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.

The motivation and process for developing a consortium-wide time and motion study to estimate resource implications of innovations in the use of genome sequencing to inform patient care.

Costs of implementing genomic testing innovations extend beyond the cost of sequencing, affecting personnel and infrastructure for which little data are available. We developed a time and motion (T&M) study within the Clinical Sequencing Evidence-Generating Research (CSER) consortium to address this gap, and herein describe challenges of conducting T&M studies within a research consortium and the approaches we developed to overcome them. CSER investigators created a subgroup to carry out the T&M study (authors). We describe logistical and administrative challenges associated with resource use data collection across heterogeneous projects conducted in real-world clinical settings, and our solutions for completing this study and harmonizing data across projects. We delineate processes for feasible data collection on workflow, personnel, and resources required to deliver genetic testing innovations in each CSER project. A critical early step involved developing detailed project-specific process flow diagrams of innovation implementation in projects' clinical settings. Analyzing diagrams across sites, we identified common process-step themes, used to organize project-specific data collection and cross-project analysis. Given the heterogeneity of innovations, study design, and workflows, which affect resources required to deliver genetic testing innovations, flexibility was necessary to harmonize data collection. Despite its challenges, this heterogeneity provides rich insights about variation in clinical processes and resource implications for implementing genetic testing innovations.

Parent-Reported Clinical Utility of Pediatric Genomic Sequencing.

BACKGROUND AND OBJECTIVES: Genomic sequencing (GS) is increasingly used for diagnostic evaluation, yet follow-up care is not well understood. We assessed clinicians' recommendations after GS, parent-reported follow-up, and actions parents initiated in response to learning their child's GS results. METHODS: We surveyed parents of children who received GS through the Clinical Sequencing Evidence Generating Research consortium ∼5 to 7 months after return of results. We compared the proportion of parents who reported discussing their child's result with a clinician, clinicians' recommendations, and parents' follow-up actions by GS result type using χ2 tests. RESULTS: A total of 1188 respondents completed survey measures on recommended medical actions (n = 1187) and/or parent-initiated actions (n = 913). Most parents who completed recommended medical actions questions (n = 833, 70.3%) reported having discussed their child's GS results with clinicians. Clinicians made recommendations to change current care for patients with positive GS results (n = 79, 39.1%) more frequently than for those with inconclusive (n = 31, 12.4%) or negative results (n = 44, 11.9%; P < .001). Many parents discussed (n = 152 completed, n = 135 planned) implications of GS results for future pregnancies with a clinician. Aside from clinical recommendations, 13.0% (n = 119) of parents initiated changes to their child's health or lifestyle. CONCLUSIONS: In diverse pediatric clinical contexts, GS results can lead to recommendations for follow-up care, but they likely do not prompt large increases in the quantity of care received.

Lessons learned while starting multi-institutional genetics research in diverse populations: A report from the Clinical Sequencing Evidence-Generating Research (CSER) consortium.

BACKGROUND: Increasing diversity in clinical trial participation is necessary to improve health outcomes and requires addressing existing social, structural, and geographic barriers. The Clinical Sequencing Evidence-Generating Research Consortium (CSER) included six research projects to enroll historically underrepresented/underserved (UR/US) populations in clinical genomics research. Delays and project re-designs emerged shortly after work began. Understanding common experiences of these projects may inform future trial implementation. METHODS: Semi-structured interviews with six CSER principal investigators and seven project managers were performed. An interview guide included questions of research/clinical infrastructure, logistics across sites, language, communication, and allocation of grant-related resources. Interviews were recorded, transcribed verbatim; transcripts were analyzed using inductive coding, thematic analysis and consensus building. RESULTS: All projects collaborating with new clinical sub-sites to recruit UR/US populations. Refining trial logistics continued long after enrollment for all projects. Themes of challenges included: sub-site customization for workflow and genetics support, conflicting input from participant advisory groups and approval bodies, developing research personnel, complex data management structures, and external changes (e.g. subcontractors ending contracts) that required redesign. Themes of beneficial lessons included: domains with prior experience were easier, develop project champions at each sub-site, structure communication within the research team, and simplify research design when possible. CONCLUSIONS: The operational aspects of expanding clinical research into novel sub-sites are significant and require investment of time and resources. The themes arising from these interviews suggest priority areas for more quantitative analyses in the future including multi-institutional approval policies and processes, data management structures, and incremental research complexity.

Conducting clinical genomics research during the COVID-19 pandemic: Lessons learned from the CSER consortium experience.

Clinical research studies have navigated many changes throughout the COVID-19 pandemic. We sought to describe the pandemic's impact on research operations in the context of a clinical genomics research consortium that aimed to enroll a majority of participants from underrepresented populations. We interviewed (July to November 2020) and surveyed (May to August 2021) representatives of six projects in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, which studies the implementation of genome sequencing in the clinical care of patients from populations that are underrepresented in genomics research or are medically underserved. Questions focused on COVID's impact on participant recruitment, enrollment, and engagement, and the transition to teleresearch. Responses were combined and thematically analyzed. Projects described factors at the project, institutional, and community levels that affected their experiences. Project factors included the project's progress at the pandemic's onset, the urgency of in-person clinical care for the disease being studied, and the degree to which teleresearch procedures were already incorporated. Institutional and community factors included institutional guidance for research and clinical care and the burden of COVID on the local community. Overall, being responsive to community experiences and values was essential to how CSER navigated evolving challenges during the COVID-19 pandemic.

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.

Cost-effectiveness frameworks for comparing genome and exome sequencing versus conventional diagnostic pathways: A scoping review and recommended methods.

PURPOSE: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. METHODS: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. RESULTS: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. CONCLUSION: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.

Lessons learned and recommendations for data coordination in collaborative research: The CSER consortium experience.

Integrating data across heterogeneous research environments is a key challenge in multi-site, collaborative research projects. While it is important to allow for natural variation in data collection protocols across research sites, it is also important to achieve interoperability between datasets in order to reap the full benefits of collaborative work. However, there are few standards to guide the data coordination process from project conception to completion. In this paper, we describe the experiences of the Clinical Sequence Evidence-Generating Research (CSER) consortium Data Coordinating Center (DCC), which coordinated harmonized survey and genomic sequencing data from seven clinical research sites from 2020 to 2022. Using input from multiple consortium working groups and from CSER leadership, we first identify 14 lessons learned from CSER in the categories of communication, harmonization, informatics, compliance, and analytics. We then distill these lessons learned into 11 recommendations for future research consortia in the areas of planning, communication, informatics, and analytics. We recommend that planning and budgeting for data coordination activities occur as early as possible during consortium conceptualization and development to minimize downstream complications. We also find that clear, reciprocal, and continuous communication between consortium stakeholders and the DCC is equally important to maintaining a secure and centralized informatics ecosystem for pooling data. Finally, we discuss the importance of actively interrogating current approaches to data governance, particularly for research studies that straddle the research-clinical divide.

Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

US private payers' perspectives on insurance coverage for genome sequencing versus exome sequencing: A study by the Clinical Sequencing Evidence-Generating Research Consortium (CSER).

PURPOSE: There is limited payer coverage for genome sequencing (GS) relative to exome sequencing (ES) in the U.S. Our objective was to assess payers' considerations for coverage of GS versus coverage of ES and requirements payers have for coverage of GS. The study was conducted by the NIH-funded Clinical Sequencing Evidence-Generating Research Consortium (CSER). METHODS: We conducted semi-structured interviews with representatives of private payer organizations (payers, N = 12) on considerations and evidentiary and other needs for coverage of GS and ES. Data were analyzed using thematic analysis. RESULTS: We described four categories of findings and solutions: demonstrated merits of GS versus ES, enhanced methods for evidence generation, consistent laboratory processes/sequencing methods, and enhanced implementation/care delivery. Payers see advantages to GS vs. ES and are open to broader GS coverage but need more proof of these advantages to consider them in coverage decision-making. Next steps include establishing evidence of benefits in specific clinical scenarios, developing quality standards, ensuring transparency of laboratory methods, developing clinical centers of excellence, and incorporating the role of genetic professionals. CONCLUSION: By comparing coverage considerations for GS and ES, we identified a path forward for coverage of GS. Future research should explicitly address payers' conditions for coverage.

Examining access to care in clinical genomic research and medicine: Experiences from the CSER Consortium.

INTRODUCTION: Ensuring equitable access to health care is a widely agreed-upon goal in medicine, yet access to care is a multidimensional concept that is difficult to measure. Although frameworks exist to evaluate access to care generally, the concept of "access to genomic medicine" is largely unexplored and a clear framework for studying and addressing major dimensions is lacking. METHODS: Comprised of seven clinical genomic research projects, the Clinical Sequencing Evidence-Generating Research consortium (CSER) presented opportunities to examine access to genomic medicine across diverse contexts. CSER emphasized engaging historically underrepresented and/or underserved populations. We used descriptive analysis of CSER participant survey data and qualitative case studies to explore anticipated and encountered access barriers and interventions to address them. RESULTS: CSER's enrolled population was largely lower income and racially and ethnically diverse, with many Spanish-preferring individuals. In surveys, less than a fifth (18.7%) of participants reported experiencing barriers to care. However, CSER project case studies revealed a more nuanced picture that highlighted the blurred boundary between access to genomic research and clinical care. Drawing on insights from CSER, we build on an existing framework to characterize the concept and dimensions of access to genomic medicine along with associated measures and improvement strategies. CONCLUSIONS: Our findings support adopting a broad conceptualization of access to care encompassing multiple dimensions, using mixed methods to study access issues, and investing in innovative improvement strategies. This conceptualization may inform clinical translation of other cutting-edge technologies and contribute to the promotion of equitable, effective, and efficient access to genomic medicine.

Sequencing-based genome-wide association studies reporting standards.

Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.

Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

BACKGROUND: Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. RESULTS: We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. CONCLUSIONS: Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

Workshop proceedings: GWAS summary statistics standards and sharing.

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.

Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies.

Harmonization of variant pathogenicity classification across laboratories is important for advancing clinical genomics. The two CLIA-accredited Electronic Medical Record and Genomics Network sequencing centers and the six CLIA-accredited laboratories and one research laboratory performing genome or exome sequencing in the Clinical Sequencing Evidence-Generating Research Consortium collaborated to explore current sources of discordance in classification. Eight laboratories each submitted 20 classified variants in the ACMG secondary finding v.2.0 genes. After removing duplicates, each of the 158 variants was annotated and independently classified by two additional laboratories using the ACMG-AMP guidelines. Overall concordance across three laboratories was assessed and discordant variants were reviewed via teleconference and email. The submitted variant set included 28 P/LP variants, 96 VUS, and 34 LB/B variants, mostly in cancer (40%) and cardiac (27%) risk genes. Eighty-six (54%) variants reached complete five-category (i.e., P, LP, VUS, LB, B) concordance, and 17 (11%) had a discordance that could affect clinical recommendations (P/LP versus VUS/LB/B). 21% and 63% of variants submitted as P and LP, respectively, were discordant with VUS. Of the 54 originally discordant variants that underwent further review, 32 reached agreement, for a post-review concordance rate of 84% (118/140 variants). This project provides an updated estimate of variant concordance, identifies considerations for LP classified variants, and highlights ongoing sources of discordance. Continued and increased sharing of variant classifications and evidence across laboratories, and the ongoing work of ClinGen to provide general as well as gene- and disease-specific guidance, will lead to continued increases in concordance.

Alaska Native genomic research: perspectives from Alaska Native leaders, federal staff, and biomedical researchers.

Meaningful engagement of Alaska Native (AN) tribes and tribal health organizations is essential in the conduct of socially responsible and ethical research. As genomics becomes increasingly important to advancements in medicine, there is a risk that populations not meaningfully included in genomic research will not benefit from the outcomes of that research. AN people have historically been underrepresented in biomedical research; AN underrepresentation in genomics research is compounded by mistrust based on past abuses, concerns about privacy and data ownership, and cultural considerations specific to this type of research. Working together, the National Human Genome Research Institute and two Alaska Native health organizations, Southcentral Foundation and the Alaska Native Health Board, cosponsored a workshop in July 2018 to engage key stakeholders in discussion, strengthen relationships, and facilitate partnership and consideration of participation of AN people in community-driven biomedical and genomic research. AN priorities related to translation of genomics research to health and health care, return of genomic results, design of research studies, and data sharing were discussed. This report summarizes the perspectives that emerged from the dialogue and offers considerations for effective and socially responsible genomic research partnerships with AN communities.

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

BACKGROUND: We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. METHODS: We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test. RESULTS: We identified 6 novels (CD36, PITX2, EMB, ZNF592, YPEL2, and BC043580) and 87 known loci (adaptive sum of powered score test P<5×10-9). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci. CONCLUSIONS: Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures.

Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.