Pleiotropy—when a single gene controls two or more seemingly unrelated traits—has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56–32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low. The genetic basis of complex traits has been thought to exhibit pleiotropy, which is the notion that a single locus can control two or more unrelated traits. Widespread reports in the human disease literature show genomic signatures of pleiotropic loci across many traits. However, little is known about the prevalence and behavior of pleiotropy in maize across a large number of phenotypes. Using association mapping of common alleles in over one hundred thousand traits, we determine how pleiotropic each region is and use these quantitative scores to functionally characterize each region of the genome. Our results show little evidence that pleiotropy is a common phenomenon in maize. We observe that maize does not exhibit the same pleiotropic characteristics as human diseases in terms of prevalence, gene expression, chromatin accessibility, or sequence conservation. Rather than pervasive pleiotropy, we hypothesize that strong selection on large and wide effect loci and the need for trait independence at the gene level keep the prevalence of pleiotropy low, thus, allowing for the adaptation of maize varieties to novel environments and conditions. Pleiotropy, a term defined by German geneticist Ludwig Plate in 1910, refers to the phenomenon where a single locus can affect two or more seemingly unrelated traits . In contVegetative to generative transition 1 (Vgt1) regulatory sequence controls leaf number and flowering time by regulating an APETALA2-like gene, ZmRap2.7, through what may be mediated pleiotropy [Vgt1 cause an additive genetic effect of roughly 2–4.5 days to pollen shed and an increase in the number of internodes. True pleiotropy of this locus was confirmed through introgression of the Vgt1 locus into transgenic plants [teosinte branched 1 (tb1) locus is an example of biological pleiotropy, which impacts the number and length of internodes in lateral branches and inflorescences due to the upregulation of gene expression in maize compared to teosinte. This upregulation results from the insertion of a Hopscotch transposable element 64 kb upstream of tb1 –12. Fine-mapping of tb1 confirmed this biological pleiotropy [Some examples of single-locus pleiotropy have been observed in maize. The eiotropy –6. Polymc plants . The teoeiotropy ,13,14. Aeiotropy .With joint linkage studies and genome-wide association studies (GWA or GWAS) in plants, the search for pleiotropy became more promising. However, few maize genes have shown true biological or mediated pleiotropy. Some pleiotropy is present between phenotypes of the same trait type, like the architecture of tassels, ears, leaves, or flowering time; yet, very few single nucleotide polymorphisms (SNPs) are shared between different trait types ,17. SomeA lack of pervasive pleiotropy was also observed in sorghum, where a large set of 234 plant architecture and agronomic traits was investigated within the Sorghum Association Panel . These rSome studies have calculated principal components over their traits to create uncorrelated variables used to determine the degree of pleiotropy . Using uPleiotropy has been described much more frequently in human disease literature. Some studies report that nearly half of the disease-associated genes within the human GWAS catalogue are pleiotropic . SimilarWhile human studies are more likely to look for pleiotropy within diseases, the extent of pleiotropy among common adaptive variants in maize remains elusive. The primary strengths of using maize for investigating pleiotropy are much higher levels of diversity and phenotypic measurements on replicated clones. However, a key drawback is that the number of recent recombination events is much lower than in some powerful human studies , limiting genomic resolution. These strengths and drawbacks have become apparent in the literature, with many maize pleiotropy studies confined to large effect domestication loci ,10,14, iIn this study, we investigated the pervasiveness of pleiotropy in maize using a large set of 120,597 previously published phenotypes in the US Nested Association Mapping population and the Goodman Association Panel  that generated 480 million significant quantitative trait nucleotides. We used these association results to calculate a quantitative estimate of how pleiotropic each genic and intergenic region is within the genome. Then, we functionally characterized these loci through gene expression, chromatin openness, and sequence conservation. We aimed to investigate instances of true biological pleiotropy where variants directly impact multiple traits while trying to limit the amount of spurious pleiotropy occurring from linkage disequilibrium.To determine the extent of pleiotropy within the maize genome, we performed GWAS on 120,597 traits across NAM  and GAP (n = 271). These traits are outlined in further detail in Supplemental Tables 1–3. These traits include 121 NAM field traits, 222 GAP field traits, 3,873 GAP leaf metabolite mass features , and 116Each population-trait category was investigated independently due to the uniqueness of the genetic architecture of each population, the variable trait sample size, and to investigate the differences in the patterns of pleiotropy between trait types. For each population-trait category, the number of unique traits mapping to each genic and intergenic interval was counted for both the observed and permuted association results to gain a quantitative metric on how pleiotropic each interval was within the genome. These pleiotropy estimates include biological, mediated, and spurious pleiotropy, as distinguishing between the different forms is difficult without a deeper understanding of how all individual traits are regulated. A total of 75,490 intervals with a mean length of 27,902 bp were used. Additionally, to gain a SNP-level estimate of pleiotropy, we randomly subsampled one million Hapmap 3.2.1 SNPs and counted all unique traits associated with each SNP. Counts for the null distributions of no pleiotropy originated from ten permutations of the field and mass features, while the expression phenotypes were permuted five times. We adjusted all our permuted phenotypes before GWA by the same principal components and PEER factors used within the GWA models to not inflate p-values due to the underlying population structure held within principal components.We compared our distribution of observed significant traits per interval to the permuted trait counts. Across populations and trait types, 67.7–98.4% of intervals have 0–1 significant traits associated with them . For ourFor intervals exhibiting five-fold higher evidence in the observed versus the mean permuted data, NAM field phenotypes display some pleiotropy at intervals putatively associated with 2–19 traits (median = 5 traits) in 1.56% of intervals. GAP field traits show pleiotropy associated with 2–65 traits (median = 9) in 32.3% of intervals genome-wide. GAP mass features showed much less pleiotropy, with 3.03% of intervals associating with between 2–695 traits (median = 63). Lastly, GAP expression data display slightly more pleiotropy with 31% of intervals associating with 2–6264 traits (median = 391). In relation to the total number of expression traits , a median of 0.33% of all expression traits impacted these highly pleiotropic intervals. For GAP mass features , a median of 1.62% of all mass features contributed to these high pleiotropy estimates.For SNPs exhibiting five-fold higher evidence in the observed versus the mean permuted data, NAM field phenotypes show some pleiotropy putatively associated with 2–9 traits (median = 2 traits) in 1.45% of SNPs. GAP field traits show pleiotropy associated with 2–35 traits (median = 2) in 1.49% of SNPs. GAP mass features show pleiotropy associated with 2–188 traits (median = 11) in 6.85% of SNPs, and the GAP expression data display pleiotropy associated with 2–879 traits (median = 21) in 42.7% of SNPs. Overall this suggests that there may be a set of intervals and SNPs with high pleiotropy, but as we will show below, this is mostly a product of factors impacting mapping.These pleiotropy estimates for GAP field and expression data may be higher than NAM field and GAP mass feature estimates. For GAP field data, these pleiotropy estimates might be slightly inflated due to residual population structure that was not controlled for well during the association mapping step  but wereTo understand how different trait types may impact the distribution of pleiotropy, rather than all traits in aggregate, NAM and GAP field traits were split into eight different types to investigate inner-category pleiotropy. These trait types included tassel, ear, flowering, disease, height, kernel, leaf, and vegetative traits. While the distribution of QTL by interval varies by the specific trait type, all individual trait types closely resemble the distribution of QTL across all traits .2 linkage disequilibrium within the NAM and GAP, the total number of SNPs per interval used for the GWA analysis, and interval size. We also included an adjusted GAP expression pleiotropy estimate for NAM field, GAP field, and GAP mass feature models to see if pleiotropy at an intermediate molecular step had any effect on field or metabolite regulation.We analyzed various biological and nuisance (GWA mapping) features to understand their importance in explaining this apparent pleiotropy. We evaluated two hypotheses: (1) that this apparent pleiotropy was due to characteristics of genetic mapping; or (2) various aspects of biological regulation and mutation-selection balance. Modeled biological features consisted of the maximum gene and protein expression values in B73 across twenty-three diverse tissues , the meaAll pleiotropy scores are highly correlated with nuisance terms . The num2 = 0.29–0.87, 2 = 0.85–0.96, With these biological, nuisance, and adjusted pleiotropy terms we ran four models (Models 1–4) that varied in their input features and model architectures. Each model type was run eight separate times for each population-trait category  and permutation type (observed versus permuted data). All models were trained using an iterative leave-one-chromosome out approach. Model 1 was a random forest model that included only nuisance, or mapping terms. Model accuracy varied by the population and trait category  over most random forest models . Models 2, 3, and 4 performed similarly, with the nuisance variables showing the highest relative importance  Figs. Al2 linkage disequilibrium. These models did not perform as well , and the average Ral terms  Figs.Due to the overwhelmingly large effect of mapping features in describing pleiotropy, an adjusted interval-based pleiotropy statistic was created. This adjusted statistic was created from Model 1 by subtracting the predicted pleiotropy from the observed pleiotropy score and was used to look at different biological relationships in more depth. This adjusted pleiotropy statistic was meant to curb, but not completely eliminate, the amount of statistical pleiotropy within the models while trying to retain some information about biological and mediated pleiotropy.2 = -2.41e-05, p-value = 0.767) and GERP  were not significant.The relationship between adjusted pleiotropy and observed pleiotropy is available in Zm00001d035776, which is a paired amphipathic helix protein shown to be involved in regulating flowering time and internode number in Arabidopsis [Zm00001d040920 is a tRNA methyltransferase pleiotropic within NAM field and GAP expression data. Lastly, we searched for overlap in our top twenty highly pleiotropic genes within a prior large-scale maize pleiotropy study [Zm00001d034600), in GAP field traits that impacted two tassel traits, and a G2-like1 gene (Zm00001d044785) in GAP mass features that impacted six root traits [With these adjusted pleiotropy scores, we then investigated the most pleiotropic genes within each population and trait type. The top twenty most pleiotropic genes by population and trait type are presented in bidopsis , pleiotrpy study . We fount traits .th percentile or highest 75th percentile of adjusted pleiotropy values within each population-trait category. We then queried how enriched the lowly and highly pleiotropic gene sets were for molecular function (MF), biological process (BP), and cellular component terms. No cellular component terms were significant.To determine if there was any differential enrichment in gene ontology terms among our putatively pleiotropic sites, we categorized our genic intervals into highly and lowly pleiotropic sets based on their adjusted pleiotropy values. Genic intervals were classified as lowly or highly pleiotropic if they had the lowest 25For molecular function, we found an enrichment of highly pleiotropic terms shared between populations involved in binding including ribonucleotide, nucleotide, nucleoside phosphate, protein, ATP, and carbohydrate derivative binding . AdditioFor biological process terms, we found an enrichment in the response to oxidative stress for lowly pleiotropic intervals in the NAM field traits. Highly pleiotropic biological processes included terms involved with signaling, signaling transduction, and cell communication in the GAP expression data and phosphorylation in the GAP field data.th and bottom 10th percentile, and top 99th and bottom 1st percentile of adjusted pleiotropy values. For the top 90th and bottom 10th percentile of adjusted pleiotropy values, numerous terms were significant for molecular function and biological process, including many terms for DNA and RNA binding for highly pleiotropic sites  shown to be involved in regulating flowering time and internode number in Arabidopsis [Zm00001d040920) was found across two populations. All other genes, within or next to the most pleiotropic intervals, were only present in one population. In GAP field traits, xylanase/glycosyl hydrolase10  has been shown to impact mesocotyl length after deep-seeding [empty pericarp2 (Zm00001d005675), which is involved in embryo morphogenesis, shoot development, and the negative regulation of the heat shock response [ZIM-transcription factor 8 , a candidate gene for Striga damage [Given our distribution of pleiotropic loci derived from common alleles, we investigated the top twenty intervals with the largest adjusted pleiotropy values. A paired amphipathic helix protein  variables to characterize the patterns of pleiotropy. Nuisance variables, including the interval size, average linkage disequilibrium, and the number of input SNPs, showed the highest relative importance across models. Of much lower relative importance were biological features, including maximum gene expression across multiple B73 tissues , the amoIn contrast to our results in maize, pleiotropy is pervasive in human and mouse literature –30,44,54Trait selection heavily influences pleiotropy results. Small subsets of measured traits originating from the same phytomer or highly correlated traits such as amino acid content  or grainteosinte branched 1 (tb1), Vegetative to generative transition 1 (Vgt1), or closely related traits that share similar developmental pathways. These closely related traits shown to exhibit some pleiotropy include key regulators of flowering time between days to silking, days to anthesis, and the anthesis-silking interval [Although many pleiotropy claims are misleading, this does not mean that pleiotropy does not exist in plants. These pleiotropic relationships may be more confined to fixed, large-effect loci such as interval , some ininterval , and co-interval ,19,20.In this study, we achieved phenotypic and marker saturation to assess the prevalence of pleiotropy within the maize genome. There is minimal pleiotropy present that, in aggregate, shows little to no relationship with gene expression, sequence conservation, or DNA accessibility and is highly impacted by mapping resolution terms including linkage disequilibrium and genomic interval size. Pleiotropy can still exist within the maize genome but is highly constrained by selection and tight linkage of multiple causal loci. We suggest that in future work of the plant genetics community, caution should be taken before claiming pleiotropy is the causal agent controlling multiple phenotypes. We recommend that: 1) mapping and marker resolution is sufficient to distinguish between tight linkage and pleiotropy, 2) there be robust testing of a null hypothesis of no pleiotropy through statistical methods or null distributions ,65, 3) tTo determine the degree of pleiotropy in the maize genome, we curated traits measured across two maize mapping populations: the US Nested Association Mapping population   and the As shown in the visual methods, we split all phenotypes into four categories based on population and trait type . These pTo create an estimate of statistical pleiotropy to compare to our estimates of biological pleiotropy, we permuted each phenotype ten times for NAM field, GAP field, and GAP mass features. Due to the large number of trait observations, we permuted GAP expression values five times. Phenotypes were permuted by fitting a linear model with their respective principal components or PEER  factors  as descrWe used the previously generated NAM and GAP Hapmap 3.2.1 SNPs imputed using Beagle 5.1  with they = Xβ1 + 3 global PCs + e. The three global principal components (PCs) were calculated on a subset of 66,527 SNPs from 3,545 diverse inbred lines in the USDA-ARS NCRPIS collection, and then GAP SNPs were rotated to such PCs. The 66,527 SNPs were chosen because they represented sites with no missing data shared between the three maize populations. This method offered similar control for population structure and kinship over PCs calculated solely within GAP and was consistent when mapping traits across multiple populations [GAP field and mass feature traits were mapped with the following model to account for population structure: ulations . This alulations .y = Xβ1 + 3 global PCs + main window PCs + e and y = Xβ1 + 3 global PCs + mid window PCs + e, where the global PCs were calculated similarly to GAP but using NAM SNPs. Main window PCs were calculated only for NAM by breaking the genome into 360 gene windows based on B73 AGPv4 gene coordinates, and PCs were calculated within each window. A total of 360 genes were chosen per window because they offered a reasonable number of degrees of freedom when mapping within NAM. For each window, enough PCs were taken to explain 15% of the total variance. To combat potential edge effects arising from these ‘main’ windows, we slid the windows over by 180 genes, recalculated window PCs (named ‘mid’ window PCs), and then used both collections of PCs in the association analysis. This resulted in 469 ‘main’ window PCs and 512 ‘mid’ window PCs. The union of significant sites between these two models was used for further analyses. In cases where both models had a significant association for a single SNP, the most significant association  was taken. All PCs used to map traits in NAM and GAP are available in Due to the population and family structure arising from NAM’s half-sib design, more stringent control of population structure was needed to reduce the false-positive rate. The following two models were used in NAM: Due to high linkage disequilibrium and the difficulty in ascertaining the true causal SNP(s) from a list of significant associations, we chose to investigate pleiotropy within genic and intergenic intervals. We did this by breaking the genome into an equal number of genic and intergenic intervals based on gene annotations in B73 AGPv4 with the GenomicRanges package in R . Any oveTo determine what factors may impact pleiotropy in maize, we investigated which annotations were the most important in determining the degree of pleiotropy within our traits and populations. We trained three random forest models using the R ranger package  and one 2 linkage disequilibrium within NAM and GAP calculated with the TASSEL MeanR2FromLDPlugin function, the total number of SNPs per interval used for the GWA analysis before p-value filtering, and the interval size. All features were calculated for each population separately.Model 1 was a random forest model that only included nuisance (mapping) features. All terms were calculated for each interval and included average RModels 2 (random forest) and 4 (gradient boosting) included both nuisance and biological features. Biological features included the mean Genomic Evolutionary Rate Profiling (GERP) scores of SNPs that overlapped exactly  with significant GWA SNPs averaged across an interval, the number of ATAC-seq sites previously analyzed , RNA andFor the SNP-level pleiotropy counts, a gradient boosting model was trained using the XGBoost package, similar to Model 4 using a subset of features. These features were linkage disequilibrium, GERP, the max RNA and protein expression of the nearest gene within 5 Kb, if the SNP was in an ATAC-seq site, and the location of the SNP (genic or intergenic).For Model 3, we included all nuisance and biological variables as Models 2 and 4; however, we also added fourteen biological process or molecular function gene ontology terms to the model. These terms describe the presence or absence of that ontology term to that interval. These gene ontology terms were scored for each interval where 1 denoted that genic interval contained a gene belonging to that gene ontology term, while 0 meant that genic and all intergenic intervals did not harbor a gene belonging to that ontology term. These terms included transcription DNA-templated , translation , regulation of transcription DNA-templated , regulation of translation , leaf development , regulation of leaf development , flower development , regulation of flower development , regulation of catalytic activity , signal transduction , nucleotide binding , DNA binding , RNA binding , and DNA-binding transcription factor activity .All models were trained and tested using a leave-one-chromosome-out approach, to avoid overfitting. This was done by cycling though all ten maize chromosomes, training the model using data from nine chromosomes, and testing on the left-out chromosome. The random forest models (Models 1–3) trained using R/ranger were built with default parameters, using the impurity variable importance mode and 500 trees. For the gradient tree boosting model (Model 4), we performed a grid search over the hyperparameter space of eta and max.depth and chose the values eta = 0.5, max.depth = 6 for each population, as these values attained approximately the lowest test error in each population. All other XGBoost parameters were set to their default values. Each model’s relative importance scores were calculated by dividing all importance scores by the maximum importance score. A correlation matrix of all model terms is available in pleiotropy ~ averageLinkageDisequilibrium + numberInputSNPs + intervalSize (Model 1) from the observed pleiotropy values for each population-trait category. The relationship between adjusted pleiotropy and observed pleiotropy at the interval level is available in Due to the high importance of linkage disequilibrium, the number of input SNPs, and interval size in the random forest models, an adjusted interval-based pleiotropy metric was created. Adjusted pleiotropy was calculated by subtracting the predicted values of the following random forest model: th percentile, 90th percentile, and 99th percentile of adjusted pleiotropy values in each population-trait category and labeled these loci as highly pleiotropic. Lowly pleiotropic values were the lowest 25th, 10th, and 1st percentile of adjusted pleiotropy values in each population-trait category. We then used the intervals in these percentiles as inputs into topGO [To investigate which gene ontology terms were enriched in highly and lowly pleiotropic regions, we subsetted our adjusted pleiotropy scores to genic regions, uplifted our B73 gene models from AGPv4 to RefGen_v5, and connected the RefGen_v5 genes with GO terms using Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.interproscan.tsv obtained from MaizeGDB . We thento topGO . We inveto topGO .S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table2 prediction accuracy is calculated using an iterative leave-one-chromosome out approach. Prediction accuracy for Model 1 , 2 (random forest), 3 (random forest with gene ontology terms), and 4 (gradient boosting) are provided for the SNP- and interval-based models.R(CSV)Click here for additional data file.S6 Table(CSV)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(CSV)Click here for additional data file.S9 Table(CSV)Click here for additional data file.S1 FigOf the SNPs showing a five-fold higher proportion of pleiotropy over their permutations . Each value along the x-axis was calculated from the natural log of the number of observed traits mapping to each SNP divided by the mean count of traits in the permuted data with a pseudo-count of plus one in the numerator and denominator. Values left of the vertical dashed line indicate higher pleiotropy in the permuted data versus the observed data suggesting the prevalence of high noise or no trait-SNP associations in either the observed or permuted data (peak at zero). Distributions are split into genic (gray) and intergenic (yellow) SNPs for (a) NAM field, (b) GAP field, (c) GAP mass features, and (d) GAP expression traits.(TIF)Click here for additional data file.S2 FigVery few intervals show a five-fold higher proportion of pleiotropy over their permutations  in the NAM field traits. Each value along the x-axis was calculated within a trait category from the natural log of the number of observed traits mapping to each interval divided by the mean count of traits in the permuted data with a pseudo-count of plus one in the numerator and denominator. Values left of the vertical dashed line indicate higher pleiotropy in the permuted data versus the observed data suggesting the prevalence of high noise or no trait-SNP associations in either the observed or permuted data (peak at zero).(TIF)Click here for additional data file.S3 FigVery few intervals show a five-fold higher proportion of pleiotropy over their permutations  in the GAP field traits. Each value along the x-axis was calculated within a trait category from the natural log of the number of observed traits mapping to each interval divided by the mean count of traits in the permuted data with a pseudo-count of plus one in the numerator and denominator. Values left of the vertical dashed line indicate higher pleiotropy in the permuted data versus the observed data suggesting the prevalence of high noise or no trait-SNP associations in either the observed or permuted data (peak at zero).(TIF)Click here for additional data file.S4 FigOnly the first permuted value from each of the four population-trait categories was included in the correlation matrix for simplicity.(TIF)Click here for additional data file.S5 FigAcross all four population-trait categories, nuisance variables showed higher relative importance over biological features. The plots show the observed data for the (a) NAM field, (b) GAP field, (c) GAP mass features, and (d) GAP expression data. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S6 FigThe dashed line represents the 1–1 identity line, while the solid line represents fitted values. Panels (a), (c), (e), and (g) show the observed results while panels (b), (d), (f), and (h) show the permuted results. Panels (a) and (b) show NAM field, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression. The plots show the observed and predicted values across all held-out chromosomes from the leave-one-chromosome-out model.(TIF)Click here for additional data file.S7 FigAcross the eight trait types, nuisance variables showed higher relative importance over biological features. The plots show the observed data for the NAM field results (a) across all traits, (b) flowering, (c) tassel, (d) ear, (e) height, (f) disease, (g) vegetative, (h) leaf, and (i) kernel traits. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S8 FigAcross the eight trait types, nuisance variables showed higher relative importance over biological features. The plots show the permuted data for the NAM field results (a) across all traits, (b) flowering, (c) tassel, (d) ear, (e) height, (f) disease, (g) vegetative, (h) leaf, and (i) kernel traits. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S9 FigAcross the eight trait types, nuisance variables showed higher relative importance over biological features. The plots show the observed data for the GAP field results (a) across all traits, (b) flowering, (c) tassel, (d) ear, (e) height, (f) disease, (g) vegetative, (h) leaf, and (i) kernel traits. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S10 Figc) tassel, (d) ear, (e) height, (f) disease, (g) vegetative, (h) leaf, and (i) kernel traits. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.Across the eight trait types, nuisance variables showed higher relative importance over biological features. The plots show the data for the GAP field results (a) across all traits, (b) flowering, ((TIF)Click here for additional data file.S11 FigAcross all four population-trait categories, nuisance variables showed higher relative importance over biological features. The plots show data for the observed data in panels (a), (c), (e), and (g) and the permuted data in panels (b), (d), (f), and (h). Panels (a) and (b) show NAM field results, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression data. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S12 FigThe dashed line represents the 1–1 identity line, while the solid line represents fitted values. Panels (a), (c), (e), and (g) show the observed results while panels (b), (d), (f), and (h) show the permuted results. Panels (a) and (b) show NAM field, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression. The plots show the observed and predicted values across all held-out chromosomes from the leave-one-chromosome-out model.(TIF)Click here for additional data file.S13 FigAcross all four population-trait categories, nuisance variables results showed higher relative importance over biological features. The plots show data for the observed data in panels (a), (c), (e), and (g) and the permuted data in panels (b), (d), (f), and (h). Panels (a) and (b) show NAM field results, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression data. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S14 FigThe dashed line represents the 1–1 identity line, while the solid line represents fitted values. Panels (a), (c), (e), and (g) show the observed results while panels (b), (d), (f), and (h) show the permuted results. Panels (a) and (b) show NAM field, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression. The plots show the observed and predicted values across all held-out chromosomes from the leave-one-chromosome-out model.(TIF)Click here for additional data file.S15 FigAcross all four population-trait categories, nuisance variables showed higher relative importance over biological features. The plots show data for the observed data in panels (a), (c), (e), and (g) and the permuted data in panels (b), (d), (f), and (h). Panels (a) and (b) show NAM field results, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression data. The bar charts depict the mean relative importance and standard error of each variable from a leave-one-chromosome-out model.(TIF)Click here for additional data file.S16 FigThe dashed line represents the 1–1 identity line, while the solid line represents fitted values. Panels (a), (c), (e), and (g) show the observed results while panels (b), (d), (f), and (h) show the permuted results. Panels (a) and (b) show NAM field, (c) and (d) GAP field, (e) and (f) GAP mass features, and (g) and (h) GAP expression. The plots show the observed and predicted values across all held-out chromosomes from the leave-one-chromosome-out model.(TIF)Click here for additional data file.S17 Fig2 values from correlating the adjusted versus unadjusted pleiotropy data.The scatter plots show the relationship between the adjusted and observed pleiotropy values for (a) NAM field, (b) GAP field, (c) GAP mass features, and (d) GAP expression traits. Values in the top right of each plot show the R(TIF)Click here for additional data file.S18 Fig2 values from correlating the observed genic and permuted genic adjusted pleiotropy data separately against the max RNA expression value across 23 B73 tissues. Shading around lines shows the 95% confidence interval.The line plots of adjusted pleiotropy for only genic ranges against the max RNA expression show the observed (gray lines) and permuted data (yellow lines). Panels show adjusted pleiotropy scores for (a) NAM field, (b) GAP field, (c) GAP mass features, and (d) GAP expression traits. Values in the top right of each plot show the R(TIF)Click here for additional data file.S19 Fig2 values from correlating the observed and permuted adjusted pleiotropy data against the mean GERP score. Shading around lines shows the 95% confidence interval.Line plots show adjusted pleiotropy against the mean GERP score for all genic and intergenic intervals. Mean GERP was calculated by averaging overlapping observed GWA SNPs with GERP SNPs for each population-trait category. Results for the observed data are in gray, while permuted results are in yellow. Panels show adjusted pleiotropy scores for (a) NAM field, (b) GAP field, (c) GAP mass features, and (d) GAP expression traits. Values in the top right of each plot show the R(TIF)Click here for additional data file.S20 FigAll molecular function, biological process, and cellular component terms were tested. Terms with FDR corrected p-values below p-value < 0.05 were retained. The x-axis represents highly or lowly pleiotropic intervals split by the population and trait type, and the y-axis shows the top significant gene ontology terms. The value of the FDR significance level is colored in blue and the size of the dots represents the proportion of genes found in that GO category.(TIF)Click here for additional data file.S21 FigAll molecular function, biological process, and cellular component terms were tested. Terms with FDR corrected p-values below p-value < 0.05 were retained. The x-axis represents highly or lowly pleiotropic intervals split by the population and trait type, and the y-axis shows the top significant gene ontology terms. The value of the FDR significance level is colored in blue and the size of the dots represents the proportion of genes found in that GO category.(TIF)Click here for additional data file.


NW273KVOCA2 contained within a haplotype of African origin; its allele frequency was 0.03 and single allele effect size was –8 melanin units. Derived allele frequencies of A111TSLC24A5 and L374FSLC45A2 were 0.14 and 0.06, with single allele effect sizes of –6 and –4, respectively. Native American genetic ancestry by itself reduced pigmentation by more than 20 melanin units (range 24–29). The responsible hypopigmenting genetic variants remain to be identified, since none of the published polymorphisms predicted in prior literature to affect skin color in Native Americans caused detectable hypopigmentation in the Kalinago.Our interest in the genetic basis of skin color variation between populations led us to seek a Native American population with genetically African admixture but low frequency of European light skin alleles. Analysis of 458 genomes from individuals residing in the Kalinago Territory of the Commonwealth of Dominica showed approximately 55% Native American, 32% African, and 12% European genetic ancestry, the highest Native American genetic ancestry among Caribbean populations to date. Skin pigmentation ranged from 20 to 80 melanin units, averaging 46. Three albino individuals were determined to be homozygous for a causative multi-nucleotide polymorphism  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).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.A variant of the gene 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.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.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 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. A111T allele in SLC24A5  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 . Oral history and beliefs among the Kalinago, numbering about 3000 living within the Prior to European contact, the Caribbean islands were inhabited by populations who migrated from the northern coast of South America . During  Reserve   is a recessive trait characterized by visual system abnormalities and hypopigmentation of skin, hair, and eyes  that is 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  and the Department of Health of the Commonwealth of Dominica, and relationship-building with three administrations of the Kalinago Council over 15 years.Our DNA and skin color sampling program encompassed 458 individuals, representing 15% of the population of the territory and all three known albino individuals. Ages ranged from 6 to 93 . We wereThe earliest western mention of the Kalinago  was in Christopher Columbus’s journal dated November 26, 1492 . Little To study Kalinago population structure, we analyzed an aggregate of our Kalinago SNP genotype data and HGDP data  using ADAt 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  , leadingOur analysis of Kalinago genetic ancestry revealed considerably more Native American and less European genetic ancestry than the Caribbean samples of The 55% Native American genetic ancestry calculated from autosomal genotype in the Kalinago is greater than the reported 13% in Puerto Rico , 10–15% 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%) . Thus, tMelanin index unit (MI) calculated from skin reflectance measured at the inner upper arm  was used as a quantitative measure of melanin pigmentation . MI deteOCA 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 . The thrOCA2 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 OCA2: SNP rs1800401 (c.913C>T or p.Arg305Trp in exon 9), R305W, and multi-nucleotide polymorphism rs797044784 in exon 8 , NW273KV.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 , includiOCA2 genotypes, 26 carried NW273KV and 60 carried R305W . All three albino individuals share a homozygous segment of ~1.7 Mb that encompasses several genes in addition to OCA2 . The albNW273KV 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 (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 NW273KVOCA2 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 NW273KVOCA2 variant in the public data is inconsistent, with the variant assigned to the wrong chromosome for the ACB siblings.The Kalinago albino individuals are the only reported individuals where the albinism was caused by homozygosity for the romosome . Conservromosome  that is A111TSLC24A5 and L374FSLC45A2. The phenotypic effects of these variants and NW273KVOCA2 are shown in NW273KVOCA2 homozygotes , as previously noted. The frequencies of the derived alleles of A111TSLC24A5 and L374FSLC45A2 in the Kalinago sample are 0.14 and 0.06, respectively.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 A111TSLC24A5 compared to L374FSLC45A2 is not explained solely by European admixture, given that most Europeans are nearly fixed for both alleles  have negligible A111TSLC24A5 frequencies, moderate frequencies are found in the Mende of Sierra Leone   that is consistent with low observed L374FSLC45A2 frequency.The markedly higher frequency of  alleles . This de = 0.08) , while s = 0.08) . Such AfA111TSLC25A5 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 A111TSLC24A5, 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 A111SLC24A5; this observation is consistent with epistasis of OCA2 hypopigmentation over that of A111TSLC24A5. 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 SLC24AA111T5, 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.In order to investigate the potential effect of the A111TSLC24A5, L374FSLC45A2, and NW273KVOCA2  for A111TSLC24A5, –4 units for L374FSLC45A2, and –8 units for the first NW273KVOCA2 allele.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 nificant . However-value from each of the five regions containing BCN2, TYR, OCA2, MC1R, and OPRM1 only modestly altered effect sizes for the other covariates . Estimated effect sizes for significant covariates were little affected by the inclusion of age as a covariate . Analysivariates .A111TSLC24A5 measured here is consistent with previously reported results of –5 melanin units calculated from an African-American sample . Estimated power for these analyses is shown in 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.Participants from among the Kalinago populations were recruited with the help of nurses from the Kalinago Territory in 2014. Recruitment took place throughout the territory’s eight hamlets. Place and date of birth, reported genealogical ancestry of parents and grandparents, number of siblings, and response to sun exposure  were obtained by interview. Hair color and texture and eye color  were noted visually but not measured quantitatively.PLUS spectrophotometer and converted to melanin unit as we have previously described . DNA integrity was checked by agarose gel electrophoresis and quantitated using a NanoDrop spectrophotometer . Further quantification was done using Qubit Fluorometer  as needed, following the manufacturer’s instructions.SLC24A5, SLC45A2, OCA2, and MFSD12 were amplified in all sampled individuals as described in OCA variants previously identified in African and Native Americans  were ampIllumina SNP genotyping using the Infinium Omni2.5–8 BeadChip was performed for all the individuals sampled. This was performed in three cohorts, using slightly different versions of the array, and the results combined. Due to ascertainment differences between the cohorts, analysis is presented here only for the combined sample. After quality control to eliminate duplicates and monomorphic variants, and to remove variants and individuals with genotype failure rates >0.05, 358 Kalinago individuals and 1,638,140 unique autosomal SNPs remained.In order to identify the causative variant for albinism in the Kalinago, two samples  were selected for whole exome sequencing. Following shearing of input DNA (1 µg) using a Covaris E220 Focused-ultrasonicator , exome enrichment and library preparation was done using the Agilent SureSelect V5+UTR kit . The samples were sequenced at 50× coverage using a HiSeq 2500 sequencer .fastq files were aligned back to Human Reference Genome GRCh37 (HG19) using BWA . Using the same criteria, a maximal subset of 184 individuals was also generated (n=184 subset).The genotyped individuals were randomly partitioned into nine subsets of 50 or 51 individuals (n=50 subsets) in which no pair exhibited greater than second-order relationship (PI_HAT >0.25 using the PCA was performed using the smartpca program (version 13050) in the eigensoft package . For com--cv to the ADMIXTURE command.Admixture analysis was performed using the ADMIXTURE program . Each ofSLC24A5), rs16891982 (SLC45A2), and the albino variant rs797044784 (OCA2) as covariates  were used: a standard GRM calculated using GCTA’s --make-grm command and an ancestry-aware GRM calculated using relationships deduced by REAP  with the default window size of 0.2 cM . A subse This pigmentation study focuses on a community from Kalinago Territory from the Caribbean islands that on average possess high percentages of Indigenous American ancestry, and broadens the effort of quantifying the genetic effects on skin pigmentation in humans. This paper describes an analysis of the genetic structure of the Kalinago population in the Commonwealth of Dominica, and the relationship between ancestry and skin pigmentation in that population. They provide valuable new insights into the skin-lightening effect of Native American alleles, which likely have been obscured by the effect of European alleles in previous studies of admixed Native American populations. Additionally, this paper provides an interesting analysis of previously reported albinism alleles, which paints a more complex picture of the genetic architecture of pigmentation. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review: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.Thank you for submitting your article "Native American Ancestry and Pigmentation Allele Contributions to Skin Color in a Caribbean Population" for consideration by 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 analysis2) 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 .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)+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.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 blacKI 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  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 ; variants on lighter skin in Native Americans in MFSD12 , and selection-test suggested OPRM1 and EGFR , 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  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 ? 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.eLife. Your revised article has been evaluated by Satyajit Rath (Senior Editor) and a Reviewing Editor.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 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  in line with guidelines.NASEM report:https://nap.nationalacademies.org/catalog/26902/using-population-descriptors-in-genetics-and-genomics-research-a-newSafra Center working group recommendations:https://www.science.org/doi/10.1126/science.abm7530Attached 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 – 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 .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)+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.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 blacKI 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.A111TSLC24A5 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.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 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  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 ; variants on lighter skin in Native Americans in MFSD12 , and selection-test suggested OPRM1 and EGFR , 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  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 ? 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.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  in line with guidelines.NASEM report:https://nap.nationalacademies.org/catalog/26902/using-population-descriptors-in-genetics-and-genomics-research-a-newSafra Center working group recommendations:https://www.science.org/doi/10.1126/science.abm7530Attached 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 – 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/6467ad190627bb0001de9b55We thank the editors for pointing out the error in the citations, which we have corrected accordingly.