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Genome-wide mapping of global-to-local genetic effects on human facial shape

Nature Genetics volume 50, pages 414–423 (2018)Cite this article

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Abstract

Genome-wide association scans of complex multipartite traits like the human face typically use preselected phenotypic measures. Here we report a data-driven approach to phenotyping facial shape at multiple levels of organization, allowing for an open-ended description of facial variation while preserving statistical power. In a sample of 2,329 persons of European ancestry, we identified 38 loci, 15 of which replicated in an independent European sample (n = 1,719). Four loci were completely new. For the others, additional support (n = 9) or pleiotropic effects (n = 2) were found in the literature, but the results reported here were further refined. All 15 replicated loci highlighted distinctive patterns of global-to-local genetic effects on facial shape and showed enrichment for active chromatin elements in human cranial neural crest cells, suggesting an early developmental origin of the facial variation captured. These results have implications for studies of facial genetics and other complex morphological traits.

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Fig. 1: Hierarchical spectral clustering of facial shape.
Fig. 2: Fifteen replicating loci.
Fig. 3: Facial shape effects.
Fig. 4: Preferential activity in CNCCs.
Fig. 5: Regulatory regions active in CNCCs.

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References

  1. Roosenboom, J., Hens, G., Mattern, B. C., Shriver, M. D. & Claes, P. Exploring the underlying genetics of craniofacial morphology through various sources of knowledge. BioMed Res. Int. 2016, 3054578 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Paternoster, L. et al. Genome-wide association study of three-dimensional facial morphology identifies a variant in PAX3 associated with nasion position. Am. J. Hum. Genet. 90, 478–485 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Liu, F. et al. A genome-wide association study identifies five loci influencing facial morphology in Europeans. PLoS Genet. 8, e1002932 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Adhikari, K. et al. A genome-wide association scan implicates DCHS2, RUNX2, GLI3, PAX1 and EDAR in human facial variation. Nat. Commun. 7, 11616 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cole, J. B. et al. Genomewide association study of African children identifies association of SCHIP1 and PDE8A with facial size and shape. PLoS Genet. 12, e1006174 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Shaffer, J. R. et al. Genome-wide association study reveals multiple loci influencing normal human facial morphology. PLoS Genet. 12, e1006149 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lee, M. K. et al. Genome-wide association study of facial morphology reveals novel associations with FREM1 and PARK2. PLoS One 12, e0176566 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  PubMed  Google Scholar 

  9. Snyders, J., Claes, P., Vandermeulen, D. & Suetens, P. Development and comparison of non-rigid surface registration and extensions (technical report KUL/ESAT/PSI/1401) 1–55 (2014).

  10. Claes, P. et al. Modeling 3D facial shape from DNA. PLoS Genet. 10, e1004224 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ng, A. Y., Jordan, M. I. & Weiss, Y. On spectral clustering: analysis and an algorithm. in Proc. 14th International Conference Neural Information Processing Systems: Natural and Synthetic 849–856 (MIT Press, Cambridge, MA, 2001).

  12. Hayton, J. C., Allen, D. G. & Scarpello, V. Factor retention decisions in exploratory factor analysis: a tutorial on parallel analysis. Organ. Res. Methods 7, 191–205 (2004).

    Article  Google Scholar 

  13. Thompson, B. Canonical correlation analysis. in Encyclopedia of Statistics in Behavioral Science (eds. Everitt, B. & Howell, D.) (John Wiley and Sons, Hoboken, NJ, 2005).

  14. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Google Scholar 

  15. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bronner, M. E. & LeDouarin, N. M. Development and evolution of the neural crest: an overview. Dev. Biol. 366, 2–9 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Green, S. A., Simoes-Costa, M. & Bronner, M. E. Evolution of vertebrates as viewed from the crest. Nature 520, 474–482 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schneider, R. A. Regulation of jaw length during development, disease, and evolution. Curr. Top. Dev. Biol. 115, 271–298 (2015).

    Article  PubMed  Google Scholar 

  19. Cordero, D. R. et al. Cranial neural crest cells on the move: their roles in craniofacial development. Am. J. Med. Genet. A 155A, 270–279 (2011).

    Article  PubMed  Google Scholar 

  20. Kulesa, P. M. & McLennan, R. Neural crest migration: trailblazing ahead. F1000Prime Rep. 7, 02 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163, 68–83 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rada-Iglesias, A. et al. Epigenomic annotation of enhancers predicts transcriptional regulators of human neural crest. Cell Stem Cell 11, 633–648 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 107, 21931–21936 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Tak, Y. G. & Farnham, P. J. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin 8, 57 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Long, H. K., Prescott, S. L. & Wysocka, J. Ever-changing landscapes: transcriptional enhancers in development and evolution. Cell 167, 1170–1187 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Corradin, O. & Scacheri, P. C. Enhancer variants: evaluating functions in common disease. Genome Med. 6, 85 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ludwig, K. U. et al. Imputation of orofacial clefting data identifies novel risk loci and sheds light on the genetic background of cleft lip ± cleft palate and cleft palate only. Hum. Mol. Genet. 26, 829–842 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Staley, J. R. et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics 32, 3207–3209 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Klingenberg, C. P. Quantitative genetics of geometric shape: heritability and the pitfalls of the univariate approach. Evolution 57, 191–195 (2003).

    Article  PubMed  Google Scholar 

  32. Pallares, L. F. et al. Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination. PLoS Genet. 11, e1005607 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Maga, A. M., Navarro, N., Cunningham, M. L. & Cox, T. C. Quantitative trait loci affecting the 3D skull shape and size in mouse and prioritization of candidate genes in-silico. Front. Physiol. 6, 92 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Coussens, A. K. & van Daal, A. Linkage disequilibrium analysis identifies an FGFR1 haplotype-tag SNP associated with normal variation in craniofacial shape. Genomics 85, 563–573 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Peng, S. et al. Detecting genetic association of common human facial morphological variation using high density 3D image registration. PLoS Comput. Biol. 9, e1003375 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Carlson, D. S. Theories of craniofacial growth in the postgenomic era. Semin. Orthod. 11, 172–183 (2005).

    Article  Google Scholar 

  37. Sperber, G. H., Guttmann, G. D. & Sperber, S. M. Craniofacial Development (Book for Windows & Macintosh) (PMPH-USA, Hamilton-London, 2001).

  38. Williams, S. E. & Slice, D. E. Regional shape change in adult facial bone curvature with age. Am. J. Phys. Anthropol. 143, 437–447 (2010).

    Article  PubMed  Google Scholar 

  39. Mitteroecker, P. & Bookstein, F. The evolutionary role of modularity and integration in the hominoid cranium. Evolution 62, 943–958 (2008).

    Google Scholar 

  40. Klingenberg, C. P. Morphological integration and developmental modularity. Annu. Rev. Ecol. Evol. Syst. 39, 115–132 (2008).

    Article  Google Scholar 

  41. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zarelli, V. E. & Dawid, I. B. Inhibition of neural crest formation by Kctd15 involves regulation of transcription factor AP-2. Proc. Natl. Acad. Sci. USA 110, 2870–2875 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ahituv, N. et al. An ENU-induced mutation in AP-2α leads to middle ear and ocular defects in Doarad mice. Mamm. Genome 15, 424–432 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Lee, Y. H. & Saint-Jeannet, J. P. Sox9 function in craniofacial development and disease. Genesis 49, 200–208 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Francis-West, P. H., Robson, L. & Evans, D. J. Craniofacial Development: The Tissue and Molecular Interactions Tha t Control Development of the Head (Springer Science & Business Media, Berlin-Heidelberg, 2012).

  46. Stearns, F. W. One hundred years of pleiotropy: a retrospective. Genetics 186, 767–773 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Weinberg, S. M. et al. The 3D Facial Norms Database: Part 1. A web-based craniofacial anthropometric and image repository for the clinical and research community. Cleft Palate Craniofac. J. 53, e185–e197 (2016).

    Article  PubMed  Google Scholar 

  49. Heike, C. L., Upson, K., Stuhaug, E. & Weinberg, S. M. 3D digital stereophotogrammetry: a practical guide to facial image acquisition. Head Face Med. 6, 18 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Claes, P., Walters, M. & Clement, J. Improved facial outcome assessment using a 3D anthropometric mask. Int. J. Oral Maxillofac. Surg. 41, 324–330 (2012).

    Article  CAS  PubMed  Google Scholar 

  51. Claes, P., Walters, M., Vandermeulen, D. & Clement, J. G. Spatially-dense 3D facial asymmetry assessment in both typical and disordered growth. J. Anat. 219, 444–455 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Rohlf, F. & Slice, D. Extensions of the procrustus method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990).

    Article  Google Scholar 

  53. Robert, P. & Escoufier, Y. A unifying tool for linear multivariate statistical methods: the RV-coefficient. Appl. Stat. 25, 257–265 (1976).

    Article  Google Scholar 

  54. Laurie, C. C. et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet. Epidemiol. 34, 591–602 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 1, 457–470 (2011).

  58. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Olson, C. L. On choosing a test statistic in multivariate analysis of variance. Psychol. Bull. 83, 579 (1976).

    Article  Google Scholar 

  61. Pe’er, I., Yelensky, R., Altshuler, D. & Daly, M. J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  PubMed  Google Scholar 

  62. Li, J. & Ji, L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95, 221–227 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Valouev, A. et al. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat. Methods 5, 829–834 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This investigation was supported by KU Leuven, BOF funds GOA, CREA and C1. The collaborators at the University of Pittsburgh were supported by the National Institute for Dental and Craniofacial Research (see URLs) through the following grants: U01-DE020078, U01-DE020057, R01-DE016148, K99-DE02560 and 1-R01-DE027023. Funding for genotyping was provided by the National Human Genome Research Institute (see URLs): X01-HG007821 and X01-HG007485. Funding for initial genomic data cleaning by the University of Washington was provided by contract HHSN268201200008I from the National Institute for Dental and Craniofacial Research (see URLs) awarded to the Center for Inherited Disease Research (CIDR). The collaborators at Penn State University were supported in part by grants from the Center for Human Evolution and Development at Penn State, the Science Foundation of Ireland Walton Fellowship (04.W4/B643), the US National Institute of Justice (see URLs; 2008-DN-BX-K125) and the US Department of Defense (see URLs). The collaborators at the Stanford University School of Medicine were supported by the Howard Hughes Medical Institute, NIH U01 DE024430 and the March of Dimes Foundation 1-FY15-312 (J.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

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

    Peter Claes, Dzemila Sero, Jiarui Li, Paul Suetens & Dirk Vandermeulen

  2. Medical Imaging Research Center, MIRC, UZ Leuven, Leuven, Belgium

    Peter Claes, Dzemila Sero, Jiarui Li, Paul Suetens & Dirk Vandermeulen

  3. Murdoch Childrens Research Institute, Melbourne, Victoria, Australia

    Peter Claes

  4. Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Jasmien Roosenboom, Myoung Keun Lee, Elizabeth J. Leslie, Mary L. Marazita & Seth M. Weinberg

  5. Department of Anthropology, Penn State University, University Park, PA, USA

    Julie D. White, Arslan Zaidi, Brooke C. Mattern, Corey Liebowitz, Laurel Pearson, Tomás González & Mark D. Shriver

  6. Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA

    Tomek Swigut & Joanna Wysocka

  7. Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA

    Jenna C. Carlson & Eleanor Feingold

  8. Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA

    Ekaterina Orlova, Eleanor Feingold, Mary L. Marazita & John R. Shaffer

  9. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA

    Joanna Wysocka

  10. Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA

    Seth M. Weinberg

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  1. Peter Claes
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Contributions

This work is the result of a four-center collaboration between KU Leuven, the University of Pittsburgh, Penn State University and the Stanford University School of Medicine, led by P.C., S.M.W., M.D.S. and J.W., respectively. All four centers contributed equally to this work. P.C. and D.S. with input from P.S. and D.V. conceptualized and implemented the global-to-local facial segmentations. J.L. provided genomic data support and analysis at the KU Leuven site and together with P.C. ran the GWAS. M.K.L., J.R., E.J.L., J.C.C., E.O., E.F., M.L.M., J.R.S. and S.M.W. organized the PITT cohort and co-analyzed the GWAS results, generated LocusZoom plots and organized the results into 38 loci. J.D.W., A.Z., B.C.M., C.L., L.P., T.G. and M.D.S. organized the PSU cohort, imputed the PSU genetic data and co-analyzed the GWAS results. T.S. and J.W. performed the GREAT analysis and the association with CNCCs. P.C. wrote the manuscript with extensive input from all co-authors.

Corresponding authors

Correspondence to Peter Claes, Joanna Wysocka, Mark D. Shriver or Seth M. Weinberg.

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Integrated supplementary information

Supplementary Figure 1 Hierarchical spectral clustering on the PSU cohort

A global-to-local facial segmentation of the PSU cohort obtained using hierarchical spectral clustering. Note that the order of quadrants, or facial segments at each level, is not necessarily the same as for Fig. 1, on the PITT cohort. The reason is the randomness in the clustering that does not preserve such order and hence the use of the normalized mutual information as a measure of overlap.

Supplementary Figure 2 Biological shape variables per facial segment

Left, the number of principal components retained after parallel analysis for each facial segment. Right, the amount of variation explained by the principal components expressed as percentage for each facial segment.

Supplementary Figure 3 GREAT analysis GREAT GO gene ontology analysis results for the 15 top replicated SNPs in

Table 1. Plotted is the binomial test FDR (cyan) and binomial enrichment (magenta) for indicated top associated biological processes, phenotypes and expression pattern categories.

Supplementary Figure 4 Statistical replication result for rs2045323

a, –log10 (P value) of the canonical correlation per facial segment ranging from 0 to –log10 (8.01 × 10–5), i.e., the Bonferroni-corrected P value for literature replication. Black-encircled facial segments have reached nominal replication (P = 0.05). b, The canonical correlation [0 1]. c, The normal displacement (displacement in the direction locally normal to the facial surface) in each quasi-landmark of facial segment 45 going from the major to the minor allele SNP variant. Blue, inward depression; red, outward protrusion.

Supplementary Figure 5 Statistical discovery result for rs2424390

a, –log10 (P value) of the canonical correlation per facial segment ranging from 0 to –log10 (1.28 × 10–9), i.e., the Bonferroni-corrected P value for discovery. Black-encircled facial segments have reached nominal genome-wide significance (P ≤ 5 × 10–8). b, The canonical correlation [0 1]. c, The normal displacement (displacement in the direction locally normal to the facial surface) in each quasi-landmark of facial segment 11, going from the major to the minor allele SNP variant. Blue, inward depression; red, outward protrusion.

Supplementary Figure 6 Two loci associated with different aspects of nasal shape

a, 6p21.1 locus with peak SNP rs227833 and candidate gene SUPT3H. b, 19q13.11 locus with peak SNP rs287104 and candidate gene KCTD15. The locus in a is primarily affecting the nasal bridge and ridge, leaving the nose tip unaffected. The locus in b is focused on the nose tip only, which could indicate potentially different underlying soft tissue regulations. Top, –log10 (P value) of the canonical correlation per facial segment ranging from 0 to –log10 (1.28 × 10–9), i.e., the Bonferroni-corrected P value for discovery. Bottom, the normal displacement (displacement in the direction locally normal to the facial surface) in each quasi-landmark of a representative facial segment per locus, going from the major to the minor allele SNP variant. Blue, inward depression; red, outward protrusion.

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Supplementary Figures 1–6, Supplementary Tables 1–7 and Supplementary Notes 1 and 2

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Claes, P., Roosenboom, J., White, J.D. et al. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nat Genet 50, 414–423 (2018). https://doi.org/10.1038/s41588-018-0057-4

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