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OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

ObjectivesThe study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS).MethodsWe conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses.ResultsWe analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity.ConclusionIn addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.

BACKGROUND:Precision medicine is set to deliver a rich new data set of genomic information. However, the number of certified specialists in the United States is small, with only 4244 genetic counselors and 1302 clinical geneticists. We conducted a national survey of 264 medical professionals to evaluate how they interpret genetic test results, determine their confidence and self-efficacy of interpreting genetic test results with patients, and capture their opinions and experiences with direct-to-consumer genetic tests (DTC-GT). METHODS:Participants were grouped into two categories, genetic specialists (genetic counselors and clinical geneticists) and medical providers (primary care, internists, physicians assistants, advanced nurse practitioners, etc.). The survey (full instrument can be found in the Additional file 1) presented three genetic test report scenarios for interpretation: a genetic risk for diabetes, genomic sequencing for symptoms report implicating a potential HMN7B: distal hereditary motor neuropathy VIIB diagnosis, and a statin-induced myopathy risk. Participants were also asked about their opinions on DTC-GT results and rank their own perceived level of preparedness to review genetic test results with patients. RESULTS:The rates of correctly interpreting results were relatively high (74.4% for the providers compared to the specialist's 83.4%) and age, prior genetic test consultation experience, and level of trust assigned to the reports were associated with higher correct interpretation rates. The self-selected efficacy and the level of preparedness to consult on a patient's genetic results were higher for the specialists than the provider group. CONCLUSION:Specialists remain the best group to assist patients with DTC-GT, however, primary care providers may still provide accurate interpretation of test results when specialists are unavailable.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

Objectives To evaluate the efficacy of ChatGPT 4 (GPT-4) in delivering genetic information about BRCA1 , HFE , and MLH1 , building on previous findings with ChatGPT 3.5 (GPT-3.5). To focus on assessing the utility, limitations, and ethical implications of using ChatGPT in medical settings. Materials and Methods A structured survey was developed to assess GPT-4's clinical value. An expert panel of genetic counselors and clinical geneticists evaluated GPT-4's responses to these questions. We also performed comparative analysis with GPT-3.5, utilizing descriptive statistics and using Prism 9 for data analysis. Results The findings indicate improved accuracy in GPT-4 over GPT-3.5 (P  < .0001). However, notable errors in accuracy remained. The relevance of responses varied in GPT-4, but was generally favorable, with a mean in the "somewhat agree" range. There was no difference in performance by disease category. The 7-question subset of the Bot Usability Scale (BUS-15) showed no statistically significant difference between the groups but trended lower in the GPT-4 version. Discussion and Conclusion The study underscores GPT-4's potential role in genetic education, showing notable progress yet facing challenges like outdated information and the necessity of ongoing refinement. Our results, while showing promise, emphasizes the importance of balancing technological innovation with ethical responsibility in healthcare information delivery. [ABSTRACT FROM AUTHOR]

Objective Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association—Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. Process A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. Conclusions Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making. [ABSTRACT FROM AUTHOR]

Epilepsy, defined by the occurrence of two or more unprovoked seizures or one unprovoked seizure with a propensity for others, affects 0.64% of the population and can lead to significant morbidity and mortality. A majority of unexplained epilepsy (seizures not attributed to an acquired etiology, such as trauma or infection) is estimated to have an underlying genetic etiology. Despite rapid progress in understanding of the genetic underpinnings of the epilepsies, there are no recent evidence‐based guidelines for genetic testing and counseling for this population. This practice guideline provides evidence‐based recommendations for approaching genetic testing in the epilepsies using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision framework. We used evidence from a recent systematic evidence review and meta‐analysis of diagnostic yield of genetic tests in patients with epilepsy. We also compiled data from other sources, including recently submitted conference abstracts and peer‐reviewed journal articles. We identified and prioritized outcomes of genetic testing as critical, important or not important and based our recommendations on outcomes deemed critical and important. We considered the desirable and undesirable effects, value and acceptability to relevant stakeholders, impact on health equity, cost‐effectiveness, certainty of evidence, and feasibility of the interventions in individuals with epilepsy. Taken together, we generated two clinical recommendations: (1) Genetic testing is strongly recommended for all individuals with unexplained epilepsy, without limitation of age, with exome/genome sequencing and/or a multi‐gene panel (>25 genes) as first‐tier testing followed by chromosomal microarray, with exome/genome sequencing conditionally recommended over multi‐gene panel. (2) It is strongly recommended that genetic tests be selected, ordered, and interpreted by a qualified healthcare provider in the setting of appropriate pre‐test and post‐test genetic counseling. Incorporation of genetic counselors into neurology practices and/or referral to genetics specialists are both useful models for supporting providers without genetics expertise to implement these recommendations. [ABSTRACT FROM AUTHOR]

Objective The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. Materials and Methods A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. Results Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. Discussion Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. Conclusions The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic. [ABSTRACT FROM AUTHOR]