8	1.5E-12
rs16891982 (SLC45A2L374F)	–4.4	6.7E-05
Albino allele (OCA2NW273KV)	–7.7	2.2E-05
Sex (female vs male)	–2.4	5.0E-04

1. ^aPer allele effect size, in melanin units, for A111T and L374F; effect of first allele for albino variant.

Additional covariates were considered but not included in our standard model. Skin pigmentation exhibited a decreasing trend with age, but its contribution was not statistically significant (adjusted p-value = 0.08). Estimated effect sizes for significant covariates were little affected by the inclusion of age as a covariate (Appendix 1—table 5). Analysis of SNPs that were previously reported as relevant to pigmentation are shown in Appendix 2—table 1. The lowest (adjusted) p-value for this collection of variants is about 0.001, considerably larger than the p-values for the variants included as covariates in our standard model. Inclusion of the SNP of lowest p-value from each of the five regions containing BCN2, TYR, OCA2, MC1R, and OPRM1 only modestly altered effect sizes for the other covariates (Appendix 1—table 5).

The effect size for SLC24A5^A111T measured here is consistent with previously reported results of –5 melanin units calculated from an African-American sample (Lamason et al., 2005; Norton et al., 2007) and –5.5 from admixed inhabitants of the Cape Verde islands (Beleza et al., 2013). Reported effect sizes for continental Africans are both higher and lower, –7.7 in Crawford et al., 2017, and –3.6 Martin et al., 2017b, while the estimated effect size in the CANDELA study (GWAS of combined admixed populations from Mexico, Brazil, Columbia, Chile, and Peru) (Adhikari et al., 2019) yielded an effect size about –3 melanin units.

A significant effect of SLC45A2^L374F on skin pigmentation reported for the African-American sample by Norton et al., 2007, and in the CANDELA study by Adhikari et al., 2019, but not for the African Caribbean sample by Norton et al., 2007. The 4 unit effect size of this allele in the Kalinago reported here is similar to the 5 unit effect reported by Norton et al., 2007. Beleza et al., 2013 reported significance for an SNP in strong linkage disequilibrium with SLC45A2^L374F, which was itself not genotyped.

Our estimate that a single OCA2^NW273KV allele causes about –8 melanin units of skin lightening is the first reported population-based effect size measurement for any albinism allele. Although albinism is generally considered recessive, our population sample offered an opportunity to compare the effect size for the first and second alleles quantitatively. We applied the estimated parameters to the three albino individuals and found that they were lighter by an average of 10 uni nm, 05W homozygotes, when controlling for OCA2^NW273KV status, OCA2^R305W had no detectable effect on skin color (Appendix 2—table 1).

To identify novel SNPs that may contribute toward skin pigmentation in the Kalinago samples, we performed GWAS using linear regression and linear mixed models (LMMs). Estimated power for these analyses is shown in Figure 5—figure supplement 1, and Q-Q plots are depicted in Figure 5—figure supplement 2. The LMM approaches exhibited less statistic inflation than linear regression, likely because they better accounted for closely related individuals. Although the lowest p-values from the LMM-based methods meet the conventional criterion of 5e-08 for genome-wide significance (Appendix 3—table 1), our interpretation is that none of these variants warrant further investigation. Low observed minor allele frequencies (<2%) are inconsistent with those expected for variants responsible for pigmentation differences between the African and Native American populations because the frequencies of alleles responsible for population differences are expected to be highly differentiated between these source populations.

Additional Native American hypopigmenting alleles of significant effect size remain to be identified. Previously characterized variants do not explain this difference. It is possible that multiple hypopigmenting variants of small effect sizes are together required to reach Native American and/or East Asian levels of hypopigmentation, individually having insufficient effect to detect in the Kalinago, given our power limitations. If this is the case, multiple variants are required to explain the observed net difference in pigmentation. Alternatively, if there are variants with large effect sizes, it appears likely that they were not genotyped and are poorly tagged by the genotyped SNPs. Additional work will be required to find hypopigmentation alleles of significant effect size that are responsible for the lighter color of Native Americans.

The whole exome sequencing and whole genome SNP genotyping data underlying this article cannot be shared publicly due to the privacy of individuals and stipulation by the Kalinago community. Only de-identified filtered SNP data used in analyses will be shared. Additional data will be shared on request to the corresponding author, pending approval from the Kalinago Council. M-index and specific genotyping data (SLC24A5 A111T, SLC45A2 L374F, OCA2 NW273KV and OCA2 305W) and genotyping data for Admixture have been uploaded to Dryad https://doi.org/10.5061/dryad.sf7m0cg7z. The data cannot be used for any commercial purposes. We did not create any new software or script for analysis.

1. 1. Ang KC
   2. Canfield VA
   3. Foster TC
   4. Harbaugh TD
   5. Early KA
   6. Harter R
   7. Harbaugh T
   8. Early K
   9. Harter R
   10. Reid KP
   11. Leong S
   12. Imamura Kawasawa Y
   13. Liu D
   14. Hawley JW
   15. Cheng KC

   (2023) Dryad Digital Repository

   Native American Genetic Ancestry and Pigmentation Allele Contributions to Skin Color in a Caribbean Population.

   https://doi.org/10.5061/dryad.sf7m0cg7z

1. 1. Adhikari K
   2. Mendoza-Revilla J
   3. Sohail A
   4. Fuentes-Guajardo M
   5. Lampert J
   6. Chacón-Duque JC
   7. Hurtado M
   8. Villegas V
   9. Granja V
   10. Acuña-Alonzo V
   11. Jaramillo C
   12. Arias W
   13. Lozano RB
   14. Everardo P
   15. Gómez-Valdés J
   16. Villamil-Ramírez H
   17. Silva de Cerqueira CC
   18. Hunemeier T
   19. Ramallo V
   20. Schuler-Faccini L
   21. Salzano FM
   22. Gonzalez-José R
   23. Bortolini M-C
   24. Canizales-Quinteros S
   25. Gallo C
   26. Poletti G
   27. Bedoya G
   28. Rothhammer F
   29. Tobin DJ
   30. Fumagalli M
   31. Balding D
   32. Ruiz-Linares A

   (2019) A GWAS in Latin Americans highlights the Convergent evolution of lighter skin Pigmentation in Eurasia

   Nature Communications 10:358.

   https://doi.org/10.1038/s41467-018-08147-0

   • PubMed
   • Google Scholar
2. 1. Alexander DH
   2. Novembre J
   3. Lange K

   (2009) Fast model-based estimation of ancestry in unrelated individuals

   Genome Research 19:1655–1664.

   https://doi.org/10.1101/gr.094052.109

   • PubMed
   • Google Scholar
3. 1. Ang KC
   2. Ngu MS
   3. Reid KP
   4. Teh MS
   5. Aida ZS
   6. Koh DXR
   7. Berg A
   8. Oppenheimer S
   9. Salleh H
   10. Clyde MM
   11. Md-Zain BM
   12. Canfield VA
   13. Cheng KC
   14. Kivisild T

   (2012) Skin color variation in Orang Asli tribes of Peninsular Malaysia

   PLOS ONE 7:e42752.

   https://doi.org/10.1371/journal.pone.0042752

   • Google Scholar
4. 1. Auton A
   2. Brooks LD
   3. Durbin RM
   4. Garrison EP
   5. Kang HM
   6. Korbel JO
   7. Marchini JL
   8. McCarthy S
   9. McVean GA
   10. Abecasis GR
   11. 1000 Genomes Project Consortium

   (2015) A global reference for human genetic variation

   Nature 526:68–74.

   https://doi.org/10.1038/nature15393

   • PubMed
   • Google Scholar
5. 1. Barsh GS

   (2003) What controls variation in human skin color? Plos Biol 1:E27

   PLOS Biology 1:e27.

   https://doi.org/10.1371/journal.pbio.0000027

   • Google Scholar
6. 1. Beleza S
   2. Campos J
   3. Lopes J
   4. Araújo II
   5. Hoppfer Almada A
   6. Correia e Silva A
   7. Parra EJ
   8. Rocha J

   (2012) The Admixture structure and genetic variation of the archipelago of Cape Verde and its implications for Admixture mapping studies

   PLOS ONE 7:e51103.

   https://doi.org/10.1371/journal.pone.0051103

   • PubMed
   • Google Scholar
7. 1. Beleza S
   2. Santos AM
   3. McEvoy B
   4. Alves I
   5. Martinho C
   6. Cameron E
   7. Shriver MD
   8. Parra EJ
   9. Rocha J

   (2013) The timing of Pigmentation lightening in Europeans

   Molecular Biology and Evolution 30:24–35.

   https://doi.org/10.1093/molbev/mss207

   • PubMed
   • Google Scholar
8. 1. Benn-Torres J
   2. Bonilla C
   3. Robbins CM
   4. Waterman L
   5. Moses TY
   6. Hernandez W
   7. Santos ER
   8. Bennett F
   9. Aiken W
   10. Tullock T
   11. Coard K
   12. Hennis A
   13. Wu S
   14. Nemesure B
   15. Leske MC
   16. Freeman V
   17. Carpten J
   18. Kittles RA

   (2008) Admixture and population stratification in African Caribbean populations

   Annals of Human Genetics 72:90–98.

   https://doi.org/10.1111/j.1469-1809.2007.00398.x

   • PubMed
   • Google Scholar
9. 1. Benn Torres JB
   2. Stone AC
   3. Kittles R

   (2013) An anthropological genetic perspective on Creolization in the Anglophone Caribbean

   American Journal of Physical Anthropology 151:135–143.

   https://doi.org/10.1002/ajpa.22261

   • PubMed
   • Google Scholar
10. 1. Benn Torres J
    2. Vilar MG
    3. Torres GA
    4. Gaieski JB
    5. Bharath Hernandez R
    6. Browne ZE
    7. Stevenson M
    8. Walters W
    9. Schurr TG
    10. Genographic Consortium

    (2015) Genetic diversity in the lesser Antilles and its implications for the settlement of the Caribbean Basin

    PLOS ONE 10:e0139192.

    https://doi.org/10.1371/journal.pone.0139192

    • PubMed
    • Google Scholar
11. 1. Brown LA
    2. Sofer T
    3. Stilp AM
    4. Baier LJ
    5. Kramer HJ
    6. Masindova I
    7. Levy D
    8. Hanson RL
    9. Moncrieft AE
    10. Redline S
    11. Rosas SE
    12. Lash JP
    13. Cai J
    14. Laurie CC
    15. Browning S
    16. Thornton T
    17. Franceschini N

    (2017) Admixture mapping identifies an Amerindian ancestry locus associated with albuminuria in Hispanics in the United States

    Journal of the American Society of Nephrology 28:2211–2220.

    https://doi.org/10.1681/ASN.2016091010

    • PubMed
    • Google Scholar
12. 1. Browning SR
    2. Browning BL

    (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome Association studies by use of localized haplotype clustering

    The American Journal of Human Genetics 81:1084–1097.

    https://doi.org/10.1086/521987

    • Google Scholar
13. 1. Browning BL
    2. Browning SR

    (2013) Improving the accuracy and efficiency of identity-by-descent detection in population data

    Genetics 194:459–471.

    https://doi.org/10.1534/genetics.113.150029

    • PubMed
    • Google Scholar
14. 1. Carrasco A
    2. Forbes EM
    3. Jeambrun P
    4. Brilliant MH

    (2009) A splice site Mutation is the cause of the high prevalence of Oculocutaneous Albinism type 2 in the KUNA population

    Pigment Cell & Melanoma Research 22:645–647.

    https://doi.org/10.1111/j.1755-148X.2009.00575.x

    • PubMed
    • Google Scholar
15. 1. Chang CC
    2. Chow CC
    3. Tellier LC
    4. Vattikuti S
    5. Purcell SM
    6. Lee JJ

    (2015) Second-generation PLINK: rising to the challenge of larger and richer Datasets

    GigaScience 4:7.

    https://doi.org/10.1186/s13742-015-0047-8

    • Google Scholar
16. 1. Cheung KH
    2. Miller PL
    3. Kidd JR
    4. Kidd KK
    5. Osier MV
    6. Pakstis AJ

    (2000) ALFRED: a web-accessible allele frequency database

    Pacific Symposium on Biocomputing 2000:639–650.

    https://doi.org/10.1142/9789814447331_0062

    • PubMed
    • Google Scholar
17. 1. Choe YB
    2. Jang SJ
    3. Jo SJ
    4. Ahn KJ
    5. Youn JI

    (2006) The difference between the Constitutive and Facultative skin color does not reflect skin Phototype in Asian skin

    Skin Research and Technology 12:68–72.

    https://doi.org/10.1111/j.0909-725X.2006.00167.x

    • PubMed
    • Google Scholar
18. 1. Crawford NG
    2. Kelly DE
    3. Hansen MEB
    4. Beltrame MH
    5. Fan S
    6. Bowman SL
    7. Jewett E
    8. Ranciaro A
    9. Thompson S
    10. Lo Y
    11. Pfeifer SP
    12. Jensen JD
    13. Campbell MC
    14. Beggs W
    15. Hormozdiari F
    16. Mpoloka SW
    17. Mokone GG
    18. Nyambo T
    19. Meskel DW
    20. Belay G
    21. Haut J
    22. Rothschild H
    23. Zon L
    24. Zhou Y
    25. Kovacs MA
    26. Xu M
    27. Zhang T
    28. Bishop K
    29. Sinclair J
    30. Rivas C
    31. Elliot E
    32. Choi J
    33. Li SA
    34. Hicks B
    35. Burgess S
    36. Abnet C
    37. Watkins-Chow DE
    38. Oceana E
    39. Song YS
    40. Eskin E
    41. Brown KM
    42. Marks MS
    43. Loftus SK
    44. Pavan WJ
    45. Yeager M
    46. Chanock S
    47. Tishkoff SA
    48. NISC Comparative Sequencing Program

    (2017) Loci associated with skin Pigmentation identified in African populations

    Science 358:eaan8433.

    https://doi.org/10.1126/science.aan8433

    • PubMed
    • Google Scholar
19. Book

    1. Crawford MH
    2. Phillips-Krawczak C
    3. Beaty KG
    4. Boaz N

    (2021) Human migration

    In: Beaty KG, editors. Migration of Garifuna: Evolutionary Success StoryHuman Migration. New York: Oxford University Press. 153.

    https://doi.org/10.1093/oso/9780190945961.003.0013

    • Google Scholar
20. 1. Derenko M
    2. Malyarchuk B
    3. Grzybowski T
    4. Denisova G
    5. Rogalla U
    6. Perkova M
    7. Dambueva I
    8. Zakharov I

    (2010) Origin and post-Glacial dispersal of mitochondrial DNA Haplogroups C and D in northern Asia

    PLOS ONE 5:e15214.

    https://doi.org/10.1371/journal.pone.0015214

    • PubMed
    • Google Scholar
21. 1. Dessinioti C
    2. Stratigos AJ
    3. Rigopoulos D
    4. Katsambas AD

    (2009) A review of genetic disorders of Hypopigmentation: lessons learned from the biology of Melanocytes

    Experimental Dermatology 18:741–749.

    https://doi.org/10.1111/j.1600-0625.2009.00896.x

    • PubMed
    • Google Scholar
22. 1. Diffey BL
    2. Oliver RJ
    3. Farr PM

    (1984) A portable instrument for Quantifying erythema induced by ultraviolet radiation

    The British Journal of Dermatology 111:663–672.

    https://doi.org/10.1111/j.1365-2133.1984.tb14149.x

    • PubMed
    • Google Scholar
23. 1. Edwards M
    2. Bigham A
    3. Tan J
    4. Li S
    5. Gozdzik A
    6. Ross K
    7. Jin L
    8. Parra EJ

    (2010) Association of the Oca2 polymorphism His615Arg with Melanin content in East Asian populations: further evidence of Convergent evolution of skin Pigmentation

    PLOS Genetics 6:e1000867.

    https://doi.org/10.1371/journal.pgen.1000867

    • PubMed
    • Google Scholar
24. 1. Engelsen O

    (2010) The relationship between ultraviolet radiation exposure and vitamin D status

    Nutrients 2:482–495.

    https://doi.org/10.3390/nu2050482

    • PubMed
    • Google Scholar
25. 1. Fullerton A
    2. Fischer T
    3. Lahti A
    4. Wilhelm KP
    5. Takiwaki H
    6. Serup J

    (1996) Guidelines for measurement of skin colour and erythema. A report from the Standardization Group of the European Society of Contact Dermatitis

    Contact Dermatitis 35:1–10.

    https://doi.org/10.1111/j.1600-0536.1996.tb02258.x

    • PubMed
    • Google Scholar
26. 1. Gao J
    2. D’Souza L
    3. Wetherby K
    4. Antolik C
    5. Reeves M
    6. Adams DR
    7. Tumminia S
    8. Wang X

    (2017) Retrospective analysis in Oculocutaneous Albinism patients for the 2.7 KB deletion in the Oca2 Gene revealed a Co-segregation of the controversial variant

    Cell & Bioscience 7:22.

    https://doi.org/10.1186/s13578-017-0149-3

    • PubMed
    • Google Scholar
27. 1. Gargiulo A
    2. Testa F
    3. Rossi S
    4. Di Iorio V
    5. Fecarotta S
    6. de Berardinis T
    7. Iovine A
    8. Magli A
    9. Signorini S
    10. Fazzi E
    11. Galantuomo MS
    12. Fossarello M
    13. Montefusco S
    14. Ciccodicola A
    15. Neri A
    16. Macaluso C
    17. Simonelli F
    18. Surace EM

    (2011) Molecular and clinical characterization of Albinism in a large cohort of Italian patients

    Investigative Opthalmology & Visual Science 52:1281.

    https://doi.org/10.1167/iovs.10-6091

    • Google Scholar
28. 1. Garrison NA
    2. Yi Z
    3. Cohen-Barak O
    4. Huizing M
    5. Hartnell LM
    6. Gahl WA
    7. Brilliant MH

    (2004) P Gene mutations in patients with Oculocutaneous Albinism and findings suggestive of Hermansky-Pudlak syndrome

    Journal of Medical Genetics 41:e86.

    https://doi.org/10.1136/jmg.2003.014902

    • PubMed
    • Google Scholar
29. 1. Gravel S
    2. Zakharia F
    3. Moreno-Estrada A
    4. Byrnes JK
    5. Muzzio M
    6. Rodriguez-Flores JL
    7. Kenny EE
    8. Gignoux CR
    9. Maples BK
    10. Guiblet W
    11. Dutil J
    12. Via M
    13. Sandoval K
    14. Bedoya G
    15. Oleksyk TK
    16. Ruiz-Linares A
    17. Burchard EG
    18. Martinez-Cruzado JC
    19. Bustamante CD
    20. 1000 Genomes Project

    (2013) Reconstructing native American migrations from whole-genome and whole-Exome data

    PLOS Genetics 9:e1004023.

    https://doi.org/10.1371/journal.pgen.1004023

    • PubMed
    • Google Scholar
30. 1. Greaves M

    (2014) Was skin cancer a selective force for black Pigmentation in early Hominin evolution

    Proceedings Biological Sciences 281:20132955.

    https://doi.org/10.1098/rspb.2013.2955

    • PubMed
    • Google Scholar
31. 1. Grønskov K
    2. Ek J
    3. Brondum-Nielsen K

    (2007) Oculocutaneous Albinism

    Orphanet Journal of Rare Diseases 2:43.

    https://doi.org/10.1186/1750-1172-2-43

    • PubMed
    • Google Scholar
32. 1. Grønskov K
    2. Dooley CM
    3. Østergaard E
    4. Kelsh RN
    5. Hansen L
    6. Levesque MP
    7. Vilhelmsen K
    8. Møllgård K
    9. Stemple DL
    10. Rosenberg T

    (2013) Mutations in C10Orf11, a melanocyte-differentiation gene, cause autosomal-recessive albinism

    American Journal of Human Genetics 92:415–421.

    https://doi.org/10.1016/j.ajhg.2013.01.006

    • PubMed
    • Google Scholar
33. 1. Grønskov K
    2. Brøndum-Nielsen K
    3. Lorenz B
    4. Preising MN

    (2014) Clinical utility gene card for: oculocutaneous albinism

    European Journal of Human Genetics 22:307.

    https://doi.org/10.1038/ejhg.2013.307

    • PubMed
    • Google Scholar
34. 1. Han J
    2. Kraft P
    3. Nan H
    4. Guo Q
    5. Chen C
    6. Qureshi A
    7. Hankinson SE
    8. Hu FB
    9. Duffy DL
    10. Zhao ZZ
    11. Martin NG
    12. Montgomery GW
    13. Hayward NK
    14. Thomas G
    15. Hoover RN
    16. Chanock S
    17. Hunter DJ

    (2008) A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation

    PLOS Genetics 4:e1000074.

    https://doi.org/10.1371/journal.pgen.1000074

    • PubMed
    • Google Scholar
35. 1. Hanel A
    2. Carlberg C

    (2020) Skin colour and vitamin D: an update

    Experimental Dermatology 29:864–875.

    https://doi.org/10.1111/exd.14142

    • PubMed
    • Google Scholar
36. 1. Harvey RG
    2. Godber MJ
    3. Kopeć AC
    4. Mourant AE
    5. Tills D

    (1969) Frequency of genetic traits in the Caribs of Dominica

    Human Biology 41:342–364.

    https://doi.org/10.1007/BF00278729

    • PubMed
    • Google Scholar
37. 1. Holick MF

    (1981) The cutaneous Photosynthesis of Previtamin D3: a unique Photoendocrine system

    The Journal of Investigative Dermatology 77:51–58.

    https://doi.org/10.1111/1523-1747.ep12479237

    • PubMed
    • Google Scholar
38. 1. Hong ES
    2. Zeeb H
    3. Repacholi MH

    (2006) Albinism in Africa as a public health issue

    BMC Public Health 6:212.

    https://doi.org/10.1186/1471-2458-6-212

    • PubMed
    • Google Scholar
39. Book

    1. Honychurch L

    (1998)

    Review of the lesser Antilles in the age of European expansion

    In: Honychurch L, editors. NWIG: New West Indian Guide / Nieuwe West-Indische Gids. Brill. pp. 305–307.

    • Google Scholar
40. Book

    1. Honychurch L

    (2012)

    The Dominica Story: A History of the Island

    Macmillan.

    • Google Scholar
41. Website

    1. Island Caribs

    (2016) Wikipedia, the free encyclopedia

    Accessed September 8, 2016.

    https://en.wikipedia.org/wiki/Kalinago
42. 1. Jablonski NG
    2. Chaplin G

    (2000) The evolution of human skin coloration

    Journal of Human Evolution 39:57–106.

    https://doi.org/10.1006/jhev.2000.0403

    • PubMed
    • Google Scholar
43. 1. Ju D
    2. Mathieson I

    (2021) The evolution of skin pigmentation-associated variation in West Eurasia

    PNAS 118:e2009227118.

    https://doi.org/10.1073/pnas.2009227118

    • PubMed
    • Google Scholar
44. 1. Kamaraj B
    2. Purohit R

    (2014) Computational screening of disease-associated mutations in Oca2 Gene

    Cell Biochemistry and Biophysics 68:97–109.

    https://doi.org/10.1007/s12013-013-9697-2

    • PubMed
    • Google Scholar
45. 1. Kausar T
    2. Bhatti MA
    3. Ali M
    4. Shaikh RS
    5. Ahmed ZM

    (2013) Oca5, a novel locus for non-Syndromic Oculocutaneous Albinism, maps to Chromosome 4Q24

    Clinical Genetics 84:91–93.

    https://doi.org/10.1111/cge.12019

    • PubMed
    • Google Scholar
46. 1. Keith MH
    2. Flinn MV
    3. Durbin HJ
    4. Rowan TN
    5. Blomquist GE
    6. Taylor KH
    7. Taylor JF
    8. Decker JE

    (2021) Genetic ancestry, Admixture, and population structure in rural Dominica

    PLOS ONE 16:e0258735.

    https://doi.org/10.1371/journal.pone.0258735

    • PubMed
    • Google Scholar
47. 1. King RA
    2. Pietsch J
    3. Fryer JP
    4. Savage S
    5. Brott MJ
    6. Russell-Eggitt I
    7. Summers CG
    8. Oetting WS

    (2003) Tyrosinase Gene mutations in Oculocutaneous Albinism 1 (Oca1): definition of the phenotype

    Human Genetics 113:502–513.

    https://doi.org/10.1007/s00439-003-0998-1

    • Google Scholar
48. 1. Klimentidis YC
    2. Miller GF
    3. Shriver MD

    (2009) Genetic Admixture, self-reported Ethnicity, self-estimated Admixture, and skin Pigmentation among Hispanics and native Americans

    American Journal of Physical Anthropology 138:375–383.

    https://doi.org/10.1002/ajpa.20945

    • PubMed
    • Google Scholar
49. 1. Lamason RL
    2. Mohideen M
    3. Mest JR
    4. Wong AC
    5. Norton HL
    6. Aros MC
    7. Jurynec MJ
    8. Mao X
    9. Humphreville VR
    10. Humbert JE
    11. Sinha S
    12. Moore JL
    13. Jagadeeswaran P
    14. Zhao W
    15. Ning G
    16. Makalowska I
    17. McKeigue PM
    18. O’donnell D
    19. Kittles R
    20. Parra EJ
    21. Mangini NJ
    22. Grunwald DJ
    23. Shriver MD
    24. Canfield VA
    25. Cheng KC

    (2005) Slc24A5, a putative Cation exchanger, affects Pigmentation in Zebrafish and humans

    Science 310:1782–1786.

    https://doi.org/10.1126/science.1116238

    • PubMed
    • Google Scholar
50. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • Google Scholar
51. 1. Lee ST
    2. Nicholls RD
    3. Schnur RE
    4. Guida LC
    5. Lu-Kuo J
    6. Spinner NB
    7. Zackai EH
    8. Spritz RA

    (1994)

    Diverse mutations of the P gene among African-Americans with type II

    Human Molecular Genetics 3:2047–2051.

    • Google Scholar
52. 1. Li JZ
    2. Absher DM
    3. Tang H
    4. Southwick AM
    5. Casto AM
    6. Ramachandran S
    7. Cann HM
    8. Barsh GS
    9. Feldman M
    10. Cavalli-Sforza LL
    11. Myers RM

    (2008) Worldwide human relationships inferred from genome-wide patterns of variation

    Science 319:1100–1104.

    https://doi.org/10.1126/science.1153717

    • PubMed
    • Google Scholar
53. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with burrows–Wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
54. 1. Liu F
    2. Visser M
    3. Duffy DL
    4. Hysi PG
    5. Jacobs LC
    6. Lao O
    7. Zhong K
    8. Walsh S
    9. Chaitanya L
    10. Wollstein A
    11. Zhu G
    12. Montgomery GW
    13. Henders AK
    14. Mangino M
    15. Glass D
    16. Bataille V
    17. Sturm RA
    18. Rivadeneira F
    19. Hofman A
    20. van IJcken WFJ
    21. Uitterlinden AG
    22. Palstra R
    23. Spector TD
    24. Martin NG
    25. Nijsten TEC
    26. Kayser M

    (2015) Genetics of skin color variation in Europeans: genome-wide association studies with functional follow-up

    Human Genetics 134:823–835.

    https://doi.org/10.1007/s00439-015-1559-0

    • PubMed
    • Google Scholar
55. 1. Loomis WF

    (1967) Skin-pigment regulation of vitamin-D biosynthesis in man

    Science 157:501–506.

    https://doi.org/10.1126/science.157.3788.501

    • PubMed
    • Google Scholar
56. 1. Maples BK
    2. Gravel S
    3. Kenny EE
    4. Bustamante CD

    (2013) Rfmix: A Discriminative modeling approach for rapid and robust local-ancestry inference

    American Journal of Human Genetics 93:278–288.

    https://doi.org/10.1016/j.ajhg.2013.06.020

    • PubMed
    • Google Scholar
57. 1. Marcheco-Teruel B
    2. Parra EJ
    3. Fuentes-Smith E
    4. Salas A
    5. Buttenschøn HN
    6. Demontis D
    7. Torres-Español M
    8. Marín-Padrón LC
    9. Gómez-Cabezas EJ
    10. Alvarez-Iglesias V
    11. Mosquera-Miguel A
    12. Martínez-Fuentes A
    13. Carracedo A
    14. Børglum AD
    15. Mors O

    (2014) Cuba: exploring the history of Admixture and the genetic basis of Pigmentation using Autosomal and Uniparental markers

    PLOS Genetics 10:e1004488.

    https://doi.org/10.1371/journal.pgen.1004488

    • PubMed
    • Google Scholar
58. 1. Martin AR
    2. Gignoux CR
    3. Walters RK
    4. Wojcik GL
    5. Neale BM
    6. Gravel S
    7. Daly MJ
    8. Bustamante CD
    9. Kenny EE

    (2017a) Human demographic history impacts genetic risk prediction across diverse populations

    American Journal of Human Genetics 100:635–649.

    https://doi.org/10.1016/j.ajhg.2017.03.004

    • PubMed
    • Google Scholar
59. 1. Martin AR
    2. Lin M
    3. Granka JM
    4. Myrick JW
    5. Liu X
    6. Sockell A
    7. Atkinson EG
    8. Werely CJ
    9. Möller M
    10. Sandhu MS
    11. Kingsley DM
    12. Hoal EG
    13. Liu X
    14. Daly MJ
    15. Feldman MW
    16. Gignoux CR
    17. Bustamante CD
    18. Henn BM

    (2017b) An unexpectedly complex architecture for skin pigmentation in Africans

    Cell 171:1340–1353.

    https://doi.org/10.1016/j.cell.2017.11.015

    • PubMed
    • Google Scholar
60. 1. McKenna A
    2. Hanna M
    3. Banks E
    4. Sivachenko A
    5. Cibulskis K
    6. Kernytsky A
    7. Garimella K
    8. Altshuler D
    9. Gabriel S
    10. Daly M
    11. DePristo MA

    (2010) The genome analysis Toolkit: A Mapreduce framework for analyzing next-generation DNA sequencing data

    Genome Research 20:1297–1303.

    https://doi.org/10.1101/gr.107524.110

    • PubMed
    • Google Scholar
61. 1. Micheletti SJ
    2. Bryc K
    3. Ancona Esselmann SG
    4. Freyman WA
    5. Moreno ME
    6. Poznik GD
    7. Shastri AJ
    8. 23andMe Research Team
    9. Beleza S
    10. Mountain JL

    (2020) Genetic consequences of the transatlantic slave trade in the Americas

    American Journal of Human Genetics 107:265–277.

    https://doi.org/10.1016/j.ajhg.2020.06.012

    • PubMed
    • Google Scholar
62. 1. Moreno-Mayar JV
    2. Potter BA
    3. Vinner L
    4. Steinrücken M
    5. Rasmussen S
    6. Terhorst J
    7. Kamm JA
    8. Albrechtsen A
    9. Malaspinas AS
    10. Sikora M
    11. Reuther JD
    12. Irish JD
    13. Malhi RS
    14. Orlando L
    15. Song YS
    16. Nielsen R
    17. Meltzer DJ
    18. Willerslev E

    (2018) Terminal Pleistocene Alaskan genome reveals first founding population of native Americans

    Nature 553:203–207.

    https://doi.org/10.1038/nature25173

    • PubMed
    • Google Scholar
63. 1. Norton HL
    2. Kittles RA
    3. Parra E
    4. McKeigue P
    5. Mao X
    6. Cheng K
    7. Canfield VA
    8. Bradley DG
    9. McEvoy B
    10. Shriver MD

    (2007) Genetic evidence for the Convergent evolution of light skin in Europeans and East Asians

    Molecular Biology and Evolution 24:710–722.

    https://doi.org/10.1093/molbev/msl203

    • PubMed
    • Google Scholar
64. 1. Park JH
    2. Lee MH

    (2005) A study of skin color by Melanin index according to site, gestational age, birth weight and season of birth in Korean neonates

    Journal of Korean Medical Science 20:105–108.

    https://doi.org/10.3346/jkms.2005.20.1.105

    • PubMed
    • Google Scholar
65. 1. Price AL
    2. Patterson NJ
    3. Plenge RM
    4. Weinblatt ME
    5. Shadick NA
    6. Reich D

    (2006) Principal components analysis corrects for stratification in genome-wide Association studies

    Nature Genetics 38:904–909.

    https://doi.org/10.1038/ng1847

    • PubMed
    • Google Scholar
66. 1. Purcell S
    2. Neale B
    3. Todd-Brown K
    4. Thomas L
    5. Ferreira MAR
    6. Bender D
    7. Maller J
    8. Sklar P
    9. de Bakker PIW
    10. Daly MJ
    11. Sham PC

    (2007) PLINK: A tool set for whole-genome Association and population-based linkage analyses

    American Journal of Human Genetics 81:559–575.

    https://doi.org/10.1086/519795

    • PubMed
    • Google Scholar
67. 1. Quillen EE
    2. Bauchet M
    3. Bigham AW
    4. Delgado-Burbano ME
    5. Faust FX
    6. Klimentidis YC
    7. Mao X
    8. Stoneking M
    9. Shriver MD

    (2012) Oprm1 and EGFR contribute to skin Pigmentation differences between indigenous Americans and Europeans

    Human Genetics 131:1073–1080.

    https://doi.org/10.1007/s00439-011-1135-1

    • PubMed
    • Google Scholar
68. 1. Quillen EE
    2. Norton HL
    3. Parra EJ
    4. Lona-Durazo F
    5. Ang KC
    6. Illiescu FM
    7. Pearson LN
    8. Shriver MD
    9. Lasisi T
    10. Gokcumen O
    11. Starr I
    12. Lin YL
    13. Martin AR
    14. Jablonski NG

    (2019) Shades of complexity: new perspectives on the evolution and genetic architecture of human skin

    American Journal of Physical Anthropology 168 Suppl 67:4–26.

    https://doi.org/10.1002/ajpa.23737

    • PubMed
    • Google Scholar
69. 1. Rajeevan H
    2. Soundararajan U
    3. Kidd JR
    4. Pakstis AJ
    5. Kidd KK

    (2012) ALFRED: an allele frequency resource for research and teaching

    Nucleic Acids Research 40:D1010–D1015.

    https://doi.org/10.1093/nar/gkr924

    • PubMed
    • Google Scholar
70. 1. Reich D
    2. Patterson N
    3. Campbell D
    4. Tandon A
    5. Mazieres S
    6. Ray N
    7. Parra MV
    8. Rojas W
    9. Duque C
    10. Mesa N
    11. García LF
    12. Triana O
    13. Blair S
    14. Maestre A
    15. Dib JC
    16. Bravi CM
    17. Bailliet G
    18. Corach D
    19. Hünemeier T
    20. Bortolini MC
    21. Salzano FM
    22. Petzl-Erler ML
    23. Acuña-Alonzo V
    24. Aguilar-Salinas C
    25. Canizales-Quinteros S
    26. Tusié-Luna T
    27. Riba L
    28. Rodríguez-Cruz M
    29. Lopez-Alarcón M
    30. Coral-Vazquez R
    31. Canto-Cetina T
    32. Silva-Zolezzi I
    33. Fernandez-Lopez JC
    34. Contreras AV
    35. Jimenez-Sanchez G
    36. Gómez-Vázquez MJ
    37. Molina J
    38. Carracedo A
    39. Salas A
    40. Gallo C
    41. Poletti G
    42. Witonsky DB
    43. Alkorta-Aranburu G
    44. Sukernik RI
    45. Osipova L
    46. Fedorova SA
    47. Vasquez R
    48. Villena M
    49. Moreau C
    50. Barrantes R
    51. Pauls D
    52. Excoffier L
    53. Bedoya G
    54. Rothhammer F
    55. Dugoujon JM
    56. Larrouy G
    57. Klitz W
    58. Labuda D
    59. Kidd J
    60. Kidd K
    61. Di Rienzo A
    62. Freimer NB
    63. Price AL
    64. Ruiz-Linares A

    (2012) Reconstructing native American population history

    Nature 488:370–374.

    https://doi.org/10.1038/nature11258

    • PubMed
    • Google Scholar
71. 1. Rinchik EM
    2. Bultman SJ
    3. Horsthemke B
    4. Lee ST
    5. Strunk KM
    6. Spritz RA
    7. Avidano KM
    8. Jong MT
    9. Nicholls RD

    (1993) A Gene for the mouse pink-eyed dilution locus and for human type II Oculocutaneous Albinism

    Nature 361:72–76.

    https://doi.org/10.1038/361072a0

    • PubMed
    • Google Scholar
72. Book

    1. Rogoziński J

    (2000)

    A brief history of the Caribbean: from the Arawak and Carib to the present: Rosenberg NA

    In: Rogoziński J, editors. Annals of Human Genetics. Wiley. pp. 841–847.

    • Google Scholar
73. 1. Rosenberg NA

    (2006) Standardized subsets of the HGDP-CEPH Human Genome Diversity Cell Line Panel, accounting for atypical and duplicated samples and pairs of close relatives

    Annals of Human Genetics 70:841–847.

    https://doi.org/10.1111/j.1469-1809.2006.00285.x

    • PubMed
    • Google Scholar
74. 1. Schroeder H
    2. Sikora M
    3. Gopalakrishnan S
    4. Cassidy LM
    5. Maisano Delser P
    6. Sandoval Velasco M
    7. Schraiber JG
    8. Rasmussen S
    9. Homburger JR
    10. Ávila-Arcos MC
    11. Allentoft ME
    12. Moreno-Mayar JV
    13. Renaud G
    14. Gómez-Carballa A
    15. Laffoon JE
    16. Hopkins RJA
    17. Higham TFG
    18. Carr RS
    19. Schaffer WC
    20. Day JS
    21. Hoogland M
    22. Salas A
    23. Bustamante CD
    24. Nielsen R
    25. Bradley DG
    26. Hofman CL
    27. Willerslev E

    (2018) Origins and genetic legacies of the Caribbean Taino.PNAS 201716839

    PNAS 115:2341–2346.

    https://doi.org/10.1073/pnas.1716839115

    • PubMed
    • Google Scholar
75. 1. Shriver MD
    2. Parra EJ
    3. Dios S
    4. Bonilla C
    5. Norton H
    6. Jovel C
    7. Pfaff C
    8. Jones C
    9. Massac A
    10. Cameron N
    11. Baron A
    12. Jackson T
    13. Argyropoulos G
    14. Jin L
    15. Hoggart CJ
    16. McKeigue PM
    17. Kittles RA

    (2003) Skin pigmentation, biogeographical ancestry and admixture mapping

    Human Genetics 112:387–399.

    https://doi.org/10.1007/s00439-002-0896-y

    • PubMed
    • Google Scholar
76. 1. Soejima M
    2. Koda Y

    (2007) Population differences of two coding SNPs in Pigmentation-related genes Slc24A5 and Slc45A2

    International Journal of Legal Medicine 121:36–39.

    https://doi.org/10.1007/s00414-006-0112-z

    • PubMed
    • Google Scholar
77. 1. Spritz RA
    2. Fukai K
    3. Holmes SA
    4. Luande J

    (1995)

    Frequent intragenic deletion of the p gene in tanzanian patients with type ii oculocutaneous albinism (Oca2)

    American Journal of Human Genetics 56:1320–1323.

    • PubMed
    • Google Scholar
78. 1. Stevens G
    2. van Beukering J
    3. Jenkins T
    4. Ramsay M

    (1995)

    An intragenic deletion of the p gene is the common mutation causing tyrosinase-positive oculocutaneous albinism in southern african negroids

    American Journal of Human Genetics 56:586–591.

    • PubMed
    • Google Scholar
79. 1. Stevens G
    2. Ramsay M
    3. Jenkins T

    (1997) Oculocutaneous Albinism (Oca2) in sub-Saharan Africa: distribution of the common 2.7-KB P gene deletion Mutation

    Human Genetics 99:523–527.

    https://doi.org/10.1007/s004390050400

    • PubMed
    • Google Scholar
80. 1. Stokowski RP
    2. Pant PVK
    3. Dadd T
    4. Fereday A
    5. Hinds DA
    6. Jarman C
    7. Filsell W
    8. Ginger RS
    9. Green MR
    10. van der Ouderaa FJ
    11. Cox DR

    (2007) A genomewide association study of skin pigmentation in A South Asian population

    American Journal of Human Genetics 81:1119–1132.

    https://doi.org/10.1086/522235

    • PubMed
    • Google Scholar
81. 1. Sturm RA

    (2009) Molecular genetics of human pigmentation diversity

    Human Molecular Genetics 18:9–17.

    https://doi.org/10.1093/hmg/ddp003

    • PubMed
    • Google Scholar
82. 1. Sulem P
    2. Gudbjartsson DF
    3. Stacey SN
    4. Helgason A
    5. Rafnar T
    6. Magnusson KP
    7. Manolescu A
    8. Karason A
    9. Palsson A
    10. Thorleifsson G
    11. Jakobsdottir M
    12. Steinberg S
    13. Pálsson S
    14. Jonasson F
    15. Sigurgeirsson B
    16. Thorisdottir K
    17. Ragnarsson R
    18. Benediktsdottir KR
    19. Aben KK
    20. Kiemeney LA
    21. Olafsson JH
    22. Gulcher J
    23. Kong A
    24. Thorsteinsdottir U
    25. Stefansson K

    (2007) Genetic determinants of hair, eye and skin pigmentation in Europeans

    Nature Genetics 39:1443–1452.

    https://doi.org/10.1038/ng.2007.13

    • PubMed
    • Google Scholar
83. 1. Tamm E
    2. Kivisild T
    3. Reidla M
    4. Metspalu M
    5. Smith DG
    6. Mulligan CJ
    7. Bravi CM
    8. Rickards O
    9. Martinez-Labarga C
    10. Khusnutdinova EK
    11. Fedorova SA
    12. Golubenko MV
    13. Stepanov VA
    14. Gubina MA
    15. Zhadanov SI
    16. Ossipova LP
    17. Damba L
    18. Voevoda MI
    19. Dipierri JE
    20. Villems R
    21. Malhi RS
    22. Carter D

    (2007) Beringian standstill and spread of native American founders

    PLOS ONE 2:e829.

    https://doi.org/10.1371/journal.pone.0000829

    • PubMed
    • Google Scholar
84. Website

    1. Territory K

    (2021) Wikipedia

    Accessed July 8, 2021.

    https://www.wikipedia.org/
85. 1. Thornton T
    2. Tang H
    3. Hoffmann TJ
    4. Ochs-Balcom HM
    5. Caan BJ
    6. Risch N

    (2012) Estimating kinship in admixed populations

    American Journal of Human Genetics 91:122–138.

    https://doi.org/10.1016/j.ajhg.2012.05.024

    • PubMed
    • Google Scholar
86. 1. Thorvaldsdóttir H
    2. Robinson JT
    3. Mesirov JP

    (2013) Integrative Genomics viewer (IGV): high-performance Genomics data visualization and exploration

    Briefings in Bioinformatics 14:178–192.

    https://doi.org/10.1093/bib/bbs017

    • PubMed
    • Google Scholar
87. 1. Tsetskhladze ZR
    2. Canfield VA
    3. Ang KC
    4. Wentzel SM
    5. Reid KP
    6. Berg AS
    7. Johnson SL
    8. Kawakami K
    9. Cheng KC

    (2012) Functional assessment of human coding mutations affecting skin Pigmentation using Zebrafish

    PLOS ONE 7:e47398.

    https://doi.org/10.1371/journal.pone.0047398

    • Google Scholar
88. 1. van Geel N
    2. Speeckaert M
    3. Chevolet I
    4. De Schepper S
    5. Lapeere H
    6. Boone B
    7. Speeckaert R

    (2013) Hypomelanoses in children

    Journal of Cutaneous and Aesthetic Surgery 6:65–72.

    https://doi.org/10.4103/0974-2077.112665

    • PubMed
    • Google Scholar
89. 1. Vogel P
    2. Read RW
    3. Vance RB
    4. Platt KA
    5. Troughton K
    6. Rice DS

    (2008) Ocular Albinism and Hypopigmentation defects in Slc24A5-/- mice

    Veterinary Pathology 45:264–279.

    https://doi.org/10.1354/vp.45-2-264

    • PubMed
    • Google Scholar
90. 1. Woolf CM

    (2005) Albinism (Oca2) in Amerindians

    American Journal of Physical Anthropology Suppl 41:118–140.

    https://doi.org/10.1002/ajpa.20357

    • PubMed
    • Google Scholar
91. 1. Yang J
    2. Lee SH
    3. Goddard ME
    4. Visscher PM

    (2011) GCTA: A tool for genome-wide complex trait analysis

    American Journal of Human Genetics 88:76–82.

    https://doi.org/10.1016/j.ajhg.2010.11.011

    • PubMed
    • Google Scholar
92. 1. Yi Z
    2. Garrison N
    3. Cohen-Barak O
    4. Karafet TM
    5. King RA
    6. Erickson RP
    7. Hammer MF
    8. Brilliant MH

    (2003) A 122.5-Kilobase deletion of the P Gene underlies the high prevalence of Oculocutaneous Albinism type 2 in the Navajo population

    American Journal of Human Genetics 72:62–72.

    https://doi.org/10.1086/345380

    • PubMed
    • Google Scholar
93. 1. Zhou H
    2. Alexander D
    3. Lange K

    (2011) A quasi-Newton acceleration for high-dimensional optimization Algorithms

    Statistics and Computing 21:261–273.

    https://doi.org/10.1007/s11222-009-9166-3

    • PubMed
    • Google Scholar

Author details

1. Khai C Ang

   1. Department of Pathology, Penn State College of Medicine, Hershey, United States
   2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   kca2@psu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7695-9953

2. Victor A Canfield

   1. Department of Pathology, Penn State College of Medicine, Hershey, United States
   2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Tiffany C Foster

   1. Department of Pathology, Penn State College of Medicine, Hershey, United States
   2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States

   Contribution

   Data curation, Investigation, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

4. Thaddeus D Harbaugh

   1. Department of Pathology, Penn State College of Medicine, Hershey, United States
   2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Kathryn A Early

   1. Department of Pathology, Penn State College of Medicine, Hershey, United States
   2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Rachel L Harter

   Department of Pathology, Penn State College of Medicine, Hershey, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Katherine P Reid

   1. Department of Pathology, Penn State College of Medicine, Hershey, United States
   2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Shou Ling Leong

   Department of Family & Community Medicine, Penn State College of Medicine, Hershey, United States

   Contribution

   Conceptualization

   Competing interests

   No competing interests declared

9. Yuka Kawasawa

   1. Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, United States
   2. Department of Pharmacology, Penn State College of Medicine, Hershey, United States
   3. Institute of Personalized Medicine, Penn State College of Medicine, Hershey, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8638-6738

10. Dajiang Liu

    1. Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, United States
    2. Department of Public Health Sciences, Penn State College of Medicine, Hershey, United States

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

11. John W Hawley

    Salybia Mission Project, Saint David Parish, Dominica

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Keith C Cheng

    1. Department of Pathology, Penn State College of Medicine, Hershey, United States
    2. Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, United States
    3. Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, United States
    4. Department of Pharmacology, Penn State College of Medicine, Hershey, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - review and editing

    For correspondence

    kcheng76@gmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5350-5825

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