What a DNA Test Can and Cannot Tell You About Antidepressants

A DNA test can tell you, with reasonable accuracy, how quickly your liver clears several common antidepressants. It cannot tell you which antidepressant will make you feel better. Clearance and benefit are different questions, and every commercial “depression gene test” blurs them. The pharmacokinetic part is real, reproducible, and encoded in two genes that do most of the work: CYP2D6 and CYP2C19. The efficacy part (which molecule will lift your mood) has no validated genetic predictor, because depression is highly polygenic and the effect of any single variant on treatment response is too small to act on ¹².

So the useful framing: a pharmacogenetic panel is a dosing-relevant lab value, like creatinine clearance. It narrows the space of exposure risk. It does not pick the drug.

The genes that carry real signal

Most SSRIs and tricyclics are cleared by cytochrome P450 enzymes, and the two relevant loci are highly polymorphic across populations ³.

• CYP2C19 is the primary route for escitalopram, citalopram, and sertraline, and contributes to amitriptyline and clomipramine. The common variants start with *2 (rs4244285, splice defect). Next is *3 (rs4986893, stop-gain), which is more frequent in East Asian populations. Last is *17 (rs12248560, increased transcription). Diplotype determines phenotype, which is poor, intermediate, normal, rapid or ultrarapid metabolizer.
• CYP2D6 handles paroxetine, fluoxetine (partly), venlafaxine, and most tricyclics. It is the hard one. It has over a hundred named alleles and a whole-gene deletion (*5). Gene duplications and multiplications give three or more functional copies. Short-read aligners routinely mangle the CYP2D6/CYP2D7 hybrid alleles (*36, *4.013, *68). Reduced-function alleles like *10 are common in East Asian populations and were characterized in the original sequencing work on the locus ⁴.
• Secondary: CYP2B6 (bupropion), CYP3A4/5, UGT1A4/2B10, and for some mood stabilizers HLA-B*15:02 and HLA-A*31:01, which carry strong, well-replicated risk signals for severe cutaneous reactions to carbamazepine.

CPIC publishes gene-drug guidelines that map a diplotype to a dosing recommendation for many of these pairs. Those documents are written for prescribers. Read them, but a change in medication or dose is a clinician’s call. The decision is not yours and not your agent’s.

Why “which antidepressant will work” is not on the table

The genes that predict response, as opposed to clearance, have not held up. Candidate-gene work on SLC6A4 (the serotonin transporter promoter polymorphism), HTR2A, and BDNF produced a long trail of small positive studies that failed replication once sample sizes grew, and the field moved to genome-wide approaches where individual effect sizes are tiny ¹. Large GWAS of depression and anxiety find many loci of very small effect, consistent with a polygenic architecture that no clinical panel summarizes ². The “combinatorial algorithm” sold by consumer psychiatric panels stacks CYP alleles with a handful of pharmacodynamic markers (commonly SLC6A4 and HTR2A) into a green/yellow/red bin. The pharmacodynamic component is the weak part, and the color bins compress a continuous exposure prediction into a judgment the underlying evidence does not support. Reviews of psychiatric pharmacogenomics have been consistent on this gap between marketing and translation ⁵⁶, and it has been an open critique of the field for more than two decades ⁷.

Adoption barriers are also practical: clinicians report low confidence interpreting reports, unclear reimbursement, and inconsistent cross-lab result formats ⁸. That is another reason to hold your own raw data.

Calling your own star alleles from WGS

If you have 30x whole-genome sequencing, you can do this yourself and get better CYP2D6 resolution than most panels. Our pipeline:

1. Start from a GRCh38 BAM/CRAM and a joint-called VCF. Array data (23andMe, AncestryDNA) is not adequate here. Arrays genotype a few CYP2D6 SNPs, cannot see the *5 deletion or duplications, and silently return “normal metabolizer” for people who are not.

2. CYP2D6 from read depth and variants. The VCF alone does not resolve this gene. Use Cyrius, which was built for this and handles the CYP2D7 paralog and hybrid alleles:

python star_caller.py --manifest bams.txt --genome 38 \
  --prefix sample01 --outDir cyp2d6/ --threads 4

Cyrius outputs a diplotype plus a raw copy-number call. An alternative is PyPGx (successor to Stargazer), which needs a control gene for depth normalization:

pypgx run-ngs-pipeline CYP2D6 out-2d6 \
  --variants cohort.vcf.gz --depth-of-coverage doc.zip \
  --control-statistics control-VDR.zip

Run both. If they disagree, treat the locus as unresolved rather than picking the answer you like.

3. Everything else through PharmCAT. It takes a VCF, matches named alleles, and emits a report with CPIC recommendations:

pharmcat_pipeline sample01.vcf.gz \
  -reporterJson -matcherHtml -o pharmcat_out/

Feed the Cyrius call in as an outside call so PharmCAT does not have to guess CYP2D6:

echo -e "CYP2D6\t*1/*4" > outside.tsv
pharmcat_pipeline sample01.vcf.gz -po outside.tsv -o pharmcat_out/

Important preprocessing detail: PharmCAT needs positions that are reference-called as well as variant sites. Variant sites alone are not enough. A VCF that only lists non-reference positions will produce no-calls. Use the bundled preprocessor with the full genome reference, and start from a gVCF or from bcftools mpileup over the PharmCAT position list if your caller dropped hom-ref sites.

Failure modes worth knowing before you trust the output

• Missing hom-ref evidence. The single most common cause of a spurious “no call” diplotype. Check the matcher HTML for uncalled positions before interpreting anything.
• CYP2D6 structural variation. Duplications of a non-functional allele (*4x2) look like increased copy number but reduce, not raise, activity. Phase matters: a duplicated *1 plus a *4 is a different phenotype from a duplicated *4 plus a *1. Short reads often cannot phase it. Long reads (PacBio HiFi, ONT) resolve the locus cleanly and are the right tool if you care about the answer.
• Ancestry coverage. Allele definitions and frequency priors are better characterized in European-ancestry cohorts. Rare or population-specific reduced-function alleles may be absent from the panel definitions you are matching against ⁴.
• Genotype is not phenotype. Enzyme activity shifts with inflammation, hepatic function, and inhibitors. Fluoxetine and paroxetine are strong CYP2D6 inhibitors, so a genetic normal metabolizer on either behaves like a poor metabolizer. This is phenoconversion, and no DNA test sees it.

That last point is why a measured drug level (therapeutic drug monitoring, an LC-MS/MS assay on plasma) beats a genotype when you want to know actual exposure. Genotype is a prior. Plasma concentration is the measurement.

Questions people also ask

How much does an antidepressant genetic test cost? Commercial psychiatric panels are typically a few hundred dollars out of pocket, with wide variation in what is billed to insurers. A 30x whole genome now costs in the same range or a little more and covers every pharmacogene plus everything else, which is why we prefer it.

What is the best DNA test for antidepressants? For raw data you control, 30x WGS processed through Cyrius plus PharmCAT. For a clinical report you can hand to a prescriber, a CLIA-certified CYP2D6/CYP2C19 panel that reports diplotypes and copy number explicitly rather than a color-coded “recommended / use with caution” list.

Is genetic testing for antidepressants reliable? The pharmacokinetic calls are reliable when the assay covers structural variation at CYP2D6. The efficacy predictions are not supported by the underlying evidence ¹⁵.

Is there a blood test to determine the best antidepressant? No. There are blood tests that measure how much drug is in your plasma once you are taking one, which is a different and more useful measurement.

Is genetic testing for antidepressants worth it? It is worth having the data, especially as a byproduct of sequencing you would do anyway. What it buys you is a dosing-relevant prior on a handful of drugs, discussed with the clinician who prescribes them.

Footnotes

1. Stanley I. Shyn, Steven P. Hamilton. The Genetics of Major Depression: Moving Beyond the Monoamine Hypothesis. Psychiatric Clinics of North America, 2010. https://doi.org/10.1016/j.psc.2009.10.004 ↩ ↩² ↩³

2. Jordan W Smoller. The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders. Neuropsychopharmacology, 2015. https://doi.org/10.1038/npp.2015.266 ↩ ↩²

3. Magnus Ingelman-Sundberg. Pharmacogenetics of cytochrome P450 and its applications in drug therapy: the past, present and future. Trends in Pharmacological Sciences, 2004. https://doi.org/10.1016/j.tips.2004.02.007 ↩

4. I Johansson, M Oscarson, Q Y Yue, et al. Genetic analysis of the Chinese cytochrome P4502D locus: characterization of variant CYP2D6 genes present in subjects with diminished capacity for debrisoquine hydroxylation. Molecular Pharmacology, 1994. https://doi.org/10.1016/s0026-895x(25)09721-4 ↩ ↩²

5. Erik H.F. Wong, Jayne C. Fox, Mandy Y.M. Ng, et al. Toward Personalized Medicine in the Neuropsychiatric Field. International Review of Neurobiology, 2011. https://doi.org/10.1016/b978-0-12-387718-5.00013-4 ↩ ↩²

6. Alessio Squassina, Mirko Manchia, Vangelis G Manolopoulos, et al. Realities and Expectations of Pharmacogenomics and Personalized Medicine: Impact of Translating Genetic Knowledge into Clinical Practice. Pharmacogenomics, 2010. https://doi.org/10.2217/pgs.10.97 ↩

7. Alison Abbott. With your genes? Take one of these, three times a day. Nature, 2003. https://doi.org/10.1038/425760a ↩

8. Justo Pinzón-Espinosa, Marte van der Horst, Janneke Zinkstok, et al. Barriers to genetic testing in clinical psychiatry and ways to overcome them: from clinicians’ attitudes to sociocultural differences between patients across the globe. Translational Psychiatry, 2022. https://doi.org/10.1038/s41398-022-02203-6 ↩