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Nature Genetics Sep 2023Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that...
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.
Topics: Humans; Artificial Intelligence; Phenotype; Algorithms; Machine Learning; Biological Variation, Population; Matrix Attachment Region Binding Proteins; DNA-Binding Proteins; Transcription Factors
PubMed: 37550531
DOI: 10.1038/s41588-023-01469-w -
Journal of Biomedical Semantics Aug 2023Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize...
MOTIVATION
Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena.
RESULTS
We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.
Topics: Animals; Humans; Phenotype; Databases, Factual; Machine Learning; Biological Ontologies; Mammals
PubMed: 37550716
DOI: 10.1186/s13326-023-00290-y -
Trends in Genetics : TIG May 2024Orofacial clefts (OFCs) are common, affecting 1:1000 live births. OFCs occur across a phenotypic spectrum - including cleft lip (CL), cleft lip and palate (CLP), or... (Review)
Review
Orofacial clefts (OFCs) are common, affecting 1:1000 live births. OFCs occur across a phenotypic spectrum - including cleft lip (CL), cleft lip and palate (CLP), or cleft palate (CP) - and can be further subdivided based on laterality, severity, or specific structures affected. Herein we review what is known about the genetic architecture underlying each of these subtypes, considering both shared and subtype-specific risks. While there are more known genetic similarities between CL and CLP than CP, recent research supports both shared and subtype-specific genetic risk factors within and between phenotypic classifications of OFCs. Larger sample sizes and deeper phenotyping data will be of increasing importance for the discovery of novel genetic risk factors for OFCs and various subtypes going forward.
Topics: Cleft Lip; Cleft Palate; Humans; Phenotype; Genetic Predisposition to Disease; Risk Factors
PubMed: 38480105
DOI: 10.1016/j.tig.2024.02.004 -
EBioMedicine May 2024Atopic dermatitis (AD) is the most common form of chronic skin inflammation with diverse clinical variants. Historically, various AD phenotypes have been grouped... (Review)
Review
Atopic dermatitis (AD) is the most common form of chronic skin inflammation with diverse clinical variants. Historically, various AD phenotypes have been grouped together without considering their heterogeneity. This approach has resulted in a lack of phenotype- and endotype-adapted therapeutic strategies. Comprehensive insights into AD pathogenesis have enabled precise medicinal approach for AD. These efforts aimed to redefine the endophenotype of AD and develop various biomarkers for diverse purposes. Among these endeavours, efforts are underway to elucidate the mechanisms (and related biomarkers) that lead to the emergence and progression of atopic diseases originating from AD (e.g., atopic march). This review focuses on diverse AD phenotypes and calls for a definition of endophenotypes. While awaiting scientific validation, these biomarkers ensure predicting disease onset and trajectory and tailoring therapeutic strategies for the future.
Topics: Dermatitis, Atopic; Humans; Biomarkers; Phenotype; Endophenotypes; Animals
PubMed: 38614010
DOI: 10.1016/j.ebiom.2024.105121 -
Human Genetics Oct 2023Congenital hearing loss affects one in 500 newborns. Sequence variations in OTOF, which encodes the calcium-binding protein otoferlin, are responsible for 1-8% of... (Review)
Review
Congenital hearing loss affects one in 500 newborns. Sequence variations in OTOF, which encodes the calcium-binding protein otoferlin, are responsible for 1-8% of congenital, nonsyndromic hearing loss and are the leading cause of auditory neuropathy spectrum disorders. The natural history of otoferlin-related hearing loss, the relationship between OTOF genotype and hearing loss phenotype, and the outcomes of clinical practices in patients with this genetic disorder are incompletely understood because most analyses have reported on small numbers of cases with homogeneous OTOF genotypes. Here, we present the first systematic, quantitative literature review of otoferlin-related hearing loss, which analyzes patient-specific data from 422 individuals across 61 publications. While most patients display a typical phenotype of severe-to-profound hearing loss with prelingual onset, 10-15% of patients display atypical phenotypes, including mild-to-moderate, progressive, and temperature-sensitive hearing loss. Patients' phenotypic presentations appear to depend on their specific genotypes. For example, non-truncating variants located in and immediately downstream of the CE calcium-binding domain are more likely to produce atypical phenotypes. Additionally, the prevalence of certain sequence variants and their associated phenotypes varies between populations due to evolutionary founder effects. Our analyses also suggest otoacoustic emissions are less common in older patients and those with two truncating OTOF variants. Critically, our review has implications for the application and limitations of clinical practices, including newborn hearing screenings, hearing aid trials, cochlear implants, and upcoming gene therapy clinical trials. We conclude by discussing the limitations of available research and recommendations for future studies on this genetic cause of hearing loss.
Topics: Infant, Newborn; Humans; Aged; Hearing Loss; Deafness; Hearing Loss, Central; Genotype; Phenotype
PubMed: 37679651
DOI: 10.1007/s00439-023-02595-5 -
Skeletal Radiology Nov 2023A joint contains many different tissues that can exhibit pathological changes, providing many potential targets for treatment. Researchers are increasingly suggesting... (Review)
Review
A joint contains many different tissues that can exhibit pathological changes, providing many potential targets for treatment. Researchers are increasingly suggesting that osteoarthritis (OA) comprises several phenotypes or subpopulations. Consequently, a treatment for OA that targets only one pathophysiologic abnormality is unlikely to be similarly efficacious in preventing or delaying the progression of all the different phenotypes of structural OA. Five structural phenotypes have been proposed, namely the inflammatory, meniscus-cartilage, subchondral bone, and atrophic and hypertrophic phenotypes. The inflammatory phenotype is characterized by marked synovitis and/or joint effusion, while the meniscus-cartilage phenotype exhibits severe meniscal and cartilage damage. Large bone marrow lesions characterize the subchondral bone phenotype. The hypertrophic and atrophic OA phenotype are defined based on the presence large osteophytes or absence of any osteophytes, respectively, in the presence of concomitant cartilage damage. Limitations of the concept of structural phenotyping are that they are not mutually exclusive and that more than one phenotype may be present. It must be acknowledged that a wide range of views exist on how best to operationalize the concept of structural OA phenotypes and that the concept of structural phenotypic characterization is still in its infancy. Structural phenotypic stratification, however, may result in more targeted trial populations with successful outcomes and practitioners need to be aware of the heterogeneity of the disease to personalize their treatment recommendations for an individual patient. Radiologists should be able to define a joint at risk for progression based on the predominant phenotype present at different disease stages.
Topics: Humans; Osteoarthritis, Knee; Knee Joint; Osteophyte; Magnetic Resonance Imaging; Cartilage, Articular; Hypertrophy; Cartilage Diseases; Bone Diseases; Phenotype
PubMed: 36161341
DOI: 10.1007/s00256-022-04191-6 -
Genetics Aug 2023Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple...
Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple phenotypes (an effect known as pleiotropy), creating observable relationships between phenotypes. A natural hypothesis is that pleiotropic effects reflect a relatively small set of common "core" cellular processes: each genetic locus affects one or a few core processes, and these core processes in turn determine the observed phenotypes. Here, we propose a method to infer such structure in genotype-phenotype data. Our approach, sparse structure discovery (SSD) is based on a penalized matrix decomposition designed to identify latent structure that is low-dimensional (many fewer core processes than phenotypes and genetic loci), locus-sparse (each locus affects few core processes), and/or phenotype-sparse (each phenotype is influenced by few core processes). Our use of sparsity as a guide in the matrix decomposition is motivated by the results of a novel empirical test indicating evidence of sparse structure in several recent genotype-phenotype datasets. First, we use synthetic data to show that our SSD approach can accurately recover core processes if each genetic locus affects few core processes or if each phenotype is affected by few core processes. Next, we apply the method to three datasets spanning adaptive mutations in yeast, genotoxin robustness assay in human cell lines, and genetic loci identified from a yeast cross, and evaluate the biological plausibility of the core process identified. More generally, we propose sparsity as a guiding prior for resolving latent structure in empirical genotype-phenotype maps.
Topics: Humans; Genotype; Saccharomyces cerevisiae; Phenotype; Mutation
PubMed: 37437111
DOI: 10.1093/genetics/iyad127 -
Trends in Endocrinology and Metabolism:... Jun 2024En masse phenotyping technology, using massively mosaic donor-derived cells and organoids, can offer enriched insights for cellotype-phenotype association in a... (Review)
Review
En masse phenotyping technology, using massively mosaic donor-derived cells and organoids, can offer enriched insights for cellotype-phenotype association in a cell-type-specific regulatory context. This emerging approach will help to discover biomarkers, inform genetic-epigenetic interactions and identify personalized therapeutic targets, offering hope for precision medicine against highly heterogeneous metabolic diseases.
Topics: Humans; Phenotype; Organoids; Precision Medicine; Animals; Metabolic Diseases
PubMed: 38575442
DOI: 10.1016/j.tem.2024.03.001 -
American Journal of Medical Genetics.... Sep 2023With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called... (Review)
Review
With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.
Topics: Humans; Face; Phenotype; Syndrome
PubMed: 37584245
DOI: 10.1002/ajmg.c.32061 -
Reproductive Sciences (Thousand Oaks,... Nov 2023This cross-sectional study examines the Doi-Alshoumer PCOS clinical phenotype classification in relation to measured clinical and biochemical characteristics of women...
This cross-sectional study examines the Doi-Alshoumer PCOS clinical phenotype classification in relation to measured clinical and biochemical characteristics of women with polycystic ovary syndrome (PCOS). Two cohorts of women (Kuwait and Rotterdam) diagnosed with PCOS (FAI > 4.5%) were examined. These phenotypes were created using neuroendocrine dysfunction (IRMA LH/FSH ratio > 1 or LH > 6 IU/L) and menstrual cycle status (oligo/amenorrhea) to create three phenotypes: (A) neuroendocrine dysfunction and oligo/amenorrhea, (B) without neuroendocrine dysfunction but with oligo/amenorrhea, and (C) without neuroendocrine dysfunction and with regular cycles. These phenotypes were compared in terms of hormonal, biochemical, and anthropometric measures. The three suggested phenotypes (A, B, and C) were shown to be sufficiently distinct in terms of hormonal, biochemical, and anthropometric measures. Patients who were classified as phenotype A had neuroendocrine dysfunction, excess LH (and LH/FSH ratio), irregular cycles, excess A4, infertility, excess T, highest FAI and E2, and excess 17αOHPG when compared to the other phenotypes. Patients classified as phenotype B had irregular cycles, no neuroendocrine dysfunction, obesity, acanthosis nigricans, and insulin resistance. Lastly, patients classified as phenotype C had regular cycles, acne, hirsutism, excess P4, and the highest P4 to E2 molar ratio. The differences across phenotypes suggested distinct phenotypic expression of this syndrome, and the biochemical and clinical correlates of each phenotype are likely to be useful in the management of women with PCOS. These phenotypic criteria are distinct from criteria used for diagnosis.
Topics: Female; Humans; Polycystic Ovary Syndrome; Cross-Sectional Studies; Amenorrhea; Phenotype; Follicle Stimulating Hormone
PubMed: 37217826
DOI: 10.1007/s43032-023-01262-4