BACKGROUND: Serine residues in the protein backbone of heavily glycosylated proteoglycans are bound to glycosaminoglycans through a tetrasaccharide linker. UXS1 encodes UDP-glucuronate decarboxylase 1, which catalyzes synthesis of UDP-xylose, the donor of the first building block in the linker. Defects in other enzymes involved in formation of the tetrasaccharide linker cause so-called linkeropathies, characterized by short stature, radio-ulnar synostosis, decreased bone density, congenital contractures, dislocations, and more.
METHODS: Whole exome sequencing was performed in a father and son who presented with a mild skeletal dysplasia, as well as the father\'s unaffected parents. Wild-type and mutant UXS1 were recombinantly expressed in Escherichia coli and purified. Enzyme activity was evaluated by LC-MS/MS. In vivo effects were studied using HeparinRed assay and metabolomics.
RESULTS: The son had short long bones, normal epiphysis, and subtle metaphyseal changes especially in his legs. The likely pathogenic heterozygous variant NM_001253875.1(UXS1):c.557T>A p.(Ile186Asn) detected in the son was de novo in the father. Purified Ile186Asn-UXS1, in contrast to the wild-type, was not able to convert UDP-glucuronic acid to UDP-xylose. Plasma glycosaminoglycan levels were decreased in both son and father.
CONCLUSIONS: This is the first report linking UXS1 to short-limbed short stature in humans.

Between 2016 and 2018, interviews with 33 persons were conducted about the history of human genetics in Germany between 1970 and the 2000s. 29 interviewees gave consent to have the interviews used for historical research. These interviews are currently being analysed with the methods of qualitative content analysis and grounded theory. The focus of this article lies on the critical examination of the method of oral history and its application to human genetics. The oral history project focuses on questions regarding (1) biographical data and careers of interviewees, (2) development and application of diagnostic and therapeutic techniques, (3) establishment and growth of institutions of human genetics and (4) social debates regarding the discipline.

Historians have repeatedly pointed to personal and institutional continuities between eugenics in Germany before 1945 and the developing human genetics in post-war Germany. But how was this external perception discussed among German human geneticists and did it play a role in the discipline\'s self-image? On the basis of printed sources and biographical interviews, the historical consciousness and the assumption of historical responsibility among German human geneticists active in the field between the 1970s and the 2010s were investigated. In the memories of the contemporary witnesses, historical awareness played a role for their discipline even before the 1980s, and since the founding of the Deutsche Gesellschaft für Humangenetik in 1987, the question of the form of showing historical responsibility has been discussed repeatedly. It has been expressed, for example, in discussions with the public and renaming of prizes.

Discrete categorization of Mendelian disease genes into dominant and recessive models often oversimplifies their underlying genetic architecture. Cardiomyopathies (CMs) are genetic diseases with complex etiologies for which an increasing number of recessive associations have recently been proposed. Here, we comprehensively analyze all published evidence pertaining to biallelic variation associated with CM phenotypes to identify high-confidence recessive genes and explore the spectrum of monoallelic and biallelic variant effects in established recessive and dominant disease genes. We classify 18 genes with robust recessive association with CMs, largely characterized by dilated phenotypes, early disease onset and severe outcomes. Several of these genes have monoallelic association with disease outcomes and cardiac traits in the UK Biobank, including LMOD2 and ALPK3 with dilated and hypertrophic CM, respectively. Our data provide insights into the complex spectrum of dominance and recessiveness in genetic heart disease and demonstrate how such approaches enable the discovery of unexplored genetic associations.

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With the rapid development of multimedia technology, the student centered flipped classroom model (FCM) and massive open online courses (MOOCs) have been increasingly introduced and implemented in higher medical education. However, comparative analyses of the offline face-to-face FCM and completely online FCM have been rarely reported. In this study, we focused specifically on a set of flipped classrooms in which prerecorded videos were provided before class. Using the Zhihuishu platform as the major online course platform, our team built a MOOC and evaluated the teaching effectiveness of the FCM in both the offline face-to-face class and the online electronic live class for medical genetics education. Questionnaires, paper-based and oral exams were used to collect data on the teaching effects of the different teaching methods. We found that student satisfaction and overall student performance in the offline FCM group was significantly higher than that in the completely online teaching group. Although online FCM allowed students to play back and review anywhere and anytime after class, students taught in offline FCM had a significantly higher degree of knowledge mastery, had a deeper understanding of theoretical knowledge, and were better at knowledge comprehensive application. The effects of their training on genetic disease clinical diagnosis and treatment skills were significantly better, and their capacity for scientific research was also significantly improved. Our research discussed the advantages of the online courses and the problems brought about by using these technologies, and it provided insight into online teaching practices in the era of internet-based medical education.

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual\'s genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

This case report sheds light on the complex management of hemihyperplasia (HHP), highlighting the difficulties associated with diagnosis and the critical importance of a multimodal approach to treatment. The story of Acharya Vinoba Bhave Rural Hospital\'s (AVBRH) successful resolution following a misdiagnosis at another clinic emphasizes the value of expert care. The successful outcome resulted from the fusion of surgical innovation, genetic insights, and psychosocial factors through genetic testing, liposuction, and postoperative rehabilitation. This example emphasizes the need to treat congenital illnesses holistically and the transforming power of individualized, multidisciplinary treatment to improve the functional and esthetic elements of life for patients with HHP.

BACKGROUND: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism.
OBJECTIVE: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt.
METHODS: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity.
RESULTS: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score\'s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores.
CONCLUSIONS: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

Genetics has become a critical component of medicine over the past five to six decades. Alongside genetics, a relatively new discipline, dysmorphology, has also begun to play an important role in providing critically important diagnoses to individuals and families. Both have become indispensable to unraveling rare diseases. Almost every medical specialty relies on individuals experienced in these specialties to provide diagnoses for patients who present themselves to other doctors. Additionally, both specialties have become reliant on molecular geneticists to identify genes associated with human disorders. Many of the medical geneticists, dysmorphologists, and molecular geneticists traveled a circuitous route before arriving at the position they occupied. The purpose of collecting the memoirs contained in this article was to convey to the reader that many of the individuals who contributed to the advancement of genetics and dysmorphology since the late 1960s/early 1970s traveled along a journey based on many chances taken, replying to the necessities they faced along the way before finding full enjoyment in the practice of medical and human genetics or dysmorphology. Additionally, and of equal importance, all exhibited an ability to evolve with their field of expertise as human genetics became human genomics with the development of novel technologies.