A patient’s diagnosis and medical treatment is frequently guided by where that patient’s laboratory values fall along a range of established “normal” values. A new “Viewpoint” (In the Era of Precision Medicine and Big Data, Who Is Normal?) in the Journal of the American Medical Association (JAMA) raises an important question in the context of laboratory values and precision medicine: if medicine is personalized, to what do we compare an individual’s laboratory values if “normal” is actually relative? The authors consider solutions to ensure that test results “be interpreted in reference to a population of ‘similar’, ‘healthy’ individuals”. For example, the authors propose 1) that longitudinal data on individual outcomes be accessible to researchers to determine whether selected reference values are truly useful 2) that large-scale analyses be carried out across data sets 3) that reference values be tailored to patients and delivered at point of care, and 3) that “computationally derived genetic ancestry” be linked to laboratory test values, so that race is not used as a proxy.
Monday, May 28, 2018
Curators' Favorite Papers
A critical component of pharmacogenetic/pharmacogenomics (PGx) implementation into clinical care is the integration of PGx data in clinical decision support (CDS) and electronic health records (EHRs). A new article in the journal Human Molecular Genetics (Genomics and electronic health record systems) discusses the benefits of, and current challenges to, the integration of genomics and PGx data, into EHRs. In particular, the article discusses how PGx data in EHRs relates to questions of standards and evidence generation, and how data should be represented so as not to be overwhelming for clinicians. The article adeptly outlines specific components of CDS pertaining to whole genome sequencing (WGS) (e.g. ordering and interpreting a test, importing data, trigger alerts and warning in EHRs, and evaluating outcomes) and describes existing tools, such as application programming interfaces (APIs), that link knowledgebases (including PharmGKB) to EHRs to assist with CDS. The authors conclude that with continued developments in technology, and ambitious research programs with large, diverse cohorts, such as the NIH-sponsored All of Us program, the “goals of generating new knowledge and clinically relevant discoveries using population-based genomics data can someday be achieved by using EHRs”.
A patient’s diagnosis and medical treatment is frequently guided by where that patient’s laboratory values fall along a range of established “normal” values. A new “Viewpoint” (In the Era of Precision Medicine and Big Data, Who Is Normal?) in the Journal of the American Medical Association (JAMA) raises an important question in the context of laboratory values and precision medicine: if medicine is personalized, to what do we compare an individual’s laboratory values if “normal” is actually relative? The authors consider solutions to ensure that test results “be interpreted in reference to a population of ‘similar’, ‘healthy’ individuals”. For example, the authors propose 1) that longitudinal data on individual outcomes be accessible to researchers to determine whether selected reference values are truly useful 2) that large-scale analyses be carried out across data sets 3) that reference values be tailored to patients and delivered at point of care, and 3) that “computationally derived genetic ancestry” be linked to laboratory test values, so that race is not used as a proxy.
A patient’s diagnosis and medical treatment is frequently guided by where that patient’s laboratory values fall along a range of established “normal” values. A new “Viewpoint” (In the Era of Precision Medicine and Big Data, Who Is Normal?) in the Journal of the American Medical Association (JAMA) raises an important question in the context of laboratory values and precision medicine: if medicine is personalized, to what do we compare an individual’s laboratory values if “normal” is actually relative? The authors consider solutions to ensure that test results “be interpreted in reference to a population of ‘similar’, ‘healthy’ individuals”. For example, the authors propose 1) that longitudinal data on individual outcomes be accessible to researchers to determine whether selected reference values are truly useful 2) that large-scale analyses be carried out across data sets 3) that reference values be tailored to patients and delivered at point of care, and 3) that “computationally derived genetic ancestry” be linked to laboratory test values, so that race is not used as a proxy.
Tuesday, May 22, 2018
New PharmVar Downloads
PharmVar is delighted to announce a new feature that just went live! Variants of genes in the PharmVar database can now be downloaded in sequence (FASTA and VCF) and table (TSV) formats. Options include to the download variants of interest, all variants of a gene or the entire PharmVar database.
Check out the link “Additional Data Download Information” on the gene page for more information.
There are currently three genes in the database, CYPs 2C9, 2C19 and 2D6 – additional genes will be transitioned soon.
The PharmVar Team
Friday, May 4, 2018
Dr. Francis Collins discusses All of Us on CBS This Morning
The National Institutes of Health (NIH) director Dr. Francis Collins appeared on CBS This Morning to promote the All of Us program, a part of the Precision Medicine Initiative, which launches this Sunday, May 6th.
When asked about what it hopes to accomplish, he explained:
“Do you ever feel when somebody is making recommendations to you about how to stay healthy or when you need a prescription and you’re wondering 'is this the right drug for me at the right dose?' A lot of what we do in medicine is one-size-fits-all. Precision medicine is this opportunity to make things much more individualized and more precise and more likely to result in a good outcome, but to get to that point we need to collect a lot of data on a lot of people…”
All of Us hopes to recruit 1 million Americans, and is particularly interested in recruiting subjects from communities that are typically underrepresented in biomedical research. Dr. Collins addressed questions about privacy concerns by explaining that all data will be de-identified, researchers pledged to not re-identify subjects and that data will be protected from use by law enforcement as a result of new legislation.
You can find more information about pharmacogenomics (PGx) such as PGx-guided dosing/prescribing guidelines at CPIC and PharmGKB where you can also find curated information about drugs and genes, from the literature as well as from drug labels and other sources.
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