Human Genetics

, Volume 131, Issue 7, pp 1073–1080 | Cite as

OPRM1 and EGFR contribute to skin pigmentation differences between Indigenous Americans and Europeans

  • Ellen E. QuillenEmail author
  • Marc Bauchet
  • Abigail W. Bigham
  • Miguel E. Delgado-Burbano
  • Franz X. Faust
  • Yann C. Klimentidis
  • Xianyun Mao
  • Mark Stoneking
  • Mark D. Shriver
Original Investigation


Contemporary variation in skin pigmentation is the result of hundreds of thousands years of human evolution in new and changing environments. Previous studies have identified several genes involved in skin pigmentation differences among African, Asian, and European populations. However, none have examined skin pigmentation variation among Indigenous American populations, creating a critical gap in our understanding of skin pigmentation variation. This study investigates signatures of selection at 76 pigmentation candidate genes that may contribute to skin pigmentation differences between Indigenous Americans and Europeans. Analysis was performed on two samples of Indigenous Americans genotyped on genome-wide SNP arrays. Using four tests for natural selection—locus-specific branch length (LSBL), ratio of heterozygosities (lnRH), Tajima’s D difference, and extended haplotype homozygosity (EHH)—we identified 14 selection-nominated candidate genes (SNCGs). SNPs in each of the SNCGs were tested for association with skin pigmentation in 515 admixed Indigenous American and European individuals from regions of the Americas with high ground-level ultraviolet radiation. In addition to SLC24A5 and SLC45A2, genes previously associated with European/non-European differences in skin pigmentation, OPRM1 and EGFR were associated with variation in skin pigmentation in New World populations for the first time.


Skin Pigmentation Single Nucleotide Polymorphism Array Pigmentation Gene Extended Haplotype Homozygosity Core Haplotype 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the individuals who volunteered their time to participate in this research and the anonymous reviewers for their comments. This project was funded by National Science Foundation Doctoral Dissertation Improvement Grant #0925976 and intramural Research and Graduate Studies Office and Hill Fellowships from Pennsylvania State University to EEQ. MB and MSt were funded by the Max Planck Society. YCK was funded by a Graduate Research Development grant from the University of New Mexico. The authors have no conflicts of interest to disclose.

Supplementary material

439_2011_1135_MOESM1_ESM.xls (942 kb)
Supplementary material 1 (XLS 942 kb)


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Ellen E. Quillen
    • 1
    • 9
    Email author
  • Marc Bauchet
    • 2
  • Abigail W. Bigham
    • 3
  • Miguel E. Delgado-Burbano
    • 4
    • 5
  • Franz X. Faust
    • 6
  • Yann C. Klimentidis
    • 7
  • Xianyun Mao
    • 8
  • Mark Stoneking
    • 2
  • Mark D. Shriver
    • 1
  1. 1.Department of AnthropologyPennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of Evolutionary GeneticsMax Planck Institute for Evolutionary AnthropologyLeipzigGermany
  3. 3.Department of AnthropologyUniversity of MichiganAnn ArborUSA
  4. 4.División Antropología, Facultad de Ciencias Naturales y MuseoUniversidad Nacional de La PlataLa PlataArgentina
  5. 5.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Buenos AiresArgentina
  6. 6.Department of AnthropologyUniversidad del CaucaPopayánColombia
  7. 7.Department of BiostatisticsUniversity of Alabama at BirminghamBirminghamUSA
  8. 8.Department of StatisticsPennsylvania State UniversityUniversity ParkUSA
  9. 9.Department of GeneticsTexas Biomedical Research InstituteSan AntonioUSA

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