Toward a fine-scale population health monitoring system.

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood.

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment.

Discovering mechanisms of human genetic variation and controlling cell states at scale.

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Ensuring best practice in genomics education: A scoping review of genomics education needs assessments and evaluations.

A health workforce capable of implementing genomic medicine requires effective genomics education. Genomics education interventions developed for health professions over the last two decades, and their impact, are variably described in the literature. To inform an evaluation framework for genomics education, we undertook an exploratory scoping review of published needs assessments for, and/or evaluations of, genomics education interventions for health professionals from 2000 to 2023. We retrieved and screened 4,659 records across the two searches with 363 being selected for full-text review and consideration by an interdisciplinary working group. 104 articles were selected for inclusion in the review-60 needs assessments, 52 genomics education evaluations, and eight describing both. Included articles spanned all years and described education interventions in over 30 countries. Target audiences included medical specialists, nurses/midwives, and/or allied health professionals. Evaluation questions, outcomes, and measures were extracted, categorized, and tabulated to iteratively compare measures across stages of genomics education evaluation: planning (pre-implementation), development and delivery (implementation), and impact (immediate, intermediate, or long-term outcomes). They are presented here along with descriptions of study designs. We document the wide variability in evaluation approaches and terminology used to define measures and note that few articles considered downstream (long-term) outcomes of genomics education interventions. Alongside the evaluation framework for genomics education, results from this scoping review form part of a toolkit to help educators to undertake rigorous genomics evaluation that is fit for purpose and can contribute to the growing evidence base of the contribution of genomics education in implementation strategies for genomic medicine.

Ensuring best practice in genomics education: A theory- and empirically informed evaluation framework.

Implementation of genomic medicine into healthcare requires a workforce educated through effective educational approaches. However, ascertaining the impact of genomics education activities or resources is limited by a lack of evaluation and inconsistent descriptions in the literature. We aim to support those developing genomics education to consider how best to capture evaluation data that demonstrate program outcomes and effectiveness within scope. Here, we present an evaluation framework that is adaptable to multiple settings for use by genomics educators with or without education or evaluation backgrounds. The framework was developed as part of a broader program supporting genomic research translation coordinated by the Australian Genomics consortium. We detail our mixed-methods approach involving an expert workshop, literature review and iterative expert input to reach consensus and synthesis of a new evaluation framework for genomics education. The resulting theory-informed and evidence-based framework encompasses evaluation across all stages of education program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences.

The NYCKidSeq randomized controlled trial: Impact of GUÍA digitally enhanced genetic results disclosure in diverse families.

Digital solutions are needed to support rapid increases in the application of genetic/genomic tests (GTs) in diverse clinical settings and patient populations. We developed GUÍA, a bilingual digital application that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GTs. The trial evaluated GUÍA's impact on understanding the GT results by randomizing families to results disclosure genetic counseling with GUÍA (intervention) or standard of care (SOC). Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6 months later. Survey measures assessed the primary study outcomes of participants' perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. The analysis included 551 diverse participants, 270 in the GUÍA arm and 281 in SOC. Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR = 2.8, CI[1.004, 7.617], p = 0.049) and maintained higher objective understanding over time (OR = 1.1, CI[1.004, 1.127], p = 0.038) compared to SOC. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR = 3.9, CI[1.603, 9.254], p = 0.003), confidence (OR = 2.7, CI[1.021, 7.277], p = 0.046), and objective understanding (OR = 1.1, CI[1.009, 1.212], p = 0.032) compared to SOC. This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions and builds a case for utilizing GUÍA to deliver complex results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics.

Population Screening in Health Systems.

Applications of genomics to population screening are expanding in the United States and internationally. Many of these programs are being implemented in the context of healthcare systems, mostly in a clinical research setting, but there are some emerging examples of clinical models. This review examines these genomic population screening programs to identify common features and differences in screened conditions, genomic technology employed, approach to results disclosure, health outcomes, financial models, and sustainability. The diversity of approaches provides opportunities to learn and better understand the optimal approach to implementation based on the contextual setting.

Establishing the Medical Actionability of Genomic Variants.

Actionability is an important concept in medicine that does not have a well-accepted standard definition, nor is there a general consensus on how to establish it. Medical actionability is often conflated with clinical utility, a related but distinct concept. This lack of clarity contributes to practice variation and inconsistent coverage decisions in genomic medicine, leading to the potential for systematic bias in the use of evidence-based interventions. We clarify how medical actionability and clinical utility are distinct and then discuss the spectrum of actionability, including benefits for the person, the family, and society. We also describe applications across the life course, including prediction, diagnosis, and treatment. Current challenges in assessing the medical actionability of identified genomic variants include gaps in the evidence, limited contexts with practice guidelines, and subjective aspects of medical actionability. A standardized and authoritative assessment of medical actionability is critical to implementing genomic medicine in a fashion that improves population health outcomes and reduces health disparities.

Equity in Genomic Medicine.

Since the completion of the Human Genome Project, considerable progress has been made in translating knowledge about the genetic basis of disease risk and treatment response into clinical services and public health interventions that have greater precision. It is anticipated that more precision approaches to early detection, prevention, and treatment will be developed and will enhance equity in healthcare and outcomes among disparity populations. Reduced access to genomic medicine research, clinical services, and public health interventions has the potential to exacerbate disparities in genomic medicine. The purpose of this article is to describe these challenges to equity in genomic medicine and identify opportunities and future directions for addressing these issues. Efforts are needed to enhance access to genomic medicine research, clinical services, and public health interventions, and additional research that examines the clinical utility of precision medicine among disparity populations should be prioritized to ensure equity in genomic medicine.

Advancing Pharmacogenomics from Single-Gene to Preemptive Testing.

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

Rare genetic variants in genes and loci linked to dominant monogenic developmental disorders cause milder related phenotypes in the general population.

Many rare monogenic diseases are known to be caused by deleterious variants in thousands of genes, however the same variants can also be found in people without the associated clinical phenotypes. The penetrance of these monogenic variants is generally unknown in the wider population, as they are typically identified in small clinical cohorts of affected individuals and families with highly penetrant variants. Here, we investigated the phenotypic effect of rare, potentially deleterious variants in genes and loci where similar variants are known to cause monogenic developmental disorders (DDs) in a large population cohort. We used UK Biobank to investigate phenotypes associated with rare protein-truncating and missense variants in 599 monoallelic DDG2P genes by using whole-exome-sequencing data from ∼200,000 individuals and rare copy-number variants overlapping known DD loci by using SNP-array data from ∼500,000 individuals. We found that individuals with these likely deleterious variants had a mild DD-related phenotype, including lower fluid intelligence, slower reaction times, lower numeric memory scores, and longer pairs matching times compared to the rest of the UK Biobank cohort. They were also shorter, had a higher BMI, and had significant socioeconomic disadvantages: they were less likely to be employed or be able to work and had a lower income and higher deprivation index. Our findings suggest that many genes routinely tested within pediatric genetics have deleterious variants with intermediate penetrance that may cause lifelong sub-clinical phenotypes in the general adult population.

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted "Tissue Risk Assessment of Causality by Expression for variants" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.

Emerging applications of machine learning in genomic medicine and healthcare.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Leveraging non-coding regions to guarantee the accuracy of small-sized panel-based tumor mutational burden estimates.

Accurate estimation of tumor mutational burden (TMB) as a predictor of responsiveness to immune checkpoint inhibitors in gene panel assays requires an adequate panel size. The current calculations of TMB only consider coding regions, while most of gene panel assays interrogate non-coding regions. Leveraging the non-coding regions is a potential solution to address this panel size limitation. However, the impact of including non-coding regions on the accuracy of TMB estimates remains unclear. This study investigated the validity of leveraging non-coding regions to supplement panel size using the OncoGuide NCC Oncopanel System (NOP). The aim of this study was to evaluate test performance against orthogonal assays and the association with responsiveness to immune checkpoint inhibitors was not included in the evaluation. We compared TMB status and values between TMB calculated only from coding regions (NOP-coding) and from both coding and non-coding regions (NOP-overall) using whole exome sequencing (WES) and FoundationOne®CDx (F1CDx) assay. Our findings revealed that NOP-overall significantly improved the overall percent agreement (OPA) with TMB status compared with NOP-coding for both WES (OPA: 96.7% vs. 73.3%, n = 30) and F1CDx (OPA: 90.0% vs. 73.3%). Additionally, the mean difference in TMB values compared with WES was lower for NOP-overall (3.55 [95% CI: 0.98-6.13]) than for NOP-coding (6.22 [95% CI: 3.73-8.70]). These results exemplify the utility of incorporating non-coding regions to maintain accurate TMB estimates in small-sized panels.

Patient survey on cancer genomic medicine in Japan under the national health insurance system.

In Japan, comprehensive genomic profiling (CGP) tests have been reimbursed under the national health care system for solid cancer patients who have finished standard treatment. More than 50,000 patients have taken the test since June 2019. We performed a nation-wide questionnaire survey between March 2021 and July 2022. Questionnaires were sent to 80 designated Cancer Genomic Medicine Hospitals. Of the 933 responses received, 370 (39.7%) were web based and 563 (60.3%) were paper based. Most patients (784, 84%) first learned about CGP tests from healthcare professionals, and 775 (83.1%) gave informed consent to their treating physician. At the time of informed consent, they were most worried about test results not leading to novel treatment (536, 57.4%). On a scale of 0-10, 702 respondents (75.2%) felt that the explanations of the test result were easy to understand (7 or higher). Ninety-one patients (9.8%) started their recommended treatment. Many patients could not receive recommended treatment because no approved drugs or clinical trials were available (102/177, 57.6%). Ninety-eight patients (10.5%) did not wish their findings to be disclosed. Overall satisfaction with the CGP test process was high, with 602 respondents (64.5%) giving a score of 7-10. The major reason for choosing 0-6 was that the CGP test result did not lead to new treatment (217/277, 78.3%). In conclusion, satisfaction with the CGP test process was high. Patients and family members need better access to information. More patients need to be treated with genomically matched therapy.

Utility and Diversity: Challenges for Genomic Medicine.

Genomic information is poised to play an increasing role in clinical care, extending beyond highly penetrant genetic conditions to less penetrant genotypes and common disorders. But with this shift, the question of clinical utility becomes a major challenge. A collaborative effort is necessary to determine the information needed to evaluate different uses of genomic information and then acquire that information. Another challenge must also be addressed if that process is to provide equitable benefits: the lack of diversity of genomic data. Current genomic knowledge comes primarily from populations of European descent, which poses the risk that most of the human population will be shortchanged when health benefits of genomics emerge. These two challenges have defined my career as a geneticist and have taught me that solutions must start with dialogue across disciplinary and social divides.

Assessing sociodemographic and regional disparities in Oncotype DX Genomic Prostate Score uptake.

BACKGROUND: The Oncotype DX Genomic Prostate Score (ODX-GPS) is a gene expression assay that predicts disease aggressiveness. The objective of this study was to identify sociodemographic and regional factors associated with ODX-GPS uptake. METHODS: Data from Surveillance Epidemiology and End Results registries on men with localized prostate cancer with a Gleason score of 3 + 3 or 3 + 4, PSA ≤20 ng/mL, and stage T1c to T2c disease from 2013 through 2017 were linked with ODX-GPS data. Census-tract level neighborhood socioeconomic status (nSES) quintiles were constructed using a composite socioeconomic score. Multivariable logistic regression was used to estimate the associations of ODX-GPS uptake with age at diagnosis, race and ethnicity, nSES, geographic region, insurance type, and marital status, accounting for National Comprehensive Cancer Network risk group, year of diagnosis, and clustering by census tract. RESULTS: Among 111,434 eligible men, 5.5% had ODX-GPS test uptake. Of these, 78.3% were non-Hispanic White, 9.6% were Black, 6.7% were Hispanic, and 3.6% were Asian American. Black men had the lowest odds of ODX-GPS uptake (odds ratio, 0.70; 95% confidence interval [CI], 0.63-0.76). Those in the highest versus lowest quintile of nSES were 1.64 times more likely (95% CI, 1.38-2.94) to have ODX-GPS uptake. The odds of ODX-GPS uptake were statistically significantly higher among men residing in the Northeast, West, and Midwest compared to the South. CONCLUSIONS: Disparities in ODX-GPS uptake by race, ethnicity, nSES, and geographical region were identified. Concerted efforts should be made to ensure that this clinical test is equitably available.

Incorporating familial risk, lifestyle factors, and pharmacogenomic insights into personalized noncommunicable disease (NCD) reports for healthcare funder beneficiaries participating in the Open Genome Project.

INTRODUCTION: An ethics-guided decision-making framework was developed for applying pathology-supported genetic testing, a multifaceted pharmacodiagnostic approach that translates population risk stratification into clinical utility. We introduce this service, supported by the Open Genome Project, which aligns with the beneficence principle in personalized medicine. METHODS: Genetic testing of six noncommunicable disease pathways was conducted as part of a pilot program, benchmarked against a readiness checklist for implementation of genomic medicine in Africa. Patient referral criteria were determined using healthcare funder claims data, employing the Adjusted Clinical Groupers and Resource Utilization Band risk rating structure to identify potential nonresponders to treatment. RESULTS: Three of the 135 doctors (2.2%) invited expressed immediate disinterest in the pilot, while 24 (17.8%) actively participated. Inherited, lifestyle-triggered, and therapy-related pathologies were simultaneously assessed in case reports, with special medical scheme reimbursement tariff codes applied to 25 patient referrals. The findings were used by the participating genetic counselor to select three patients for whole exome sequencing, utilizing a novel, level-up data processing algorithm for adaptive reporting. CONCLUSION: This study demonstrated the implementation of genomics into an evolving workflow for patients with a history of frequent clinic visits. Eliminating the cost barrier provided valuable insights to guide future reimbursement policy decisions.

"It's hard to wait": Provider perspectives on current genomic care in safety-net NICUs.

PURPOSE: Critically ill infants from marginalized populations disproportionately receive care in neonatal intensive care units (NICUs) that lack access to state-of-the-art genomic care, leading to inequitable outcomes. We sought provider perspectives to inform our implementation study (VIGOR) providing rapid genomic sequencing within these settings. METHODS: We conducted semistructured focus groups with neonatal and genetics providers at 6 NICUs at safety-net hospitals, informed by the Promoting Action on Research Implementation in Health Services framework, which incorporates evidence, context, and facilitation domains. We iteratively developed codes and themes until thematic saturation was reached. RESULTS: Regarding evidence, providers felt that genetic testing benefits infants and families. Regarding context, the major barriers identified to genomic care were genetic testing cost, lack of genetics expertise for disclosure and follow-up, and navigating the complexity of selecting and ordering genetic tests. Providers had negative feelings about the current status quo and inequity in genomic care across NICUs. Regarding facilitation, providers felt that a virtual support model such as VIGOR would address major barriers and foster family-centered care and collaboration. CONCLUSION: NICU providers at safety-net hospitals believe that access to state-of-the-art genomic care is critical for optimizing infant outcomes; yet, substantial barriers exist that the VIGOR study may address.

A national education program for rapid genomics in pediatric acute care: Building workforce confidence, competence, and capability.

PURPOSE: To develop and evaluate a scalable national program to build confidence, competence and capability in the use of rapid genomic testing (rGT) in the acute pediatric setting. METHODS: We used theory-informed approaches to design a modular, adaptive program of blended learning aimed at diverse professional groups involved in acute pediatric care. The program comprised 4 online learning modules and an online workshop and was centered on case-based learning. We evaluated the program using the Kirkpatrick 4-level model of training evaluation and report our findings using the Reporting Item Standards for Education and its Evaluation (RISE2) guidelines for genomics education and evaluation. RESULTS: Two hundred and two participants engaged with at least 1 component of the program. Participants self-reported increased confidence in using rGT, (P < .001), and quiz responses objectively demonstrated increased competence (eg, correct responses to a question on pretest counseling increased from 30% to 64%; P < .001). Additionally, their capability in applying genomic principles to simulated clinical cases increased (P < .001), as did their desire to take on more responsibility for performing rGT. The clinical interpretation of more complex test results (such as negative results or variants of uncertain significance) appeared to be more challenging, indicating a need for targeted education in this area. CONCLUSION: The program format was effective in delivering multidisciplinary and wide-scale genomics education in the acute care context. The modular approach we have developed now lends itself to application in other medical specialties or areas of health care.