Vitiligo is an autoimmune disease in which depigmented skin results from the destruction of melanocytes, with epidemiological association with other autoimmune diseases. In previous linkage and genome-wide association studies (GWAS1 and GWAS2), we identified 27 vitiligo susceptibility loci in patients of European ancestry. We carried out a third GWAS (GWAS3) in European-ancestry subjects, with augmented GWAS1 and GWAS2 controls, genome-wide imputation, and meta-analysis of all three GWAS, followed by an independent replication. The combined analyses, with 4,680 cases and 39,586 controls, identified 23 new significantly associated loci and 7 suggestive loci. Most encode immune and apoptotic regulators, with some also associated with other autoimmune diseases, as well as several melanocyte regulators. Bioinformatic analyses indicate a predominance of causal regulatory variation, some of which corresponds to expression quantitative trait loci (eQTLs) at these loci. Together, the identified genes provide a framework for the genetic architecture and pathobiology of vitiligo, highlight relationships with other autoimmune diseases and melanoma, and offer potential targets for treatment.

The FaceBase Consortium, funded by the National Institute of Dental and Craniofacial Research, National Institutes of Health, is designed to accelerate understanding of craniofacial developmental biology by generating comprehensive data resources to empower the research community, exploring high-throughput technology, fostering new scientific collaborations among researchers and human/computer interactions, facilitating hypothesis-driven research and translating science into improved health care to benefit patients. The resources generated by the FaceBase projects include a number of dynamic imaging modalities, genome-wide association studies, software tools for analyzing human facial abnormalities, detailed phenotyping, anatomical and molecular atlases, global and specific gene expression patterns, and transcriptional profiling over the course of embryonic and postnatal development in animal models and humans. The integrated data visualization tools, faceted search infrastructure, and curation provided by the FaceBase Hub offer flexible and intuitive ways to interact with these multidisciplinary data. In parallel, the datasets also offer unique opportunities for new collaborations and training for researchers coming into the field of craniofacial studies. Here, we highlight the focus of each spoke project and the integration of datasets contributed by the spokes to facilitate craniofacial research.

Numerous lines of evidence point to a genetic basis for facial morphology in humans, yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features. We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts. Analyses were performed on just under one million genotyped SNPs (Illumina OmniExpress+Exome v1.2 array) imputed to the 1000 Genomes reference panel (Phase 3). We observed genome-wide significant associations (p < 5 x 10-8) for cranial base width at 14q21.1 and 20q12, intercanthal width at 1p13.3 and Xq13.2, nasal width at 20p11.22, nasal ala length at 14q11.2, and upper facial depth at 11q22.1. Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1. We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in CACNA2D3 and PRDM16. These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features. Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis.

We previously reported a genome-wide association study (GWAS) identifying 14 susceptibility loci for generalized vitiligo. We report here a second GWAS (450 individuals with vitiligo (cases) and 3,182 controls), an independent replication study (1,440 cases and 1,316 controls) and a meta-analysis (3,187 cases and 6,723 controls) identifying 13 additional vitiligo-associated loci. These include OCA2-HERC2 (combined P = 3.80 × 10(-8)), MC1R (P = 1.82 × 10(-13)), a region near TYR (P = 1.57 × 10(-13)), IFIH1 (P = 4.91 × 10(-15)), CD80 (P = 3.78 × 10(-10)), CLNK (P = 1.56 × 10(-8)), BACH2 (P = 2.53 × 10(-8)), SLA (P = 1.58 × 10(-8)), CASP7 (P = 3.56 × 10(-8)), CD44 (P = 1.78 × 10(-9)), IKZF4 (P = 2.75 × 10(-14)), SH2B3 (P = 3.54 × 10(-18)) and TOB2 (P = 6.81 × 10(-10)). Most vitiligo susceptibility loci encode immunoregulatory proteins or melanocyte components that likely mediate immune targeting and the relationships among vitiligo, melanoma, and eye, skin and hair coloration.

Vitiligo is an autoimmune disease in which melanocyte destruction causes skin depigmentation, with 49 loci known from previous GWAS. Aiming to define vitiligo subtypes, we discovered that age-of-onset is bimodal; one-third of cases have early onset (mean 10.3 years) and two-thirds later onset (mean 34.0 years). In the early-onset subgroup we found novel association with MHC class II region indel rs145954018, and independent association with the principal MHC class II locus from previous GWAS, represented by rs9271597; greatest association was with rs145954018del-rs9271597A haplotype (P = 2.40 × 10-86, OR = 8.10). Both rs145954018 and rs9271597 are located within lymphoid-specific enhancers, and the rs145954018del-rs9271597A haplotype is specifically associated with increased expression of HLA-DQB1 mRNA and HLA-DQ protein by monocytes and dendritic cells. Thus, for vitiligo, MHC regulatory variation confers extreme risk, more important than HLA coding variation. MHC regulatory variation may represent a significant component of genetic risk for other autoimmune diseases.

Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.

Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.

Reliable, inexpensive, high-throughput genotyping methods are required for clinical trials. Traditional assays require numerous enzyme digestions or are too expensive for large sample volumes. Our objective was to develop an inexpensive, efficient, and reliable assay for CYP2D6 and ADRB1 accounting for numerous polymorphisms including gene duplications.

Failure of facial prominence fusion causes cleft lip and palate (CL/P), a common human birth defect. Several potential mechanisms can be envisioned that would result in CL/P, including failure of prominence growth and/or alignment as well as a failure of fusion of the juxtaposed epithelial seams. Here, using geometric morphometrics, we analyzed facial outgrowth and shape change over time in a novel mouse model exhibiting fully penetrant bilateral CL/P. This robust model is based upon mutations in Tfap2a, the gene encoding transcription factor AP-2α, which has been implicated in both syndromic and non-syndromic human CL/P. Our findings indicate that aberrant morphology and subsequent misalignment of the facial prominences underlies the inability of the mutant prominences to fuse. Exencephaly also occured in some of the Tfap2a mutants and we observed additional morphometric differences that indicate an influence of neural tube closure defects on facial shape. Molecular analysis of the CL/P model indicates that Fgf signaling is misregulated in the face, and that reducing Fgf8 gene dosage can attenuate the clefting pathology by generating compensatory changes. Furthermore, mutations in either Tfap2a or Fgf8 increase variance in facial shape, but the combination of these mutations restores variance to normal levels. The alterations in variance provide a potential mechanistic link between clefting and the evolution and diversity of facial morphology. Overall, our findings suggest that CL/P can result from small gene-expression changes that alter the shape of the facial prominences and uncouple their coordinated morphogenesis, which is necessary for normal fusion.

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Automated phenotyping is essential for the creation of large, highly standardized datasets from anatomical imaging data. Such datasets can support large-scale studies of complex traits or clinical studies related to precision medicine or clinical trials. We have developed a method that generates three-dimensional landmark data that meet the requirements of standard geometric morphometric analyses. The method is robust and can be implemented without high-performance computing resources. We validated the method using both direct comparison to manual landmarking on the same individuals and also analyses of the variation patterns and outlier patterns in a large dataset of automated and manual landmark data. Direct comparison of manual and automated landmarks reveals that automated landmark data are less variable, but more highly integrated and reproducible. Automated data produce covariation structure that closely resembles that of manual landmarks. We further find that while our method does produce some landmarking errors, they tend to be readily detectable and can be fixed by adjusting parameters used in the registration and control-point steps. Data generated using the method described here have been successfully used to study the genomic architecture of facial shape in two different genome-wide association studies of facial shape.

Facial morphology is highly variable, both within and among human populations, and a sizable portion of this variation is attributable to genetics. Previous genome scans have revealed more than 100 genetic loci associated with different aspects of normal-range facial variation. Most of these loci have been detected in Europeans, with few studies focusing on other ancestral groups. Consequently, the degree to which facial traits share a common genetic basis across diverse sets of humans remains largely unknown. We therefore investigated the genetic basis of facial morphology in an East African cohort. We applied an open-ended data-driven phenotyping approach to a sample of 2,595 3D facial images collected on Tanzanian children. This approach segments the face into hierarchically arranged, multivariate features that capture the shape variation after adjusting for age, sex, height, weight, facial size and population stratification. Genome scans of these multivariate shape phenotypes revealed significant (p < 2.5 × 10-8) signals at 20 loci, which were enriched for active chromatin elements in human cranial neural crest cells and embryonic craniofacial tissue, consistent with an early developmental origin of the facial variation. Two of these associations were in highly conserved regions showing craniofacial-specific enhancer activity during embryological development (5q31.1 and 12q21.31). Six of the 20 loci surpassed a stricter threshold accounting for multiple phenotypes with study-wide significance (p < 6.25 × 10-10). Cross-population comparisons indicated 10 association signals were shared with Europeans (seven sharing the same associated SNP), and facilitated fine-mapping of causal variants at previously reported loci. Taken together, these results may point to both shared and population-specific components to the genetic architecture of facial variation.

Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships.

No abstract available

Orofacial malformations resulting from genetic and/or environmental causes are frequent human birth defects yet their etiology is often unclear because of insufficient information concerning the molecular, cellular and morphogenetic processes responsible for normal facial development. We have, therefore, derived a comprehensive expression dataset for mouse orofacial development, interrogating three distinct regions - the mandibular, maxillary and frontonasal prominences. To capture the dynamic changes in the transcriptome during face formation, we sampled five time points between E10.5-E12.5, spanning the developmental period from establishment of the prominences to their fusion to form the mature facial platform. Seven independent biological replicates were used for each sample ensuring robustness and quality of the dataset. Here, we provide a general overview of the dataset, characterizing aspects of gene expression changes at both the spatial and temporal level. Considerable coordinate regulation occurs across the three prominences during this period of facial growth and morphogenesis, with a switch from expression of genes involved in cell proliferation to those associated with differentiation. An accompanying shift in the expression of polycomb and trithorax genes presumably maintains appropriate patterns of gene expression in precursor or differentiated cells, respectively. Superimposed on the many coordinated changes are prominence-specific differences in the expression of genes encoding transcription factors, extracellular matrix components, and signaling molecules. Thus, the elaboration of each prominence will be driven by particular combinations of transcription factors coupled with specific cell:cell and cell:matrix interactions. The dataset also reveals several prominence-specific genes not previously associated with orofacial development, a subset of which we externally validate. Several of these latter genes are components of bidirectional transcription units that likely share cis-acting sequences with well-characterized genes. Overall, our studies provide a valuable resource for probing orofacial development and a robust dataset for bioinformatic analysis of spatial and temporal gene expression changes during embryogenesis.

In a recent genome-wide association study of generalized vitiligo, we identified ten confirmed susceptibility loci. By testing additional loci that showed suggestive association in the genome-wide study, using two replication cohorts of European descent, we observed replicated association of generalized vitiligo with variants at 3p13 encompassing FOXP1 (rs17008723, combined P=1.04x10(-8)) and with variants at 6q27 encompassing CCR6 (rs6902119, combined P=3.94x10(-7)).

3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects.

Craniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Delineation of salient facial characteristics can guide clinicians towards a correct clinical diagnosis and understanding the pathogenesis of the disorder. Abnormal facial shape might require craniofacial surgical intervention, with the restoration of normal shape an important surgical outcome. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. However, these fail to capture the full complexity of facial shape. With the increasing availability of 3D photographs, methods of assessment that take advantage of the rich information contained in such images are needed. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face. These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. We demonstrate the use of these growth curves for assessing patients and show that they identify normal and abnormal facial morphology independent from age-specific facial features. 3D growth curves can facilitate use of state-of-the-art 3D facial shape assessment by the broader clinical and biomedical research community. This advance in phenotype description will support clinical diagnosis and the understanding of disease pathogenesis including genotype-phenotype relations.

Tumor cell fusion with motile bone marrow-derived cells (BMDCs) has long been posited as a mechanism for cancer metastasis. While there is much support for this from cell culture and animal studies, it has yet to be confirmed in human cancer, as tumor and marrow-derived cells from the same patient cannot be easily distinguished genetically.

The human face is an array of variable physical features that together make each of us unique and distinguishable. Striking familial facial similarities underscore a genetic component, but little is known of the genes that underlie facial shape differences. Numerous studies have estimated facial shape heritability using various methods. Here, we used advanced three-dimensional imaging technology and quantitative human genetics analysis to estimate narrow-sense heritability, heritability explained by common genetic variation, and pairwise genetic correlations of 38 measures of facial shape and size in normal African Bantu children from Tanzania. Specifically, we fit a linear mixed model of genetic relatedness between close and distant relatives to jointly estimate variance components that correspond to heritability explained by genome-wide common genetic variation and variance explained by uncaptured genetic variation, the sum representing total narrow-sense heritability. Our significant estimates for narrow-sense heritability of specific facial traits range from 28 to 67%, with horizontal measures being slightly more heritable than vertical or depth measures. Furthermore, for over half of facial traits, >90% of narrow-sense heritability can be explained by common genetic variation. We also find high absolute genetic correlation between most traits, indicating large overlap in underlying genetic loci. Not surprisingly, traits measured in the same physical orientation (i.e., both horizontal or both vertical) have high positive genetic correlations, whereas traits in opposite orientations have high negative correlations. The complex genetic architecture of facial shape informs our understanding of the intricate relationships among different facial features as well as overall facial development.