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

{"title":"Publication schedule for 2023","authors":"","doi":"10.1002/ajmg.c.31981","DOIUrl":"https://doi.org/10.1002/ajmg.c.31981","url":null,"abstract":"","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.31981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50148805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Table of Contents, Volume 193, Number 3, September 2023","authors":"","doi":"10.1002/ajmg.c.31980","DOIUrl":"https://doi.org/10.1002/ajmg.c.31980","url":null,"abstract":"","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.31980","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50148804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthy transition: Roadmap for young adults with Down syndrome to adulthood","authors":"Maya Weaver,&nbsp;Andrew McCormick","doi":"10.1002/ajmg.c.32065","DOIUrl":"10.1002/ajmg.c.32065","url":null,"abstract":"<p>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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41097426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainability of personal social networks of people with Down syndrome","authors":"Ayesha Harisinghani,&nbsp;Amar Dhand,&nbsp;Ellen Hollands Steffensen,&nbsp;Brian G. Skotko","doi":"10.1002/ajmg.c.32064","DOIUrl":"10.1002/ajmg.c.32064","url":null,"abstract":"<p>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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41098089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of EpiSign, facial phenotyping, and likelihood ratio interpretation of clinical abnormalities in the re-classification of an ARID1B missense variant","authors":"Caitlin Forwood,&nbsp;Katie Ashton,&nbsp;Ying Zhu,&nbsp;Futao Zhang,&nbsp;Kerith-Rae Dias,&nbsp;Krystle Standen,&nbsp;Carey-Anne Evans,&nbsp;Louise Carey,&nbsp;Michael Cardamone,&nbsp;Carolyn Shalhoub,&nbsp;Hala Katf,&nbsp;Carlos Riveros,&nbsp;Tzung-Chien Hsieh,&nbsp;Peter Krawitz,&nbsp;Peter N Robinson,&nbsp;Tracy Dudding-Byth,&nbsp;Bekim Sadikovic,&nbsp;Jason Pinner,&nbsp;Michael F. Buckley,&nbsp;Tony Roscioli","doi":"10.1002/ajmg.c.32056","DOIUrl":"10.1002/ajmg.c.32056","url":null,"abstract":"<p>Heterozygous <i>ARID1B</i> 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 <i>ARID1B</i> 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 <i>ARID1B</i> 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, <i>ARID1B</i> 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 <i>ARID1B</i> 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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.32056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10185760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is artificial intelligence getting too much credit in medical genetics?","authors":"Imen F. Alkuraya","doi":"10.1002/ajmg.c.32062","DOIUrl":"10.1002/ajmg.c.32062","url":null,"abstract":"<p>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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.32062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10037257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational facial analysis for rare Mendelian disorders","authors":"Tzung-Chien Hsieh,&nbsp;Peter M. Krawitz","doi":"10.1002/ajmg.c.32061","DOIUrl":"10.1002/ajmg.c.32061","url":null,"abstract":"<p>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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.32061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9997772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and the impact on medical genetics","authors":"Benjamin D. Solomon,&nbsp;Wendy K. Chung","doi":"10.1002/ajmg.c.32060","DOIUrl":"10.1002/ajmg.c.32060","url":null,"abstract":"<p>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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.32060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9974406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of facial analysis Technology in Clinical Genetics: Considerations for diverse populations","authors":"Paul Kruszka,&nbsp;Cedrik Tekendo-Ngongang","doi":"10.1002/ajmg.c.32059","DOIUrl":"10.1002/ajmg.c.32059","url":null,"abstract":"<p>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.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.32059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9927786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of webcam-collected and artificial-intelligence-derived social and cognitive performance measures for neurodevelopmental genetic syndromes","authors":"Thomas W. Frazier,&nbsp;Robyn M. Busch,&nbsp;Patricia Klaas,&nbsp;Katherine Lachlan,&nbsp;Shafali Jeste,&nbsp;Alexander Kolevzon,&nbsp;Eva Loth,&nbsp;Jacqueline Harris,&nbsp;Leslie Speer,&nbsp;Tom Pepper,&nbsp;Kristin Anthony,&nbsp;J. Michael Graglia,&nbsp;Christal G. Delagrammatikas,&nbsp;Sandra Bedrosian-Sermone,&nbsp;Constance Smith-Hicks,&nbsp;Katie Huba,&nbsp;Robert Longyear,&nbsp;LeeAnne Green-Snyder,&nbsp;Frederick Shic,&nbsp;Mustafa Sahin,&nbsp;Charis Eng,&nbsp;Antonio Y. Hardan,&nbsp;Mirko Uljarević","doi":"10.1002/ajmg.c.32058","DOIUrl":"10.1002/ajmg.c.32058","url":null,"abstract":"<p>This study focused on the development and initial psychometric evaluation of a set of online, webcam-collected, and artificial intelligence-derived patient performance measures for neurodevelopmental genetic syndromes (NDGS). Initial testing and qualitative input was used to develop four stimulus paradigms capturing social and cognitive processes, including social attention, receptive vocabulary, processing speed, and single-word reading. The paradigms were administered to a sample of 375 participants, including 163 with NDGS, 56 with idiopathic neurodevelopmental disability (NDD), and 156 neurotypical controls. Twelve measures were created from the four stimulus paradigms. Valid completion rates varied from 87 to 100% across measures, with lower but adequate completion rates in participants with intellectual disability. Adequate to excellent internal consistency reliability (<i>α</i> = 0.67 to 0.95) was observed across measures. Test–retest reproducibility at 1-month follow-up and stability at 4-month follow-up was fair to good (<i>r</i> = 0.40–0.73) for 8 of the 12 measures. All gaze-based measures showed evidence of convergent and discriminant validity with parent-report measures of other cognitive and behavioral constructs. Comparisons across NDGS groups revealed distinct patterns of social and cognitive functioning, including people with <i>PTEN</i> mutations showing a less impaired overall pattern and people with <i>SYNGAP1</i> mutations showing more attentional, processing speed, and social processing difficulties relative to people with <i>NFIX</i> mutations. Webcam-collected performance measures appear to be a reliable and potentially useful method for objective characterization and monitoring of social and cognitive processes in NDGS and idiopathic NDD. Additional validation work, including more detailed convergent and discriminant validity analyses and examination of sensitivity to change, is needed to replicate and extend these observations.</p>","PeriodicalId":7445,"journal":{"name":"American Journal of Medical Genetics Part C: Seminars in Medical Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.c.32058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10016091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}