Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup

OBJECTIVE

Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS

A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS

Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Representing NIH Genetic Test Registry Data in the FHIR Genomic Study Resource

The National Institute of Health (NIH) Genetic Testing Registry (GTR) provides a variety of information about genetic tests such as relevant methods, conditions, and performing laboratories. This study mapped a subset of GTR data to the newly developed HL7®-FHIR® Genomic Study resource. Using open-source tools, a web application was developed to implement data mapping and provides many GTR test records as Genomic Study resources. The developed system demonstrates the feasibility of using open-source tools and the FHIR Genomic Study resource to represent publicly available genetic testing information. This study validates the overall design of the Genomic Study resource and proposes two enhancements to support additional data elements.

Returning integrated genomic risk and clinical recommendations: The eMERGE study

PURPOSE

Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk.

METHODS

To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results.

RESULTS

GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022.

CONCLUSION

Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

Development and application of a computable genotype model in the GA4GH Variation Representation Specification

As the diversity of genomic variation data increases with our growing understanding of the role of variation in health and disease, it is critical to develop standards for precise inter-system exchange of these data for research and clinical applications. The Global Alliance for Genomics and Health (GA4GH) Variation Representation Specification (VRS) meets this need through a technical terminology and information model for disambiguating and concisely representing variation concepts. Here we discuss the recent Genotype model in VRS, which may be used to represent the allelic composition of a genetic locus. We demonstrate the use of the Genotype model and the constituent Haplotype model for the precise and interoperable representation of pharmacogenomic diplotypes, HGVS variants, and VCF records using VRS and discuss how this can be leveraged to enable interoperable exchange and search operations between assayed variation and genomic knowledgebases.

Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index

Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine

MOTIVATION

Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships. In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for 'Genes and Gene Products' and 'Treatments'.

RESULTS

Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18 653 sentences that contained both 'Genes and Gene Products' and 'Treatments', 3011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829) and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments. In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources

OBJECTIVE

The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.

MATERIALS AND METHODS

Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy.

RESULTS

Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address.

DISCUSSION

Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Abstract 1177: Introduction of the GA4GH Variation Representation Specification (VRS) and supporting tools for discovery and exchange of clinical genomic and cytogenomic knowledge in cancers

Precision oncology is the practice of interpreting the clinical significance of observed molecular changes in patient neoplasms, potentially impacting medical decision making and care. This process is labor-intensive and (among other challenges) involves accurately translating between variation representation conventions from one resource to the next. For example, differences in representations of Copy Number Variation (CNV) from genomic regions, cytogenomic bands, or gene features create challenges in knowledge matching due to lack of standards covering all of these modalities of observed variation.The Global Alliance for Genomics and Health (GA4GH; ga4gh.org) is an international collaborative of genomic data sharing initiatives (Driver Projects) developing genomic data sharing standards within a human rights framework. GA4GH recently published the Variation Representation Specification (VRS; pronounced “verse”), a standard for the computational representation of biomolecular variation. VRS is a terminology, schema, and associated conventions for creating uniquely identifiable and federatable representations of molecular variation. VRS has formal data classes well-suited to differentiating between variation on a single molecule (e.g. tandem duplications) from variation measured at a systemic level (e.g. genome-wide copy number variation). In addition to molecular sequence variation, VRS also supports variation on cytogenetic coordinate systems and genes, making it well-suited to representing variation associated with cancer biomarkers.We demonstrate the use of VRS to model reported gene-associated CNVs from the AACR Project GENIE cohort, to aid in the computational discovery of evidence from clinico-genomic knowledgebases with genomic or cytogenomic CNV representations. We highlight the use case of knowledge matching to the Atlas of Genetics and Cytogenetics in Oncology and Haematology (“the Atlas”; atlasgeneticsoncology.org), a cytogenetics resource historically driven by user website navigation. Using VRS search tools we developed for the Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) GA4GH Driver Project, we found that 64% of GENIE samples with reported CNVs matched clinically relevant knowledge in the Atlas. This work was enabled by programmatic search tools leveraging standard VRS object structures, demonstrating how VRS enables collection of real-world evidence across more resources without manual interpretation or custom normalization methods. We conclude with a survey of open-source tools supporting this analysis as well as search of other clinico-genomic knowledgebases with VRS, including CIViC (civicdb.org), BRCA Exchange (brcaexchange.org), and the Molecular Oncology Almanac (moalmanac.org).

Citation Format: Matthew Cannon, Kori Kuzma, James Stevenson, Jiachen Liu, Colin O'Sullivan, Bimal P. Chaudhari, Matthew Brush, Robert R. Freimuth, Tristan Nelson, Michael Baudis, Obi L. Griffith, Malachi Griffith, Lawrence Babb, Melissa S. Cline, Xuelu Liu, Brian Walsh, Alex H. Wagner. Introduction of the GA4GH Variation Representation Specification (VRS) and supporting tools for discovery and exchange of clinical genomic and cytogenomic knowledge in cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1177.

Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study

PURPOSE

The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping.

METHODS

Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record.

RESULTS

For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping.

CONCLUSION

Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.

Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitions

Introduction

Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness.

Methods

This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm.

Results

We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites.

Discussion and conclusion

Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01759-z.

The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification

Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced "verse"), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH).

GA4GH: International policies and standards for data sharing across genomic research and healthcare

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Abstract 449: A standard operating procedure for the curation of gene fusions

Despite the well-established role of recurrent gene fusions as oncogenic drivers, current practices for characterizing and interpreting gene fusion events in clinical testing and in biomedical literature are inconsistent. From the conceptual definition of gene fusions to the salient elements that characterize these alterations, a lack of community-driven standards for the curation of gene fusions has resulted in a disparate landscape of fusion representations and supporting tools. Consequently, the evidence-based clinical evaluation of gene fusions requires extensive expert review for accurate interpretation of observed gene fusions with respect to putative evidence from biomedical literature. Furthermore, the lack of these standards inhibits the interoperability of tools, resources, and pipelines - impeding data sharing and downstream utility.To address these challenges, a cross-consortia initiative between the Variant Interpretation for Cancer Consortium and ClinGen was formed to develop a standard operating procedure (SOP) for the curation of gene fusions. The SOP is under development by an international and diverse set of experts in the representation, detection, and clinical interpretation of gene fusions. Participating stakeholders across academic, government, and industry sectors showcased challenges and solutions, and participated in community surveys and discussions to define and develop the SOP for this diverse class of alterations.An initial result of this effort was the precise molecular definition of genomic events and features constituting gene fusions. We distinguish these from similar but distinct classes of structural alterations through clinically-relevant examples. Next, we discuss our findings on community practices around the description and evaluation of gene fusions. We provide our recommendations for characterization and representation of gene fusions from these practices, and compare these recommendations to existing variant representation standards and formats (e.g. HGVS variant nomenclature). We also discuss the concurrent application of formats for standardized human- and machine-readable representations of gene fusion events.We conclude with discussion of the salient elements to enable rapid, scalable, and consistent evaluation of fusions curated from the biomedical literature. Recommendations are provided for the standardized capture of these elements to enable both intuitive and precise characterization of this diverse class of alterations in clinical reporting and literature. In summary, we provide a clinical-practice driven framework and nomenclature for gene fusions, including recommendations for human readability, computational precision, and data integrity within the SOP. This work is a substantial advancement towards standardized communication, investigation, and sharing of gene fusion data across clinical and research domains and specialties.

Citation Format: Alex H. Wagner, Ioannis S. Vlachos, Dmitriy Sonkin, Panieh Terraf, Chimene Kesserwan, Andrea Sboner, Thomas Coard, Christian Reich, Deborah I. Ritter, Peter Horak, Ying S. Zou, Anna Tanska, Aaron M. Berlin, Anna Lu, Daniel Cameron, Heather E. Williams, Wan-Hsin Lin, Gokce Toruner, Arpad Danos, Jason Saliba, Huiling Xu, Xinjie Xu, Georgina Ryland, Michele Ceccarelli, Liying Zhang, Sarah Rapisardo, Catherine Rehder, Xuelu Liu, Aparna Pallavajjala, Nicole Park, Laveniya Satgunaseelan, Kristy Lee, Jie Liu, Obi Griffith, Robert R. Freimuth, Albrecht Stenzinger, Linda B. Baughn, Michael Baudis, Jennifer Lee, Marilyn Li, Angshumoy Roy, Gordana Raca. A standard operating procedure for the curation of gene fusions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 449.

Genomic considerations for FHIR®; eMERGE implementation lessons

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

Infobuttons for Genomic Medicine: Requirements and Barriers

OBJECTIVES

The study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS).

METHODS

We conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses.

RESULTS

We analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity.

CONCLUSION

In addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS.

Abstract 1096: Harmonization standards from the Variant Interpretation for Cancer Consortium

The use of clinical gene sequencing is now commonplace, and genome analysts and molecular pathologists are often tasked with the labor-intensive process of interpreting the clinical significance of large numbers of tumor variants. Numerous independent knowledgebases have been constructed to alleviate this manual burden, however these knowledgebases are non-interoperable. As a result, the analyst is left with a difficult tradeoff: for each knowledgebase used the analyst must understand the nuances particular to that resource and integrate its evidence accordingly when generating the clinical report, but for each knowledgebase omitted there is increased potential for missed findings of clinical significance.The Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) was formed as a driver project of the Global Alliance for Genomics and Health (GA4GH; ga4gh.org) to address this concern. VICC members include representatives from several major somatic interpretation knowledgebases including CIViC, OncoKB, Jax-CKB, the Weill Cornell PMKB, the IRB-Barcelona Cancer Biomarkers Database, and others. Previously, the VICC built and reported on a harmonized meta-knowledgebase of 19,551 biomarker associations of harmonized variants, diseases, drugs, and evidence across the constituent resources.In that study, we analyzed the frequency with which the tumor samples from the AACR Project GENIE cohort would match to harmonized associations. Variant matches increased dramatically from 57% to 86% when broader matching to regions describing categorical variants were allowed. Unlike precise sequence variants with specified alternate alleles, categorical variants describe a collection of potential variants with a common feature, such as “V600” (non-valine alleles at the 600 residue), “Exon 20 mutations” (all non-silent mutations in exon 20), or “Gain-of-function” (hypermorphic alterations that activate or amplify gene activity). However, matching observed sequence variants to categorical variants is challenging, as the latter are typically only described as unstructured text. Here we describe the expressive and computational GA4GH Variation Representation specification (vr-spec.readthedocs.io), which we co-developed as members of the GA4GH Genomic Knowledge Standards work stream. This specification provides a schema for common, precise forms of variation (e.g. SNVs and Indels) and the method for computing identifiers from these objects. We highlight key aspects of the specification and our work to apply it to the characterization of categorical variation, showcasing the variant terminology and classification tools developed by the VICC to support this effort. These standards and tools are free, open-source, and extensible, overcoming barriers to standardized variant knowledge sharing and search.

Citation Format: Alex H. Wagner, Reece K. Hart, Larry Babb, Robert R. Freimuth, Adam Coffman, Yonghao Liang, Beth Pitel, Angshumoy Roy, Matthew Brush, Jennifer Lee, Anna Lu, Thomas Coard, Shruti Rao, Deborah Ritter, Brian Walsh, Susan Mockus, Peter Horak, Ian King, Dmitriy Sonkin, Subha Madhavan, Gordana Raca, Debyani Chakravarty, Malachi Griffith, Obi L. Griffith. Harmonization standards from the Variant Interpretation for Cancer Consortium [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1096.

Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records

Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.

Participant choices for return of genomic results in the eMERGE Network

PURPOSE

Secondary findings are typically offered in an all or none fashion when sequencing is used for clinical purposes. This study aims to describe the process of offering categorical and granular choices for results in a large research consortium.

METHODS

Within the third phase of the electronic MEdical Records and GEnomics (eMERGE) Network, several sites implemented studies that allowed participants to choose the type of results they wanted to receive from a multigene sequencing panel. Sites were surveyed to capture the details of the implementation protocols and results of these choices.

RESULTS

Across the ten eMERGE sites, 4664 participants including adolescents and adults were offered some type of choice. Categories of choices offered and methods for selecting categories varied. Most participants (94.5%) chose to learn all genetic results, while 5.5% chose subsets of results. Several sites allowed participants to change their choices at various time points, and 0.5% of participants made changes.

CONCLUSION

Offering choices that include learning some results is important and should be a dynamic process to allow for changes in scientific knowledge, participant age group, and individual preference.

Sync for Genes: Making Clinical Genomics Available for Precision Medicine at the Point-of-Care

BACKGROUND

Making genomic data available at the point-of-care and for research is critical for the success of the Precision Medicine Initiative (PMI), a research initiative which seeks to change health care by "tak(ing) into account individual differences in people's genes, environments, and lifestyles." The Office of the National Coordinator for Health Information Technology (ONC) led Sync for Genes, a program to develop standards that make genomic data available when and where it matters most. This article discusses lessons learned from recent Sync for Genes activities.

OBJECTIVES

The goals of Sync for Genes were to (1) demonstrate exchange of genomic data using health data standards, (2) provide feedback for refinement of health data standards, and (3) synthesize project experiences to support the integration of genomic data at the point-of-care and for research.

METHODS

Four organizations participated in a program to test the Health Level Seven International (HL7^®) Fast Healthcare Interoperability Resources (FHIR^®) standard, which supports sharing genomic data. ONC provided access to subject matter experts, resources, tools, and technical guidance to support testing activities. Three of the four organizations participated in HL7 FHIR Connectathons to test FHIR's ability to exchange genomic diagnostic reports.

RESULTS

The organizations successfully demonstrated exchange of genomic diagnostic reports using FHIR. The feedback and artifacts that resulted from these activities were shared with HL7 and made publicly available. Four areas were identified as important considerations for similar projects: (1) FHIR proficiency, (2) developer support, (3) project scope, and (4) bridging health information technology and genomic expertise.

CONCLUSION

Precision medicine is a rapidly evolving field, and there is opportunity to continue maturing health data standards for the exchange of necessary genomic data, increasing the likelihood that the standard supports the needs of users.

A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example

The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

Genomic Information for Clinicians in the Electronic Health Record: Lessons Learned From the Clinical Genome Resource Project and the Electronic Medical Records and Genomics Network

Genomic knowledge is being translated into clinical care. To fully realize the value, it is critical to place credible information in the hands of clinicians in time to support clinical decision making. The electronic health record is an essential component of clinician workflow. Utilizing the electronic health record to present information to support the use of genomic medicine in clinical care to improve outcomes represents a tremendous opportunity. However, there are numerous barriers that prevent the effective use of the electronic health record for this purpose. The electronic health record working groups of the Electronic Medical Records and Genomics (eMERGE) Network and the Clinical Genome Resource (ClinGen) project, along with other groups, have been defining these barriers, to allow the development of solutions that can be tested using implementation pilots. In this paper, we present “lessons learned” from these efforts to inform future efforts leading to the development of effective and sustainable solutions that will support the realization of genomic medicine.

ClinGen advancing genomic data-sharing standards as a GA4GH driver project

The Clinical Genome Resource (ClinGen)'s work to develop a knowledge base to support the understanding of genes and variants for use in precision medicine and research depends on robust, broadly applicable, and adaptable technical standards for sharing data and information. To forward this goal, ClinGen has joined with the Global Alliance for Genomics and Health (GA4GH) to support the development of open, freely-available technical standards and regulatory frameworks for secure and responsible sharing of genomic and health-related data. In its capacity as one of the 15 inaugural GA4GH "Driver Projects," ClinGen is providing input on the key standards needs of the global genomics community, and has committed to participate on GA4GH Work Streams to support the development of: (1) a standard model for computer-readable variant representation; (2) a data model for linking variant data to annotations; (3) a specification to enable sharing of genomic variant knowledge and associated clinical interpretations; and (4) a set of best practices for use of phenotype and disease ontologies. ClinGen's participation as a GA4GH Driver Project will provide a robust environment to test drive emerging genomic knowledge sharing standards and prove their utility among the community, while accelerating the construction of the ClinGen evidence base.

A Decade of Translational Bioinformatics: A Retrospective Analysis of "Year-in-Review" Presentations

For the past 11 years, the year-in-review (YIR) keynote presentation at the AMIA Informatics summit has been a perennial highlight. We hypothesized that the presented material from these keynotes could be used to assess both the recent trajectory of topics in informatics-especially translational bioinformatics (TBI)-as well as the scientific merit of the crowd-sourced process used to nominate, review, and select the papers presented at the YIR. We compare YIR articles to a background set of non-YIR articles from informatics journals using structured metadata and qualitative thematic analysis, paying specific attention to trends and popularity over time. These trends were inspected both internally (comparing the YIR sessions to each other) and externally (comparing them to the overall content of scientific literature for the same time period). In doing so, we identified some unexpected patterns that suggest important opportunities for TBI research in the future.

ClinGen Allele Registry links information about genetic variants

Effective exchange of information about genetic variants is currently hampered by the lack of readily available globally unique variant identifiers that would enable aggregation of information from different sources. The ClinGen Allele Registry addresses this problem by providing (1) globally unique "canonical" variant identifiers (CAids) on demand, either individually or in large batches; (2) access to variant-identifying information in a searchable Registry; (3) links to allele-related records in many commonly used databases; and (4) services for adding links to information about registered variants in external sources. A core element of the Registry is a canonicalization service, implemented using in-memory sequence alignment-based index, which groups variant identifiers denoting the same nucleotide variant and assigns unique and dereferenceable CAids. More than 650 million distinct variants are currently registered, including those from gnomAD, ExAC, dbSNP, and ClinVar, including a small number of variants registered by Registry users. The Registry is accessible both via a web interface and programmatically via well-documented Hypertext Transfer Protocol (HTTP) Representational State Transfer Application Programming Interface (REST-APIs). For programmatic interoperability, the Registry content is accessible in the JavaScript Object Notation for Linked Data (JSON-LD) format. We present several use cases and demonstrate how the linked information may provide raw material for reasoning about variant's pathogenicity.

Prototype of a Standards-Based EHR and Genetic Test Reporting Tool Coupled with HL7-Compliant Infobuttons

Integration of genetic information is becoming increasingly important in clinical practice. However, genetic information is often ambiguous and difficult to understand, and clinicians have reported low-self-efficacy in integrating genetics into their care routine. The Health Level Seven (HL7) Infobutton standard helps to integrate online knowledge resources within Electronic Health Records (EHRs) and is required for EHR certification in the US. We implemented a prototype of a standards-based genetic reporting application coupled with infobuttons leveraging the Infobutton and Fast Healthcare Interoperability Resources (FHIR) Standards. Infobutton capabilities were provided by Open Infobutton, an open source package compliant with the HL7 Infobutton Standard. The resulting prototype demonstrates how standards-based reporting of genetic results, coupled with curated knowledge resources, can provide dynamic access to clinical knowledge on demand at the point of care. The proposed functionality can be enabled within any EHR system that has been certified through the US Meaningful Use program.

Implementing Genomic Clinical Decision Support for Drug-Based Precision Medicine

The explosive growth of patient-specific genomic information relevant to drug therapy will continue to be a defining characteristic of biomedical research. To implement drug-based personalized medicine (PM) for patients, clinicians need actionable information incorporated into electronic health records (EHRs). New clinical decision support (CDS) methods and informatics infrastructure are required in order to comprehensively integrate, interpret, deliver, and apply the full range of genomic data for each patient.

The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

Knowledge as a Service at the Point of Care

An electronic health record (EHR) can assist the delivery of high-quality patient care, in part by providing the capability for a broad range of clinical decision support, including contextual references (e.g., Infobuttons), alerts and reminders, order sets, and dashboards. All of these decision support tools are based on clinical knowledge; unfortunately, the mechanisms for managing rules, order sets, Infobuttons, and dashboards are often unrelated, making it difficult to coordinate the application of clinical knowledge to various components of the clinical workflow. Additional complexity is encountered when updating enterprise-wide knowledge bases and delivering the content through multiple modalities to different consumers. We present the experience of Mayo Clinic as a case study to examine the requirements and implementation challenges related to knowledge management across a large, multi-site medical center. The lessons learned through the development of our knowledge management and delivery platform will help inform the future development of interoperable knowledge resources.

Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

INTRODUCTION

Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes.

MATERIALS AND METHODS

Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts.

RESULTS

Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms.

DISCUSSION

The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.Genet Med 19 2, 215-223.

An informatics research agenda to support precision medicine: seven key areas

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.

Harnessing next-generation informatics for personalizing medicine: a report from AMIA's 2014 Health Policy Invitational Meeting

The American Medical Informatics Association convened the 2014 Health Policy Invitational Meeting to develop recommendations for updates to current policies and to establish an informatics research agenda for personalizing medicine. In particular, the meeting focused on discussing informatics challenges related to personalizing care through the integration of genomic or other high-volume biomolecular data with data from clinical systems to make health care more efficient and effective. This report summarizes the findings (n = 6) and recommendations (n = 15) from the policy meeting, which were clustered into 3 broad areas: (1) policies governing data access for research and personalization of care; (2) policy and research needs for evolving data interpretation and knowledge representation; and (3) policy and research needs to ensure data integrity and preservation. The meeting outcome underscored the need to address a number of important policy and technical considerations in order to realize the potential of personalized or precision medicine in actual clinical contexts.

The genomic CDS sandbox: An assessment among domain experts

Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.

Creating Shareable Clinical Decision Support Rules for a Pharmacogenomics Clinical Guideline Using Structured Knowledge Representation

Pharmacogenomics (PGx) guidelines contain drug-gene relationships, therapeutic and clinical recommendations from which clinical decision support (CDS) rules can be extracted, rendered and then delivered through clinical decision support systems (CDSS) to provide clinicians with just-in-time information at the point of care. Several tools exist that can be used to generate CDS rules that are based on computer interpretable guidelines (CIG), but none have been previously applied to the PGx domain. We utilized the Unified Modeling Language (UML), the Health Level 7 virtual medical record (HL7 vMR) model, and standard terminologies to represent the semantics and decision logic derived from a PGx guideline, which were then mapped to the Health eDecisions (HeD) schema. The modeling and extraction processes developed here demonstrate how structured knowledge representations can be used to support the creation of shareable CDS rules from PGx guidelines.

Analyzing the potential for incorrect haplotype calls with different pharmacogenomic assays in different populations: a simulation based on 1000 Genomes data

AIM

Many currently available pharmacogenomic assays and algorithms interrogate a set of 'tag' polymorphisms for inferring haplotypes. We wanted to test the accuracy of such haplotype inferences across different populations.

MATERIALS & METHODS

We simulated haplotype inferences made by existing pharmacogenomic assays for seven important pharmacogenes based on full genome data of 2504 persons in the 1000 Genomes dataset.

RESULTS

A sizable fraction of samples did not match any of the haplotypes in the star allele nomenclature systems. We found no clear population bias in the accuracy of results of simulated assays.

CONCLUSION

Haplotype nomenclatures and inference algorithms need to be improved to adequately capture pharmacogenomic diversity in human populations.

A conceptual model for translating omic data into clinical action

Genomic, proteomic, epigenomic, and other “omic” data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called “the omic funnel.” This model parallels the classic “Data, Information, Knowledge, Wisdom pyramid” and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

OBJECTIVE

Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS).

MATERIALS AND METHODS

The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement.

RESULTS

There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information.

CONCLUSION

Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies

BACKGROUND

Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.

METHODS

We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles.

RESULTS

Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners.

CONCLUSIONS

The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.

Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process

The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines.

A Template for Authoring and Adapting Genomic Medicine Content in the eMERGE Infobutton Project

The Electronic Medical Records and Genomics (eMERGE) Network is a national consortium that is developing methods and best practices for using the electronic health record (EHR) for genomic medicine and research. We conducted a multi-site survey of information resources to support integration of pharmacogenomics into clinical care. This work aimed to: (a) characterize the diversity of information resource implementation strategies among eMERGE institutions; (b) develop a master template containing content topics of important for genomic medicine (as identified by the DISCERN-Genetics tool); and (c) assess the coverage of content topics among information resources developed by eMERGE institutions. Given that a standard implementation does not exist and sites relied on a diversity of information resources, we identified a need for a national effort to efficiently produce sharable genomic medicine resources capable of being accessed from the EHR. We discuss future areas of work to prepare institutions to use infobuttons for distributing standardized genomic content.

Evaluation of RxNorm for Medication Clinical Decision Support

We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS.

Simplifying complex clinical element models to encourage adoption

Clinical Element Models (CEMs) were developed to provide a normalized form for the exchange of clinical data. The CEM specification is quite complex and specialized knowledge is required to understand and implement the models, which presents a significant barrier to investigators and study designers. To encourage the adoption of CEMs at the time of data collection and reduce the need for retrospective normalization efforts, we developed an approach that provides a simplified view of CEMs for non-experts while retaining the full semantic detail of the underlying logical models. This allows investigators to approach CEMs through generalized representations that are intended to be more intuitive than the native models, and it permits them to think conceptually about their data elements without worrying about details related to the CEM logical models and syntax. We demonstrate our approach using data elements from the Pharmacogenomics Research Network (PGRN).

Clinical Pharmacogenetics Implementation Consortium Guidelines for HLA-B Genotype and Abacavir Dosing: 2014 update

The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B Genotype and Abacavir Dosing were originally published in April 2012. We reviewed recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplementary Material online and included additional resources for applying CPIC guidelines to the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol

OBJECTIVE

To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR).

PATIENTS AND METHODS

We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR.

RESULTS

The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance.

CONCLUSION

This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.