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Genetic variants underlying differences in facial morphology in East Asian and European populations

Nature Genetics volume 54, pages 403–411 (2022)Cite this article

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Abstract

Facial morphology—a conspicuous feature of human appearance—is highly heritable. Previous studies on the genetic basis of facial morphology were performed mainly in European-ancestry cohorts (EUR). Applying a data-driven phenotyping and multivariate genome-wide scanning protocol to a large collection of three-dimensional facial images of individuals with East Asian ancestry (EAS), we identified 244 variants in 166 loci (62 new) associated with typical-range facial variation. A newly proposed polygenic shape analysis indicates that the effects of the variants on facial shape in EAS can be generalized to EUR. Based on this, we further identified 13 variants related to differences between facial shape in EUR and EAS populations. Evolutionary analyses suggest that the difference in nose shape between EUR and EAS populations is caused by a directional selection, due mainly to a local adaptation in Europeans. Our results illustrate the underlying genetic basis for facial differences across populations.

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Fig. 1: Overall results of genome-wide association meta-analyses in Han Chinese cohort.
Fig. 2: Comparison of shared and population-specific variants.
Fig. 3: Visualization of PPS-derived whole faces and nose of EAS and EUR, and statistical validation of the PPS approach.
Fig. 4: Natural selection analyses and enrichment test of the differentiation of facial-associated variants among EAS and EUR populations.

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Data availability

The Meta-analysis GWAS summary statistics are available on the National Omics Data Encyclopedia. NODE: OEP002283. The participants making up the NSPT, NHC and TZL datasets were not collected with broad data sharing consent. Given the highly identifiable nature of both facial and genomic information and unresolved issues regarding risk to participants, we opted for a more conservative approach to participant recruitment. Broad data sharing of the raw data from these collections would thus be in legal and ethical violation of the informed consent obtained from the participants. This restriction is not because of any personal or commercial interests. Additional details can be requested from L.J. for the NSPT dataset, and S. Wang for the NHC and TZL datasets. Data usage shall be in full compliance with the Regulations on Management of Human Genetic Resources in China. Publicly available data used were: the 1000GP Phase 3 data (https://www.internationalgenome.org/category/phase-3/)23, The Roadmap Epigenomics Project (http://www.roadmapepigenomics.org)26, NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/SNP)62, UCSC genome browser (http://genome.ucsc.edu)63, HaploReg v.4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)64, Ensemble genome browser (http://asia.ensembl.org/Homo_sapiens/Info/Index)65, GTEx v.8 (https://gtexportal.org/home/)66,67,68, Human genome dating (https://human.genome.dating/)49 and the transcriptome resource from separated ectoderm and mesenchyme of the developing mouse face (GSE62214).

Code availability

The statistical analyses in this work were based on functions of the statistical toolbox in MeshMonk (https://github.com/TheWebMonks/meshmonk, v.0.0.6)56, MATLAB 2018a, R (v.3.6.1), ggplot2 (v.3.1.0), Python (v.3.5.0), PLINK v.1.9, SHAPEIT2 (v.2.17), IMPUTE2 (v.2.3.2), SNPLIB (https://github.com/jiarui-li/SNPLIB), GCTA-GREML, FUMA (v.1.3.6), GREAT (v.4.0.4), GREGOR (v.1.4.0), Metascape (https://metascape.org), LocusZoom (https://genome.sph.umich.edu/wiki/LocusZoom) and REHH2 (v.3.2.0) as mentioned throughout the Methods.

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Acknowledgements

We thank the participants of the NSPT, NHC and TZL cohorts who consented to participate in research, and the related teams, including interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This project was funded by the following grants and contracts: Strategic Priority Research Program of the Chinese Academy of Sciences (XDB38020400 to S. Wang); Shanghai Municipal Science and Technology Major Project (2017SHZDZX01 to L.J. and S. Wang); National Key Research and Development Project (2018YFC0910403 to S. Wang); CAS Interdisciplinary Innovation Team Project (to S. Wang); Max Planck-CAS Paul Gerson Unna Independent Research Group Leadership Award (to S. Wang); National Natural Science Foundation of China (31521003 to L.J., 31900408 to M.Z.); National Science and Technology Basic Research Project (2015FY111700 to L.J.); CAMS Innovation Fund for Medical Sciences (2019-I2M-5-066 to L.J.); The 111 Project (B13016 to L.J.); China Postdoctoral Science Foundation (2019M651352 to M.Z., 2020M670984 to W.Q.). We are grateful for all suggestions collected during the poster exhibition of ASHG 2019 Annual Meeting and thank all the participants in these studies.

Author information

Author notes

  1. These authors contributed equally: Manfei Zhang, Sijie Wu, Siyuan Du, Wei Qian.

Authors and Affiliations

  1. State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China

    Manfei Zhang, Sijie Wu, Wei Qian, Andrés Ruiz-Linares, Jiucun Wang, Li Jin & Sijia Wang

  2. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Manfei Zhang, Sijie Wu, Siyuan Du, Wei Qian, Jieyi Chen, Lu Qiao, Qianqian Peng, Yu Liu, Kun Tang, Li Jin, Jiarui Li & Sijia Wang

  3. School of Computer Science, Fudan University, Shanghai, China

    Manfei Zhang & Wei Qian

  4. Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China

    Sijie Wu, Jieyi Chen, Yajun Yang, Jingze Tan & Andrés Ruiz-Linares

  5. Fudan-Taizhou Institute of Health Sciences, Taizhou, China

    Ziyu Yuan, Jiucun Wang & Li Jin

  6. Biogéosciences, UMR 6282 CNRS-EPHE, Université Bourgogne Franche-Comté, Dijon, France

    Nicolas Navarro

  7. Ecole Pratique des Hautes Etudes, PSL University, Paris, France

    Nicolas Navarro

  8. Aix-Marseille Université, CNRS, EFS, ADES, Marseille, France

    Andrés Ruiz-Linares

  9. Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, UK

    Andrés Ruiz-Linares

  10. Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium

    Peter Claes & Jiarui Li

  11. Medical Imaging Research Center, UZ Leuven, Leuven, Belgium

    Peter Claes & Jiarui Li

  12. Department of Human Genetics, KU Leuven, Leuven, Belgium

    Peter Claes

  13. Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

    Peter Claes

  14. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Sijia Wang

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  1. Manfei Zhang
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  2. Sijie Wu
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Contributions

S. Wang, L.J., J.L. and M.Z. conceptualized the study (ideas; formulation or evolution of overarching research goals and aims). M.Z., S. Wu, S.D., W.Q. and J.L. carried out data curation (management activities to annotate, scrub data and maintain research data for initial use and later re-use). M.Z., S. Wu, S.D., W.Q., J.L. and J.C. carried out the formal analysis (application of statistical, mathematical, computational or other formal techniques to analyze or synthesize study data). M.Z., S. Wu, S.D., W.Q. and J.L. did the visualization (preparation, creation and/or presentation of the published work, specifically visualization/data presentation). K.T., L.Q., Y.Y. and J.T. collected the 3D facial scans of the TZL cohort. J.W., Z.Y., J.T, K.T. and L.Q. collected the 3D facial scans of the NSPT cohort. S. Wu, Y.L. and Q.P. contributed to generating the SNP array data. S. Wu, S.D. and J.L. registered the 3D facial scans of the Northern Han Chinese cohort and conducted the PCA of discovery cohort. N.N. and A.R.-L. performed the analysis of MPRS22/Mprs22 and human/mouse craniofacial shape. M.Z., S. Wu, S.D., W.Q. and J.L. wrote the original draft. S. Wang, L.J., P.C., J.L., M.Z., S. Wu, S.D. and W.Q. reviewed and edited the final manuscript. All authors participated in preparing the manuscript by reading and commenting on drafts before submission.

Corresponding authors

Correspondence to Li Jin, Jiarui Li or Sijia Wang.

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Competing interests

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Nature Genetics thanks Hongtu Zhu and Fan Liu for their contribution to the peer review of this work. Peer reviewer reports are available with this article.

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Extended data

Extended Data Fig. 1 Study design.

We first start with a face segmentation procedure to get 63 face segments from which we defined 10 anatomical face regions. Then by using a CCA based GWAS, we identified 244 variants with a P value lower than 5×10-8, in which 151 are also lower than 9.8×10-10. To investigate what affects the similarity of an EAS face, we used polygenic population shape (PPS) analyses to fit EUR and EAS faces and identified 13 variants mainly contributing to EUR-EAS facial differences. To investigate selection on facial variation, we used FST and XP-EHH to find which parts of the face are under selection. These results, we further compared with random drift and random PPS to find out, which from the two populations, EUR or EAS, experienced selection.

Extended Data Fig. 2 Enrichment analysis of leading variants.

(a) Geno Ontology enrichment for genes annotated from leading variants by GREAT24. (b) Heatmap indicating the global enrichment of trait-associated variants in different chromatin state (y axis) and in different tissue (x axis). The fold change was calculated by GREGOR28. The embryonic craniofacial tissue was previously published by epigenomic atlas, while the other was previously published by Roadmap Epigenome27. The description of the 25-state chromatin model can be found at: https://egg2.wustl.edu/roadmap/web_portal/imputed.html#chr_imp. (c) Expression levels of the candidate genes in craniofacial tissues. Each point (n=3 biologically independent replicates for each condition) represents an estimated fold change compared to control genes at different times (E10.5, E11.5, E12.5), in different prominences (Frontonasal, FNP: circle; Maxillary, MxP: square; Mandibular, MnP: triangle), and tissue layer (Ectoderm, Ect: red; Mesenchyme, Mes: blue). Data are presented as mean values +/- 95% confidence intervals (1.96×SEM).

Extended Data Fig. 3 XP-EHH and FST enrichment analysis for shared and differentiated variants.

XP-EHH and FST enrichment analysis for (a, d) EUR differentiated variants, (b, e) EAS differentiated variants, and (c, f) shared variants in EAS study. The blue color is the null distribution. The red line is the mean XP-EHH or FST score of shared or differentiated variants. The black line is the 95% quantile of the null distribution.

Extended Data Fig. 4 Validation of PPS in 10 anatomical segments.

(a) The null distribution (blue) of Euclidean distance, cosine similarity with EUR average face and EAS average face using 1,000 simulations derived from random variants on the 10 anatomical regions, red line infers the statistics of the leading variants associated with corresponding regions; black line infers 95% quantile of distribution from the random variants with corresponding regions; (b) The genetic effects of rs12632544 and (c) rs12473319 weighted by their effect allele number difference of EUR and EAS (visualized using the local surface normal displacement).

Extended Data Fig. 5 The EAS-FA of polygenic shapes in 10 anatomical regions for EAS and EUR individuals in 1000GP.

The EAS-FA of polygenic shapes in a) mandible, b) forehead, c) lower mouth, d) upper mouth, e) nose, f) maxillary, g) glabella, h) eye, i) tempora, and j) zygoma for EAS and EUR individuals in 1000GP. The squares represent the mean EAS-FA score in 10 anatomical regions and the horizontal lines represent 1st and 3rd quantile.

Extended Data Fig. 6 EAS-FA of the 244 leading variants on the EUR-EAS difference.

The distributions (blue) of EAS-FA derived from 244 leading variants associated with a) whole face and b) - k) 10 anatomical segments. The black dotted line is the EAS-FA threshold of each region (mean + 3×SD). The red arrow is the variant over threshold.

Extended Data Fig. 7 Multi peak in 17q24.3 region.

(a) Association variants in the SOX9 locus and genomic environment surrounding SOX9 across a 2-Mb window. Four independent variants, represented by (1) rs34476511 (blue), (2) rs9900242 (green), (3) rs8068343 (red), and (4) rs2193052 (purple) are observed; (b) Allele frequency in AMR, SAS, AFR, EUR and EAS population of the four variants from 1000GP; (c) The effects of the four variants in the nose region.

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Zhang, M., Wu, S., Du, S. et al. Genetic variants underlying differences in facial morphology in East Asian and European populations. Nat Genet 54, 403–411 (2022). https://doi.org/10.1038/s41588-022-01038-7

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