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.

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Decision letter after peer review:

Thank you for submitting your article "Native American Ancestry and Pigmentation Allele Contributions to Skin Color in a Caribbean Population" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Satyajit Rath as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. We believe that the cohort and sampling effort is interesting and admirable and would help to diversify genomic studies and studies of skin pigmentation. However, the reviewers have found substantial issues with respect to the technical details of the analyses, the magnitude of the advance reported for the field, the availability of the data, and the consideration of previous studies and justification of analysis choices made. The authors will need to consider and respond to each of the reviewer points in a revised version for the manuscript to be considered further.

Essential revisions:

1) Why does the paper only consider the possibility for Native American-derived pigmentation alleles to contribute by decreasing pigmentation? The paper frames Native American and East Asian ancestry as necessarily tied to lighter pigmentation. However, this does not consider that there have been previous reports of Native American-European populations where Native American ancestry has been associated with darker skin. The authors need to offer a justification of the search for depigmentation alleles only or extend their analysis

2) The authors need to provide more clear details of all the analyses performed such as ADMIXTURE and association analysis and re-do these analyses as necessary given the technical points raised by reviewers #2 and #3. These include choice of K in the admixture analyses, potential new analyses of local ancestry to add rigor, considerations of relatedness in the association analysis and how predictors are chosen.

3) The paper should credit and include previous studies and hypotheses on skin pigmentation in different ancestries as mentioned by all reviewers, and update their models as necessary to consider more genes or justify their choices.

4) The authors should try to identify the specific NAM-specific variants that contribute to pigmentation either through GWAS or admixture mapping. If they can do that and actually discover new variants it would be a more novel contribution, and make the paper more likely to be eventually accepted for publication.

5) The authors need to provide a clearer data availability framework. If we understand correctly the data cannot be shared publicly due to community stipulations, but they can be shared at the discretion of the corresponding author with no further resort to the community? That seems inconsistent. Is this actually what is specified in the IRB and other agreements? Even if so, "reasonable request" is not well-defined and there needs to be a clear statement about under what conditions exactly the author commits to sharing the data. The authors need to share enough so that others can replicate their results, and there are ways they can do that without compromising privacy.

Reviewer #1 (Recommendations for the authors):

The authors should be consistent with the capitalization of 'Mixed' and 'Black.' Mixed is sometimes capitalized and sometimes not, and if there is no reason for the lowercase in 'black,' it should be capitalized to denote ethnicity as with the other terms. In the comparison of Kalinago vs. Mixed individuals, no reason is given to exclude those who identified as Black. Please provide an explanation or include them in the ancestry comparisons --this would only strengthen your point if African ancestry is higher in those groups.

Also, it would be helpful if there was some explanation as to how individuals were socially categorized (what phenotypes did people use to explain this? Was there any relation to parental identity?).

At one point, the authors describe the albino individuals as having "golden blonde" hair. There is no reason to add "golden"; it is not descriptive and gives the impression of superior valuation of this phenotype over others.

If possible, the authors should consider adding age and sex to the phenotype txt file.

Lastly, the authors need to justify why they only looked for albinism variants that were homozygous in the albino individual and heterozygous in the parent. I understand that this is a recessive trait; however, they discuss the allele they identify as having produced the phenotype in a compound heterozygote, so it bears elaborating on their reasoning for only considering homozygous variants in the Kalinago.

Reviewer #2 (Recommendations for the authors):

I thought the identification of the putative albinism allele was interesting and I don't have any issues with that analysis (would be nice to see an in vivo model eg in zebrafish but I certainly wouldn't require it for this paper).

I think the result that AFR ancestry is associated with higher MI relative to NAM is very unsurprising, unless I am missing something. It seems like the obvious next step would be to try to identify specific variants and I don't understand why the authors didn't do that.

I'm not totally convinced by the ancestry analysis, at least quantitatively. In general, performing local ancestry inference would enable additional analyses and more accurate approaches to some of the analyses you do perform (for example ancestry inference)

1) I don't think you can necessarily interpret quantitatively ADMIXTURE components in the way you want to. In general "ancestry" in that sense is not a very well-defined concept. For example, at K=4 what you describe as the "Native American component" is indeed maximized in Native American populations, but also present in East Asian populations. Similarly, the yellow ancestry cluster could equally well be described as "Middle Eastern" (yellow+black) rather than "European" (yellow+green) ancestry based on your admixture plots. In fact, some of your African populations actually have blacK⁺yellow ancestry. So you could equally interpret this as the Kalinago having NAM ancestry, and ancestry from some unsampled (e.g. East) African population that has about 70/30 of the black/yellow component. That would also explain the high correlation between your inferred African and European ancestry in Figure 2A.

I think the reason you call the yellow component "European", is because that's what you expect to see based on your knowledge of recent history. So you mean ancestry in sense of recent admixture, which is probably better-defined. But in that case, I do not think you can rely on ADMIXTURE to give you unbiased estimates. Local ancestry-based methods would probably be better and would give you confidence based on the tract length distribution that you are actually detecting recent European admixture.

2) Even if there is recent European ancestry, the two variants at SLC45A2 and SLC45A2 are the largest-effect European pigmentation alleles but they are by no means the only ones. For example, there are over 100 genomes-wide significant associations with skin pigmentation in UK Biobank, many of which have been reported as targets of selection and/or as contributing to differences in pigmentation between European and other populations. Some examples include TYRP1 (PMID: 17233754), TYR (24616518), KITLG (27738015), MC1R (24045876), as well as other variants at OCA2/HERC (17182896). This is not an exhaustive list of genes or references for each gene but you get the idea. Therefore, I don't think it's necessarily the case that including the two large-effect alleles will account for all the effect of European ancestry in your analysis. That said, since EUR ancestry is negatively correlated with NAM ancestry in your sample, I don't think that qualitatively affects the result.

3) But even ignoring the European ancestry, the framing is a bit odd. Figure 5 shows that NAM ancestry is associated with lower MI or, equivalently that higher AFR ancestry is associated with higher MI. But isn't this already known. We know that (even relatively unadmixed) Native American populations have lighter skin pigmentation than West African populations, so don't we expect that? Or are you claiming that we do not know that already?

4) It seems that the next step would be to try to identify the specific NAM-specific variants that contribution to pigmentation either through GWAS or admixture mapping. If you could do that and actually discover new variants it would be a more novel contribution.

5) You have a very large age range in this study and should probably include age and age^2 at least as covariates in the regression models to account for age-related variation in pigmentation (which may be confounded with age-related changes in ancestry). I'd actually question whether children should be included at all, both from a scientific (and perhaps also an ethical) perspective.

6) I am a bit confused about the data availability. If I understand correctly the data cannot be shared publicly due to community stipulations, but they CAN be shared at the discretion of the corresponding author with no further resort to the community? That seems inconsistent. Is this actually what is specified in the IRB and other agreements? Even if so, "reasonable request" is not well-defined and there needs to be a clear statement about under what conditions exactly the author commits to sharing the data. If the data actually cannot be shared, then say so.

Reviewer #3 (Recommendations for the authors):

1. The introduction has claimed that "the genetic basis for lighter skin pigmentation in Native American and East Asian populations…has yet to be established". This is misleading as there have been studies on the convergent evolution and shared genetic basis of light skin between East Asians and Europeans on KITLG, and East Asian specific allele in OCA2 (Beleza et al., 2012; Yang et al., 2016); variants on lighter skin in Native Americans in MFSD12 (Adhikari et al., 2019), and selection-test suggested OPRM1 and EGFR (Quillen et al., 2011), to name a few. Perhaps a starting place to gather the relevant literature would be Quillen et al., 2018.

This brings in two additional questions for this study that the authors might want to clarify in the manuscript:

(1) The significance of this study in the presence of other pigmentation studies in individuals with Native American ancestry, especially with so few genes that this study highlighted that were already assessed in other Latin American cohorts. This is perhaps something the authors should put into the discussion.

(2) The rationale behind choosing only three variants from three genes (SLC24A5, SLC45A2, OCA2) as covariates to characterize their effects in.

2. The ancestry decomposition was done using ADMIXTURE, while a lot of details were lacking for sufficient judgment and independent replication. The necessary information includes: What was the final K used to obtain the "Native American" ancestry estimate used in all subsequent analyses (presumably K=6?), and how was it determined (e.g. cross validation)? How many iterations with random seeds were used to obtain the estimates, and how many minor modes appeared? Was data properly pruned based on linkage disequilibrium, and if so what was the threshold and how many variants were left for ADMIXTURE? What is the maximal K ever tested? If the authors used k=6 to obtain the indigenous ancestry estimate, K>6 should be run to make sure this is the stable performance and reasonable decision.

3. The authors acknowledged presence of close relatedness, which can introduce bias in lot of analyses, unless properly handled. The description is lacking here, for example, in association analyses, were close relatedness pruned or a GRM is used in the model? If the authors merely partitioned relatedness into different groups for separate GWAS, then meta-analyzed the summary stats from all groups, this is still equivalent to including every sample into one GWAS. Moreover, the kinship threshold the authors used to partition samples seemed relaxed (i.e. to exclude second degree relatedness), when cryptic relatedness can largely present, since it was a community recruitment for the participants in this study with 15% of the total population involved. I would encourage the authors to try using a standard linear mixed model to account for relatedness, if not done so, and examining the genome inflation factor for indication of any leftover structures.

4. The association analysis: There are no summary stats reported, or any conclusions found in the manuscript with regard to the result of GWAS: even if no variants are significant given the limitation in power, this needs to be reported. Additionally, the authors might consider reporting the effect size and p values for variants in the suggestive Native American specific pigmentation genes from previous literatures (see point 1).

5. The predictors in the linear model: how are the predictors chosen is unclear, why does a discovery GWAS needs to account for specifically three variants from SLC24A5, SLC45A2, and OCA2, not none, less, or more? The participants included children, as young as below 10, thus was age tested for a significant covariate in this case? (similar to point 2) what ancestries are included as covariates, and which K were the estimates derived from?

6. The effect size of OCA2NW273KV as -8 MI: this is from the discovery cohort, and no replication of the effect size is done. This is worth bringing into discussion for winner's curse effect.

7. Line 107 above Figure 2: relatively recent divergence between any two populations does not necessarily suggest sharing of any causal alleles of a particular trait. This sentence reads as an overstatement.

8. Line 217 after Figure 3: The authors mentioned the non-albino individuals with lighter pigmentation, yet their quantitative description of the phenotype is lacking. What is their hair color, and MI of skin that make them classified as "lighter" than the rest of the samples, leading to the further analysis of their genotypes?

9. Line 243 above Table 2: The excess of 3% allele frequency in SLC24A5 compared to global European ancestry is a very small amount for the authors to claim that there is another source of ancestry to bring in the derived allele. 3% can easily be a stochastic /estimation error from global ancestry.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Native American Ancestry and Pigmentation Allele Contributions to Skin Color in a Caribbean Population" for further consideration by eLife. Your revised article has been evaluated by Satyajit Rath (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

– The authors are urged to do more critical/thorough engagement with how people are socially categorized in the study. The authors should review recent guidelines highlighted below and take the opportunity to make their manuscript (title, abstract, main text) in line with guidelines.

NASEM report:

https://nap.nationalacademies.org/catalog/26902/using-population-descriptors-in-genetics-and-genomics-research-a-new

Safra Center working group recommendations:

https://www.science.org/doi/10.1126/science.abm7530

Attached ethical framework for research that uses genetic ancestry (soon to be in print)

– The authors should check their citations as they have one author (Jada Benn Torres) down as both Torres JB and Benn-Torres, J (it should be Benn Torres, J.)

– The authors should make the full GWAS summary statistics public, either in the supplement or on the GWAS catalog. Since the raw data are not available, it's important to release the summary stats for others to build on e.g. with meta analysis or other analysis.

Essential revisions:

Reviewer #1 (Recommendations for the authors):

The authors should be consistent with the capitalization of 'Mixed' and 'Black.' Mixed is sometimes capitalized and sometimes not, and if there is no reason for the lowercase in 'black,' it should be capitalized to denote ethnicity as with the other terms. In the comparison of Kalinago vs. Mixed individuals, no reason is given to exclude those who identified as Black. Please provide an explanation or include them in the ancestry comparisons --this would only strengthen your point if African ancestry is higher in those groups.

Thank you for the comment. We have now consistently capitalized Black and Mixed. Black individuals were not excluded from analysis; however, none of the small number of individuals meeting this description were sampled.

Also, it would be helpful if there was some explanation as to how individuals were socially categorized (what phenotypes did people use to explain this? Was there any relation to parental identity?).

They were community-defined based on their skin color, but this subject was not explored further in our interviews.

At one point, the authors describe the albino individuals as having "golden blonde" hair. There is no reason to add "golden"; it is not descriptive and gives the impression of superior valuation of this phenotype over others.

Thank you. We removed the word ‘golden’.

If possible, the authors should consider adding age and sex to the phenotype txt file.

We included the phenotype and sex to the text file (PC_Covariates_Age_Sex.xlxs) found in Dryad.

Lastly, the authors need to justify why they only looked for albinism variants that were homozygous in the albino individual and heterozygous in the parent. I understand that this is a recessive trait; however, they discuss the allele they identify as having produced the phenotype in a compound heterozygote, so it bears elaborating on their reasoning for only considering homozygous variants in the Kalinago.

Thank you for pointing out this. We changed our wording to make this clearer. Had we not found a homozygous variant, we would have proceeded to examine the sequence data for heterozygous variants.

Reviewer #2 (Recommendations for the authors):

I thought the identification of the putative albinism allele was interesting and I don't have any issues with that analysis (would be nice to see an in vivo model eg in zebrafish but I certainly wouldn't require it for this paper).

Thank you. We agree that in vivo model will be nice and will be pursuing it separately from the focus of this manuscript.

I think the result that AFR ancestry is associated with higher MI relative to NAM is very unsurprising, unless I am missing something. It seems like the obvious next step would be to try to identify specific variants and I don't understand why the authors didn't do that.

GWAS results are reported in more detail than before. No novel variant was significant in our sample.

I'm not totally convinced by the ancestry analysis, at least quantitatively. In general, performing local ancestry inference would enable additional analyses and more accurate approaches to some of the analyses you do perform (for example ancestry inference)

1) I don't think you can necessarily interpret quantitatively ADMIXTURE components in the way you want to. In general "ancestry" in that sense is not a very well-defined concept. For example, at K=4 what you describe as the "Native American component" is indeed maximized in Native American populations, but also present in East Asian populations. Similarly, the yellow ancestry cluster could equally well be described as "Middle Eastern" (yellow+black) rather than "European" (yellow+green) ancestry based on your admixture plots. In fact, some of your African populations actually have blacK⁺yellow ancestry. So you could equally interpret this as the Kalinago having NAM ancestry, and ancestry from some unsampled (e.g. East) African population that has about 70/30 of the black/yellow component. That would also explain the high correlation between your inferred African and European ancestry in Figure 2A.

I think the reason you call the yellow component "European", is because that's what you expect to see based on your knowledge of recent history. So you mean ancestry in sense of recent admixture, which is probably better-defined. But in that case, I do not think you can rely on ADMIXTURE to give you unbiased estimates. Local ancestry-based methods would probably be better and would give you confidence based on the tract length distribution that you are actually detecting recent European admixture.

We are aware that it can be tempting to over-interpret the outputs from Admixture. For example, the appearance of the 'Native American' component in some East Asian populations does not man that these groups have actual Native American ancestry. Thank you for pointing out that we have described the clusters in ways that could be misleading. We changed our description to referring to the clusters by color. We have also added comparison to the results of local ancestry analysis, which are largely concordant.

2) Even if there is recent European ancestry, the two variants at SLC45A2 and SLC45A2 are the largest-effect European pigmentation alleles but they are by no means the only ones. For example, there are over 100 genomes-wide significant associations with skin pigmentation in UK Biobank, many of which have been reported as targets of selection and/or as contributing to differences in pigmentation between European and other populations. Some examples include TYRP1 (PMID: 17233754), TYR (24616518), KITLG (27738015), MC1R (24045876), as well as other variants at OCA2/HERC (17182896). This is not an exhaustive list of genes or references for each gene but you get the idea. Therefore, I don't think it's necessarily the case that including the two large-effect alleles will account for all the effect of European ancestry in your analysis. That said, since EUR ancestry is negatively correlated with NAM ancestry in your sample, I don't think that qualitatively affects the result.

We report effects (if any) of previously reported pigmentation-associated variants (Appendix 2 – Table 6).

3) But even ignoring the European ancestry, the framing is a bit odd. Figure 5 shows that NAM ancestry is associated with lower MI or, equivalently that higher AFR ancestry is associated with higher MI. But isn't this already known. We know that (even relatively unadmixed) Native American populations have lighter skin pigmentation than West African populations, so don't we expect that? Or are you claiming that we do not know that already?

We have rearranged our results in a way that emphasizes that we are interested in the size of the pigmentation difference that remains to be explained.

4) It seems that the next step would be to try to identify the specific NAM-specific variants that contribution to pigmentation either through GWAS or admixture mapping. If you could do that and actually discover new variants it would be a more novel contribution.

We have now added Linear Mixed Models (LMM) based genome wide assessment to our earlier linear regression methods to identify NAM-specific variants. While the lowest p-values from the LMM-based methods meet the conventional criterion of 5e-08 for genome wide significance, the low observed minor allele frequencies (<2%) are inconsistent with what would be expected for variants responsible for pigmentation differences between the African and Native American populations. For example, a hypothetical allele that is fixed in Native Americans like SLC24A5^A111T is in CEU would be expected to have a minor allele frequency close to the average percent Native American Ancestry in the Kalinago, about 0.6.

5) You have a very large age range in this study and should probably include age and age^2 at least as covariates in the regression models to account for age-related variation in pigmentation (which may be confounded with age-related changes in ancestry). I'd actually question whether children should be included at all, both from a scientific (and perhaps also an ethical) perspective.

We show in the revised manuscript that the effect of age on pigmentation is not significant, and that inclusion in the linear regression model has very little effect on parameter estimates (Appendix 1 – Table 5).

6) I am a bit confused about the data availability. If I understand correctly the data cannot be shared publicly due to community stipulations, but they CAN be shared at the discretion of the corresponding author with no further resort to the community? That seems inconsistent. Is this actually what is specified in the IRB and other agreements? Even if so, "reasonable request" is not well-defined and there needs to be a clear statement about under what conditions exactly the author commits to sharing the data. If the data actually cannot be shared, then say so.

We have updated our data availability statement.

Reviewer #3 (Recommendations for the authors):

1. The introduction has claimed that "the genetic basis for lighter skin pigmentation in Native American and East Asian populations…has yet to be established". This is misleading as there have been studies on the convergent evolution and shared genetic basis of light skin between East Asians and Europeans on KITLG, and East Asian specific allele in OCA2 (Beleza et al., 2012; Yang et al., 2016); variants on lighter skin in Native Americans in MFSD12 (Adhikari et al., 2019), and selection-test suggested OPRM1 and EGFR (Quillen et al., 2011), to name a few. Perhaps a starting place to gather the relevant literature would be Quillen et al., 2018.

This brings in two additional questions for this study that the authors might want to clarify in the manuscript:

(1) The significance of this study in the presence of other pigmentation studies in individuals with Native American ancestry, especially with so few genes that this study highlighted that were already assessed in other Latin American cohorts. This is perhaps something the authors should put into the discussion.

(2) The rationale behind choosing only three variants from three genes (SLC24A5, SLC45A2, OCA2) as covariates to characterize their effects in.

We have clarified that there are indeed genetic variants that contribute to skin color variation among East Asians and between East Asians or Native Americans and Africans, but none of the identified variants has an effect equivalent to that of the well-characterized variants found in Europeans. We have added (Appendix 2 – Table 6) reported p-values and effect sizes in our sample for previously reported variants.

2. The ancestry decomposition was done using ADMIXTURE, while a lot of details were lacking for sufficient judgment and independent replication. The necessary information includes: What was the final K used to obtain the "Native American" ancestry estimate used in all subsequent analyses (presumably K=6?), and how was it determined (e.g. cross validation)? How many iterations with random seeds were used to obtain the estimates, and how many minor modes appeared? Was data properly pruned based on linkage disequilibrium, and if so what was the threshold and how many variants were left for ADMIXTURE? What is the maximal K ever tested? If the authors used k=6 to obtain the indigenous ancestry estimate, K>6 should be run to make sure this is the stable performance and reasonable decision.

As we show, the ancestry estimates from K=4 to K=6 are very similar, since only a small number of individuals have substantial amounts of East Asian ancestry, and negligible amounts of Central/South Asian or Oceanian ancestry. Analysis at higher values of K does not improve our knowledge of Kalinago ancestry.

3. The authors acknowledged presence of close relatedness, which can introduce bias in lot of analyses, unless properly handled. The description is lacking here, for example, in association analyses, were close relatedness pruned or a GRM is used in the model? If the authors merely partitioned relatedness into different groups for separate GWAS, then meta-analyzed the summary stats from all groups, this is still equivalent to including every sample into one GWAS. Moreover, the kinship threshold the authors used to partition samples seemed relaxed (i.e. to exclude second degree relatedness), when cryptic relatedness can largely present, since it was a community recruitment for the participants in this study with 15% of the total population involved. I would encourage the authors to try using a standard linear mixed model to account for relatedness, if not done so, and examining the genome inflation factor for indication of any leftover structures.

We have removed the meta-analysis and added analysis using LMM-based models to the linear regression model. The decrease in λ using a simple GRM (and omitting PC-based covariates) suggests that this approach better accounts for relatedness. Models that include a GRM and add back the PC-based covariates exhibit greater statistic inflation that does not precisely follow the expected distribution.

4. The association analysis: There are no summary stats reported, or any conclusions found in the manuscript with regard to the result of GWAS: even if no variants are significant given the limitation in power, this needs to be reported. Additionally, the authors might consider reporting the effect size and p values for variants in the suggestive Native American specific pigmentation genes from previous literatures (see point 1).

We have added Appendix 2 – Table 6 to report the variants with the lowest p-values, and have displayed QQ-plots in Figure 5-Supplement Figure 2.

5. The predictors in the linear model: how are the predictors chosen is unclear, why does a discovery GWAS needs to account for specifically three variants from SLC24A5, SLC45A2, and OCA2, not none, less, or more? The participants included children, as young as below 10, thus was age tested for a significant covariate in this case? (similar to point 2) what ancestries are included as covariates, and which K were the estimates derived from?

Age has an effect that is not significant in our sample. We have included more detail on the effects of adding additional covariates such as age or other pigmentation-associated variants (Appendix 1 – Table 5).

6. The effect size of OCA2NW273KV as -8 MI: this is from the discovery cohort, and no replication of the effect size is done. This is worth bringing into discussion for winner's curse effect.

We agree that replication, preferably in a different population, would be useful. Such experiments lie outside the scope of the current work.

7. Line 107 above Figure 2: relatively recent divergence between any two populations does not necessarily suggest sharing of any causal alleles of a particular trait. This sentence reads as an overstatement.

We have removed this statement.

8. Line 217 after Figure 3: The authors mentioned the non-albino individuals with lighter pigmentation, yet their quantitative description of the phenotype is lacking. What is their hair color, and MI of skin that make them classified as "lighter" than the rest of the samples, leading to the further analysis of their genotypes?

We have clarified these points, including that the individuals described as lighter have m-index lower than 30.

9. Line 243 above Table 2: The excess of 3% allele frequency in SLC24A5 compared to global European ancestry is a very small amount for the authors to claim that there is another source of ancestry to bring in the derived allele. 3% can easily be a stochastic /estimation error from global ancestry.

We have revised this discussion considerably.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

– The authors are urged to do more critical/thorough engagement with how people are socially categorized in the study. The authors should review recent guidelines highlighted below and take the opportunity to make their manuscript (title, abstract, main text) in line with guidelines.

NASEM report:

https://nap.nationalacademies.org/catalog/26902/using-population-descriptors-in-genetics-and-genomics-research-a-new

Safra Center working group recommendations:

https://www.science.org/doi/10.1126/science.abm7530

Attached ethical framework for research that uses genetic ancestry (soon to be in print)

– The authors should check their citations as they have one author (Jada Benn Torres) down as both Torres JB and Benn-Torres, J (it should be Benn Torres, J.)

– The authors should make the full GWAS summary statistics public, either in the supplement or on the GWAS catalog. Since the raw data are not available, it's important to release the summary stats for others to build on e.g. with meta analysis or other analysis.

As suggested by the editors, we have reworded appropriate parts of the paper to comply to the latest NASEM report. For example, we added the term ‘genetic’ or ‘genealogical’ before ancestry, as specific population descriptors.

As requested, we have also added GWAS summary statistic to GWAS-Catalog with the corresponding submission ID: https://www.ebi.ac.uk/gwas/deposition/submission/6467ad190627bb0001de9b55

We thank the editors for pointing out the error in the citations, which we have corrected accordingly.

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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