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The cranial vault – the portion of the skull surrounding the brain and cerebellum – is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conducted a joint multi-ancestry and admixed multivariate GWAS on 3D cranial vault shape extracted from magnetic resonance images of 6,772 children from the ABCD study cohort, identifying 30 genome-wide significant genetic loci and replicating 20 of these signals in 16,947 additional individuals of the UK Biobank. This joint multi-ancestry GWAS was enriched for genetic components of cranial vault shape shared across ancestral groups and yielded a greater discovery than a European-only GWAS. We present supporting evidence for parietal versus frontal bone localization for several of the identified genes based on expression patterns in E15.5 mice. Collectively, our GWAS loci were enriched for processes related to skeletal development and showed elevated activity in cranial neural crest cells, suggesting a role during early craniofacial development. Among the identified genes, wereRUNX2and several of its upstream and downstream actors, highlighting the prominent role of intramembranous ossification – which takes place at the cranial sutures – in influencing cranial vault shape. We found that mutations in many genes associated with craniosynostosis exert their pathogenicity by modulating the same pathways involved in normal cranial vault development. This was further demonstrated in a non-syndromic sagittal craniosynostosis case-parent trio dataset of 63 probands (n = 189), where our GWAS signals nearBMP2, BBS9, andZIC2contributed significantly to disease risk. Moreover, we found strong evidence of overlap with genes influencing the morphology of the face and the brain, suggesting a common genetic architecture connecting these developmentally adjacent structures. Overall, our study provides a comprehensive overview of the genetics underlying normal cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.

The human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study 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 indicate 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 summary, our analyses provide insights into the understanding of how complex morphological traits are shaped by both individual and coordinated genetic actions. ispartof: Nature Genetics vol:53 issue:1 ispartof: location:United States status: Published online

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., Author summary Genetic factors play an important role in shaping human facial features. Over the last decade, studies have identified numerous genes associated with various facial traits. The vast majority of these studies have focused on European or Asian populations, while African populations have been underrepresented. Increasing the diversity of these analyses can reveal novel associations and cross-population analyses can help deepen our understanding of known genetic associations. We therefore performed a genome scan of 3D facial features in African children from Tanzania and then compared our results to Europeans. We found 20 regions of the genome associated with facial shape in Tanzanian children, 10 of which were also present in Europeans, indicating evidence for a partly shared genetic basis for human facial shape across populations. In addition, about half of the genetic associations observed in Tanzanians were not present in Europeans, and some of the shared signals differed between populations in the specific genetic variants associated or specific facial traits affected. These results shed light on the shared and population-specific genetic contributors to normal-range facial variation.

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 of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face cross-talk. 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 genome-wide association study 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. A multivariate genome-wide association study highlighting loci that influence both face and brain shape suggesting shared developmental axes during early embryogenesis. These loci did not overlap with those governing behavioral-cognitive traits or neuropsychiatric risk indicating divergence between early brain development and cognitive function. J.W. was supported by the Howard Hughes Medical Institute, a Lorry Lokey endowed professorship and a Stinehart Reed award. S.N. was supported by a Helen Hay Whitney Fellowship. The KU Leuven research team and analyses were supported by the National Institutes of Health (NIH; 1-R01-DE027023 and 2-R01-DE027023), The Research Fund KU Leuven (BOF-C1, C14/15/081 and C14/20/081) and The Research Program of the Research Foundation in Flanders (FWO; G078518N). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the FWO and the Flemish Government (department EWI). J.P.S. was supported by an NIH training grant (5T32HG000044-23). J.K.P. was supported by the NIH (HG008140 and HG009431). Pittsburgh personnel, data collection and analyses were supported by the National Institute of Dental and Craniofacial Research (U01-DE020078, R01-DE016148 and R01-DE027023). Funding for genotyping by the National Human Genome Research Institute (X01-HG007821 and X01-HG007485) and funding for initial genomic data cleaning by the University of Washington were provided by contract HHSN268201200008I from the National Institute for Dental and Craniofacial Research awarded to the Center for Inherited Disease Research (https://www.cidr.jhmi.edu/).J.T.was supported by the NIH (5R01-DA033431-07) and the National Science Foundation (1922598). This research has been conducted, in part, using the UKB resource under application no. 43193 (understanding the genetic architecture of human brain shape from MRI using global-to-local shape segmentations), and we are grateful for all the participants in that resource. This manuscript reflects the views of the authors and may not reflect the opinions or views of the UKB funders and investigators. Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the National Institute of Mental Health Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9-10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the NIH and additional federal partners under award nos. U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093 and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.A list of participating sites and study investigators can be found at https://abcdstudy.org/wp-content/uploads/2019/04/Consortium_Members.pdf.ABCD consortium investigators provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. Naqvi, S; Wysocka, J (corresponding author), Stanford Univ, Sch Med, Dept Chem & Syst Biol, Stanford, CA 94305 USA ; Stanford Univ, Sch Med, Dept Genet, Stanford, CA 94305 USA ; Stanford Univ, Sch Med, Dept Biol, Stanford, CA 94305 USA. Claes, P (corresponding author), Katholieke Univ Leuven, Dept Human Genet, Leuven, Belgium. ; Univ Hosp Leuven, Med Imaging Res Ctr, Leuven, Belgium ; Katholieke Univ Leuven, ESAT PSI, Dept Elect Engn, Leuven, Belgium ; Murdoch Childrens Res Inst, Melbourne, Vic, Australia. Wysocka, J (corresponding author), Stanford Univ, Sch Med, Dept Dev Biol, Stanford, CA 94305 USA ; Stanford Univ, Sch Med, Howard Hughes Med Inst, Stanford, CA 94305 USA.

Evidence from both model organisms and clinical genetics suggests close coordination between the developing brain and face1–8, but it remains unknown whether this developmental link extends to genetic variation that drives normal-range diversity of face and brain shape. Here, we performed a multivariate genome-wide association study of cortical surface morphology in 19,644 European-ancestry individuals and identified 472 genomic loci influencing brain shape at multiple levels. We discovered a substantial overlap of these brain shape association signals with those linked to facial shape variation, with 76 common to both. These shared loci include transcription factors with cell-intrinsic roles in craniofacial development, as well as members of signaling pathways involved in brain-face crosstalk. Brain shape heritability is equivalently enriched near regulatory regions active in either brain organoids or in facial progenitor cells. However, brain shape association signals shared with face shape are distinct from those shared with behavioral-cognitive traits or neuropsychiatric disorder risk. Together, we uncover common genetic variants and candidate molecular players underlying brain-face interactions. We propose that early in embryogenesis, the face and the brain mutually shape each other through a combination of structural effects and paracrine signaling, but this interplay may have little impact on later brain development associated with cognitive function.

Estimates of individual-level genomic ancestry are routinely used in human genetics, and related fields. The analysis of population structure and genomic ancestry can yield insights in terms of modern and ancient populations, allowing us to address questions regarding admixture, and the numbers and identities of the parental source populations. Unrecognized population structure is also an important confounder to correct for in genome-wide association studies. However, it remains challenging to work with heterogeneous datasets from multiple studies collected by different laboratories with diverse genotyping and imputation protocols. This work presents a new approach and an accompanying open-source toolbox that facilitates a robust integrative analysis for population structure and genomic ancestry estimates for heterogeneous datasets. We show robustness against individual outliers and different protocols for the projection of new samples into a reference ancestry space, and the ability to reveal and adjust for population structure in a simulated case-control admixed population. Given that visually evident and easily recognizable patterns of human facial characteristics co-vary with genomic ancestry, and based on the integration of three different sources of genome data, we generate average 3D faces to illustrate genomic ancestry variations within the 1,000 Genome project and for eight ancient-DNA profiles, respectively. ispartof: Scientific Reports vol:10 issue:1 ispartof: location:England status: published

The human face is complex and multipartite, and characterization of its genetic architecture remains intriguingly challenging. Applying GWAS to multivariate shape 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 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 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 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.

The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17–0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation., Author summary The human face is a highly variable trait composed of distinct features, each influenced by genetic and environmental factors. The strong genetic component is primarily evidenced by the facial similarity between identical twins and the clear facial resemblances within families. Over the past decade, a powerful methodological toolbox of computational and statistical genetics has been developed to study the genetic architecture of complex traits. However, these methods typically require one-dimensional phenotypic measurements (e.g. width of the nose or spacing between the eyes) that fail to accurately describe 3D facial shape. In this paper, we learn from 3D facial data itself, a series of relevant traits that are guided by the facial similarity observed between sibling pairs. Importantly, while preserving the structural convolution of the face, these traits also fit the requirements as input to the well-established statistical tools. In doing so, we have identified many genetic loci that are associated with a wide range of facial features. Some of these regions contained genes related to embryonic facial development, and craniofacial malformations. An improved understanding of the genetic basis of facial shape can have several important applications, for example in developmental biology, medical genetics and forensic sciences.

Background Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses. Results In an effort to streamline the entire process with easy-to-use steps for scientists working with big data, the Odyssey pipeline was developed. Odyssey is a simplified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. Odyssey is a pipeline that integrates programs such as PLINK, SHAPEIT, Eagle, IMPUTE, Minimac, and several R packages, to create a seamless, easy-to-use, and modular workflow controlled via a single user-friendly configuration file. Odyssey was built with compatibility in mind, and thus utilizes the Singularity container solution, which can be run on Linux, MacOS, and Windows platforms. It is also easily scalable from a simple desktop to a High-Performance System (HPS). Conclusion Odyssey facilitates efficient and fast genome-wide association analysis automation and can go from raw genetic data to genome: phenome association visualization and analyses results in 3–8 h on average, depending on the input data, choice of programs within the pipeline and available computer resources. Odyssey was built to be flexible, portable, compatible, scalable, and easy to setup. Biologists less familiar with programing can now work hands on with their own big data using this easy-to-use pipeline. Electronic supplementary material The online version of this article (10.1186/s12859-019-2964-5) contains supplementary material, which is available to authorized users.

Accurate inference of genomic ancestry is critically important in human genetics, epidemiology, and related fields. Geneticists today have access to multiple heterogeneous population-based datasets from studies collected under different protocols. Therefore, joint analyses of these datasets require robust and consistent inference of ancestry, where a common strategy is to yield an ancestry space generated by a reference dataset. However, such a strategy is sensitive to batch artefacts introduced by different protocols. In this work, we propose a novel robust genome-wide ancestry inference method; referred to as SUGIBS, based on an unnormalized genomic (UG) relationship matrix whose spectral (S) decomposition is generalized by an Identity-by-State (IBS) similarity degree matrix. SUGIBS robustly constructs an ancestry space from a single reference dataset, and provides a robust projection of new samples, from different studies. In experiments and simulations, we show that, SUGIBS is robust against individual outliers and batch artifacts introduced by different genotyping protocols. The performance of SUGIBS is equivalent to the widely used principal component analysis (PCA) on normalized genotype data in revealing the underlying structure of an admixed population and in adjusting for false positive findings in a case-control admixed GWAS. We applied SUGIBS on the 1000 Genome project, as a reference, in combination with a large heterogeneous dataset containing auxiliary 3D facial images, to predict population stratified average or ancestry faces. In addition, we projected eight ancient DNA profiles into the 1000 Genome ancestry space and reconstructed their ancestry face. Based on the visually strong and recognizable human facial phenotype, comprehensive facial illustrations of the populations embedded in the 1000 Genome project are provided. Furthermore, ancestry facial imaging has important applications in personalized and precision medicine along with forensic and archeological DNA phenotyping.Author SummaryEstimates of individual-level genomic ancestry are routinely used in human genetics, epidemiology, and related fields. The analysis of population structure and genomic ancestry can yield significant insights in terms of modern and ancient population dynamics, allowing us to address questions regarding the timing of the admixture events, and the numbers and identities of the parental source populations. Unrecognized or cryptic population structure is also an important confounder to correct for in genome-wide association studies (GWAS). However, to date, it remains challenging to work with heterogeneous datasets from multiple studies collected by different laboratories with diverse genotyping and imputation protocols. This work presents a new approach and an accompanying open-source software toolbox that facilitates a robust integrative analysis for population structure and genomic ancestry estimates for heterogeneous datasets. Given that visually evident and easily recognizable patterns of human facial characteristics covary with genomic ancestry, we can generate predicted ancestry faces on both the population and individual levels as we illustrate for the 26 1000 Genome populations and for eight eminent ancient-DNA profiles, respectively.