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A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF

Graduate medical students must demonstrate competency in clinical skills. Current tracking methods rely either on manual efforts or on simple electronic entry to record clinical experience. We evaluated automated methods to locate 10 institution-defined core clinical problems from three medical students' clinical notes (n=290). Each note was processed with section header identification algorithms and the KnowledgeMap concept identifier to locate Unified Medical Language System (UMLS) concepts. The best performing automated search strategies accurately classified documents containing primary discussions to the core clinical problems with area under receiver operator characteristic curve of 0.90-0.94. Recall and precision for UMLS concept identification was 0.91 and 0.92, respectively. Of the individual note section, concepts found within the chief complaint, history of present illness, and assessment and plan were the strongest predictors of relevance. This automated method of tracking can provide detailed, pertinent reports of clinical experience that does not require additional work from medical trainees. The coupling of section header identification and concept identification holds promise for other natural language processing tasks, such as clinical research or phenotype identification. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to classify genetic variants in RYR1 and CACNA1S associated with malignant hyperthermia using biobank genotyping data in patients exposed to triggering anesthetics without malignant hyperthermia phenotype., Methods: We identified individuals who underwent surgery and were exposed to triggering anesthetics without malignant hyperthermia phenotype and who had RYR1 or CACNA1S genotyping data available in our biobank. We classified all variants in the cohort using a Bayesian framework of the American College of Medical Genetics and Genomics and the Association of Molecular Pathologists guidelines for variant classification and updated the posterior probabilities from this model with the new information from our biobank cohort., Results: We identified 253 patients with 95 RYR1 variants and 12 CACNA1S variants. After applying a Bayesian framework, we classified 17 variants as benign (B), 31 as likely benign (LB), 57 as uncertain (VUS), and 2 as likely pathogenic (LP). When we incorporated evidence about unique exposures to malignant hyperthermia triggering anesthetic agents, 48 of 107 (45%) variants were downgraded (9 to B, 37 to LB, and 2 to VUS). Notably, 41 (72%) of 57 VUSs were downgraded to B or LB. When repeat anesthetics in the same individual were counted as one exposure, 42 of 107 (39%) of variants were downgraded (5 to B, 35 to LB, and 2 to VUS). Specifically, 37 (65%) of 57 VUSs were downgraded to LB., Conclusion: Deidentified biorepositories linked with anesthetic data offer a new method of integrating clinical evidence into the assessment of variant probability of pathogenicity., (Copyright © 2024 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

Purpose: School-age children with unilateral hearing loss are at an increased risk of exhibiting academic difficulties. Yet, approximately half of children with unilateral hearing loss will not require additional support. There is a dearth of information to assist in determining which of these children will express academic deficits and which will not. The purpose of this study was to identify hearing- and health-related factors that contribute to adverse educational progress in children with permanent unilateral hearing loss. Specific indicators of academic concern identified during school age included the need for specialized academic services, receipt of speech-language therapy, or parent/teacher concerns for academics or speech-language development., Method: This study provides an in-depth analysis of a previously described patient cohort developed from de-identified electronic health records. Factors of interest included potentially relevant hearing-related risk factors (e.g., degree, type, and laterality of hearing loss), in addition to health-related factors that could be extracted from the electronic health records (e.g., sex, premature birth, history of significant otitis media)., Results: Being born preterm, having a history of pressure equalization tubes or having conductive or mixed hearing loss more than doubled the risk of demonstrating adverse educational progress. Laterality and degree of loss were generally not significantly related to academic progress., Conclusions: Approximately half of school-age children with permanent unilateral hearing loss in this cohort experienced some academic challenges. Birth history and middle ear pathology were important predictors of adverse educational progress.

Background: Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction after surgery. Because traditional risk factors do not completely explain variability in risk, this study hypothesized that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV., Methods: Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine (Ann Arbor, Michigan) and Vanderbilt University Medical Center (Nashville, Tennessee) were studied. PONV was defined as nausea or emesis occurring and documented in the postanesthesia care unit. In the discovery phase, genome-wide association studies were performed on each genetic cohort, and the results were meta-analyzed. Next, the polygenic phase assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (c-statistic) and net reclassification index., Results: Of 64,523 total patients, 5,703 developed PONV (8.8%). The study identified 46 genetic variants exceeding the threshold of P < 1 × 10-5, occurring with minor allele frequency greater than 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex (adjusted odds ratio, 1.027 per SD increase in overall genetic risk; 95% CI, 1.001 to 1.053; P = 0.044), a model based on known clinical risks (adjusted odds ratio, 1.029; 95% CI, 1.003 to 1.055; P = 0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (adjusted odds ratio, 1.029; 95% CI, 1.002 to 1.056; P = 0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic, 0.616 compared to 0.613; P = 0.028) and improved net reclassification of 4.6% of cases., Conclusions: Standardized polygenic risk was associated with PONV in all three of the study's models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score greater than 1 SD above the mean has 2 to 3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV or motion sickness (55%), having a history of migraines (17%), or being female (83%) and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc., on behalf of the American Society of Anesthesiologists.)

Background: Kidney dysfunction is a common complication in septic patients. Studies have identified numerous risk factors for sepsis-associated acute kidney injury (S-AKI), yet there is wide variability in the incidence even among patients with similar risk factors, suggesting the presence of additional uncharacterized risk factors, including genetic differences. The expansion of biobanks, advances in genotyping, and standardized diagnostic criteria have enabled large-scale, hypothesis-generating studies into the genetic mechanisms underlying S-AKI. We hypothesize that the genetic pathway behind S-AKI has overlapping mechanisms with key differences based upon the specific subtype of acute kidney injury (AKI)., Methods: To test this hypothesis, we performed a genome-wide association study (GWAS) of S-AKI in three logistic regression models. Model 1, controlled for 1) age, 2) sex, 3) genotyping chip, and 4) the first five principal components. In Model 2, pre-sepsis baseline serum creatinine was added to the variables in Model 1. Finally, in Model 3, we controlled for the full range of patient, clinical, and ICU-related risk factors. Each of the 3-models were repeated in a pre-specified sensitivity analysis of higher severity S-AKI, defined as KDIGO Stage 2 or 3. We then compare associated variants and genes from our GWAS with previously published AKI sub-types and model other factors associated with S-AKI in our dataset., Findings: 3,348 qualifying Sepsis-3 patients have been genotyped in our dataset. Of these patients, 383 (11.4%) developed Stage 1, 2, or 3 AKI (primary outcome) and 181 (5.4%) developed Stage 2 or 3 AKI (sensitivity analysis). The median age was 61 years (interquartile range (IQR): 51,69), 42% were female, and the increase in SOFA score (between 48-hours before to 24-hours after the onset of suspected infection) was 2 (2-3). No variants exceeded our threshold for genome-wide significance (P<5x10-8), however, a total of 13 variants exceeded the suggestive (P<1x10-6) threshold. Notably, rs184516290 (chr1:199814965:G:A), near the NR5A2 gene, chr1:199805801:T:TA, also near the NR5A2 gene, and rs117313146 (chr15:31999784:G:C), near the CHRNA7 gene, were associated with S-AKI at the suggestive level in all three models presented. Variants in the suppressor of fused homolog (SUFU) gene, previously shown to be correlated with renal function in bacteremic patients, consistently exceeded the P<0.05 threshold in our models., Conclusions: While failing to identify any novel association for S-AKI at the level of genome-wide significance, our study did suggest multiple variants in previously characterized pathways for S-AKI including CHRNA7, NR5A2, and SUFU. We failed to replicate associations from multiple prior studies which may result from differences in how the phenotype was defined or, alternatively, limited genetic contribution and low heritability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Douville et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Importance: The genetic factors that modulate the reduced penetrance and variable expressivity of heritable dilated cardiomyopathy (DCM) are largely unknown. BAG3 genetic variants have been implicated in both DCM and hypertrophic cardiomyopathy (HCM), nominating BAG3 as a gene that harbors potential modifier variants in DCM., Objective: To interrogate the clinical traits and diseases associated with BAG3 coding variation., Design, Setting, and Participants: This was a cross-sectional study in the Penn Medicine BioBank (PMBB) enrolling patients of the University of Pennsylvania Health System's clinical practice sites from 2014 to 2023. Whole-exome sequencing (WES) was linked to electronic health record (EHR) data to associate BAG3 coding variants with EHR phenotypes. This was a health care population-based study including individuals of European and African genetic ancestry in the PMBB with WES linked to EHR phenotypes, with replication studies in BioVU, UK Biobank, MyCode, and DCM Precision Medicine Study., Exposures: Carrier status for BAG3 coding variants., Main Outcomes and Measures: Association of BAG3 coding variation with clinical diagnoses, echocardiographic traits, and longitudinal outcomes., Results: In PMBB (n = 43 731; median [IQR] age, 65 [50-76] years; 21 907 female [50.1%]), among 30 324 European and 11 198 African individuals, the common C151R variant was associated with decreased risk for DCM (odds ratio [OR], 0.85; 95% CI, 0.78-0.92) and simultaneous increased risk for HCM (OR, 1.59; 95% CI, 1.25-2.02), which was confirmed in the replication cohorts. C151R carriers exhibited improved longitudinal outcomes compared with noncarriers as assessed by age at death (hazard ratio [HR], 0.85; 95% CI, 0.74-0.96; median [IQR] age, 71.8 [63.1-80.7] in carriers and 70.3 [61.6-79.2] in noncarriers) and heart transplant (HR, 0.81; 95% CI, 0.66-0.99; median [IQR] age, 56.7 [46.1-63.1] in carriers and 55.6 [45.2-62.9] in noncarriers). C151R was associated with reduced risk of DCM (OR, 0.42; 95% CI, 0.24-0.74) and heart failure (OR, 0.27; 95% CI, 0.14-0.50) among individuals harboring truncating TTN variants in exons with high cardiac expression (n = 358)., Conclusions and Relevance: BAG3 C151R was identified as a bidirectional modulator of risk along the DCM-HCM spectrum, as well as an important genetic modifier variant in TTN-mediated DCM. This work expands on the understanding of the etiology and penetrance of DCM, suggesting that BAG3 C151R is an important genetic modifier variant contributing to the variable expressivity of DCM, warranting further exploration of its mechanisms and of genetic modifiers in DCM more broadly.

Telomeres protect chromosomal integrity, and telomere length (TL) is influenced by environmental and genetic factors. While short-telomeres are linked to rare telomeropathies, this study explored the hypothesis that a "long-telomeropathy" is associated with a cancer-predisposing syndrome. Using genomic and health data from 113,861 individuals, a trans-ancestry polygenic risk score for TL (PRS TL ) was developed. A phenome-wide association study (PheWAS) identified 65 tumor traits linked to elevated PRS TL . Using this result, a trans-ancestry phenotype risk score for a long-TL (PheRS LTL ) was develop and validated. Rare variant analyses revealed 13 genes associated with PheRS LTL . Individuals who were carriers of these rare variants had a predisposition for long-TL validating original hypothesis. Most of these genes were new to both cancer and telomere biology. In conclusion, this study identified a novel tumor-predisposing syndrome shaped by both common and rare genetic variants, broadening the understanding of telomeropathies to those with a predisposition for long telomeres.

Objective: 2017 blood pressure (BP) categories focus on cardiac risk. We hypothesize that studying the balance between mechanisms that increase or decrease BP across the medical phenome will lead to new insights. We devised a classifier that uses BP measures to assign individuals to mutually exclusive categories centered in the upper (Htn), lower (Hotn) and middle (Naf) zones of the BP spectrum; and examined the epidemiologic and phenotypic patterns of these BP-categories., Methods: We classified a cohort of 832,560 deidentified electronic health records by BP-category; compared the frequency of BP-categories and four subtypes of Htn and Hotn by sex and age-decade; visualized the distributions of systolic, diastolic, mean arterial and pulse pressures stratified by BP-category; and ran Phenome-wide Association Studies (PheWAS) for Htn and Hotn. We paired knowledgebases for hypertension and hypotension and computed aggregate knowledgebase status (KB-status) indicating known associations. We assessed alignment of PheWAS results with KB-status for phecodes in the knowledgebase, and paired PheWAS correlations with KB-status to surface phenotypic patterns., Results: BP-categories represent distinct distributions within the multimodal distributions of systolic and diastolic pressure. They are centered in the upper, lower, and middle zones of mean arterial pressure and provide a different signal than pulse pressure. For phecodes in the knowledgebase, 85% of positive correlations align with KB-status. Phenotypic patterns for Htn and Hotn overlap for several phecodes and are separate for others. Our analysis suggests five candidates for hypothesis testing research, two where the prevalence of the association with Htn or Hotn may be under appreciated, three where mechanisms that increase and decrease blood pressure may be affecting one another's expression., Conclusion: PairedPheWAS methods may open a phenome-wide path to disentangling hypertension and chronic hypotension. Our classifier provides a starting point for assigning individuals to BP-categories representing the upper, lower, and middle zones of the BP spectrum. 4.7 % of individuals matching 2017 BP categories for normal, elevated BP or isolated hypertension, have diastolic pressure < 60. Research is needed to fine-tune the classifier, provide external validation, evaluate the clinical significance of diastolic pressure < 60, and test the candidate hypotheses., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: William Stead and Lisa Bastarache report financial support was provided by National Center for Advancing Translational Sciences. Lisa Bastarache reports financial support was provided by National Library of Medicine. William Stead reports a relationship with National Heart Lung and Blood Institute that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The Undiagnosed Disease Network (UDN) is comprised of clinical and research experts collaborating to diagnose rare disease. The UDN is funded by the National Institutes of Health and includes 12 different clinical sites (About Us, 2022). Here we highlight the success of collaborative efforts within the UDN Clinical Site at Vanderbilt University Medical Center (VUMC) in utilizing a cohort of experts in bioinformatics, structural biology, and genetics specialists in diagnosing rare disease. Our UDN team identified a de novo mosaic CACNA1D variant c.2299T>C in a 5-year-old female with a history of global developmental delay, dystonia, dyskinesis, and seizures. Using a collaborative multidisciplinary approach, our VUMC UDN team diagnosed the participant with Primary Aldosteronism, Seizures, and Neurologic abnormalities (PASNA) OMIM: 615474 due to a rare mosaic CACNA1D variant (O'Neill, 2013). Interestingly, this patient was mosaic, a phenotypic trait previously unreported in PASNA cases. This report highlights the importance of a multidisciplinary approach in diagnosing rare disease., (© 2024 The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals LLC.)

This study addresses rising incidence of pediatric venous thromboembolism by validating a VTE phenotype and developing a polygenic risk score (PRS) using UK Biobank data. Our findings demonstrate predictive value of the PRS, enhancing VTE risk assessment in clinical settings. Future steps involve integrating the PRS into risk stratification models., (©2024 AMIA - All rights reserved.)

Voriconazole exposure is associated with skin cancer, but it is unknown how the full spectrum of its metabolizer phenotypes impacts this association. We conducted a retrospective cohort study to determine how variation in metabolism of voriconazole as measured by metabolizer status of CYP2C19 is associated with the total number of skin cancers a patient develops and the rate of development of the first skin cancer after treatment. There were 1,739 organ transplant recipients with data on CYP2C19 phenotype. Of these, 134 were exposed to voriconazole. There was a significant difference in the number of skin cancers after transplant based on exposure to voriconazole, metabolizer phenotype, and the interaction of these two (p < 0.01 for all three). This increase was driven primarily by number of squamous cell carcinomas among rapid metabolizes with voriconazole exposure (p < 0.01 for both). Patients exposed to voriconazole developed skin cancers more rapidly than those without exposure (Fine-Grey hazard ratio 1.78, 95% confidence interval 1.19-2.66). This association was similarly driven by development of SCC (Fine-Grey hazard ratio 1.83, 95% confidence interval 1.14-2.94). Differences in voriconazoles metabolism are associated with an increase in the number of skin cancers developed after transplant, particularly SCC., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Disease specific cohort studies have reported details on X linked (XL) disorders affecting females. We investigated the spectrum and penetrance of XL disorders seen in electronic health records (EHR). We generated a cohort of individuals diagnosed with XL disorders at Vanderbilt University Medical Center over 20 years. Our cohort included 477 males and 203 females diagnosed with 108 different XL genetic disorders. We found large differences between the female/male (F/M) ratios for various XL disorders regardless of their OMIM annotated mode of inheritance. We identified four XL recessive disorders affecting women previously only described in men. Biomarkers for XL disease had unique gender-specific patterns differing between modes of inheritance. EHRs provide large cohorts of XL genetic disorders that give new insights compared to the literature. Differences in the F/M ratios and biomarkers of XL disorders observed likely result from disease specific and sex dependent penetrance. We conclude that observed gender ratios associated with specific XL disorders may be more useful than those predicted by Mendelian genetics provided by OMIM. Our findings of a gender specific penetrance and severity for XL disorders show unexpected differences from Mendelian predictions. Further work is required to validate our findings in larger combined EHR cohorts., (© 2024 Wiley Periodicals LLC.)

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGS WBC ) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGS WBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10 -5 ) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit., (© 2024. The Author(s).)

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of R R that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that R R is unbiased without compiling exclusion criteria lists. With R R as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes., Competing Interests: Competing Interest Statement The authors have declared no competing interest.

Purpose: Diagnostic delay in monogenic disease is reportedly common. We conducted a scoping review investigating variability in study design, results, and conclusions., Methods: We searched the academic literature on January 17, 2023, for original peer reviewed journals and conference articles that quantified diagnostic delay in monogenic disease. We abstracted the reported diagnostic delay, relevant study design features, and definitions., Results: Our search identified 259 articles quantifying diagnostic delay in 111 distinct monogenetic diseases. Median reported diagnostic delay for all studies collectively in monogenetic diseases was 5.0 years (IQR 2-10). There was major variation in the reported delay within individual monogenetic diseases. Shorter delay was associated with disorders of childhood metabolism, immunity, and development. The majority (67.6%) of articles that studied delay reported an improvement with calendar time. Study design and definitions of delay were highly heterogenous. Three gaps were identified: (1) no studies were conducted in the least developed countries, (2) delay has not been studied for the majority of known, or (3) most prevalent genetic diseases., Conclusion: Heterogenous study design and definitions of diagnostic delay inhibit comparison across studies. Future efforts should focus on standardizing delay measurements, while expanding the research to low-income countries., Competing Interests: Conflict of Interest Lisa Bastarache is a consultant for Galatea Bio. Josh Peterson is a consultant for Natera. All other authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: This methodological study describes a technique for extracting information from de-identified electronic health records (EHRs) to identify occurrences of permanent unilateral hearing loss (UHL) and associated educational comorbidities., Method: This was an exploratory methodological study utilizing approximately 3.3 million de-identified medical records. Structured and unstructured data were extracted using both automated and manual methods. When both methods were available, positive and negative predictive values were calculated to evaluate the utility of using automated methods., Results: We defined a cohort of 471 records that met our criteria of school-age children with permanent UHL and no additional significant disabilities/diagnoses. Fifty-one percent of the children reflected in this cohort had indicators of adverse educational progress, defined as documentation of receiving educational services, speech-language therapy, and/or parental/teacher concern, with 12% of records reflecting overlapping services/concerns. Negative predictive values were generally high and positive predictive values were generally low, suggesting automated searches are useful for excluding factors of interest, but not finding them., Conclusions: This study demonstrates the feasibility of using EHRs in examining UHL in school-age children. By restricting our cohort to individuals who were seen in audiology clinic, we were able to capture variables such as educational difficulty that are not routinely ascertained in medical contexts. The proportion of children in this cohort demonstrating a marker of adverse educational progress is consistent with numerous prior observational studies, thus providing validity to this ascertainment approach. We describe challenges encountered in creating this cohort and detail our hybrid approach to ascertaining key variables accurately.

Purpose: Phenotyping is critical for informing rare disease diagnosis and treatment, but disease phenotypes are often embedded in unstructured text. While natural language processing (NLP) can automate extraction, a major bottleneck is developing annotated corpora. Recently, prompt learning with large language models (LLMs) has been shown to lead to generalizable results without any (zero-shot) or few annotated samples (few-shot), but none have explored this for rare diseases. Our work is the first to study prompt learning for identifying and extracting rare disease phenotypes in the zero- and few-shot settings., Methods: We compared the performance of prompt learning with ChatGPT and fine-tuning with BioClinicalBERT. We engineered novel prompts for ChatGPT to identify and extract rare diseases and their phenotypes (e.g., diseases, symptoms, and signs), established a benchmark for evaluating its performance, and conducted an in-depth error analysis., Results: Overall, fine-tuning BioClinicalBERT resulted in higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.610 in the zero- and few-shot settings, respectively). However, ChatGPT achieved higher accuracy for rare diseases and signs in the one-shot setting (F1 of 0.778 and 0.725). Conversational, sentence-based prompts generally achieved higher accuracy than structured lists., Conclusion: Prompt learning using ChatGPT has the potential to match or outperform fine-tuning BioClinicalBERT at extracting rare diseases and signs with just one annotated sample. Given its accessibility, ChatGPT could be leveraged to extract these entities without relying on a large, annotated corpus. While LLMs can support rare disease phenotyping, researchers should critically evaluate model outputs to ensure phenotyping accuracy., Competing Interests: Competing of interestThe authors declare no competing interests., (© The Author(s) 2024.)

Objective: The All of Us Research Program (All of Us) aims to recruit over a million participants to further precision medicine. Essential to the verification of biobanks is a replication of known associations to establish validity. Here, we evaluated how well All of Us data replicated known cigarette smoking associations., Materials and Methods: We defined smoking exposure as follows: (1) an EHR Smoking exposure that used International Classification of Disease codes; (2) participant provided information (PPI) Ever Smoking; and, (3) PPI Current Smoking, both from the lifestyle survey. We performed a phenome-wide association study (PheWAS) for each smoking exposure measurement type. For each, we compared the effect sizes derived from the PheWAS to published meta-analyses that studied cigarette smoking from PubMed. We defined two levels of replication of meta-analyses: (1) nominally replicated: which required agreement of direction of effect size, and (2) fully replicated: which required overlap of confidence intervals., Results: PheWASes with EHR Smoking, PPI Ever Smoking, and PPI Current Smoking revealed 736, 492, and 639 phenome-wide significant associations, respectively. We identified 165 meta-analyses representing 99 distinct phenotypes that could be matched to EHR phenotypes. At P < .05, 74 were nominally replicated and 55 were fully replicated. At P < 2.68 × 10-5 (Bonferroni threshold), 58 were nominally replicated and 40 were fully replicated., Discussion: Most phenotypes found in published meta-analyses associated with smoking were nominally replicated in All of Us. Both survey and EHR definitions for smoking produced similar results., Conclusion: This study demonstrated the feasibility of studying common exposures using All of Us data., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2023.)

Purpose: Automated use of electronic health records may aid in decreasing the diagnostic delay for rare diseases. The phenotype risk score (PheRS) is a weighted aggregate of syndromically related phenotypes that measures the similarity between an individual's conditions and features of a disease. For some diseases, there are individuals without a diagnosis of that disease who have scores similar to diagnosed patients. These individuals may have that disease but not yet be diagnosed., Methods: We calculated the PheRS for cystic fibrosis (CF) for 965,626 subjects in the Vanderbilt University Medical Center electronic health record., Results: Of the 400 subjects with the highest PheRS for CF, 248 (62%) had been diagnosed with CF. Twenty-six of the remaining participants, those who were alive and had DNA available in the linked DNA biobank, underwent clinical review and sequencing analysis of CFTR and SERPINA1. This uncovered a potential diagnosis for 2 subjects, 1 with CF and 1 with alpha-1-antitrypsin deficiency. An additional 7 subjects had pathogenic or likely pathogenic variants, 2 in CFTR and 5 in SERPINA1., Conclusion: These findings may be clinically actionable for the providers caring for these patients. Importantly, this study highlights feasibility and challenges for future implications of this approach., Competing Interests: Conflict of Interest L.B. is a consultant for Galatea Bio. L.B. and J.C.D. receive royalties from Nashville Biosciences for their application of phenome-wide association studies within BioVU. All other authors declare no conflicts of interest., (Copyright © 2023 American College of Medical Genetics and Genomics. All rights reserved.)

Motivation: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure., Results: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research., Availability and Implementation: phecodeX is available at https://github.com/PheWAS/phecodeX., (© The Author(s) 2023. Published by Oxford University Press.)

The contribution of mosaicism to diagnosed genetic disease and presumed de novo variants (DNV) is under investigated. We determined the contribution of mosaic genetic disease (MGD) and diagnosed parental mosaicism (PM) in parents of offspring with reported DNV (in the same variant) in the (1) Undiagnosed Diseases Network (UDN) (N = 1946) and (2) in 12,472 individuals electronic health records (EHR) who underwent genetic testing at an academic medical center. In the UDN, we found 4.51% of diagnosed probands had MGD, and 2.86% of parents of those with DNV exhibited PM. In the EHR, we found 6.03% and 2.99% and (of diagnosed probands) had MGD detected on chromosomal microarray and exome/genome sequencing, respectively. We found 2.34% (of those with a presumed pathogenic DNV) had a parent with PM for the variant. We detected mosaicism (regardless of pathogenicity) in 4.49% of genetic tests performed. We found a broad phenotypic spectrum of MGD with previously unknown phenotypic phenomena. MGD is highly heterogeneous and provides a significant contribution to genetic diseases. Further work is required to improve the diagnosis of MGD and investigate how PM contributes to DNV risk., (© 2023 Wiley Periodicals LLC.)

Nuclear receptor subfamily 2 group F member 2 (NR2F2 or COUP-TF2) encodes a transcription factor which is expressed at high levels during mammalian development. Rare heterozygous Mendelian variants in NR2F2 were initially identified in individuals with congenital heart disease (CHD), then subsequently in cohorts of congenital diaphragmatic hernia (CDH) and 46,XX ovotesticular disorders/differences of sexual development (DSD); however, the phenotypic spectrum associated with pathogenic variants in NR2F2 remains poorly characterized. Currently, less than 40 individuals with heterozygous pathogenic variants in NR2F2 have been reported. Here, we review the clinical and molecular details of 17 previously unreported individuals with rare heterozygous NR2F2 variants, the majority of which were de novo. Clinical features were variable, including intrauterine growth restriction (IUGR), CHD, CDH, genital anomalies, DSD, developmental delays, hypotonia, feeding difficulties, failure to thrive, congenital and acquired microcephaly, dysmorphic facial features, renal failure, hearing loss, strabismus, asplenia, and vascular malformations, thus expanding the phenotypic spectrum associated with NR2F2 variants. The variants seen were predicted loss of function, including a nonsense variant inherited from a mildly affected mosaic mother, missense and a large deletion including the NR2F2 gene. Our study presents evidence for rare, heterozygous NR2F2 variants causing a highly variable syndrome of congenital anomalies, commonly associated with heart defects, developmental delays/intellectual disability, dysmorphic features, feeding difficulties, hypotonia, and genital anomalies. Based on the new and previous cases, we provide clinical recommendations for evaluating individuals diagnosed with an NR2F2-associated disorder., (© 2023. The Author(s), under exclusive licence to European Society of Human Genetics.)

Purpose: To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR)., Methods: We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders., Results: We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10 -4 , Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results., Conclusion: Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage., Competing Interests: Conflict of Interest Lisa Bastarache is a consultant for Galatea Bio. Josh Peterson is a consultant for Natera. Rory J. Tinker has provided ad hoc consulting to Gerson Lehrman Group., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results., Competing Interests: Declaration of interests Vanderbilt University Medical Center licensed PheWAS on Vanderbilt’s DNA biobank to Nashville Biosciences. L.B. and J.C.D. receive a portion of those royalty payments. L.B. and R.B.A. are advisors to the UK Biobank. R.B.A. is an advisor to All of Us, the Swiss Personalized Health Network, and the Danish National Genome Center. R.B.A. is an advisor and stockholder of Personalis, as well as a stockholder of 23andme, and is a paid advisor to Myome, Invitae, and BridgeBio., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)