The human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study (GWAS) meta-analysis of 8,246 European individuals, we identified 203 genome-wide significant signals (120 also study-wide significant) associated with normal-range facial variation. Follow-up analyses find that the regions surrounding these signals are enriched for enhancer activity in cranial neural crest cells and craniofacial tissues, several regions harbor multiple signals with associations to different facial phenotypes, and there is evidence for potential coordinated actions of variants. In sum, our analyses provide insights for understanding how complex morphological traits are shaped by both individual and coordinated genetic actions.
Evidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study (GWAS) of cortical surface morphology in 19,644 European-ancestry individuals, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to facial shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face crosstalk. Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect significant overlap between shared brain-face GWAS signals and variants affecting behavioral-cognitive traits. These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function.
The human face is complex and multipartite, and characterization of its genetic architecture remains intriguingly challenging. Applying GWAS to multivariate shape 2 phenotypes, we identified 203 genomic regions associated with normal-range facial variation, 117 of which are novel. The associated regions are enriched for both genes relevant to 4 craniofacial and limb morphogenesis and enhancer activity in cranial neural crest cells and craniofacial tissues. Genetic variants grouped by their contribution to similar aspects of facial 6 variation show high within-group correlation of enhancer activity, and four SNP pairs display evidence of epistasis, indicating potentially coordinated actions of variants within the same cell 8 types or tissues. In sum, our analyses provide new insights for understanding how complex morphological traits are shaped by both individual and coordinated genetic actions. 10 Main Text: 12 "One of the major problems confronting modern biology is to understand how complex morphological structures arise during development and how they are altered during evolution""complicated developmental choreography" in which intrinsic genetic factors, epigenetic factors, and interactions between the two make up the progeny genotype, which engages with the 20 environment to ultimately produce a complex morphological trait, defined thus by its composition from a number of separate component parts 1 . We now understand that the intrinsic 22 genetic factors ultimately contributing to complex morphological traits consist not only of single 2 variants altering protein structure and/or function, but also non-coding variants and interactions 24 among variants, each affecting multiple tissues and developmental timepoints. This realization necessitates the development and utilization of methods capable of describing the genetic 26 architecture of complex morphological traits, which includes identifying the individual genetic variants contributing to morphological variation as well as their interactions 2,3 . 28 The human face is an exemplar complex morphological structure. It is a highly multipartite structure resulting from the intricate coordination of genetic, cellular, and 30 environmental factors 4-6 . Through prior genetic association studies of quantitative traits, 51 loci have been implicated in normal-range craniofacial morphology, and an additional 50 loci have 32 been associated with self-reported nose size or chin dimples in a large cohort study 7 (Table S1).However, as with all complex morphological traits, our ability to identify and describe the 34 genetic architecture of the face is limited by our ability to accurately characterize its phenotypic variation 4 , identify variants of both large and small effect 8 , and identify interactions between 36 variants. We previously described a novel data-driven approach to facial phenotyping, which facilitated the identification and replication of 15 loci involved in global-to-local variation in 38 facial morphology 9 . Here, we apply this phenotyping approach...
Many factors influence human facial morphology, including genetics, age, nutrition, biomechanical forces, and endocrine factors. Moreover, facial features clearly differ between males and females, and these differences are driven primarily by the influence of sex hormones during growth and development. Specific genetic variants are known to influence circulating sex hormone levels in humans, which we hypothesize, in turn, affect facial features. In this study, we investigated the effects of testosterone-related genetic variants on facial morphology. We tested 32 genetic variants across 22 candidate genes related to levels of testosterone, sex hormone-binding globulin (SHGB) and dehydroepiandrosterone sulfate (DHEAS) in three cohorts of healthy individuals for which 3D facial surface images were available (Pittsburgh 3DFN, Penn State and ALSPAC cohorts; total n = 7418). Facial shape was described using a recently developed extension of the dense-surface correspondence approach, in which the 3D facial surface was partitioned into a set of 63 hierarchically organized modules. Each variant was tested against each of the facial surface modules in a multivariate genetic association-testing framework and meta-analyzed. Additionally, the association between these candidate SNPs and five facial ratios was investigated in the Pittsburgh 3DFN cohort. Two significant associations involving intronic variants of SHBG were found: both rs12150660 (p = 1.07E-07) and rs1799941 (p = 6.15E-06) showed an effect on mandible shape. Rs8023580 (an intronic variant of NR2F2-AS1) showed an association with the total and upper facial width to height ratios (p = 9.61E-04 and p = 7.35E-04, respectively). These results indicate that testosterone-related genetic variants affect normal-range facial morphology, and in particular, facial features known to exhibit strong sexual dimorphism in humans.
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