An active area of genomic medicine implementation at many health care organizations and academic medical centers includes development of decision support and return of results around pharmacogenomics. One of the challenges in implementing pharmacogenomics is the representation of the information in clinical dosing guidelines, including star-allele haplotypes, and extracting these variants and haplotypes from genetic datasets. In a collaboration between the Pharmacogenomics Knowledgebase (PharmGKB) and the former PGRN Statistical Analysis Resource (P-STAR), with input from other groups, we are developing a software tool to extract guideline variants from a genetic dataset (represented as a vcf), interpret the variant alleles, and generate a report with genotype-based prescribing recommendations which can be used to inform treatment decisions. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has established guidelines surrounding gene-drug pairs that can and should lead to treatment modifications based on genetic variants. These guidelines are used for the initial version of PharmCAT, and other sources of PGx information and guidelines will be included in the future.
For more details read the published commentary.
PharmCAT is under development and has not been officially released. We will post an announcement when PharmCAT is ready for beta testing.
Refer to the project’s wiki on GitHub for documentation about the technical aspects of PharmCAT.
Read the PharmCAT wiki page on named allele matching to learn how PharmCAT matches genotype data to allele definitions.
There are detailed documents on how a few particular genes are handled by PharmCAT. See the gene definition exceptions for a rundown of exceptional circumstances when analyzing particular genes. The genes UGT1A1 and CFTR also have documentation of their non-standard allele matching algorithms.
HLA’s and G6PD are not included in the initial version of PharmCAT, see [PMID: 31306493] (https://www.ncbi.nlm.nih.gov/pubmed/?term=31306493) for further explanations. The allele definition tables used in PharmCAT are based on CPIC allele definition tables from 2018. Newer guidelines (published after 2017) are NOT currently included in PharmCAT reports, covering additional genes RYR1, CACNA1S, NUTD15, CYP2B6 and recommendations for tamoxifen, atomoxetine, efavirenz, potent volatile anesthetic agents, succinylcholine and the DPYD update related to changes in recommendation for DPYD Intermediate Metabolizers. The CYP2D6 metabolizer grouping and the activity value for *10 are NOT updated to the newly released consensus metabolizer groups for CYP2D6.
If you’d like to view example input files and example HTML reports generated by PharmCAT check out the examples page.
If you are interested in testing the tool or have questions, please contact firstname.lastname@example.org.
Co-PIs: Teri Klein (Stanford) and Marylyn Ritchie (University of Pennsylvania)
Katrin Sangkuhl (Stanford)
Ryan Whaley (Stanford)
Michelle Whirl-Carrillo (Stanford)
Mark Woon (Stanford)
|Solomon Adams||University of Pittsburgh|
|Lester Carter||Stanford (formerly)|
|Mark Dunnenberger||Northshore University Health System|
|Philip Empey||University of Pittsburgh|
|Alex Frase||University of Pennsylvania|
|Robert Freimuth||Mayo Clinic|
|Andrea Gaedigk||Children’s Mercy Hospital|
|Adam Gordon||University of Washington|
|Cyrine Haidar||St Jude Children’s Research Hospital|
|James Hoffman||St Jude Children’s Research Hospital|
|Kevin Hicks||Moffitt Cancer Center & Research Institute|
|Ming Ta (Mike) Lee||Geisinger|
|Neil Miller||Children’s Mercy Hospital|
|Sean Mooney||University of Washington|
|Minoli Perera||Northwestern University|
|Josh Peterson||Vanderbilt University|
|Stuart Scott||Icahn School of Medicine at Mount Sinai|
|Greyson Twist||Children’s Mercy Hospital|
|Chunlei Wu||Scrips Research Institute|
|Anurag Verma||University of Pennsylvania|
|Wenjian Yang||St Jude Children’s Research Hospital|