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Integrative functional genomic analyses identify genetic variants influencing skin pigmentation in Africans

Nature Genetics volume 56, pages 258–272 (2024)Cite this article

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Abstract

Skin color is highly variable in Africans, yet little is known about the underlying molecular mechanism. Here we applied massively parallel reporter assays to screen 1,157 candidate variants influencing skin pigmentation in Africans and identified 165 single-nucleotide polymorphisms showing differential regulatory activities between alleles. We combine Hi-C, genome editing and melanin assays to identify regulatory elements for MFSD12, HMG20B, OCA2, MITF, LEF1, TRPS1, BLOC1S6 and CYB561A3 that impact melanin levels in vitro and modulate human skin color. We found that independent mutations in an OCA2 enhancer contribute to the evolution of human skin color diversity and detect signals of local adaptation at enhancers of MITF, LEF1 and TRPS1, which may contribute to the light skin color of Khoesan-speaking populations from Southern Africa. Additionally, we identified CYB561A3 as a novel pigmentation regulator that impacts genes involved in oxidative phosphorylation and melanogenesis. These results provide insights into the mechanisms underlying human skin color diversity and adaptive evolution.

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Fig. 1: Massively parallel screening of genetic variants associated with African skin pigmentation.
Fig. 2: Regulatory variant rs6497271 impacts OCA2 expression and contributes to human skin color variations.
Fig. 3: Continuing evolution of OCA2 enhancer E2 contributes to African skin pigmentation diversity.
Fig. 4: Regulatory variant rs111969762 near MITF contributes to the light skin color of the San.
Fig. 5: Regulatory SNPs of LEF1 and TRPS1 contribute to the light skin color of the San.
Fig. 6: CYB561A3 and TMEM138 are the primary target genes of GWAS-SNP rs7948623.
Fig. 7: CYB561A3 affects melanin levels in MNT-1.

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Data availability

The epigenomic data, Hi-C and HiChIP data were uploaded to UCSC browser and are available at https://genome.ucsc.edu/s/fengyq/Tishkoff_Lab%2Dhg38%2DMPRA%2DHiC_Pigmentation. All RNA-seq and epigenomic data generated in this study are available at GEO GSE240717. Genotype data for GWAS are in dbGaP phs001396.vl.pl. Source data are provided with this paper.

Code availability

Public software and packages were used following the developer’s manuals. The custom code used for data analysis has been deposited at GitHub (https://github.com/fengyq/nature_gentics_codes) and Zenodo101 (https://zenodo.org/records/10198223).

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Acknowledgements

This research was supported by the following grants: NIH grants R35 GM134957-01, 3UM1HG009408-02S1, 1R01GM113657-01 and 5R01AR076241-02. We thank the Skin Biology and Disease Resource-based Center (SBDRC, NIH P30-AR069589) at the University of Pennsylvania for funding and providing human primary melanocytes. The sequencing of MPRA was carried out by the DNA Technologies and Expression Analysis Core at the University of California Davis Genome Center, supported by the NIH Shared Instrumentation Grant 1S10OD010786-01. We thank E. Burton for assistance on part of the plasmid cloning. We thank Z. (J.) Zhou from the Department of Genetics at the University of Pennsylvania for sharing their tissue culture room. We thank J. Phillips-Cremins from the Department of Genetics at the University of Pennsylvania for constructive suggestions on Hi-C. We thank H. Wong and H. Wu at the University of Pennsylvania for sharing their experimental equipment. We thank the African participants for their contributions to this study.

Author information

Author notes

  1. Fumitaka Inoue

    Present address: Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan

  2. Shaohua Fan

    Present address: Human Phenome Institute, School of Life Science, Fudan University, Shanghai, China

Authors and Affiliations

  1. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Yuanqing Feng, Ning Xie, Shaohua Fan, Chao Zhang, Fang Zhang, Matthew E. B. Hansen & Sarah A. Tishkoff

  2. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA

    Fumitaka Inoue & Nadav Ahituv

  3. Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA

    Fumitaka Inoue & Nadav Ahituv

  4. Department of Neuroscience, Brown University, Providence, RI, USA

    Joshua Saskin & Elena Oancea

  5. Department of Biochemistry and Molecular Biology, Hubert Kairuki Memorial University, Dar es Salaam, Tanzania

    Thomas Nyambo

  6. Department of Biological Sciences, Faculty of Sciences, University of Botswana, Gaborone, Botswana

    Sununguko Wata Mpoloka

  7. Department of Biomedical Sciences, University of Botswana, Gaborone, Botswana

    Gaonyadiwe George Mokone

  8. Department of Pharmacotoxicology and Pharmacokinetics, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon

    Charles Fokunang

  9. Department of Biology, Addis Ababa University, Addis Ababa, Ethiopia

    Gurja Belay

  10. Brain Research Africa Initiative (BRAIN); Neuroscience Lab, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Department of Neurology, Central Hospital Yaoundé, Yaoundé, Cameroon

    Alfred K. Njamnshi

  11. Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA

    Michael S. Marks

  12. Department of Biology, University of Pennsylvania, Philadelphia, PA, USA

    Sarah A. Tishkoff

  13. Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, USA

    Sarah A. Tishkoff

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  1. Yuanqing Feng
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Contributions

Y.F. and S.A.T. designed the study and wrote the original draft. Y.F. performed the Hi-C, H3K27ac HiChIP, CRISPR, RNA-seq, ATAC-seq and CUT&RUN experiments and related data analysis. Y.F. and F.I. conducted the MPRA under supervision of N.A. Y.F. and C.Z. analyzed the MPRA data. S.F. and M.E.B.H played a role in quality control and analysis of WGS and SNP array data. Y.F. and S.F. conducted the GWAS and Di analysis. Y.F. and N.X. performed CRISPR editing and related assays in MNT-1. S.A.T., T.N., S.W.M., G.G.M., A.K.N., C.F. and G.B. played a role in collecting data from Africa. J.S. and E.O. performed the CYB561A3 immunofluorescence imaging and analyses. E.O. and M.S.M. provided resources and additional experimental insights. All authors assisted with manuscript review and editing. S.A.T. supervised the project.

Corresponding author

Correspondence to Sarah A. Tishkoff.

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Competing interests

N.A. is an equity holder of Encoded Therapeutics, a gene regulation therapeutics company and is a cofounder and scientific advisor of Regel Therapeutics and Neomer Diagnostics. The remaining authors declare no competing financial interests.

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quality statistics of the MPRA experiments.

(a) Statistics for FLASH-merged reads in the association library. The plot shows that 46.1% are 200 bp fragments as designed. (b) Statistics of BWA-mapped reads in the association library. The plot shows that 44.1% are 200 bp fragments as designed. (c) Statistics of barcode types per oligo in the association library. On average, each oligo is linked with 126 different barcodes. (d) Statistics of barcode types per oligo in reference (n = 1102), alternative (n = 1103), negative control (n = 153), and positive control (n = 30) oligos. Data is from the association library. (e) Statistics of barcode counts per oligo in reference (n = 1102), alternative (n = 1103), negative control (n = 153), and positive control (n = 30) oligos. Data is from the association library. (f) Barcode types for reference and alternative alleles are comparable. Pearson’s r = 0.91, p < 2 × 10−16. (g) Principal component analysis of DNA and RNA libraries from MNT-1 and WM88 cells. Three replicates. (h) Summary of enhancer activities estimated by MPRA. Enhancer activities were defined as the barcode counts per million in the RNA library divided by the barcode counts per million in the DNA library. Alt: oligos containing alternative alleles (n = 1103). Ref: oligos containing reference alleles (n = 1102). Negative, negative control oligos (n = 148). Positive, positive control oligos (n = 30). For boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 2 MPRA identifies six allelic skewed variants near MFSD12.

(a) Plot showing allelic skewed variants in regulatory regions near MFSD12. Blue tracks indicate DNase-Seq, ATAC-Seq, and ChIP-Seq from melanocytes; orange tracks indicate ChIP-Seq from melanoma (501-mel) cells; green tracks indicate DNase-Seq from ENCODE cell lines. E1-E4, enhancers. P, promoter. (b-g) Relative enhancer activities of the two alleles at rs142317543, rs6510759, rs734454, rs10416746, rs6510760, rs7246261 estimated by MPRA (n = 3). For b, c, d, f, g, p-values were estimated with a random effects model for mpralm and paired t-tests with multiple testing adjustments; e was without multiple testing adjustments. (h-k) Relative enhancer activities estimated by LRA. Two-tailed paired t-tests (For LRA in MNT1, n = 6. For LRA in WM88, 2 h n = 8; others n = 9). Data were presented as mean ± SEM. ns p > 0.05. (l) rs6510760 and rs7246261 disrupt the binding motifs of AHR and TFAP2, respectively. Predicted by ‘MotifBreakR’98. (m) The LD pattern of candidate functional variants near MFSD12. LD was calculated using the 180 G18 data by the LDheatmap102 package. For boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 3 The enhancer E4 interacts with the promoter of MFSD12 and affects the expression of MFSD12.

(a, b) Chromatin interactions near MFSD12 identified by Hi-C and H3K27ac HiChIP with Hae3 digestion. The upper matrix is from MNT-1 Hi-C data, and the lower matrix is from MNT-1 H3K27ac HiChIP data. TADs were called by onTAD100 and colored by nested TAD levels. The solid arch was a loop defined using FitHiChIP29 software, the dashed arch was a potential loop based on the observed interaction matrix. The interaction matrix between MFSD12 and HMG20B was highlighted with orange angles. The DNase track of melanocytes was downloaded from ENCODE68. rs6510760 and rs657246261 in E4 were colored in red. The plotted region is chr19:3519998-3589998 (hg19). (c) Schematic showing the location of the two sgRNAs targeting the enhancer E4 of MFSD12. (d) PCR results showing efficient knockout of the enhancer by the two sgRNAs. Three independent experiments. (e) qPCR showed that CRISPRi of E4 reduces the gene expression of MFSD12 and HMG20B in MNT-1 cells. Two-sided Dunnett’s test with adjustments for multiple comparisons (n = 3). (f) CRISPRi of E4 slightly increases melanin levels in MNT-1 cells. Two-tailed unpaired t-tests (n = 19). (g) qPCR showed that CRISPR knockout of E4 decreases the gene expression of MFSD12 and HMG20B in MNT-1 cells. Two-tailed unpaired t-tests without multiple testing adjustments (n = 6). Data are presented as mean ± SEM.

Source data

Extended Data Fig. 4 Identification of functional variants associated with skin pigmentation near OCA2.

(a) SNP rs6497271 is in a melanocyte-specific enhancer. Blue tracks indicate DNase-Seq, ATAC-Seq, and ChIP-Seq data from melanocytes; orange tracks indicate ChIP-Seq data from melanoma (501-mel) cells; green tracks indicate DNase-Seq data from ENCODE cell lines. E1-E4, enhancers. The plotted region is chr15: 28,335,146-28,385,146 (hg19). (b) MPRA and LRA reveals that rs4778242 significantly affects the enhancer activity of E1 in MNT-1 and WM88 cells. MPRA (n = 3), LRA (n = 9). (c) MPRA showed that rs6497271 affects the enhancer activity of E2 in MNT-1 and WM88 cells (n = 3). (d) MPRA shows that rs7495989 affects the enhancer activity of E3 in MNT-1 and WM88 cells (n = 3). (e) MPRA and LRA reveals that rs4778141 affects the enhancer activity of E4 in MNT-1 and WM88 cells. MPRA (n = 3), LRA (MNT-1, n = 9; WM88, n = 6). (f) rs6497271 overlaps transcription factor binding sites. Left panel shows rs6497271 disrupts the binding motif of LEF1 and SOX10. Right panel shows that rs6497271 overlaps ChIP-seq peaks from Cistrome database103. LRA data are presented as mean ± SEM, tested with two-tailed paired t-tests. MPRA p-values are estimated with a random effects model for mpralm and paired t-tests with multiple testing adjustments. For MPRA boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 5 Identification of functional variants near MITF related to skin pigmentation in the San.

(a) A Plot showing functional Di-SNP rs111969762 is in a melanocyte-specific regulatory region. Blue tracks indicate DNase-Seq, ATAC-Seq, and ChIP-Seq from melanocytes; orange tracks indicate ChIP-Seq from melanoma (501-mel) cells; green tracks indicate DNase-Seq from ENCODE cell lines. E1-E2, enhancers. (b) MPRA showed that rs111969762 affects enhancer activity in WM88 cells (n = 3). (c) MPRA shows that rs7430957 impacts enhancer activity in WM88 cells (n = 3). (d) LRA shows that rs7430957 does not significantly alter the activity of the E2 enhancer near MITF. P values were estimated by two-tailed paired t-tests, MNT-1(n = 6), WM88 (n = 11). Data were presented as mean ± SEM. ns p > 0.05. (e) rs111969762 overlaps transcription factor binding sites. Left panel showed rs6497271 disrupts the binding motif of FOXP3. Right panel showed that rs111969762 overlaps ChIP-seq peaks from the Cistrome database103. MPRA p-values were estimated with a random effects model for mpralm and paired t-tests with multiple testing adjustments. For MPRA boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 6 Functional testing of Di-SNPs near LEF1.

(a) MFVs and regulatory elements near LEF1. rs17038630 and rs11939273 are Di-SNPs from the San population. (b) Plot showing allelic skews at rs17038630 in MNT-1 and WM88 cells estimated by MPRA (n = 3). (c) rs17038630 overlaps SOX10 and LEF1 binding sites. Left panel shows that rs17038630 disrupts the binding motif of SOX10 and LEF1. Right panel shows that rs11939273 overlaps ChIP-seq peaks from the Cistrome database103. (d) MPRA and LRA results showing allelic skews at Di-SNP rs11939273 in MNT-1 and WM88 cells, the allele frequency data was from the 180 G18 and 1000 G31 dataset. MPRA (n = 3), LRA (MNT-1, n = 6; WM88, n = 9). LRA data are presented as mean ± SEM, tested with two-tailed paired t-tests. (e) CRISPR-KO of the enhancer E1 of LEF1 does not affect LEF1 expression and melanin levels in MNT-1 cells. Left panel shows genotyping results of CRISPR-KO of the enhancer E1 of LEF1, three independent experiments. Middle panel shows the RT-qPCR results of CRISPR-KO of the enhancer E1 of LEF1 (n = 9). Right panel shows the melanin levels of CRISPR-KO of the enhancer E1 of LEF1 (n = 9). Two-tailed unpaired t-tests. For MPRA boxplots in b and d, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box. MPRA p-values were estimated with a random effects model for mpralm and paired t-tests with multiple testing adjustments.

Source data

Extended Data Fig. 7 MPRA and LRA identified three functional Di-SNPs near NLK.

(a) MFVs and regulatory elements near NLK. rs75827647, rs10468581 and rs113940275 are Di-SNPs from the San population. (b) LRA and MPRA results showing allelic skews at rs75827647 in MNT-1 and WM88 cells. MPRA (n = 3), LRA (MNT-1, n = 6; WM88, n = 9). (c) LRA and MPRA results showing allelic skews at rs10468581 in MNT-1 and WM88 cells. MPRA (n = 3), LRA (MNT-1, n = 6; WM88, n = 9). (d) LRA and MPRA results showing allelic skews at rs113940275 in MNT-1 and WM88 cells. MPRA (n = 3), LRA (MNT-1, n = 6; WM88, n = 11). From b to d, the barplots are results of LRA, two-tailed paired t-tests without adjustments for multiple comparisons; data were presented as mean ± SEM. ns p > 0.05. The boxplots are results from MPRA, p-values were estimated with a random effects model for mpralm and paired t-tests with multiple testing adjustments. The right panels are allele frequency maps constructed using the 180G18 and 1000 G31 dataset. For boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 8 Functional testing of Di-SNPs near TRPS1.

(a) SNP rs11985280 overlaps a regulatory element of TPRS1. Blue tracks show ATAC-Seq, and ChIP-Seq data from melanocytes; orange tracks indicate ChIP-Seq data from melanoma (501-mel) cells, green tracks indicate ATAC-Seq and DNase-Seq data from ENCODE cell lines. (b) MPRA results showing allelic skews at rs11985280 in MNT-1 and WM88 cells (n = 3). p-values were estimated with a random effects model for mpralm and paired t-tests with multiple testing adjustments. For boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box. (c) rs11985280 disrupts the binding motif of CEBPA and CEBPB. Right panel shows that rs11985280 overlaps ChIP-seq peaks from the Cistrome database103. (d) CRISPR-KO of the enhancer E1 of TRPS1 affects TRPS1 expression but not melanin levels in MNT-1 cells. Left panel shows genotyping results of CRISPR-KO of the enhancer E1 of TRPS1, three independent experiments. Middle panel shows the RT-qPCR results of CRISPR-KO of the enhancer E1 of TRPS1 (n = 9). Right panel shows the melanin levels of CRISPR-KO of the enhancer E1 of TRPS1 in MNT-1 cells (n = 9). Two-tailed unpaired t-tests. Data are presented as mean ± SEM and p values are listed above the bars.

Source data

Extended Data Fig. 9 Identification of functional regulatory variants near the BLOC1S6 locus.

(a). rs72713175 overlaps a regulatory region in melanocytes. Green tracks indicate ATAC-seq for MNT-1 and WM88 cells; blue tracks indicate ATAC-seq and ChIP-Seq from NHM; orange tracks indicate CUT&RUN from MNT-1 cells. (b) MPRA results showing allelic skews at rs11985280 in WM88 cells but not in MNT-1 cells (n = 3). P values were estimated with a random effects model for mpralm and paired t-tests without multiple testing adjustments. For boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box. (c) Allele frequencies at rs72713175 in global populations, data were from the 180 G18 and 1000 G31 datasets. (d) LRA results showing that rs72713175 did not affect enhancer activity in WM88 and MNT-1 cells. Two-tailed paired t-tests (n = 6). (e) CRISPRi of the enhancer containing rs72713175 significantly reduced the expression of BLOC1S6 (control, n = 8; others, n = 6; Two-sided Dunnett’s test with adjustments for multiple comparisons). (f) CRISPRi of the enhancer containing rs72713175 significantly reduced melanin levels in MNT-1 cells (control, n = 18; others, n = 9, Two-sided Dunnett’s test with adjustments for multiple comparisons). Data are presented as mean ± SEM and p values are listed above the bars.

Source data

Extended Data Fig. 10 Identification of functional regulatory variants near the DDB1 locus.

(a) Plots showing allelic skewed variants in regulatory elements near the DDB1 locus. rs7948623 overlaps an open chromatin region in melanocytes and many other cell types. rs2277285 and rs2943806 are located within CTCF binding sites and TAD boundaries. Blue tracks indicate DNase-seq, ATAC-seq, and ChIP-Seq data from melanocytes; orange tracks indicate ChIP-Seq data from melanoma (501-mel) cells; gray tracks indicate CTCF ChIP-Seq data from three cell lines; and green tracks indicate DNase-Seq data from ENCODE68. (b-d) Allelic skews at rs7948623, rs2277285 and rs2943806 as estimated by MPRA (n = 3). P values were estimated with a random effects model for mpralm and paired t-tests without multiple testing adjustments. For boxplots, central lines are median, with boxes extending from the 25th to the 75th percentiles. Whiskers further extend by ±1.5 times the interquartile range from the limits of each box. (e) rs7948623 disrupts a MITF binding motif and overlaps ChIP-seq peaks from the Cistrome database103. (f, g) LD pattern between the MFVs at the DDB1 locus. LD was calculated using the 180G18 dataset.

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Supplementary information

Supplementary Information

Supplementary Notes 1–5 and Figs. 1–30.

Reporting Summary

Supplementary Tables

Supplementary Table 1. GWAS statistics of significant GWAS-All SNPs. Supplementary Table 2. GWAS statistics of significant GWAS-Bots SNPs. Supplementary Table 3. MPRA oligo sequences. Supplementary Table 4. MPRA results. Supplementary Table 5. LRA results. Supplementary Table 6. SNP–gene pairs determined by Hi-C and H3K27ac HiChIP. Supplementary Table 7. Allele frequencies of MPRA significant SNPs in global populations. Supplementary Table 8. Primer sequences.

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Feng, Y., Xie, N., Inoue, F. et al. Integrative functional genomic analyses identify genetic variants influencing skin pigmentation in Africans. Nat Genet 56, 258–272 (2024). https://doi.org/10.1038/s41588-023-01626-1

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