DNA Info Lab team · Editorial review · Aug 24, 2026 · 10 min read

Pharmacogenomics from Raw DNA Data: What Your Genes Say About Medications

The same pill, at the same dose, can be ineffective in one person and cause side effects in another. A large part of that difference is written in the genes that metabolise drugs — and a surprising amount of it is already sitting in the raw DNA file you can download from 23andMe or AncestryDNA.

What pharmacogenomics is

Pharmacogenomics (PGx) studies how genetic variation changes the way a person responds to a drug: how fast it is activated or broken down, how strongly it acts, and how likely side effects are. The field has matured from anecdotes into published, peer-reviewed prescribing guidance — the CPIC guidelines (Clinical Pharmacogenetics Implementation Consortium) and the PharmGKB database, which curates gene–drug annotations with graded levels of evidence.

The central actors are enzymes with memorable names: CYP2D6, CYP2C19, CYP2C9, plus transporters like SLCO1B1 and targets like VKORC1. Variants in these genes can make you break a drug down unusually slowly or unusually fast — the difference between a standard dose doing nothing and the same dose becoming an overdose.

What a raw DNA file can actually tell you

Consumer arrays do not sequence your genes; they measure pre-selected positions. Fortunately, many of the variants the CPIC guidelines rely on are single-letter variants (SNPs) that sit exactly on those chips. For those, your raw file already contains the relevant genotype:

• CYP2C19*2 (rs4244285) and CYP2C19*17 (rs12248560) — the main loss- and gain-of-function alleles for clopidogrel and several antidepressants, both simple SNPs present on consumer chips.
• SLCO1B1 rs4149056 — the simvastatin myopathy variant, a single SNP.
• VKORC1 rs9923231 and the CYP2C9 *2 and *3 alleles (rs1799853, rs1057910) — the backbone of warfarin dosing algorithms, all SNPs.

When you upload your file to our report, the pharmacogenomics section matches your genotypes against PharmGKB annotations and shows each match with the drug, the gene and the published annotation behind it — research context, in plain language, with the source linked.

The honest limit: SNPs yes, star alleles not always

Here is the caveat most consumer pages skip. Pharmacogenetic star alleles are not all single-letter variants. Some are structural: whole-gene deletions or duplications. The most important case is CYP2D6 — an enzyme involved in roughly a quarter of common prescriptions:

• An ultrarapid CYP2D6 metaboliser often carries extra copies of the gene. An array measuring individual positions cannot count copies, so it cannot see this.
• A whole-gene deletion (*5) is invisible to SNP positions for the same reason.
• The result: an array can genotype the SNP-based CYP2D6 alleles it covers, but it cannot certify a complete CYP2D6 phenotype. "No variant found" on an array is not proof of a normal metaboliser.

Any tool that prints a definitive CYP2D6 metaboliser status from consumer array data is overstating what the file contains. Ours reports the per-genotype annotations for the variants actually measured, and says so.

Four worked examples

CYP2C19 and clopidogrel

Clopidogrel is a prodrug: CYP2C19 must activate it. Carriers of two loss-of-function alleles (typically two *2 alleles) activate it poorly and remain at higher risk of stent thrombosis — which is why the CPIC guideline recommends a different antiplatelet for poor metabolisers after coronary stenting (PMID 35034351). The key alleles are SNPs your raw file covers.

CYP2D6 and codeine

Codeine is also a prodrug — CYP2D6 converts it to morphine. Poor metabolisers get little pain relief; ultrarapid metabolisers convert it dangerously fast, which is why codeine carries warnings in children and breastfeeding mothers (PMID 24458010). The SNP alleles are on the chips, but the ultrarapid case is usually a duplication — exactly the structural variant an array cannot detect. This example is the honest limit in action.

SLCO1B1 and simvastatin

SLCO1B1 transports simvastatin into the liver. The rs4149056 C allele reduces that transport, raising blood levels of the drug and the risk of muscle damage (myopathy) at higher doses. The CPIC guideline recommends lower doses or a different statin for carriers (PMID 22617227). One SNP, directly on the chip.

VKORC1, CYP2C9 and warfarin

Warfarin dosing is notoriously individual. VKORC1 rs9923231 changes how sensitive the drug target is, and the CYP2C9 *2 and *3 alleles change how fast the drug is cleared; together with clinical factors they drive the dosing algorithms in the CPIC guideline (PMID 28198005). All three variants are SNPs present in consumer files.

What "poor" and "rapid" metaboliser mean

PGx guidelines classify people into metaboliser phenotypes per enzyme: poor, intermediate, normal, rapid, ultrarapid. A poor metaboliser clears the drug slowly — it accumulates, and standard doses overshoot. A rapid or ultrarapid metaboliser clears it fast — for a prodrug like codeine that means more active drug, for a normal active drug it means less effect. The label is per enzyme, not per person: you can be a normal CYP2C19 metaboliser and an intermediate CYP2D6 one at the same time.

What to do with this information

1. Do not change any medication on your own. Not the dose, not the schedule, not the drug. PGx annotations describe group-level associations, not your prescription.
2. Bring it to your doctor or pharmacist. A PharmGKB annotation plus your genotype is a legitimate starting point for that conversation — clinicians increasingly recognise CPIC guidelines.
3. If a real prescribing decision depends on it, get a clinical-grade test. Hospital-grade PGx panels include the copy-number analysis arrays lack, and are interpreted under clinical quality standards.

Frequently asked questions

Related reading

• How to interpret your 23andMe raw data → — the full method guide this article belongs to.
• What you can and cannot learn from raw data → — where PGx sits inside the honest scope.
• Polygenic risk scores (PRS) for non-scientists → — the other big thing research variants can say.
• Analyse your raw DNA file → — the report, including the pharmacogenomics section.
• Our methodology and sources → — how we use PharmGKB and the other databases.
• Check your file in the browser → — free, private, nothing is uploaded.

This article is for educational purposes only and does not constitute medical advice, diagnosis or treatment. Always consult a healthcare professional for decisions about your health.