Pharmacogenomics and Drug Metabolism Genes
This dataset provides detailed pharmacogenomic testing results, including CYP450 enzyme variants, drug transporter genes, HLA alleles, and other key drug metabolism genes, along with clinical implications for drug selection and dosing. It is ideal for precision medicine initiatives, clinical decision support, and research into gene-drug interactions.
Sample rows
preview · 8 of 100 rows · all 15 columns| test_idstring | test_methodstring | phenotypestring | patient_idstring | test_datedate | gene_symbolstring | variant_namestring | genotypestring | clinical_implicationstring | recommended_drug_adjustmentstring | drug_namestring | reporting_labstring | reference_rangestring | hla_allelestring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PGX10001 | PCR | poor metabolizer | PAT001 | 2022-02-14 | CYP2D6 | *4 | homozygous | High risk of adverse effects with codeine. | Avoid codeine, consider non-opioid analgesics. | codeine | GenLab East | Normal: *1/*1 | blank | Family history of opioid sensitivity. |
| PGX10002 | NGS | intermediate metabolizer | PAT002 | 2022-03-05 | TPMT | *3A | heterozygous | Increased risk of thiopurine toxicity. | Reduce azathioprine dose by 30-70%. | azathioprine | PharmaGene North | Normal: *1/*1 | blank | Patient previously tolerated standard dosing. |
| PGX10003 | PCR | intermediate metabolizer | PAT003 | 2023-01-16 | DPYD | *2A | heterozygous | Risk of fluoropyrimidine toxicity. | Reduce fluorouracil dose by 50%. | fluorouracil | GenLab East | Wild-type: *1/*1 | blank | blank |
| PGX10004 | Microarray | ultrarapid metabolizer | PAT004 | 2022-05-21 | CYP2C19 | *17 | homozygous | Reduced efficacy of clopidogrel. | Consider alternative antiplatelet. | clopidogrel | NorthGene | Normal: *1/*1 | blank | Patient scheduled for PCI. |
| PGX10005 | PCR | intermediate metabolizer | PAT005 | 2022-06-03 | CYP2C9 | *3 | heterozygous | Increased bleeding risk with warfarin. | Start warfarin at lower dose. | warfarin | GeneExpress | Normal: *1/*1 | blank | blank |
| PGX10006 | NGS | blank | PAT006 | 2022-07-17 | HLA-B | *57:01 | heterozygous | Risk of hypersensitivity to abacavir. | Avoid abacavir. | abacavir | PharmaGene North | Negative: Not present | HLA-B*57:01 | Patient HIV-positive. |
| PGX10007 | Microarray | poor metabolizer | PAT007 | 2022-07-29 | CYP3A5 | *3 | homozygous | Higher tacrolimus levels expected. | Reduce tacrolimus dose. | tacrolimus | CentralGenomics | Normal: *1/*1 | blank | Renal transplant recipient. |
| PGX10008 | NGS | intermediate metabolizer | PAT008 | 2022-08-10 | SLCO1B1 | rs4149056 | heterozygous | Increased risk of statin-induced myopathy. | Consider lower dose or alternative statin. | simvastatin | GeneExpress | Wild-type: TT | blank | blank |
| PGX10009 | PCR | normal metabolizer | PAT009 | 2022-09-01 | CYP2D6 | *1/*2 | heterozygous | Standard dosing for antidepressants. | No adjustment needed. | nortriptyline | GenLab East | Normal: *1/*1 | blank | blank |
| PGX10010 | Microarray | poor metabolizer | PAT010 | 2022-09-15 | CYP2C19 | *2 | homozygous | Reduced efficacy of proton pump inhibitors. | Increase PPI dose or switch drug. | omeprazole | PharmaGene North | Normal: *1/*1 | blank | Frequent heartburn. |
| PGX10011 | NGS | intermediate metabolizer | PAT011 | 2022-10-03 | CYP2C9 | *2 | heterozygous | Moderate warfarin sensitivity. | Start at moderate dose. | warfarin | GeneExpress | Normal: *1/*1 | blank | blank |
| PGX10012 | PCR | intermediate metabolizer | PAT012 | 2022-10-17 | CYP2D6 | *10 | heterozygous | Monitor for antidepressant side effects. | Start with lower dose. | amitriptyline | CentralGenomics | Normal: *1/*1 | blank | blank |
| PGX10013 | NGS | normal metabolizer | PAT013 | 2022-10-25 | DPYD | rs67376798 | wild-type | Low risk of fluoropyrimidine toxicity. | Standard dosing. | capecitabine | PharmaGene North | Wild-type: C/C | blank | blank |
| PGX10014 | PCR | intermediate metabolizer | PAT014 | 2022-11-09 | CYP2C19 | *1/*2 | heterozygous | Possible reduced efficacy of clopidogrel. | Monitor response. | clopidogrel | NorthGene | Normal: *1/*1 | blank | blank |
| PGX10015 | Microarray | poor metabolizer | PAT015 | 2022-11-23 | CYP2D6 | *5 | homozygous | High risk of adverse effects with antidepressants. | Consider dose reduction. | paroxetine | GenLab East | Normal: *1/*1 | blank | Patient reports insomnia. |
| PGX10016 | NGS | poor metabolizer | PAT016 | 2022-12-04 | TPMT | *3C | homozygous | High risk of bone marrow suppression. | Consider alternative therapy. | mercaptopurine | CentralGenomics | Normal: *1/*1 | blank | blank |
| PGX10017 | PCR | normal metabolizer | PAT017 | 2022-12-18 | CYP3A5 | *1 | heterozygous | Standard tacrolimus dosing. | No adjustment needed. | tacrolimus | GeneExpress | Normal: *1/*1 | blank | Stable post-transplant. |
| PGX10018 | NGS | poor metabolizer | PAT018 | 2023-01-04 | SLCO1B1 | rs4149056 | homozygous | High risk of statin-induced myopathy. | Avoid simvastatin. | simvastatin | PharmaGene North | Wild-type: TT | blank | Family history of muscle pain. |
| PGX10019 | Microarray | blank | PAT019 | 2023-01-18 | HLA-A | *31:01 | heterozygous | Risk of carbamazepine hypersensitivity. | Avoid carbamazepine. | carbamazepine | CentralGenomics | Negative: Not present | HLA-A*31:01 | Patient with epilepsy. |
| PGX10020 | PCR | intermediate metabolizer | PAT020 | 2023-02-06 | CYP2C9 | *1/*3 | heterozygous | Increased warfarin sensitivity. | Reduce initial dose. | warfarin | NorthGene | Normal: *1/*1 | blank | blank |
What the 100 rows show
from the 100-row sample- 4genotypes
- 8reporting labs
- 9gene symbols
- 16reference ranges
- string 14
- date 1
Columns
15 columns in two groups| column | type | description | example |
|---|---|---|---|
| Text 14 columns | |||
test_id | string | Unique identifier for the pharmacogenomic test resultunique | PGX10001 |
patient_id | string | Unique identifier for the patient | PAT001 |
gene_symbol | string | Standard symbol for the gene tested (e.g., CYP2D6, TPMT, DPYD, HLA-B)9 symbols | CYP2D6 |
variant_name | string | Name or identifier of the genetic variant (e.g., *2, rsID, allele name) | *4 |
genotype | string | Genotype result for the variant (e.g., homozygous, heterozygous, wild-type)homozygous · heterozygous · wild-type · unknown | homozygous |
phenotype | string | Predicted metabolic phenotype (e.g., poor metabolizer, intermediate metabolizer, normal metabolizer, rapid metabolizer)6 values · optional | poor metabolizer |
clinical_implication | string | Summary of clinical implications for drug selection and dosing based on the genotype/phenotypeoptional | Standard statin dosing. |
recommended_drug_adjustment | string | Recommended drug(s) and dosing adjustment(s) based on the test resultoptional | Avoid abacavir. |
drug_name | string | Name of the drug(s) affected by the gene variantoptional | codeine |
test_method | string | Method used for genotyping (e.g., PCR, NGS, microarray)3 methods · optional | PCR |
reporting_lab | string | Name of the laboratory that performed the test8 labs · optional | GenLab East |
reference_range | string | Reference range or interpretation guideline for the variantoptional | Normal: *1/*1 |
hla_allele | string | Specific HLA allele result if applicable (e.g., HLA-B*57:01)4 alleles · optional | HLA-B*57:01 |
notes | string | Additional notes or comments from the reporting laboratory or clinicianoptional | Patient HIV-positive. |
| Dates and times 1 column | |||
test_date | date | Date when the pharmacogenomic test was performed | 2022-02-14 |
Use it for
A healthcare dashboard
A breakdown of phenotype. Excel, Power BI or Tableau.
What pattern can you find in 100 rows?
A class exercise
Hand out the rows and one question. Everyone works from the same 100 rows.
- Tests100PGX10001PCRPGX10002NGSPGX10003PCR
A software demo
Believable tests with patient_
id, test_ date and gene_ symbol to fill a screen in front of a buyer.
This dataset100 rows15 columns
Yours1,000 rows15 columns
blueprint · pharmacogenomics-and-drug-metabolism-genes
Behind this dataset
Same schema. As many rows as you need.
These 100 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Test ID unique per patient
- Gene tested: CYP2D6, CYP2C19, CYP2C9, VKORC1, TPMT, DPYD, etc.
- Diplotype: combination of alleles (e.g., *1/*2)
- Phenotype: poor, intermediate, normal, rapid, ultra-rapid metabolizer
- Affected drugs listed with clinical implications
- CYP2D6 for codeine, tamoxifen, SSRIs
- CYP2C19 for clopidogrel, PPIs
- VKORC1 and CYP2C9 for warfarin dosing
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- pharmacogenomics-and-drug-metabolism-genes