This study aimed to quantify heterogeneity in the value for money of precision medicine (PM) by application types across contexts and conditions and to quantify sources of heterogeneity to areas of particular promises or concerns as the field of PM moves forward.
A systemic search was performed in Embase, Medline, EconLit, and CRD databases for studies published between 2011 and 2021 on cost-effectiveness analysis (CEA) of PM interventions. Based on a willingness-to-pay threshold of one-time GDP per capita of each study country, the net monetary benefit (NMB) of PM was pooled using random-effects meta-analyses. Sources of heterogeneity and study biases were examined using random-effects meta-regressions, jackknife sensitivity analysis, and the biases in economic studies checklist.
Among the 275 unique CEAs of PM, publicly sponsored studies found neither genetic testing nor gene therapy cost-effective in general, which was contradictory to studies funded by commercial entities and early stage evaluations. Evidence of PM being cost-effective was concentrated in a genetic test for screening, diagnosis, or as companion diagnostics (pooled NMBs, $48,152, $8,869, $5,693, p < 0.001), in the form of multigene panel testing (pooled NMBs = $31,026, p < 0.001), which only applied to a few disease areas such as cancer and high-income countries. Incremental effectiveness was an essential value driver for varied genetic tests but not gene therapy.
Precision medicine\'s value for money across application types and contexts was difficult to conclude from published studies, which might be subject to systematic bias. The conducting and reporting of CEA of PM should be locally based and standardized for meaningful comparisons.

Autosomal dominant variants in ELOVL4 cause spinocerebellar ataxia type 34 (SCA34; ATX-ELOVL4), classically associated with a skin condition known as erythrokeratoderma. Here, we report a large Italian-Maltese-Australian family with spinocerebellar ataxia. Notably, while there were dermatological manifestations (eczema), erythrokeratoderma was not present. Using a next-generation sequencing panel, we identified a previously reported ELOVL4 variant, NM_022726.4: c.698C > T p.(Thr233Met). The variant was initially classified as a variant of uncertain significance; however, through segregation studies, we reclassified the variant as likely pathogenic. We next identified an individual from another family (Algerian-Maltese-Australian) with the same ELOVL4 variant with spinocerebellar ataxia but without dermatological manifestations. We subsequently performed the first dedicated literature review of ELOVL4-associated ataxia to gain further insights into genotype-phenotype relationships. We identified a total of 60 reported cases of SCA34 to date. The majority had gait ataxia (88.3%), limb ataxia (76.7%), dysarthria (63.3%), and nystagmus (58.3%). Of note, skin lesions related to erythrokeratoderma were seen in a minority of cases (33.3%). Other extracerebellar manifestations included pyramidal tract signs, autonomic disturbances, retinitis pigmentosa, and cognitive impairment. For brain MRI data, cerebellar atrophy was seen in all cases (100%), whereas the hot cross bun sign (typically associated with multiple system atrophy type C) was seen in 32.4% of cases. Our family study and literature review highlight the variable phenotypic spectrum of SCA34. Importantly, it shows that erythrokeratoderma is not found in most cases and that, while a dermatological assessment may be helpful in these patients, SCA34 diagnosis should be considered irrespective of dermatological manifestations.

Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.

Autism spectrum disorder (ASD) and schizophrenia (SCZ) are separate disorders, with distinct clinical profiles and natural histories. ASD, typically diagnosed in childhood, is characterized by restricted or repetitive interests or behaviors and impaired social communication, and it tends to have a stable course. SCZ, typically diagnosed in adolescence or adulthood, is characterized by hallucinations and delusions, and tends to be associated with declining function. However, youth with ASD are three to six times more likely to develop SCZ than their neurotypical counterparts, and increasingly, research has shown that ASD and SCZ converge at several levels. We conducted a systematic review of studies since 2013 relevant to understanding this convergence, and present here a narrative synthesis of key findings, which we have organized into four broad categories: symptoms and behavior, perception and cognition, biomarkers, and genetic and environmental risk. We then discuss opportunities for future research into the phenomenology and neurobiology of overlap between ASD and SCZ. Understanding this overlap will allow for researchers, and eventually clinicians, to understand the factors that may make a child with ASD vulnerable to developing SCZ. LAY SUMMARY: Autism spectrum disorder and schizophrenia are distinct diagnoses, but people with autism and people with schizophrena share several characteristics. We review recent studies that have examined these areas of overlap, and discuss the kinds of studies we will need to better understand how these disorders are related. Understanding this will be important to help us identify which autistic children are at risk of developing schizophrenia.

Human imprinting disorders cause a range of dysmorphic and neurocognitive phenotypes, and they may elude traditional molecular diagnosis such exome sequencing. The discovery of novel disorders related to imprinted genes has lagged behind traditional Mendelian disorders because current diagnostic technology, especially unbiased testing, has limited utility in their discovery. To identify novel imprinting disorders, we reviewed data for every human gene hypothesized to be imprinted, identified each mouse ortholog, determined its imprinting status in the mouse, and analyzed its function in humans and mice. We identified 17 human genes that are imprinted in both humans and mice, and have functional data in mice or humans to suggest that dysregulated expression would lead to an abnormal phenotype in humans. These 17 genes, along with known imprinted genes, were preferentially flagged 538 clinical exome sequencing tests. The identified genes were: DIRAS3 [1p31.3], TP73 [1p36.32], SLC22A3 [6q25.3], GRB10 [7p12.1], DDC [7p12.2], MAGI2 [7q21.11], PEG10 [7q21.3], PPP1R9A [7q21.3], CALCR [7q21.3], DLGAP2 [8p23.3], GLIS3 [9p24.2], INPP5F [10q26.11], ANO1 [11q13.3], SLC38A4 [12q13.11], GATM [15q21.1], PEG3 [19q13.43], and NLRP2 [19q13.42]. In the 538 clinical cases, eight cases (1.7%) reported variants in a causative known imprinted gene. There were 367/758 variants (48.4%) in imprinted genes that were not known to cause disease, but none of those variants met the criteria for clinical reporting. Imprinted disorders play a significant role in human disease, and additional human imprinted disorders remain to be discovered. Therefore, evolutionary conservation is a potential tool to identify novel genes involved in human imprinting disorders and to identify them in clinical testing.

Genetics is an essential subject to be mastered by health professional students of all types. However, technological advances in genomics and recent pedagogical research have changed the way in which many medical training programs teach genetics to their students. These advances favor a more experience-based education focused primarily on developing student\'s critical thinking skills. In this review, we examine the current state of genetics education at both the preclinical and clinical levels and the ways in which medical and pedagogical research have guided reforms to current and emerging teaching practices in genetics. We discover exciting trends taking place in which genetics is integrated with other scientific disciplines both horizontally and vertically across medical curricula to emphasize training in scientific critical thinking skills among students via the evaluation of clinical evidence and consultation of online databases. These trends will produce future health professionals with the skills and confidence necessary to embrace the new tools of medical practice that have emerged from scientific advances in genetics, genomics, and bioinformatics.

OBJECTIVE: To systematically review studies that have analyzed communication within medical consultations involving genetic specialists and report on their findings and design.
METHODS: Drawing from PRISMA and appropriate guidelines for reviewing qualitative research, a systematic search of seven databases was conducted, followed by selection of studies for inclusion based on a set of criteria. Three authors conducted data extraction and narrative synthesis.
RESULTS: Twenty-two studies were identified and were heterogeneous in setting, design, and methods, with many including limited descriptions of health professionals involved. Despite this variability, studies generally pursued the following three main objectives: searching for structural patterns within consultations, investigating communication and genetic counseling concepts, and linking process with input- and outcome-measures. Structural patterns identified included clinician dialog dominating consultations, and talk being mostly biomedical. Counseling and communication concepts investigated were: risk communication, the negotiation of power and knowledge, and adherence to genetic counseling ideals. Attempts to link consultation data to input- or outcome-measures were often unsuccessful.
CONCLUSIONS: More interdisciplinary research, grounded in appropriate theoretical frameworks, is needed to explore inherent complexities in this setting.
CONCLUSIONS: Findings from this review can be used to guide the design of future research into the process of genetic consultations.