1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Genomics and Human Genetics
  4. Volume 23, 2022
  5. Article

Annual Review of Genomics and Human Genetics

Volume 23, 2022

Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology

Abstract

Variations in the form of the human face, which plays a role in our individual identities and societal interactions, have fascinated scientists and artists alike. Here, we review our current understanding of the genetics underlying variation in craniofacial morphology and disease-associated dysmorphology, synthesizing decades of progress on Mendelian syndromes in addition to more recent results from genome-wide association studies of human facial shape and disease risk. We also discuss the various approaches used to phenotype and quantify facial shape, which are of particular importance due to the complex, multipartite nature of the craniofacial form. We close by discussing how experimental studies have contributed and will further contribute to our understanding of human genetic variation and then proposing future directions and applications for the field.

Keyword(s): craniofacial, development, gene regulation, genome-wide association study, GWAS, phenotyping, syndromes
Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-120121-102607
2022-08-31
2025-02-17
Loading full text...

Full text loading...

/deliver/fulltext/genom/23/1/annurev-genom-120121-102607.html?itemId=/content/journals/10.1146/annurev-genom-120121-102607&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Adhikari K, Fuentes-Guajardo M, Quinto-Sánchez M, Mendoza-Revilla J, Chacón-Duque JC et al. 2016. A genome-wide association scan implicates DCHS2, RUNX2, GLI3, PAX1 and EDAR in human facial variation. Nat. Commun. 7:11616
     [Google Scholar]
  2. 2.
    Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ et al. 2021. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53:195–204
     [Google Scholar]
  3. 3.
    Baldi P. 2018. Deep learning in biomedical data science. Annu. Rev. Biomed. Data Sci. 1:181–205
     [Google Scholar]
  4. 4.
    Bannister JJ, Crites SR, Aponte JD, Katz DC, Wilms M et al. 2020. Fully automatic landmarking of syndromic 3D facial surface scans using 2D images. Sensors 20:3171
     [Google Scholar]
  5. 5.
    Barton RA, Venditti C. 2014. Rapid evolution of the cerebellum in humans and other great apes. Curr. Biol. 24:2440–44
     [Google Scholar]
  6. 6.
    Beaty TH, Murray JC, Marazita ML, Munger RG, Ruczinski I et al. 2010. A genome-wide association study of cleft lip with and without cleft palate identifies risk variants near MAFB and ABCA4. Nat. Genet. 42:525–29
     [Google Scholar]
  7. 7.
    Beaty TH, Taub MA, Scott AF, Murray JC, Marazita ML et al. 2013. Confirming genes influencing risk to cleft lip with/without cleft palate in a case-parent trio study. Hum. Genet. 132:771–81
     [Google Scholar]
  8. 8.
    Birnbaum S, Ludwig KU, Reutter H, Herms S, Steffens M et al. 2009. Key susceptibility locus for nonsyndromic cleft lip with or without cleft palate on chromosome 8q24. Nat. Genet. 41:473–77
     [Google Scholar]
  9. 9.
    Boehringer S, van der Lijn F, Liu F, Günther M, Sinigerova S et al. 2011. Genetic determination of human facial morphology: links between cleft-lips and normal variation. Eur. J. Hum. Genet. 19:1192–97
     [Google Scholar]
  10. 10.
    Böhringer S, de Jong MA. 2019. Quantification of facial traits. Front. Genet. 10:397
     [Google Scholar]
  11. 11.
    Bonfante B, Faux P, Navarro N, Mendoza-Revilla J, Dubied M et al. 2021. A GWAS in Latin Americans identifies novel face shape loci, implicating VPS13B and a Denisovan introgressed region in facial variation. Sci. Adv. 7:eabc6160
     [Google Scholar]
  12. 12.
    Boulet SL, Rasmussen SA, Honein MA. 2008. A population-based study of craniosynostosis in metropolitan Atlanta, 1989–2003. Am. J. Med. Genet. A 146A:984–91
     [Google Scholar]
  13. 13.
    Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P. 2017. Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34:418–42
     [Google Scholar]
  14. 14.
    Butaric LN, Klocke RP. 2018. Nasal variation in relation to high-altitude adaptations among Tibetans and Andeans. Am. J. Hum. Biol. 30:e23104
     [Google Scholar]
  15. 15.
    Calo E, Gu B, Bowen ME, Aryan F, Zalc A et al. 2018. Tissue-selective effects of nucleolar stress and rDNA damage in developmental disorders. Nature 554:112–17
     [Google Scholar]
  16. 16.
    Carlson JC, Standley J, Petrin A, Shaffer JR, Butali A et al. 2017. Identification of 16q21 as a modifier of nonsyndromic orofacial cleft phenotypes. Genet. Epidemiol. 41:887–97
     [Google Scholar]
  17. 17.
    Casey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM et al. 2018. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32:43–54
     [Google Scholar]
  18. 18.
    Castel SE, Cervera A, Mohammadi P, Aguet F, Reverter F et al. 2018. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat. Genet. 50:1327–34
     [Google Scholar]
  19. 19.
    Cha S, Lim JE, Park AY, Do J-H, Lee SW et al. 2018. Identification of five novel genetic loci related to facial morphology by genome-wide association studies. BMC Genom 19:481
     [Google Scholar]
  20. 20.
    Chaitanya L, Breslin K, Zuñiga S, Wirken L, Pośpiech E et al. 2018. The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: introduction and forensic developmental validation. Forensic Sci. Int. Genet. 35:123–35
     [Google Scholar]
  21. 21.
    Chan CKF, Gulati GS, Sinha R, Tompkins JV, Lopez M et al. 2018. Identification of the human skeletal stem cell. Cell 175:43–56.e21
     [Google Scholar]
  22. 22.
    Chan CKF, Seo EY, Chen JY, Lo D, McArdle A et al. 2015. Identification and specification of the mouse skeletal stem cell. Cell 160:285–98
     [Google Scholar]
  23. 23.
    Claes P, Hill H, Shriver MD. 2014. Toward DNA-based facial composites: preliminary results and validation. Forensic Sci. Int. Genet. 13:208–16
     [Google Scholar]
  24. 24.
    Claes P, Liberton DK, Daniels K, Rosana KM, Quillen EE et al. 2014. Modeling 3D facial shape from DNA. PLOS Genet 10:e1004224
     [Google Scholar]
  25. 25.
    Claes P, Roosenboom J, White JD, Swigut T, Sero D et al. 2018. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nat. Genet. 50:414–23
     [Google Scholar]
  26. 26.
    Claes P, Walters M, Clement J 2012. Improved facial outcome assessment using a 3D anthropometric mask. Int. J. Oral Maxillofac. Surg. 41:324–30
     [Google Scholar]
  27. 27.
    Cole JB, Manyama M, Kimwaga E, Mathayo J, Larson JR et al. 2016. Genomewide association study of African children identifies association of SCHIP1 and PDE8A with facial size and shape. PLOS Genet 12:e1006174
     [Google Scholar]
  28. 28.
    Cole JB, Manyama M, Larson JR, Liberton DK, Ferrara TM et al. 2017. Human facial shape and size heritability and genetic correlations. Genetics 205:967–78
     [Google Scholar]
  29. 29.
    Cordero DR, Brugmann S, Chu Y, Bajpai R, Jame M et al. 2011. Cranial neural crest cells on the move: their roles in craniofacial development. Am. J. Med. Genet. A 155A:270–79
     [Google Scholar]
  30. 30.
    Crouch DJM, Winney B, Koppen WP, Christmas WJ, Hutnik K et al. 2018. Genetics of the human face: identification of large-effect single gene variants. PNAS 115:E676–85
     [Google Scholar]
  31. 31.
    Crouzon O. 1912. La dysostose cranio-faciale héréditaire. Bull. Mém. Soc. Med. Hôp. Paris 33:545–55
     [Google Scholar]
  32. 32.
    Cuomo ASE, Seaton DD, McCarthy DJ, Martinez I, Bonder MJ et al. 2020. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nat. Commun. 11:810
     [Google Scholar]
  33. 33.
    Curtis SW, Chang D, Lee MK, Shaffer JR, Indencleef K et al. 2021. The PAX1 locus at 20p11 is a potential genetic modifier for bilateral cleft lip. Hum. Genet. Genom. Adv. 2:100025
     [Google Scholar]
  34. 34.
    Curtis SW, Chang D, Sun MR, Epstein MP, Murray JC et al. 2021. FAT4 identified as a potential modifier of orofacial cleft laterality. Genet. Epidemiol. 45:721–35
     [Google Scholar]
  35. 35.
    Dardani C, Howe LJ, Mukhopadhyay N, Stergiakouli E, Wren Y et al. 2020. Cleft lip/palate and educational attainment: cause, consequence or correlation? A Mendelian randomization study. Int. J. Epidemiol. 49:1282–93
     [Google Scholar]
  36. 36.
    de Jong MA, Wollstein A, Ruff C, Dunaway D, Hysi P et al. 2016. An automatic 3D facial landmarking algorithm using 2D Gabor wavelets. IEEE Trans. Image Process. 25:580–88
     [Google Scholar]
  37. 37.
    Debusmann P. 1940. Familiare kombinierte Gesichtsmissbildung im Bereich des ersten Viszeralbogens. Arch. Kinderheil. 120:133–39
     [Google Scholar]
  38. 38.
    Dixon J, Edwards SJ, Gladwin AJ, Dixon MJ, Loftus SK et al. 1996. Positional cloning of a gene involved in the pathogenesis of Treacher Collins syndrome. Nat. Genet. 12:130–36
     [Google Scholar]
  39. 39.
    Djordjevic J, Zhurov AI, Richmond S, Visigen Consort. 2016. Genetic and environmental contributions to facial morphological variation: a 3D population-based twin study. PLOS ONE 11:e0162250 Correction 2016. PLOS ONE 11:e0164961
     [Google Scholar]
  40. 40.
    Dudas M, Kaartinen V. 2005. Tgf-β superfamily and mouse craniofacial development: interplay of morphogenetic proteins and receptor signaling controls normal formation of the face. Curr. Top. Dev. Biol. 66:65–133
     [Google Scholar]
  41. 41.
    Dworkin S, Boglev Y, Owens H, Goldie SJ. 2016. The role of Sonic hedgehog in craniofacial patterning, morphogenesis and cranial neural crest survival. J. Dev. Biol. 4:24
     [Google Scholar]
  42. 42.
    Endo C, Johnson TA, Morino R, Nakazono K, Kamitsuji S et al. 2018. Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations. Sci. Rep. 8:8974
     [Google Scholar]
  43. 43.
    Enlow DH, Hans MG. 1996. Essentials of Facial Growth Philadelphia: Saunders
     [Google Scholar]
  44. 44.
    Falconer DS. 1996. Introduction to Quantitative Genetics Harlow, UK: Prentice Hall
     [Google Scholar]
  45. 45.
    Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y et al. 2015. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47:1228–35
     [Google Scholar]
  46. 46.
    Fuad MTH, Fime AA, Sikder D, Iftee MAR, Rabbi J et al. 2021. Recent advances in deep learning techniques for face recognition. IEEE Access 9:99112–42
     [Google Scholar]
  47. 47.
    Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT et al. 2019. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51:1664–69
     [Google Scholar]
  48. 48.
    Giambartolomei C, Seo J-H, Schwarz T, Freund MK, Johnson RD et al. 2021. H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility. Am. J. Hum. Genet. 108:2284–300
     [Google Scholar]
  49. 49.
    Gokhman D, Mishol N, de Manuel M, de Juan D, Shuqrun J et al. 2019. Reconstructing Denisovan anatomy using DNA methylation maps. Cell 179:180–92.e10
     [Google Scholar]
  50. 50.
    Graf D, Malik Z, Hayano S, Mishina Y. 2016. Common mechanisms in development and disease: BMP signaling in craniofacial development. Cytokine Growth Factor Rev 27:129–39
     [Google Scholar]
  51. 51.
    Grant SFA, Wang K, Zhang H, Glaberson W, Annaiah K et al. 2009. A genome-wide association study identifies a locus for nonsyndromic cleft lip with or without cleft palate on 8q24. J. Pediatr. 155:909–13
     [Google Scholar]
  52. 52.
    Greenberg RS, Long HK, Swigut T, Wysocka J. 2019. Single amino acid change underlies distinct roles of H2A.Z subtypes in human syndrome. Cell 178:1421–36.e24
     [Google Scholar]
  53. 53.
    Guo J, Tan J, Yang Y, Zhou H, Hu S et al. 2014. Variation and signatures of selection on the human face. J. Hum. Evol. 75:143–52
     [Google Scholar]
  54. 54.
    Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N et al. 2019. Identifying facial phenotypes of genetic disorders using deep learning. Nat. Med. 25:60–64
     [Google Scholar]
  55. 55.
    Hallgrimsson B, Mio W, Marcucio RS, Spritz R. 2014. Let's face it—complex traits are just not that simple. PLOS Genet 10:e1004724
     [Google Scholar]
  56. 56.
    Hallgrimsson B, Percival CJ, Green R, Young NM, Mio W et al. 2015. Morphometrics, 3D imaging, and craniofacial development. Craniofacial Development Y Chai 561–97 Curr. Top. Dev. Biol. 115 Waltham, MA: Academic
     [Google Scholar]
  57. 57.
    Hammond P. 2007. The use of 3D face shape modelling in dysmorphology. Arch. Dis. Child. 92:1120–26
     [Google Scholar]
  58. 58.
    Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. 2005. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33:D514–17
     [Google Scholar]
  59. 59.
    Hirsch N, Dahan I, D'haene E, Avni M, Vergult S et al. 2021. HDAC9 structural variants disrupting TWIST1 transcriptional regulation lead to craniofacial and limb malformations. bioRxiv 2021.08.10.455254. https://doi.org/10.1101/2021.08.10.455254
    [Crossref]
  60. 60.
    Hoskens H, Li J, Indencleef K, Gors D, Larmuseau MHD et al. 2018. Spatially dense 3D facial heritability and modules of co-heritability in a father-offspring design. Front. Genet. 9:554
     [Google Scholar]
  61. 61.
    Hoskens H, Liu D, Naqvi S, Lee MK, Eller RJ et al. 2021. 3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies. PLOS Genet 17:e1009528
     [Google Scholar]
  62. 62.
    Howe LJ, Lee MK, Sharp GC, Davey Smith G, St. Pourcain B et al. 2018. Investigating the shared genetics of non-syndromic cleft lip/palate and facial morphology. PLOS Genet 14:e1007501
     [Google Scholar]
  63. 63.
    Hrdlička A. 1920. Anthropometry Philadelphia: Wistar Inst. Anat. Biol.
     [Google Scholar]
  64. 64.
    Huang L, Jia Z, Shi Y, Du Q, Shi J et al. 2019. Genetic factors define CPO and CLO subtypes of nonsyndromicorofacial cleft. PLOS Genet 15:e1008357
     [Google Scholar]
  65. 65.
    Huang Y, Li D, Qiao L, Liu Y, Peng Q et al. 2021. A genome-wide association study of facial morphology identifies novel genetic loci in Han Chinese. J. Genet. Genom. 48:198–207
     [Google Scholar]
  66. 66.
    Hutton TJ, Buxton BR, Hammond P. 2001. Dense surface point distribution models of the human face. Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001)153–60 Piscataway, NJ: IEEE
     [Google Scholar]
  67. 67.
    Indencleef K, Hoskens H, Lee MK, White JD, Liu C et al. 2021. The intersection of the genetic architectures of orofacial clefts and normal facial variation. Front. Genet. 12:170
     [Google Scholar]
  68. 68.
    Indencleef K, Roosenboom J, Hoskens H, White JD, Shriver MD et al. 2018. Six NSCL/P loci show associations with normal-range craniofacial variation. Front. Genet. 9:502
     [Google Scholar]
  69. 69.
    Inoue F, Ahituv N. 2015. Decoding enhancers using massively parallel reporter assays. Genomics 106:159–64
     [Google Scholar]
  70. 70.
    Jacobs LC, Liu F, Bleyen I, Gunn DA, Hofman A et al. 2014. Intrinsic and extrinsic risk factors for sagging eyelids. JAMA Dermatol 150:836–43
     [Google Scholar]
  71. 71.
    Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J et al. 2021. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53:304–12
     [Google Scholar]
  72. 72.
    Jheon AH, Oberoi S, Solem RC, Kapila S. 2017. Moving towards precision orthodontics: an evolving paradigm shift in the planning and delivery of customized orthodontic therapy. Orthod. Craniofac. Res. 20:Suppl. 1106–13
     [Google Scholar]
  73. 73.
    Johannsdottir B, Thorarinsson F, Thordarson A, Magnusson TE. 2005. Heritability of craniofacial characteristics between parents and offspring estimated from lateral cephalograms. Am. J. Orthod. Dentofac. Orthop. 127:200–61
     [Google Scholar]
  74. 74.
    Jones MC. 1988. Etiology of facial clefts: prospective evaluation of 428 patients. Cleft Palate J 25:16–20
     [Google Scholar]
  75. 75.
    Justice CM, Cuellar A, Bala K, Sabourin JA, Cunningham ML et al. 2020. A genome-wide association study implicates the BMP7 locus as a risk factor for nonsyndromic metopic craniosynostosis. Hum. Genet. 139:1077–90
     [Google Scholar]
  76. 76.
    Justice CM, Yagnik G, Kim Y, Peter I, Jabs EW et al. 2012. A genome-wide association study identifies susceptibility loci for nonsyndromic sagittal craniosynostosis near BMP2 and within BBS9. Nat. Genet. 44:1360–64
     [Google Scholar]
  77. 77.
    Karzbrun E, Khankhel AH, Megale HC, Glasauer SMK, Wyle Y et al. 2021. Human neural tube morphogenesis in vitro by geometric constraints. Nature 599:268–72
     [Google Scholar]
  78. 78.
    Katsanis SH, Claes P, Doerr M, Cook-Deegan R, Tenenbaum JD et al. 2021. A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts. PLOS ONE 16:e0257923
     [Google Scholar]
  79. 79.
    Katz DC, Grote MN, Weaver TD. 2017. Changes in human skull morphology across the agricultural transition are consistent with softer diets in preindustrial farming groups. PNAS 114:9050–55
     [Google Scholar]
  80. 80.
    Kau CH, Richmond S 2008. Three-dimensional analysis of facial morphology surface changes in untreated children from 12 to 14 years of age. Am. J. Orthod. Dentofac. Orthop. 134:751–60
     [Google Scholar]
  81. 81.
    Kau CH, Richmond S, Zhurov A, Ovsenik M, Tawfik W et al. 2010. Use of 3-dimensional surface acquisition to study facial morphology in 5 populations. Am. J. Orthod. Dentofac. Orthop. 137:Suppl.S56.e1–9
     [Google Scholar]
  82. 82.
    Kayser M. 2015. Forensic DNA phenotyping: predicting human appearance from crime scene material for investigative purposes. Forensic Sci. Int. Genet. 18:33–48
     [Google Scholar]
  83. 83.
    Kayser M, de Knijff P. 2011. Improving human forensics through advances in genetics, genomics and molecular biology. Nat. Rev. Genet. 12:179–92
     [Google Scholar]
  84. 84.
    Kesterke MJ, Raffensperger ZD, Heike CL, Cunningham ML, Hecht JT et al. 2016. Using the 3D Facial Norms Database to investigate craniofacial sexual dimorphism in healthy children, adolescents, and adults. Biol. Sex Differ. 7:23
     [Google Scholar]
  85. 85.
    Knol MJ, Pawlak MA, Lamballais S, Terzikhan N, Hofer E et al. 2022. Genetic architecture of orbital telorism. Hum. Mol. Genet. 31:1531–43
     [Google Scholar]
  86. 86.
    Kohn LAP. 1991. The role of genetics in craniofacial morphology and growth. Annu. Rev. Anthropol. 20:261–78
     [Google Scholar]
  87. 87.
    Lajeunie E, Le Merrer M, Bonaïti-Pellie C, Marchac D, Renier D 1995. Genetic study of nonsyndromic coronal craniosynostosis. Am. J. Med. Genet. 55:500–4
     [Google Scholar]
  88. 88.
    Lee MK, Shaffer JR, Leslie EJ, Orlova E, Carlson JC et al. 2017. Genome-wide association study of facial morphology reveals novel associations with FREM1 and PARK2. PLOS ONE 12:e0176566
     [Google Scholar]
  89. 89.
    Leslie EJ, Carlson JC, Shaffer JR, Butali A, Buxó CJ et al. 2017. Genome-wide meta-analyses of nonsyndromic orofacial clefts identify novel associations between FOXE1 and all orofacial clefts, and TP63 and cleft lip with or without cleft palate. Hum. Genet. 136:275–86
     [Google Scholar]
  90. 90.
    Li M, Cole JB, Manyama M, Larson JR, Liberton DK et al. 2017. Rapid automated landmarking for morphometric analysis of three-dimensional facial scans. J. Anat. 230:607–18
     [Google Scholar]
  91. 91.
    Lieberman P. 2007. The evolution of human speech: its anatomical and neural bases. Curr. Anthropol. 48:39–66
     [Google Scholar]
  92. 92.
    Lippert C, Sabatini R, Maher MC, Kang EY, Lee S et al. 2017. Identification of individuals by trait prediction using whole-genome sequencing data. PNAS 114:10166–71
     [Google Scholar]
  93. 93.
    Liu C, Lee MK, Naqvi S, Hoskens H, Liu D et al. 2021. Genome scans of facial features in East Africans and cross-population comparisons reveal novel associations. PLOS Genet 17:e1009695
     [Google Scholar]
  94. 94.
    Liu D, Alhazmi N, Matthews H, Lee MK, Li J et al. 2021. Impact of low-frequency coding variants on human facial shape. Sci. Rep. 11:748
     [Google Scholar]
  95. 95.
    Liu D, Ban H-J, El Sergani AM, Lee MK, Hecht JT et al. 2021. PRICKLE1 × FOCAD interaction revealed by genome-wide vQTL analysis of human facial traits. Front. Genet. 12:674642
     [Google Scholar]
  96. 96.
    Liu F, van der Lijn F, Schurmann C, Zhu G, Chakravarty MM et al. 2012. A genome-wide association study identifies five loci influencing facial morphology in Europeans. PLOS Genet 8:e1002932
     [Google Scholar]
  97. 97.
    Long HK, Osterwalder M, Welsh IC, Hansen K, Davies JOJ et al. 2020. Loss of extreme long-range enhancers in human neural crest drives a craniofacial disorder. Cell Stem Cell 27:765–83.e14
     [Google Scholar]
  98. 98.
    Ludwig KU, Ahmed ST, Böhmer AC, Sangani NB, Varghese S et al. 2016. Meta-analysis reveals genome-wide significance at 15q13 for nonsyndromic clefting of both the lip and the palate, and functional analyses implicate GREM1 as a plausible causative gene. PLOS Genet 12:e1005914
     [Google Scholar]
  99. 99.
    Ludwig KU, Böhmer AC, Bowes J, Nikolic M, Ishorst N et al. 2017. 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. 26:829–42
     [Google Scholar]
  100. 100.
    Ludwig KU, Mangold E, Herms S, Nowak S, Reutter H et al. 2012. Genome-wide meta-analyses of nonsyndromic cleft lip with or without cleft palate identify six new risk loci. Nat. Genet. 44:968–71
     [Google Scholar]
  101. 101.
    Mahdi SS, Nauwelaers N, Joris P, Bouritsas G, Gong S et al. 2022. Matching 3D facial shape to demographic properties by geometric metric learning: a part-based approach. IEEE Trans. Biometr. Behav. Identity Sci. 4:163–72
     [Google Scholar]
  102. 102.
    Mangold E, Ludwig KU, Birnbaum S, Baluardo C, Ferrian M et al. 2010. Genome-wide association study identifies two susceptibility loci for nonsyndromic cleft lip with or without cleft palate. Nat. Genet. 42:24–26
     [Google Scholar]
  103. 103.
    Marazita ML, Lidral AC, Murray JC, Field LL, Maher BS et al. 2009. Genome scan, fine-mapping, and candidate gene analysis of non-syndromic cleft lip with or without cleft palate reveals phenotype-specific differences in linkage and association results. Hum. Hered. 68:151–70
     [Google Scholar]
  104. 104.
    Matthews HS, Palmer RL, Baynam GS, Quarrell OW, Klein OD et al. 2021. Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism. Sci. Rep. 11:12175
     [Google Scholar]
  105. 105.
    Matthews HS, Penington AJ, Hardiman R, Fan Y, Clement JG et al. 2018. Modelling 3D craniofacial growth trajectories for population comparison and classification illustrated using sex-differences. Sci. Rep. 8:4771
     [Google Scholar]
  106. 106.
    Mitteroecker P, Gunz P, Bernhard M, Schaefer K, Bookstein FL. 2004. Comparison of cranial ontogenetic trajectories among great apes and humans. J. Hum. Evol. 46:679–97
     [Google Scholar]
  107. 107.
    Mostowska A, Gaczkowska A, Żukowski K, Ludwig KU, Hozyasz KK et al. 2018. Common variants in DLG1 locus are associated with non-syndromic cleft lip with or without cleft palate. Clin. Genet. 93:784–93
     [Google Scholar]
  108. 108.
    Muggli E, Matthews H, Penington A, Claes P, O'Leary C et al. 2017. Association between prenatal alcohol exposure and craniofacial shape of children at 12 months of age. JAMA Pediatr 171:771–80
     [Google Scholar]
  109. 109.
    Mukhopadhyay N, Feingold E, Moreno-Uribe L, Wehby G, Valencia-Ramirez LC et al. 2021. Genome-wide association study of non-syndromic orofacial clefts in a multiethnic sample of families and controls identifies novel regions. Front. Cell Dev. Biol. 9:621482
     [Google Scholar]
  110. 110.
    Mumbach MR, Satpathy AT, Boyle EA, Dai C, Gowen BG et al. 2017. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49:1602–12
     [Google Scholar]
  111. 111.
    Naini FB, Moss JP. 2004. Three-dimensional assessment of the relative contribution of genetics and environment to various facial parameters with the twin method. Am. J. Orthod. Dentofac. Orthop. 126:655–65
     [Google Scholar]
  112. 112.
    Naqvi S, Sleyp Y, Hoskens H, Indencleef K, Spence JP et al. 2021. Shared heritability of human face and brain shape. Nat. Genet. 53:830–39
     [Google Scholar]
  113. 113.
    Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR et al. 2021. Genome-wide enhancer maps link risk variants to disease genes. Nature 593:238–43
     [Google Scholar]
  114. 114.
    Neubauer S, Hublin J-J, Gunz P. 2018. The evolution of modern human brain shape. Sci. Adv. 4:eaao5961
     [Google Scholar]
  115. 115.
    Nie X, Luukko K, Kettunen P. 2006. FGF signalling in craniofacial development and developmental disorders. Oral Dis 12:102–11
     [Google Scholar]
  116. 116.
    Niemi MEK, Martin HC, Rice DL, Gallone G, Gordon S et al. 2018. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562:268–71
     [Google Scholar]
  117. 117.
    Parker SE, Mai CT, Canfield MA, Rickard R, Wang Y et al. 2010. Updated national birth prevalence estimates for selected birth defects in the United States, 2004–2006. Birth Defects Res. A 88:1008–16
     [Google Scholar]
  118. 118.
    Paternoster L, Zhurov AI, Toma AM, Kemp JP, St. Pourcain B et al. 2012. Genome-wide association study of three-dimensional facial morphology identifies a variant in PAX3 associated with nasion position. Am. J. Hum. Genet. 90:478–85
     [Google Scholar]
  119. 119.
    Pecora NG, Baccetti T, McNamara JA. 2008. The aging craniofacial complex: a longitudinal cephalometric study from late adolescence to late adulthood. Am. J. Orthod. Dentofac. Orthop. 134:496–505
     [Google Scholar]
  120. 120.
    Petrides G, Clark JR, Low H, Lovell N, Eviston TJ. 2021. Three-dimensional scanners for soft-tissue facial assessment in clinical practice. J. Plast. Reconstr. Aesthet. Surg. 74:605–14
     [Google Scholar]
  121. 121.
    Pickrell JK, Berisa T, Liu JZ, Ségurel L, Tung JY et al. 2016. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48:709–17
     [Google Scholar]
  122. 122.
    Prescott SL, Srinivasan R, Marchetto MC, Grishina I, Narvaiza I et al. 2015. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163:68–83
     [Google Scholar]
  123. 123.
    Proffit W, Fields H, Larson B, Sarver D. 2018. Contemporary Orthodontics Philadelphia: Elsevier. , 6th ed..
     [Google Scholar]
  124. 124.
    Qian Y, Xiong Z, Li Y, Zhou H, Kayser M et al. 2021. Knocking-out the human face genes TBX15 and PAX1 in mice alters facial and other physical morphology. bioRxiv 2021.05.26.445773. https://doi.org/10.1101/2021.05.26.445773
    [Crossref]
  125. 125.
    Qiao L, Yang Y, Fu P, Hu S, Zhou H et al. 2018. Genome-wide variants of Eurasian facial shape differentiation and a prospective model of DNA based face prediction. J. Genet. Genom. 45:419–32
     [Google Scholar]
  126. 126.
    Ransom RC, Carter AC, Salhotra A, Leavitt T, Marecic O et al. 2018. Mechanoresponsive stem cells acquire neural crest fate in jaw regeneration. Nature 563:514–21
     [Google Scholar]
  127. 127.
    Reardon W, Winter RM, Rutland P, Pulleyn LJ, Jones BM et al. 1994. Mutations in the fibroblast growth factor receptor 2 gene cause Crouzon syndrome. Nat. Genet. 8:98–103
     [Google Scholar]
  128. 128.
    Reynolds K, Kumari P, Sepulveda Rincon L, Gu R, Ji Y et al. 2019. Wnt signaling in orofacial clefts: crosstalk, pathogenesis and models. Dis. Models Mech. 12:dmm037051
     [Google Scholar]
  129. 129.
    Ritchie MD, Van Steen K. 2018. The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation. Ann. Transl. Med. 6:157
     [Google Scholar]
  130. 130.
    Roosenboom J, Indencleef K, Lee MK, Hoskens H, White JD et al. 2018. SNPs associated with testosterone levels influence human facial morphology. Front. Genet. 9:497
     [Google Scholar]
  131. 131.
    Rudy HL, Wake N, Yee J, Garfein ES, Tepper OM. 2020. Three-dimensional facial scanning at the fingertips of patients and surgeons: accuracy and precision testing of iPhone X three-dimensional scanner. Plast. Reconstr. Surg. 146:1407–17
     [Google Scholar]
  132. 132.
    Schaid DJ, Chen W, Larson NB 2018. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19:491–504
     [Google Scholar]
  133. 133.
    Schneider RA. 2018. Neural crest and the origin of species-specific pattern. Genesis 56:e23219
     [Google Scholar]
  134. 134.
    Schwartzentruber J, Foskolou S, Kilpinen H, Rodrigues J, Alasoo K et al. 2018. Molecular and functional variation in iPSC-derived sensory neurons. Nat. Genet. 50:54–61
     [Google Scholar]
  135. 135.
    Sero D, Zaidi A, Li J, White JD, Zarzar TBG et al. 2019. Facial recognition from DNA using face-to-DNA classifiers. Nat. Commun. 10:2557
     [Google Scholar]
  136. 136.
    Sforza C, de Menezes M, Ferrario V. 2013. Soft- and hard-tissue facial anthropometry in three dimensions: what's new. J. Anthropol. Sci. 91:159–84
     [Google Scholar]
  137. 137.
    Shaffer JR, Orlova E, Lee MK, Leslie EJ, Raffensperger ZD et al. 2016. Genome-wide association study reveals multiple loci influencing normal human facial morphology. PLOS Genet. 12:e1006149
     [Google Scholar]
  138. 138.
    Sheehan MJ, Nachman MW. 2014. Morphological and population genomic evidence that human faces have evolved to signal individual identity. Nat. Commun. 5:4800
     [Google Scholar]
  139. 139.
    Shema E, Bernstein BE, Buenrostro JD. 2019. Single-cell and single-molecule epigenomics to uncover genome regulation at unprecedented resolution. Nat. Genet. 51:19–25
     [Google Scholar]
  140. 140.
    Shin JH, Persing JA 2007. Nonsyndromic craniosynostosis and deformational plagiocephaly. Grabb and Smith's Plastic Surgery CH Thorne, RW Beasley, SJ Aston, SP Bartlett, GC Gurtner, SL Spear 226–36 Philadelphia: Lippincott Williams & Wilkins. , 6th ed..
     [Google Scholar]
  141. 141.
    Shrimpton S, Daniels K, de Greef S, Tilotta F, Willems G et al. 2014. A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction. Forensic Sci. Int. 234:103–10
     [Google Scholar]
  142. 142.
    Simões-Costa M, Bronner ME. 2015. Establishing neural crest identity: a gene regulatory recipe. Development 142:242–57
     [Google Scholar]
  143. 143.
    Smith DW. 1982. Recognizable Patterns of Human Malformation: Genetic, Embryologic and Clinical Aspects Philadelphia: Saunders. , 3rd ed..
     [Google Scholar]
  144. 144.
    Som PM, Naidich TP. 2013. Illustrated review of the embryology and development of the facial region, part 1: early face and lateral nasal cavities. Am. J. Neuroradiol. 34:2233–40
     [Google Scholar]
  145. 145.
    Som PM, Streit A, Naidich TP. 2014. Illustrated review of the embryology and development of the facial region, part 3: an overview of the molecular interactions responsible for facial development. Am. J. Neuroradiol. 35:223–29
     [Google Scholar]
  146. 146.
    Strober BJ, Elorbany R, Rhodes K, Krishnan N, Tayeb K et al. 2019. Dynamic genetic regulation of gene expression during cellular differentiation. Science 364:1287–90
     [Google Scholar]
  147. 147.
    Subtelny JD. 1959. A longitudinal study of soft tissue facial structures and their profile characteristics, defined in relation to underlying skeletal structures. Am. J. Orthod. 45:481–507
     [Google Scholar]
  148. 148.
    Sudlow C, Gallacher J, Allen N, Beral V, Burton P et al. 2015. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med 12:e1001779
     [Google Scholar]
  149. 149.
    Sun Y, Huang Y, Yin A, Pan Y, Wang Y et al. 2015. Genome-wide association study identifies a new susceptibility locus for cleft lip with or without a cleft palate. Nat. Commun. 6:6414
     [Google Scholar]
  150. 150.
    Suttie M, Foroud T, Wetherill L, Jacobson JL, Molteno CD et al. 2013. Facial dysmorphism across the fetal alcohol spectrum. Pediatrics 131:e779–88
     [Google Scholar]
  151. 151.
    Thayer ZM, Dobson SD. 2013. Geographic variation in chin shape challenges the universal facial attractiveness hypothesis. PLOS ONE 8:e60681
     [Google Scholar]
  152. 152.
    Thieme F, Ludwig KU. 2017. The role of noncoding genetic variation in isolated orofacial clefts. J. Dent. Res. 96:1238–47
     [Google Scholar]
  153. 153.
    Timberlake AT, Choi J, Zaidi S, Lu Q, Nelson-Williams C et al. 2016. Two locus inheritance of non-syndromic midline craniosynostosis via rare SMAD6 and common BMP2 alleles. eLife 5:e20125
     [Google Scholar]
  154. 154.
    Tsagkrasoulis D, Hysi P, Spector T, Montana G. 2017. Heritability maps of human face morphology through large-scale automated three-dimensional phenotyping. Sci. Rep. 7:45885
     [Google Scholar]
  155. 155.
    Umans BD, Battle A, Gilad Y. 2021. Where are the disease-associated eQTLs?. Trends Genet 37:109–24
     [Google Scholar]
  156. 156.
    Van Noorden R. 2020. The ethical questions that haunt facial-recognition research. Nature 587:354–58
     [Google Scholar]
  157. 157.
    Venkatesaramani R, Malin BA, Vorobeychik Y. 2021. Re-identification of individuals in genomic datasets using public face images. Sci. Adv. 7:eabg3296
     [Google Scholar]
  158. 158.
    Visscher PM, Hill WG, Wray NR. 2008. Heritability in the genomics era—concepts and misconceptions. Nat. Rev. Genet. 9:255–66
     [Google Scholar]
  159. 159.
    Vortkamp A, Gessler M, Grzeschik K-H. 1991. GLI3 zinc-finger gene interrupted by translocations in Greig syndrome families. Nature 352:539–40
     [Google Scholar]
  160. 160.
    Wagner T, Wirth J, Meyer J, Zabel B, Held M et al. 1994. Autosomal sex reversal and campomelic dysplasia are caused by mutations in and around the SRY-related gene SOX9. Cell 79:1111–20
     [Google Scholar]
  161. 161.
    Wang S, Zhang M, Wu S, Du S, Qian W et al. 2021. Genetic mechanisms underlying East Asian and European facial differentiation. Res. Square 604881 https://doi.org/10.21203/rs.3.rs-604881/v1
    [Crossref] [Google Scholar]
  162. 162.
    Weinberg SM, Parsons TE, Marazita ML, Maher BS. 2013. Heritability of face shape in twins: a preliminary study using 3D stereophotogrammetry and geometric morphometrics. Dent. 3000 1:14
     [Google Scholar]
  163. 163.
    Welzenbach J, Hammond NL, Nikolić M, Thieme F, Ishorst N et al. 2021. Integrative approaches generate insights into the architecture of non-syndromic cleft lip with or without cleft palate. Hum. Genet. Genom. Adv. 2:100038
     [Google Scholar]
  164. 164.
    White JD, Indencleef K, Naqvi S, Eller RJ, Hoskens H et al. 2021. Insights into the genetic architecture of the human face. Nat. Genet. 53:45–53
     [Google Scholar]
  165. 165.
    White JD, Ortega-Castrillon A, Virgo C, Indencleef K, Hoskens H et al. 2020. Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems. Sci. Rep. 10:4443
     [Google Scholar]
  166. 166.
    Wienroth M. 2020. Socio-technical disagreements as ethical fora: Parabon NanoLab's forensic DNA Snapshot™ service at the intersection of discourses around robust science, technology validation, and commerce. Biosocieties 15:28–45
     [Google Scholar]
  167. 167.
    Wilderman A, VanOudenhove J, Kron J, Noonan JP, Cotney J. 2018. High-resolution epigenomic atlas of human embryonic craniofacial development. Cell Rep 23:1581–97
     [Google Scholar]
  168. 168.
    Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J et al. 2019. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570:514–18
     [Google Scholar]
  169. 169.
    Wu W, Zhai G, Xu Z, Hou B, Liu D et al. 2019. Whole-exome sequencing identified four loci influencing craniofacial morphology in northern Han Chinese. Hum. Genet. 138:601–11
     [Google Scholar]
  170. 170.
    Xiong Z, Dankova G, Howe LJ, Lee MK, Hysi PG et al. 2019. Novel genetic loci affecting facial shape variation in humans. eLife 8:e49898
     [Google Scholar]
  171. 171.
    Yang J, Lee SH, Goddard ME, Visscher PM. 2011. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88:76–82
     [Google Scholar]
  172. 172.
    Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR et al. 2018. Meta-analysis of genome-wide association studies for height and body mass index in ∼700 000 individuals of European ancestry. Hum. Mol. Genet. 27:3641–49
     [Google Scholar]
  173. 173.
    Yu Y, Zuo X, He M, Gao J, Fu Y et al. 2017. Genome-wide analyses of non-syndromic cleft lip with palate identify 14 novel loci and genetic heterogeneity. Nat. Commun. 8:14364
     [Google Scholar]
  174. 174.
    Zaidi AA, Mattern BC, Claes P, McEcoy B, Hughes C et al. 2017. Investigating the case of human nose shape and climate adaptation. PLOS Genet 13:e1006616
     [Google Scholar]
  175. 175.
    Zelditch ML, Swiderski DL, Sheets HD, Fink WL. 2004. Geometric Morphometrics for Biologists: A Primer Boston: Academic
     [Google Scholar]
/content/journals/10.1146/annurev-genom-120121-102607
Loading
Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology
Annual Review of Genomics and Human Genetics 23, 383 (2022); https://doi.org/10.1146/annurev-genom-120121-102607
/content/journals/10.1146/annurev-genom-120121-102607
/content/journals/10.1146/annurev-genom-120121-102607
Loading

Data & Media loading...

Supplementary Data

  • Download Supplemental Tables 1-3 (XLSX).

    Supplemental Table descriptions:

    Supplemental Table 1. Genes and pathways mutated in rare craniofacial disorders with Mendelian inheritance. Genes were identified by searching the Online Mendelian Inheritance in Man (OMIM) database for the term ‘craniofacial,’ and then manually reviewing clinical synopses to confirm a craniofacial defect in the accompanying syndrome. Genes implicated by GWAS were identified through literature review of all facial shape and nsCL/P GWAS (Supplementary Tables 2 and 3). Genes for which mouse mutants have craniofacial phenotypes were identified by searching the Mouse Genome Informatics database (MGI; http://www.informatics.jax.org/allele) for the phenotype term ‘craniofacial’ and further screening entries for loss-of-function mutations.

    Supplemental Table 2. SNPs associated with normal-range facial variation through GWAS. SNPs as well as corresponding candidate genes were collected from previously published GWAS.

    Supplemental Table 3. SNPs associated with orofacial clefts through GWAS. SNPs as well as corresponding candidate genes were collected from previously published GWAS.

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error