Please enable JavaScript.
Coggle requires JavaScript to display documents.
Genome-wide mapping of global-to-local genetic effects on human facial…
Genome-wide mapping of global-to-local genetic effects on human facial shape
Abstruct
Genome-wide association scans of complex multipartite traits like the human face typically use preselected phenotypic measures.
Here we report a data-driven approach to phenotyping facial shape at multiple levels of organization, allowing for an open-ended description of facial variation, while preserving statistical power.
In a sample of 2,329 persons of European ancestry we identified 38 loci, 15 of which replicated in an independent European sample (n=1,719).
Four loci were completely novel.
For the others, additional support (n=9) or pleiotropic effects (n=2) were found in the literature, but the results reported here were further refined. All 15 replicated loci revealed distinctive patterns of global-to local genetic effects on facial shape and showed enrichment for active chromatin elements in human cranial neural crest cells, suggesting an early developmental origin of the facial variation captured.
These results have implications for studies of facial genetics and other complex morphological traits.
Results
Study Samples
Global-to-local facial segmentations
Figure 1
Genetic mapping of global-to-local shape
Table 1
Figure 2
Figure 3
Association between 15 replicated GWAS regions and signatures of active regulatory elements in Cranial Neural Crest Cells (CNCCs)
Figure 4
Figure 5
Candidate genes and integration with facial GWAS literature
Discussion
Online Methods
Sample and Recruitment Details
Facial Phenotyping, 3D imaging quality control and shape variables
3D facial surface imaging
Spatially-dense facial quasi-landmarking
Facial quality control
Phenotyping the discovery panel
Phenotyping the replication panel
Genotyping, quality checks, imputation, population structure and annotation
The Pittsburgh Sample
The Penn State Sample
Top SNPS and annotation
Statistics and epigenomic analysis
SNP—statistical association
Multiple testing corrections
SNP—statistical replication
SNP—tissue specific enhancer association
SNP—CNCC chromatin state association and statistics
Chromatin modifications visualization in the genome browser
Gene Ontology term enrichment analysis
URLs
Ethics Statement
Data and Code availability statements
Supplementary Material
Acknowledgments
Footnotes
Author Contributions
Competing financial interest
References
Introduction
The human face is a multipartite trait composed of distinct features (i.e., eyes, nose, chin, and mouth), whose size, shape and composition are clearly heritable.
However, our knowledge of which genetic variants are responsible for human facial variation is still lacking1.
Several genome-wide association studies (GWAS) have each identified a handful of loci associated with a small number of facial traits, few of which have been replicated2–7.
While a GWAS benefits from being an unbiased approach to gene mapping, the phenotypic descriptions used in such studies are typically preselected and used to classify individuals in a “phenotype first” way of thinking. This approach may be appropriate in certain instances (e.g., affected and unaffected disease status), but is less so for complex multipartite traits like the human face.
The result is that facial shape has been reduced to a limited series of measurements (e.g., linear distances) that are analyzed individually, resulting in a loss of information.
In this study, we present a data-driven approach to facial phenotyping that exploits both the partable and integrated information contained in 3D facial images, allowing for the identification of genetic effects on facial shape at multiple levels of organization – from global-to local.
This approach generates a nested series of multivariate GWAS, with a low computational burden and, more importantly, controlled multiple testing burden.
We applied this new approach to a European-derived discovery cohort and then tested significant variants for replication in an independent European-derived cohort.
In an effort to provide additional validation, we integrated our work with previously published human facial GWAS.
We show a number of novel genetic loci supported by strong statistical evidence, revealing unseen patterns in global-to-local genetic effects on facial shape.
Furthermore, these loci are preferentially marked by active chromatin signatures in human cranial neural crest cells, an embryonic cell type that gives rise to most of the craniofacial structures.
This suggests a developmental origin of much of the facial variation uncovered by our study.
These results offer novel insights on the genetic basis of human facial shape with potentially far reaching implications.
More generally, the results present an alternative to the prevailing phenotype-first mindset and is widely applicable to any GWAS on complex, quantitative and multipartite traits, especially those captured thoroughly using images.