Dysmorphology in a Genomic Era

Published:December 24, 2019DOI:https://doi.org/10.1016/j.clp.2019.10.009

      Keywords

      Key points

      • Clinical dysmorphology evolved out of the need to standardize descriptive terminology used to define human variation, primarily in the context of malformations and syndromic disorders.
      • DNA analysis may provide the genotype and confirm a diagnosis, but it cannot define the phenotype.
      • The advances made in molecular technology have led to the identification and ongoing discovery of the underlying genetic pathoetiology of many syndromes.

      Introduction

      Clinical dysmorphology evolved out of the need to standardize descriptive terminology used to define human variation, primarily in the context of malformations and syndromic disorders. The classic dysmorphology approach of establishing a clear history and, through detailed examination, listing abnormal findings ordered in a sequence ranked by perceived importance and then using these data to aid in establishing a working clinical diagnosis, is tried and tested but effective in only a small subset of recognized syndromes. This science and art of syndrome recognition is gradually being lost as newer technologies both blur the margins between various syndromes and enable syndrome recognition through the application of various computational tools. In this article, we review the utility of deep phenotyping and the application of dysmorphology in a genomic era.
      DNA analysis may provide the genotype and confirm a diagnosis, but it cannot define the phenotype the advances made in molecular technology have led to the identification and ongoing discovery of the underlying genetic pathoetiology of many syndromes. The field of molecular dysmorphology grew out of the fusion of knowledge of normal human embryologic development and aberrations in gene signaling pathways giving rise to syndromic disorders.
      • Biesecker L.G.
      Molecular dysmorphology.
      Many of these pathways interact, either directly or indirectly, and thus syndromes previously considered distinct entities now share common or overlapping genetic etiologies, which has further compounded the challenge of clearly defining syndromes. As a result, the era of the gene-x-opathy descriptor is our current reality. A commonly used example of this involves the RAS pathway. RASopathies represent disorders impacting the RAS-MEK-ERK pathway, which includes a diverse group of disorders, including neurofibromatosis type I, Legius, Noonan with and without multiple lentigines, Costello, and cardiofaciocutaneous syndromes, as represented in Fig. 1. Noonan syndrome has the greatest overlap, and certain genes within the pathway may present as either Noonan syndrome or cardiofaciocutaneous syndrome. Neurofibromatosis is associated only with neurofibromin, and Costello syndrome is similarly seen only with pathogenic variants in HRAS. In addition to the genetic heterogeneity of common pathways, disorders that use metabolites in signal transduction can share common pathoetiology through these common factors. Disorders of cholesterol metabolism are a good representation of such syndromic overlap. Aside from cholesterol being a key precursor in the general steroid biosynthesis pathway, it is additionally a key signaling molecule in the Sonic Hedgehog pathway, and as such is implicated in related syndromic disorders that include Smith Lemli Opitz, Gorlin syndrome, Rubinstein Taybi, holoprosencephaly, and Pallister Hall and Greig syndromes.
      Figure thumbnail gr1
      Fig. 1The RASK/MAPK signal transduction pathway.
      (From Rauen KA. The RASopathies, Annu Rev Genom Hum Genet 14:355–369, 2013; with permission.)

      Variation and ontology

      Human variation is estimated at approximately 0.1%, which equates to roughly 6 coding variants per gene. Sequencing of the human genome clarified that humans have close to 23,000 genes, of which only 5500 have corresponding phenotype data. Given that approximately two-thirds of our genes do not have a clear disease association, it is apparent that we need tools to facilitate and assist in the identification of disorders in undiagnosed and rare diseases. There is still much to learn about our genetic code, let alone how tertiary chromatin structure impacts disease and gene regulation. Even if we could perform genomic sequencing on every patient, in most patients the molecular diagnosis would remain elusive. The current diagnostic rate for exome sequencing is approximately 25%, this increases to approximately 30% with exome trio analysis in which selected relatives are sequenced and the additional data are used to assess allele segregation with the phenotype. It should not be inferred from this that genomic sequencing has limited clinical utility, as clinically actionable decisions are made in most cases undergoing sequencing, either through direct pathophysiological mechanisms in a positive result or through exclusion in negative test outcomes.
      Sequencing in isolation of supportive phenotypic data significantly limits the analyst’s ability to undertake a comprehensive evaluation of variants identified through sequencing. Several publications have highlighted the value of supportive phenotype data in focusing the analysis to identify causal variants in genomic sequencing.
      • Seaby E.G.
      • Pengelly R.J.
      • Ennis S.
      Exome sequencing explained: a practical guide to its clinical application.
      • Maver A.
      • Lovrecic L.
      • Volk M.
      • et al.
      Phenotype-driven gene target definition in clinical genome-wide sequencing data interpretation.
      • Tomar S.
      • Sethi R.
      • Lai P.S.
      Specific phenotype semantics facilitate gene prioritization in clinical exome sequencing.
      Gripp and colleagues
      • Gripp K.W.
      • Baker L.
      • Telegrafi A.
      • et al.
      The role of objective facial analysis using FDNA in making diagnoses following whole exome analysis. Report of two patients with mutations in the BAF complex genes.
      published a unique twist on this concept by using artificial intelligence (AI) to aid variant identification. This automated phenotyping through facial recognition software will be discussed in greater detail.
      To standardize phenotype terminology and create a structured hierarchical basis for computational analysis, the Human Phenotype Ontology (HPO) was developed in 2008 and through ongoing international collaboration with the Monarch Initiative and other organizations, several tools have been developed to aid diagnostics in undiagnosed diseases.
      • Robinson P.N.1
      • Köhler S.
      • Bauer S.
      • et al.
      The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease.
      Using HPO terminology, developmental malformations can be cross referenced across a number of different species, which can in turn help define function in human genes. Conversely, genes identified in undiagnosed human malformations can be evaluated for plausible disease association by comparing phenotypic presentations across various species
      • Robinson P.N.
      • Köhler S.
      • Oellrich A.
      • et al.
      Sanger Mouse Genetics Project
      Improved exome prioritization of disease genes through cross-species phenotype comparison.
      (Fig. 2).
      Figure thumbnail gr2
      Fig. 2Comparison of phenotypes.
      (From the Monarch Intiative. Available at: https://monarchinitiative.org/.)
      There are too many analytical tools available to allow for a detailed discussion of them all. It would, however, be useful to outline a few of the more common tools used in clinical practice. In general, these tools help define a differential diagnosis based on HPO terminology derived from a thorough dysmorphic evaluation. The details of how to approach a dysmorphic examination have been previously covered
      • Basel D.
      Dysmorphology.
      ,
      • Davies D.P.
      • Evans D.J.R.
      Clinical dysmorphology: understanding congenital abnormalities.
      and are not detailed here, but it is helpful to conceptualize the etiologic basis of the deformity, as outlined in Fig. 3.
      Figure thumbnail gr3
      Fig. 3Etiologic mechanisms for congenital anomalies.
      (Adapted from Spranger J, Benirschke K, Hall JG, Errors of morphogenesis: concepts and terms. Recommendations of an international working group. J Pediatr. 1982 Jan;100(1):160-5; with permission.)
      To adequately phenotype a patient, it is expected that a detailed head-to-toe evaluation is undertaken, capturing all major (Table 1) and minor anomalies (Table 2). In addition, a complete review of the medical record is required to understand the historical progression and extract the phenotypic elements provided through medical investigation.
      Table 1Major malformations
      Not an inclusive list.
      • Neurologic
        • Severe hydrocephalus
        • Lissencephaly
        • Schizencephaly
        • Megalencephaly
        • Neural tube defect
        • Spina bifida
        • Meningomyelocele
        • Encephalocele
      • Cardiovascular
        • Various congenital heart malformations
        • Cardiomyopathy
        • Severe arrhythmia
      • Genitourinary
        • Ambiguous genitalia
        • Kidney malformations
        • Urachal defects
      • Respiratory
        • Congenital pulmonary airway malformation
        • Tracheoesophageal fistula
      • Abdominal wall
        • Gastroschisis
        • Omphalocele
      • Craniofacial
        • Craniosynostosis
        • Facial cleft
        • Cleft lip and palate
        • Structural eye defect
        • Coloboma
        • Aniridia
        • Structural ear defects
        • Microtia
        • Aplasia of the auditory canal
      • Limb
        • Amelia
        • Split/hand foot malformation
      a Not an inclusive list.
      Table 2Minor malformations
      Not an inclusive list.
      • Craniofacial
        • Large fontanel
        • Flat or low nasal bridge
        • Saddle nose, upturned nose
        • Micrognathia
        • Cutis aplasia of the scalp
      • Eye
        • Palpebral fissures
          • Telecanthus or epicanthus
          • Up or down slanting
        • Hypertelorism
        • Brushfield spots
      • Ear
        • Posteriorly rotated pinna
        • Lack of helical fold
        • Preauricular with or without auricular skin tags
        • Small pinna
        • Auricular (preauricular) pit or sinus
        • Folding of helix
        • DARWINIAN tubercle
        • Crushed (crinkled) ear
        • Asymmetric ear sizes
        • Low-set ears
      • Skin
        • Dimpling over the bones
        • Capillary hemangioma (face/posterior neck)
        • Dermal melanosis (African, Asian)
        • Sacral dimple
        • Pigmented nevi
        • Redundant skin folds
        • Cutis marmorata
        • Café au lait macules
      • Hand
        • Simian crease
        • Bridged upper palmar creases
        • Fifth finger clinodactyly
        • Joint hypermobility (hyperextension of thumb)
        • Cutaneous syndactyly
        • Polydactyly
        • Short, broad thumb
        • Narrow or hyperconvex nails
        • Hypoplastic nails
        • Camptodactyly
        • Short fourth metacarpal
      • Foot
        • Syndactyly of second/third toe
        • Asymmetric toe length
        • Clinodactyly of second toe
        • Overlapping toes
        • Nail hypoplasia
        • Wide gap between hallux and second toe
        • Deep plantar crease between hallux and second toe
      • Other
        • Mild calcaneovalgus
        • Hydrocele
        • Shawl scrotum
        • Hypospadias
        • Hypoplasia of labia majora
        • Supernumerary nipples
        • Undescended testes
        • Tongue tie
      a Not an inclusive list.
      An international initiative to standardize the nosology used in clinical dysmorphology has been adapted to the Internet as an online resource supported by the National Human Genome Research Institute: https://elementsofmorphology.nih.gov/. These terms provide the basis for the HPO definitions, which can then be applied to computational tools such as Phenomizer (http://compbio.charite.de/phenomizer/), a tool that creates ranked lists of diseases from OMIM (Online Mendelian Inheritance in Man; www.omim.org) based on the probability of the HPO terms listed presenting concurrently (Fig. 4). It is important to realize that none of the computational tools provide diagnoses, rather they present ranked lists of possible disorders to consider in the differential diagnosis. The onus for making a diagnosis relies on the critical thinking of the physician.
      Figure thumbnail gr4
      Fig. 4HPO features linking searched terms with disease-associated findings.
      (Data from Köhler S, Schulz MH, Krawitz P, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet 2009;85(4):457-64; Köhler S, Vasilevsky NA, Engelstad M, et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res 2017;45(D1):D865-76.
      OMIM can be searched directly using any phenotypic descriptor. This character string is processed through a Boolean search engine of its internal database to define the rank list of probable disorders based on the characters used in the search. The lists generated are highly dependent on the search terms used and are, in general, less accurate than implementing specific HPO terms.

      Artificial intelligence in dysmorphology

      Evolving beyond the reliance of the evaluating physician to accurately define dysmorphic ontological terminology, several groups are working on facial recognition software for syndromic identification. These tools too are not diagnostic, but offer rank-listed possibilities for diagnostic consideration. There are various machine learning tools used (eg, Support Vector Machines, KNearest Neighbors, Deformable Models, Hidden Markov Models) that yield variable results. The class of machine learning architectures called Deep Learning, such as convolutional neural networks, have seen substantial gains in recent years. These methods extract features from training images and perform classification of images, often identifying features, such as image texture, that would not have been chosen by diagnosticians. A classic critique of machine learning methods is that they are black-box; meaning that it can be difficult to understand why it arrived at a particular outcome once it has been trained. New algorithms, such as attention-based networks, allow the possibility of opening the box to understand why the classifier made its decision. These new features provide a robust data set for comparative analysis. One of the more widely implemented tools is the free resource: Face2Gene, which in addition to the facial computational analysis, enables adjunct HPO terminology to be entered to further refine the phenotype. The depiction in Fig. 5 outlines the process implemented by FDNA Inc. to establish a bioinformatic reference for comparative image analysis.
      • Hadj-Rabia S.
      • Schneider H.
      • Navarro E.
      • et al.
      Automatic recognition of the XLHED phenotype from facial images.
      The machine learning aspect of these tools ensures that, over time, their accuracy will improve. The knowledge base is continually expanded through “crowdsourcing” the expertise of recognized experts who continue to train the system by entering molecularly confirmed diagnoses. The AI utility of Face2Gene has been termed DeepGestalt, and in a recent publication Gurovich and colleagues
      • Gurovich Y.
      • Hanani Y.
      • Bar O.
      • et al.
      Identifying facial phenotypes of genetic disorders using deep learning.
      have shown that this tool is able to list the correct diagnosis within the top 10 differential 91% of the time. The model also has been shown to be able to differentiate the numerous genotypes causing Noonan syndrome with high accuracy in addition to being able to differentiate disorders within a common pathway (eg, RASopathies). Pascolini and colleagues
      • Pascolini G.
      • Fleischer N.
      • Ferraris A.
      • et al.
      The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers.
      describe similarities in phenotype in various disorders associated with chromatin remodeling that were identified through DeepGestalt. There is some overlap in the context of the epigenetic modification that occurs at a molecular level, but phenotypic overlap had not been previously appreciated. The next evolution of this technology is to incorporate the data derived from phenotyping and apply that directly to machine learning tools used for variant identification and classification. In a recent publication, Hsieh and colleagues described such an approach to variant prioritization termed PEDIA (prioritization of exome data by image analysis) in which data from DeepGestalt is used within the variant analysis pipeline to improve the likelihood of identifying causal variants in genomic analysis.
      • Hsieh T.C.
      • Mensah M.A.
      • Pantel J.T.
      • et al.
      PEDIA: prioritization of exome data by image analysis.
      Figure thumbnail gr5
      Fig. 5Image analysis process of the automated facial recognition technology used in this study. (A) A face is detected in the frontal image and anatomic points are automatically identified. The face is divided into multiple regions, whose appearance is analyzed. (B) Last, a mask depicting the characteristic appearance of each syndrome is created.
      (From Hadj-Rabia S, Schneider H, Navarro E, et al. Automatic recognition of the XLHED phenotype from facial images. Am J Med Genet A. 2017 Sep;173(9):2408-2414. https://doi.org/10.1002/ajmg.a.38343. Epub 2017 Jul 10; with permission.)

      Closing considerations

      The development of these various tools and their application to molecular dysmorphology will change the way that we approach phenotyping as a whole. Computer-assisted image processing can be applied to histology, radiology, and any other data sets that allow for a digital reference to be established. This “next-generation phenotyping” will become the basis for comparative analysis to aid further understanding of genomic data. Where is the limit? Vocal analysis, gait analysis, handwriting, visual tracking; there are many avenues for phenotype expansion and with the power of cloud computing and integration of machine learning, we are limited only by our imagination.

      Disclosure

      D. Basel is an unpaid member of the FDNA Scientific Advisory Board.

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