How much of your medication genetics can DNA raw data actually read?
Your DNA raw data can hint at how you handle some medications — but how much of the picture does it really capture? Clinically, roughly 34 genes guide prescribing (per CPIC, the pharmacogenomics standard), and about 50 pharmacogenes have solid evidence overall. We analysed how many a curated consumer-DNA panel actually reaches. The answer is honest and a little sobering: 10 of them — just 3 of the 10 most important — and it misses the single biggest one, CYP2D6.
This is general educational information, not medical advice. Consumer DNA markers are statistical associations, not diagnoses, and are not a pharmacogenomic test — confirm anything that matters with clinical-grade testing and a professional.
Key findings
- The clinical pharmacogenome is ~34–50 genes. CPIC publishes prescribing guidelines for 34 genes across 164 drugs; a broader evidence review counts ~50 pharmacogenes influencing 152 drugs. (External, cited figures — see Methodology.)
- A consumer-DNA panel reaches only part of it. Our curated 66-marker interpretation set covers 10 of those pharmacogenes.
- It misses the most important ones. Of the 10 most-impactful pharmacogenes (each affecting ≥5 drugs), the panel covers just 3 — and misses 7, including CYP2D6, the single biggest drug-metabolising gene.
- CYP2D6 is a structural blind spot, not an oversight: its gene deletions, duplications and hybrids can't be read by the single-position SNP arrays behind consumer raw data.
How big is the "pharmacogenome," really?
The genes that shape drug response are well catalogued. Two reference points:
- CPIC — the Clinical Pharmacogenetics Implementation Consortium, the global standard for turning genotype into prescribing advice — has guidelines spanning 34 genes and 164 drugs (Caudle et al., 2025).
- A broader evidence review identified ~50 pharmacogenes with high-to-moderate association evidence, touching 152 drugs (Pharmacogenes review, 2022).
The same review names the 10 most impactful pharmacogenes — each predicted to affect five or more drugs:
CYP2D6 · CYP2C9 · CYP2C19 · G6PD · HLA-B · SLCO1B1 · CACNA1S · RYR1 · MT-RNR1 · IFNL4
CYP2D6 tops that list. It's involved in metabolising an estimated 20–25% of prescription drugs — antidepressants, opioids like codeine and tramadol, tamoxifen, some beta-blockers and antipsychotics.
What a consumer-DNA panel actually reaches
Our curated interpretation panel covers 10 pharmacogenes: ABCG2, CYP2C19, CYP2C9, CYP4F2, DPYD, NUDT15, SLCO1B1, TPMT, UGT1A1 and VKORC1. These are real, clinically-meaningful markers — they inform genuinely high-stakes drugs:
| Gene(s) in the panel | Medications it informs |
|---|---|
| CYP2C19 | clopidogrel (Plavix), some antidepressants, PPIs, voriconazole |
| CYP2C9 · VKORC1 · CYP4F2 | warfarin (dose sensitivity) |
| SLCO1B1 | simvastatin and other statins (muscle-symptom risk) |
| TPMT · NUDT15 | thiopurines (azathioprine, mercaptopurine) |
| DPYD | fluoropyrimidine chemotherapy (5-FU, capecitabine) |
| UGT1A1 | irinotecan, atazanavir |
| ABCG2 | rosuvastatin, allopurinol |
But line that up against the top-10 most impactful pharmacogenes and the gap is clear: the panel covers CYP2C9, CYP2C19 and SLCO1B1 — and misses CYP2D6, G6PD, HLA-B, CACNA1S, RYR1, MT-RNR1 and IFNL4. The misses aren't obscure: HLA-B flags dangerous hypersensitivity to drugs like abacavir and carbamazepine; G6PD matters for oxidative-stress drugs; CACNA1S/RYR1 relate to malignant hyperthermia under anaesthesia.
The CYP2D6 problem
The most telling gap is CYP2D6 — the biggest pharmacogene of all, missing from the reach of consumer raw data not by choice but by technology. CYP2D6 is defined by structural variation: people carry whole-gene deletions, duplications (extra copies that speed metabolism up), and CYP2D6/CYP2D7 hybrids. Consumer raw data comes from SNP genotyping arrays, which read one base at a single position — they simply can't see copy number or gene rearrangements. So even when a raw file lists a CYP2D6 SNP, it can't tell you your true metaboliser status. Clinical pharmacogenomic tests use dedicated methods (copy-number assays, long-read or targeted sequencing) for exactly this reason.
What this means for reading your own file
Two honest takeaways:
- Consumer raw data carries real pharmacogenomic signal — CYP2C19, warfarin genetics, statin-response markers are genuinely in your file, and that's worth understanding. See what your CYP2C19 raw data means for one worked example.
- It is a partial view, and it's blind to the biggest gene. A raw file reaches a minority of the pharmacogenome and structurally can't read CYP2D6. So treat anything you find as a prompt to ask a clinician or pharmacist about proper pharmacogenomic testing — never as a reason to start, stop, or change a medication.
That honesty is the point: a raw DNA file is a great starting place for curiosity and context, not a clinical test. You can explore yours without uploading it anywhere — our free DNA raw data explorer reads a 23andMe or AncestryDNA file entirely in your browser.
Methodology
- Our original figures — the pharmacogenes a consumer panel reaches (10), the top-10 overlap (3 covered, 7 missed), and the category breakdown — were computed from Quanome's committed 66-marker reference panel (
genePanel) by a reproducible script (scripts/dna_panel_study.py). Nothing is estimated; each marker carries verified GRCh37 and GRCh38 coordinates (66/66). - External, cited facts (not our data): CPIC guideline scope of 34 genes / 164 drugs (Caudle et al., 2025); ~50 evidence-backed pharmacogenes / 152 drugs and the top-10 list (PMC9640910); CYP2D6's drug share and structural complexity are well-documented in the pharmacogenomics literature.
- Scope and limitations: "reached by a consumer-DNA panel" means our curated, well-studied interpretation set — a defined, consumer-oriented selection, not the full physical content of any one chip (23andMe, AncestryDNA and MyHeritage read overlapping but different SNPs, and versions change). The point holds regardless: SNP-array raw data cannot resolve structurally-complex pharmacogenes like CYP2D6. Educational associations, not clinical diagnoses.
- Date: August 2026. Citation welcome — please link to this page.
Read the markers behind the numbers
Several markers here have their own plain-English guides: CYP2C19, CYP1A2 caffeine metabolism, APOE4, MTHFR, and Factor V Leiden. For the full picture of reading a raw file, see the complete guide to 23andMe raw data and how to read your AncestryDNA raw data, or browse the genetics section.
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Read your own raw DNA — privately, on your device
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Frequently asked questions
How many genes affect how you respond to medications?
There's no single number — it depends on the evidence bar. The clinical standard, CPIC (Clinical Pharmacogenetics Implementation Consortium), publishes prescribing guidelines for 34 genes covering 164 drugs. A broader count finds roughly 50 pharmacogenes with high-to-moderate evidence, influencing about 152 drugs. So the 'pharmacogenome' that matters clinically is on the order of 34–50 genes.
Can 23andMe or AncestryDNA raw data tell me about my medications?
Partly. Consumer raw data can flag some well-studied pharmacogenomic markers — in our analysis, a curated interpretation panel reached 10 of the ~34 CPIC pharmacogenes, including CYP2C19 (clopidogrel), CYP2C9/VKORC1 (warfarin) and SLCO1B1 (statins). But it's a partial view: it covered just 3 of the 10 most impactful pharmacogenes and missed the single most important one, CYP2D6. Treat any finding as educational, never as a basis to change a medication.
Why can't consumer DNA tests read CYP2D6 well?
CYP2D6 is the most important drug-metabolising gene — it's involved in processing an estimated 20–25% of prescription drugs — but it's also one of the hardest to read. It has extensive structural variation (whole-gene deletions, duplications, and hybrid genes) that the single-position SNP genotyping arrays behind consumer raw data can't resolve accurately. That's why clinical pharmacogenomic testing uses dedicated methods for it.
Is consumer raw data a substitute for a pharmacogenomic test?
No. A consumer genotyping file reads selected single positions, not the full, structurally-complex pharmacogenome, and it isn't clinically validated. It can be a useful prompt to ask a clinician or pharmacist about pharmacogenomic testing — but real prescribing decisions need clinical-grade testing, not a consumer chip.
How was this analysed?
The panel numbers were computed from Quanome's committed 66-marker reference set by a reproducible script; the pharmacogenome figures (34 CPIC genes, ~50 evidence-backed pharmacogenes, the top-10 list) are established published facts, cited below. Full method and limitations are in the Methodology section.
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