Language
English
Publication Date
10-1-2024
Journal
Scientific Reports
DOI
10.1038/s41598-024-73925-4
PMID
39354046
PMCID
PMC11445378
PubMedCentral® Posted Date
10-1-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Opioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records. Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity. We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype. Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits.
Keywords
Humans, Codeine, Genome-Wide Association Study, Male, Female, Analgesics, Opioid, Electronic Health Records, Drug Prescriptions, Middle Aged, Adult, Phenotype, Opioid-Related Disorders, Polymorphism, Single Nucleotide, Aged, Medication use phenotype, Electronic health record, Genome-wide association study, Opioid use disorder, Polygenic risk score, Opioid prescription phenotype
Published Open-Access
yes
Recommended Citation
Song, Wenyu; Lam, Max; Liu, Ruize; et al., "A Genome-Wide Association Study of the Count of Codeine Prescriptions" (2024). Faculty, Staff and Students Publications. 8466.
https://digitalcommons.library.tmc.edu/baylor_docs/8466