Morphometrics, craniofacial disease genes, and the quest for the genetic basis of facial morphology

2017 ◽  
Vol 6 ◽  
pp. e59-e61
Author(s):  
Bailey Harrington
Keyword(s):  
Genetic Basis ◽  
Disease Genes ◽  
Facial Morphology

Genetic basis of human left–right asymmetry disorders

2014 ◽  
Vol 16 ◽  
Author(s):  
Hao Deng ◽  
Hong Xia ◽  
Sheng Deng
Keyword(s):  
Situs Inversus ◽  
Genetic Basis ◽  
Chromosomal Abnormalities ◽  
The Body ◽  
Disease Genes ◽  
Internal Organs ◽  
Abdominal Organs ◽  
Pathogenic Genes ◽  
Left Right Asymmetry ◽  
Reduced Penetrance

Humans and other vertebrates exhibit left–right (LR) asymmetric arrangement of the internal organs, and failure to establish normal LR asymmetry leads to internal laterality disorders, includingsitus inversusandheterotaxy.Situs inversusis complete mirror-imaged arrangement of the internal organs along LR axis, whereasheterotaxyis abnormal arrangement of the internal thoraco-abdominal organs across LR axis of the body, most of which are associated with complex cardiovascular malformations. Both disorders are genetically heterogeneous with reduced penetrance, presumably because of monogenic, polygenic or multifactorial causes. Research in genetics of LR asymmetry disorders has been extremely prolific over the past 17 years, and a series of loci and disease genes involved insitus inversusandheterotaxyhave been described. The review highlights the classification, chromosomal abnormalities, pathogenic genes and the possible mechanism of human LR asymmetry disorders.

scholarly journals Genome scans of facial features in East Africans and cross-population comparisons reveal novel associations

PLoS Genetics ◽  
2021 ◽  
Vol 17 (8) ◽  
pp. e1009695
Author(s):  
Chenxing Liu ◽  
Myoung Keun Lee ◽  
Sahin Naqvi ◽  
Hanne Hoskens ◽  
Dongjing Liu ◽  
...  
Keyword(s):  
Genetic Basis ◽  
Human Populations ◽  
Facial Morphology ◽  
Shape Variation ◽  
East African ◽  
Enhancer Activity ◽  
Genome Scans ◽  
Multiple Phenotypes ◽  
Population Comparisons ◽  
Cranial Neural Crest Cells

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.

scholarly journals Genetic Aspects of Cerebrovascular Malformations

2000 ◽  
Vol 6 (2) ◽  
pp. 107-111 ◽  
Author(s):  
C.L. Shovlin
Keyword(s):  
Genetic Basis ◽  
Autosomal Dominant ◽  
Vascular Malformations ◽  
Vascular Development ◽  
Molecular Methods ◽  
Disease Genes ◽  
New Therapies ◽  
Physiological Studies ◽  
Cerebral Vascular Malformations ◽  
Cerebral Vascular

In the last decade there have been fundamental advances in our understanding of the pathogenesis of vascular malformations. These advances have resulted from the application of molecular methods to identify disease genes, rather than from immunohistochemical or physiological studies. This presentation reviews the genetic basis of a variety of cerebral vascular malformations which occur as part of well-characterised diseases inherited in an autosomal dominant manner. These highlight the diversity of mechanisms which can perturb vascular development, and should have significant implications for the development of new therapies.

scholarly journals GestaltMatcher: Overcoming the limits of rare disease matching using facial phenotypic descriptors

2021 ◽  
Author(s):  
Tzung-Chien Hsieh ◽  
Aviram Bar-Haim ◽  
Shahida Moosa ◽  
Nadja Ehmke ◽  
Karen W. Gripp ◽  
...  
Keyword(s):  
Facial Dysmorphism ◽  
Disease Genes ◽  
Facial Morphology ◽  
Craniofacial Abnormalities ◽  
Rare Disorders ◽  
Training Set ◽  
Monogenic Disorders ◽  
Computer Aided ◽  
Phenotype Space ◽  
Space Distance

AbstractThe majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.

scholarly journals GestaltMatcher: Overcoming the limits of rare disease matching using facial phenotypic descriptors

2021 ◽  
Author(s):  
Peter Krawitz ◽  
Tzung-Chien Hsieh ◽  
Aviram Bar-Haim ◽  
Shahida Moosa ◽  
Nadja Ehmke ◽  
...  
Keyword(s):  
Facial Dysmorphism ◽  
Disease Genes ◽  
Facial Morphology ◽  
Craniofacial Abnormalities ◽  
Rare Disorders ◽  
Training Set ◽  
Monogenic Disorders ◽  
Computer Aided ◽  
Phenotype Space ◽  
Space Distance

Abstract The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.

scholarly journals Gene prioritization based on systems biology revealed new insight into genetic basis and pathophysiology underlying schizophrenia

2020 ◽  
Author(s):  
Jia-Feng Li ◽  
Lei Wang ◽  
Xiao Dang ◽  
Wei-Min Feng ◽  
Zi-Wei Wang ◽  
...  
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Interaction Network ◽  
Copy Number Variant ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Holistic Model ◽  
Full Spectrum ◽  
Risk Genes ◽  
Causal Genes

AbstractSequencing-based studies have recognized hundreds of genetic variants that increase the risk of schizophrenia (SCZ), but only a few percents of heritability can be attributed to these loci. It is challenging to discover the full spectrum of schizophrenia genes and reveal the dysregulated functions underlying the disease. Here, we proposed a holistic model for predicting disease genes (HMPDG), a novel machine learning prediction strategy integrated by Protein-Protein Interaction Network (PPIN), pathogenicity score, and RNA expression data. Applying HMPDG, 1946 potential risk genes (PRGs) as a complement of the genetic basis of SCZ were predicted. Among these, the first decile genes were highlighted as high confidence genes (HCGs). PRGs were validated by multiple independent studies of schizophrenia, including genome-wide association studies (GWASs), gene expression studies, and epigenetic studies. Remarkably, the strategy revealed causal genes of schizophrenia in GWAS loci and regions of copy number variant (CNV), providing a new insight to identify key genes in disease-related loci with multi genes. Leveraging our predictions, we depict the spatiotemporal expression pattern and functional groups of schizophrenia risk genes, which can help us figure out the pathophysiology of schizophrenia and facilitate the discovery of biomarkers. Taken together, our strategy will advance the understanding of schizophrenia genetic basis and the development of diagnosis and therapeutics.

scholarly journals Genetic mechanisms underlying East Asian and European Facial differentiation

2021 ◽  
Author(s):  
Sijia Wang ◽  
Manfei Zhang ◽  
Sijie Wu ◽  
Siyuan Du ◽  
Wei Qian ◽  
...  
Keyword(s):  
Genetic Basis ◽  
East Asian ◽  
Asian Population ◽  
Population Based ◽  
European Population ◽  
Facial Morphology ◽  
Facial Shape ◽  
Genetic Mechanisms ◽  
The Difference ◽  
Facial Differences

Abstract Facial morphology, the most conspicuous feature of human appearance, is highly heritable. Previous studies on the genetic basis of facial morphology were mainly performed in European populations. Applying a proven data-driven phenotyping and multivariate genome-wide scanning protocol to the largest collection of 3D facial images of an East Asian population to date, we identified 244 leading variants associated with normal-range facial variation, of which 130 are novel. A newly proposed polygenic shape analysis indicates that the effects of the variants on East Asian facial shape can be generalized into the European population. Based on this analysis, we further identified 13 variants mainly related to differences between European and East Asian facial shape. Natural selection analyses suggest that the difference in European and East Asian nose shape is caused by a directional selection, mainly due to a local adaptation in Europeans. Our results expand the knowledge of human facial genetics and illustrates for the first time the underlying genetic basis for facial differences across populations.

POPDC proteins and cardiac function

2019 ◽  
Vol 47 (5) ◽  
pp. 1393-1404 ◽  
Author(s):  
Thomas Brand
Keyword(s):  
Potential Role ◽  
Striated Muscle ◽  
Short Review ◽  
Effector Proteins ◽  
Muscle Disease ◽  
Disease Genes ◽  
Protein Protein Interaction ◽  
Interaction Partners ◽  
And Function

Abstract The Popeye domain-containing gene family encodes a novel class of cAMP effector proteins in striated muscle tissue. In this short review, we first introduce the protein family and discuss their structure and function with an emphasis on their role in cyclic AMP signalling. Another focus of this review is the recently discovered role of POPDC genes as striated muscle disease genes, which have been associated with cardiac arrhythmia and muscular dystrophy. The pathological phenotypes observed in patients will be compared with phenotypes present in null and knockin mutations in zebrafish and mouse. A number of protein–protein interaction partners have been discovered and the potential role of POPDC proteins to control the subcellular localization and function of these interacting proteins will be discussed. Finally, we outline several areas, where research is urgently needed.

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