Advancing genomics to improve health equity.

Health equity is the state in which everyone has fair and just opportunities to attain their highest level of health. The field of human genomics has fallen short in increasing health equity, largely because the diversity of the human population has been inadequately reflected among participants of genomics research. This lack of diversity leads to disparities that can have scientific and clinical consequences. Achieving health equity related to genomics will require greater effort in addressing inequities within the field. As part of the commitment of the National Human Genome Research Institute (NHGRI) to advancing health equity, it convened experts in genomics and health equity research to make recommendations and performed a review of current literature to identify the landscape of gaps and opportunities at the interface between human genomics and health equity research. This Perspective describes these findings and examines health equity within the context of human genomics and genomic medicine.

To boldly go: Unpacking the NHGRI's bold predictions for human genomics by 2030.

The 2020 strategic vision for human genomics, written by the National Human Genome Research Institute (NHGRI), was punctuated by a set of provocatively audacious "bold predictions for human genomics by 2030." Starting here, these will be unpacked and discussed in an upcoming series in the American Journal of Human Genetics.

The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

The Dockstore: enhancing a community platform for sharing reproducible and accessible computational protocols.

Dockstore (https://dockstore.org/) is an open source platform for publishing, sharing, and finding bioinformatics tools and workflows. The platform has facilitated large-scale biomedical research collaborations by using cloud technologies to increase the Findability, Accessibility, Interoperability and Reusability (FAIR) of computational resources, thereby promoting the reproducibility of complex bioinformatics analyses. Dockstore supports a variety of source repositories, analysis frameworks, and language technologies to provide a seamless publishing platform for authors to create a centralized catalogue of scientific software. The ready-to-use packaging of hundreds of tools and workflows, combined with the implementation of interoperability standards, enables users to launch analyses across multiple environments. Dockstore is widely used, more than twenty-five high-profile organizations share analysis collections through the platform in a variety of workflow languages, including the Broad Institute's GATK best practice and COVID-19 workflows (WDL), nf-core workflows (Nextflow), the Intergalactic Workflow Commission tools (Galaxy), and workflows from Seven Bridges (CWL) to highlight just a few. Here we describe the improvements made over the last four years, including the expansion of system integrations supporting authors, the addition of collaboration features and analysis platform integrations supporting users, and other enhancements that improve the overall scientific reproducibility of Dockstore content.

The genomics workforce must become more diverse: a strategic imperative.

The National Human Genome Research Institute (NHGRI) recently published a new strategic vision for the future of human genomics, the product of an extensive, multi-year engagement with numerous research, medical, educational, and public communities. The theme of this 2020 vision-The Forefront of Genomics-reflects NHGRI's critical role in providing responsible stewardship of the field of human genomics, especially as genomic methods and approaches become increasingly disseminated throughout biomedicine. Embracing that role, the new NHGRI strategic vision features a set of guiding principles and values that provide an ethical and moral framework for the field. One principle emphasizes the need to champion a diverse genomics workforce because "the promise of genomics cannot be fully achieved without attracting, developing, and retaining a diverse workforce, which includes individuals from groups that are currently underrepresented in the genomics enterprise." To build on the remarkable metamorphosis of the field over the last three decades, enhancing the diversity of the genomics workforce must be embraced as an urgent priority. Toward that end, NHGRI recently developed an "action agenda" for training, employing, and retaining a genomics workforce that reflects the diversity of the US population.

The NHGRI Short Course in Genomics: energizing genetics and genomics education in classrooms through direct engagement between educators and scientists.

PURPOSE: The National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH) recognizes an urgent need for educator resources on cutting edge scientific topics due to increased public interest in genetics and genomics. We developed a Short Course in Genomics ("Short Course") to inspire new teaching materials through collaborative course development sessions and lectures, to expand access to cutting edge scientific information, and to provide a framework to consider when crafting new coursework related to scientific education. METHODS: We compared publicly available participant data from 2015 to 2019 with data from the National Center for Education Statistics to assess our progress in serving diverse educator and student populations. We also evaluated course agendas and interviewed participants and instructors. RESULTS: Middle school, high school, community college, and tribal college course attendees from the last five years were more likely to teach students from diverse communities underrepresented in science, technology, engineering, and mathematics (STEM). Both attendees and Short Course instructors emphasized the importance of bidirectional learning through interactive curriculum development. CONCLUSION: This course has the potential to facilitate the engagement of educators and students at all levels, recruit and maintain a diverse STEM workforce, and improve genomic literacy and future health decision-making.

Strategic vision for improving human health at The Forefront of Genomics.

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.

Facilitating phenotype transfer using a common data model.

BACKGROUND: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. METHODS: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. RESULTS: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. CONCLUSION: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

Establishing the Genomic Knowledge Matrix for Nursing Science.

PURPOSE: To establish the knowledge needed to integrate the multiple branches of omics into nursing research to accelerate achieving the research recommendations of the Genomic Nursing Science Blueprint. METHODS: The creation of the Genomic Knowledge Matrix occurred in three phases. In phase 1, the Omics Nursing Science and Education Network (ONSEN) Education Workgroup completed an evidence, bioinformatics, and technology review to inform the components of the Matrix. The ONSEN Advisory Panel then reviewed and integrated revisions. Phase 3 solicited targeted public comment focused on education and research experts, and applicable revisions were made. FINDINGS: The Genomic Knowledge Matrix establishes the following content areas: cellular and molecular biology, system physiology, microbiology, and translational bioinformatics as the minimum required preparation for nurse scientists to understand omics and to integrate this knowledge into research. The Matrix also establishes levels of understanding needed to function based on the role of the nurse scientist. CONCLUSIONS: The Genomic Knowledge Matrix addresses knowledge important for nurse scientists to integrate genomics into their research. Building on prior recommendations and existing genomic competencies, the Matrix was designed to present key knowledge elements critical to understand omics that underpin health and disease. Knowledge depth varies based on the research role. CLINICAL RELEVANCE: The Genomic Knowledge Matrix provides the vital guidance for training nurse scientists in the integration of genomics. The flexibility of the Matrix also provides guidance to inform fundamental genomic content needed in core science content in undergraduate and graduate level nursing curricula.

Normative and conceptual ELSI research: what it is, and why it's important.

The Ethical, Legal, and Social Implications (ELSI) Research Program of the National Human Genome Research Institute sponsors research examining ethical, legal, and social issues arising in the context of genetics/genomics. The ELSI Program endorses an understanding of research not as the sole province of empirical study, but instead as systematic study or inquiry, of which there are many types and methods. ELSI research employs both empirical and nonempirical methods. Because the latter remain relatively unfamiliar to biomedical and translational scientists, this paper seeks to elucidate the relationship between empirical and nonempirical methods in ELSI research. It pays particular attention to the research questions and methods of normative and conceptual research, which examine questions of value and meaning, respectively. To illustrate the distinct but interrelated roles of empirical and nonempirical methods in ELSI research, including normative and conceptual research, the paper demonstrates how a range of methods may be employed both to examine the evolution of the concept of incidental findings (including the recent step toward terming them 'secondary findings'), and to address the normative question of how genomic researchers and clinicians should manage incidental such findings.

Opportunities to implement a sustainable genomic medicine program: lessons learned from the IGNITE Network.

PURPOSE: While there is growing scientific evidence for and significant advances in the use of genomic technologies in medicine, there is a significant lag in the clinical adoption and sustainability of genomic medicine. Here we describe the findings from the National Human Genome Research Institute's (NHGRI) Implementing GeNomics In pracTicE (IGNITE) Network in identifying key constructs, opportunities, and challenges associated with driving sustainability of genomic medicine in clinical practice. METHODS: Network members and affiliates were surveyed to identify key drivers associated with implementing and sustaining a genomic medicine program. Tallied results were used to develop and weigh key constructs/drivers required to support sustainability of genomic medicine programs. RESULTS: The top three driver-stakeholder dyads were (1) genomic training for providers, (2) genomic clinical decision support (CDS) tools embedded in the electronic health record (EHR), and (3) third party reimbursement for genomic testing. CONCLUSION: Priorities may differ depending on healthcare systems when comparing the current state of key drivers versus projected needs for supporting genomic medicine sustainability. Thus we provide gap-filling guidance based on IGNITE members' experiences. Although results are limited to findings from the IGNITE network, their implementation, scientific, and clinical experience may be used as a road map by others considering implementing genomic medicine programs.

Comprehensive identification of pleiotropic loci for body fat distribution using the NHGRI-EBI Catalog of published genome-wide association studies.

We conducted a hypothesis-free cross-trait analysis for waist-to-hip ratio adjusted for body mass index (WHRadjBMI ) loci derived through genome-wide association studies (GWAS). Summary statistics from published GWAS were used to capture all WHRadjBMI single-nucleotide polymorphisms (SNPs), and their proxy SNPs were identified. These SNPs were used to extract cross-trait associations between WHRadjBMI SNPs and other traits through the NHGRI-EBI GWAS Catalog. Pathway analysis was conducted for pleiotropic WHRadjBMI SNPs. We found 160 WHRadjBMI SNPs and 3675 proxy SNPs. Cross-trait analysis identified 239 associations, of which 100 were for obesity traits. The remaining 139 associations were filtered down to 101 unique linkage disequilibrium block associations, which were grouped into 13 categories: lipids, red blood cell traits, white blood cell counts, inflammatory markers and autoimmune diseases, type 2 diabetes-related traits, adiponectin, cancers, blood pressure, height, neuropsychiatric disorders, electrocardiography changes, urea measurement, and others. The highest number of cross-trait associations were found for triglycerides (n = 10), high-density lipoprotein cholesterol (n = 9), and reticulocyte counts (n = 8). Pathway analysis for WHRadjBMI pleiotropic SNPs found immune function pathways as the top canonical pathways. Results from our original methodology indicate a novel genetic association between WHRadjBMI and reticulocyte counts and highlight the pleiotropy between abdominal obesity, immune pathways, and other traits.

Development of a Genomic Data Flow Framework: Results of a Survey Administered to NIH-NHGRI IGNITE and eMERGE Consortia Participants.

Precision health's more individualized molecular approach will enrich our understanding of disease etiology and patient outcomes. Universal implementation of precision health will not be feasible, however, until there is much greater automation of processes related to genomic data transmission, transformation, and interpretation. In this paper, we describe a framework for genomic data flow developed by the Clinical Informatics Work Group of the NIH National Human Genome Research Institute (NHGRI) IGNITE Network consortium. We subsequently report the results of a genomic data flow survey administered to sites funded by NIH-NHGRI for large scale genomic medicine implementations. Finally, we discuss insights and challenges identified through these survey results as they relate to both the current and a desirable future state of genomic data flow.

The Clinical Sequencing Evidence-Generating Research Consortium: Integrating Genomic Sequencing in Diverse and Medically Underserved Populations.

The Clinical Sequencing Evidence-Generating Research (CSER) consortium, now in its second funding cycle, is investigating the effectiveness of integrating genomic (exome or genome) sequencing into the clinical care of diverse and medically underserved individuals in a variety of healthcare settings and disease states. The consortium comprises a coordinating center, six funded extramural clinical projects, and an ongoing National Human Genome Research Institute (NHGRI) intramural project. Collectively, these projects aim to enroll and sequence over 6,100 participants in four years. At least 60% of participants will be of non-European ancestry or from underserved settings, with the goal of diversifying the populations that are providing an evidence base for genomic medicine. Five of the six clinical projects are enrolling pediatric patients with various phenotypes. One of these five projects is also enrolling couples whose fetus has a structural anomaly, and the sixth project is enrolling adults at risk for hereditary cancer. The ongoing NHGRI intramural project has enrolled primarily healthy adults. Goals of the consortium include assessing the clinical utility of genomic sequencing, exploring medical follow up and cascade testing of relatives, and evaluating patient-provider-laboratory level interactions that influence the use of this technology. The findings from the CSER consortium will offer patients, healthcare systems, and policymakers a clearer understanding of the opportunities and challenges of providing genomic medicine in diverse populations and settings, and contribute evidence toward developing best practices for the delivery of clinically useful and cost-effective genomic sequencing in diverse healthcare settings.