Ramos, Erin M.

Abstract Pharmacogenomics (PGx) is focused on the relationship between an individual's genetic makeup and their response to medications, with the overarching aim of guiding prescribing decisions to improve drug efficacy and reduce adverse events. The PGx and genomic medicine communities have worked independently for over 2 decades, developing separate standards and terminology, making implementation of PGx across all areas of genomic medicine difficult. To address this issue, the Clinical Genome Resource (ClinGen) Pharmacogenomics Working Group (PGxWG) was established by the National Institutes of Health (NIH)-funded ClinGen to initially create frameworks for evaluating gene–drug response clinical validity and actionability aligned with the ClinGen frameworks for evaluating monogenic gene–disease relationships, and a framework for classifying germline PGx variants similar to the American College of Medical Genetics (ACMG) and Association of Molecular Pathology (AMP) system for interpretation of disease-causing variants. These frameworks will leverage decades of work from well-established PGx resources facilitating buy-in among PGx stakeholders. In this report, we describe the background and major activities of the ClinGen PGxWG, and how this initiative will facilitate the critical inclusion of PGx into the larger context of genomic medicine.

Abstract 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.

AbstractOver the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.Genet. Epidemiol. 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc.