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Last Posted: Sep 29, 2022
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Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach
D Charitou et al, J PGX Journal, September 29, 2022

We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders.

The genetic landscape of major drug metabolizing cytochrome P450 genes—an updated analysis of population-scale sequencing data
Y Zhou et al, The PGX journal, September 6, 2022

Genes encoding cytochrome P450 enzymes (CYPs) are extremely polymorphic and multiple CYP variants constitute clinically relevant biomarkers for the guidance of drug selection and dosing. We previously reported the distribution of the most relevant CYP alleles using population-scale sequencing data. Here, we update these findings by making use of the increasing wealth of data, incorporating whole exome and whole genome sequencing data from 141,614 unrelated individuals across 12 human populations.

Dutch pharmacogenetics working group guideline for the gene-drug interaction of ABCG2, HLA-B and Allopurinol, and MTHFR, folic acid and methotrexate
KH van der Pol et al, EJHG, September 2, 2022

This guideline describes the gene-drug interaction of ABCG2 with allopurinol, HLA-B with allopurinol, MTHFR with folic acid, and MTHFR with methotrexate, relevant for the treatment of gout, cancer, and rheumatoid arthritis. A systematic review was performed based on which pharmacotherapeutic recommendations were developed.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes
A Siemens et al, J Per Med, August 29, 2022

The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort.

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.