Pharmacogenomic Guideline-Label Divergence Dataset: CYP2C19, CYP2D6, and HLA-B in Neuropsychiatric Medications

This dataset supports a comparative analysis of clinical pharmacogenomic guideline recommendations (CPIC, DPWG) against FDA drug label status for CYP2C19-, CYP2D6-, and HLA-B-actionable neuropsychiatric medications, including SSRIs, TCAs, atomoxetine, carbamazepine/oxcarbazepine/phenytoin, and antipsychotics. Contents: Three gene-specific master workbooks (CYP2C19, CYP2D6, HLA-B): allele definitions, diplotype-to-phenotype mappings, and population frequency data drawn from CPIC, PharmGKB/ClinPGx, PharmVar, and the Allele Frequency Net Database A cross-gene clinical recommendations workbook comparing CPIC/DPWG guideline strength against FDA label status for 19 gene-drug pairs A supplementary HLA-B population frequency dataset (HLA-B*15:02 and HLA-B*57:01) across multiple countries and ethnic groups A findings summary consolidating the key guideline-label divergence results Methodology: All data was extracted from primary or near-primary published sources rather than estimated or interpolated. Every data point is traceable to a named, dated source record. Where a value could not be verified to this standard, it was left unpopulated and explicitly flagged rather than approximated — each workbook includes its own Data_Quality_Flags sheet documenting known limitations and open verification items, consistent with the transparency principle underlying this project. See the included README.md for full details on each file's structure and contents.