American Journal of Medical Genetics Part C: Seminars in Medical Genetics最新文献

Cover legend: Aradhya etal., Am J Med Genet C Semin Med Genet 2023, 10.1002/ajmg.c.32057.

Healthcare transition is the purposeful and planned process for preparing young adults with Down syndrome for an adult oriented healthcare system. Significant gaps of a delayed, incomplete, siloed and decentered transition can be avoided when transition is approached in a longitudinal and holistic manner. Young adults with Down syndrome are specifically vulnerable to these gaps as the combination of intellectual differences and healthcare complexity leads to the need for a process that allows for appropriate preparation to develop the skills and process for an appropriate. To establish a successful transition care plan, the six core elements of policy, tracking, readiness, planning, transfer of care, and complete transition will compose the scaffolding of the transition process and address these gaps in care. A comprehensive tool kit including policy statements, healthcare transition tracking forms, the TRAQ tool, and template portable medical summaries will operationalize those elements and counteract any gaps in the transition process.

Research continues to demonstrate that the characteristics of one's social network could have an impact on the development of Alzheimer's disease. Given the predisposition of people with Down syndrome to develop Alzheimer's disease, analysis of their social networks has become an emerging focus. Previous pilot research demonstrated that the personal networks of people with DS could be quantitatively analyzed, with no difference between self-report and parent-proxy report. This manuscript focuses on a 12-month follow-up period with the same original participants (24 adults with Down syndrome). Their social networks demonstrated sustainability, but not improvement, as reported by people with DS (mean network size: 8.88; mean density: 0.73; mean constraint: 0.44; mean effective size: 3.58; mean max degree: 6.04; mean degree: 4.78) and their proxies (mean network size: 7.90; mean density: 0.82; mean constraint: 53.13; mean effective size: 2.87; mean max degree: 5.19; mean degree: 4.30). Intentional and continued efforts are likely needed in order to improve the social network measures of people with Down syndrome.

Heterozygous ARID1B variants result in Coffin–Siris syndrome. Features may include hypoplastic nails, slow growth, characteristic facial features, hypotonia, hypertrichosis, and sparse scalp hair. Most reported cases are due to ARID1B loss of function variants. We report a boy with developmental delay, feeding difficulties, aspiration, recurrent respiratory infections, slow growth, and hypotonia without a clinical diagnosis, where a previously unreported ARID1B missense variant was classified as a variant of uncertain significance. The pathogenicity of this variant was refined through combined methodologies including genome-wide methylation signature analysis (EpiSign), Machine Learning (ML) facial phenotyping, and LIRICAL. Trio exome sequencing and EpiSign were performed. ML facial phenotyping compared facial images using FaceMatch and GestaltMatcher to syndrome-specific libraries to prioritize the trio exome bioinformatic pipeline gene list output. Phenotype-driven variant prioritization was performed with LIRICAL. A de novo heterozygous missense variant, ARID1B p.(Tyr1268His), was reported as a variant of uncertain significance. The ACMG classification was refined to likely pathogenic by a supportive methylation signature, ML facial phenotyping, and prioritization through LIRICAL. The ARID1B genotype–phenotype has been expanded through an extended analysis of missense variation through genome-wide methylation signatures, ML facial phenotyping, and likelihood-ratio gene prioritization.

Artificial intelligence has lately proven useful in the field of medical genetics. It is already being used to interpret genome sequences and diagnose patients based on facial recognition. More recently, large-language models (LLMs) such as ChatGPT have been tested for their capacity to provide medical genetics information. It was found that ChatGPT performed similarly to human respondents in factual and critical thinking questions, albeit with reduced accuracy in the latter. In particular, ChatGPT's performance in questions related to calculating the recurrence risk was dismal, despite only having to deal with a single disease. To see if challenging ChatGPT with more difficult problems may reveal its flaws and their bases, it was asked to solve recurrence risk problems dealing with two diseases instead of one. Interestingly, it managed to correctly understand the mode of inheritance of recessive diseases, yet it incorrectly calculated the probability of having a healthy child. Other LLMs were also tested and showed similar noise. This highlights a major limitation for clinical use. While this shortcoming may be solved in the near future, LLMs may not be ready yet to be used as an effective clinical tool in communicating medical genetics information.

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.

Virtually all areas of biomedicine will be increasingly affected by applications of artificial intelligence (AI). We discuss how AI may affect fields of medical genetics, including both clinicians and laboratorians. In addition to reviewing the anticipated impact, we provide recommendations for ways in which these groups may want to evolve in light of the influence of AI. We also briefly discuss how educational and training programs can play a key role in preparing the future workforce given these anticipated changes.

Facial analysis technology in rare diseases has the potential to shorten the diagnostic odyssey by providing physicians with a valuable diagnostic tool. Given that most clinical genetic resources focus on populations of European descent, we compare craniofacial features in genetic syndromes across different populations and review how machine learning algorithms perform on diagnosing genetic syndromes in geographically and ethnically diverse populations. We also discuss the value of populations from ancestrally diverse backgrounds in the training set of machine learning algorithms. Finally, this review demonstrates that across diverse population groups, machine learning models have outstanding accuracy as supported by the area under the curve values greater than 0.9. Artificial intelligence is only in its infancy in the diagnosis of rare disease in diverse populations and will become more accurate as larger and more diverse training sets, including a wider spectrum of ages, particularly infants, are studied.