DNA Info Lab team · Editorial review · Aug 24, 2026 · 12 min read

How to Interpret Your 23andMe Raw Data: A Practical Guide (Updated 2026)

If you've downloaded your raw DNA file from 23andMe (or AncestryDNA, or MyHeritage — the principle is the same), you have a plain text file with around 650,000 rows. Each row is a genetic position the testing chip measured. The natural question: what can you actually do with it?

This guide walks through three ways to interpret your raw data — from the simplest (looking up specific variants by hand) to the most useful (cross-referencing against published research). Plus the honest limits, and how to read what you find without fooling yourself.

First: check your file is usable

Before interpreting anything, spend one minute on the file itself. A surprising share of "my results look wrong" stories are really file problems: a truncated download, the wrong export, or a format a tool cannot read.

The fastest check is our free raw DNA file checker, which runs entirely in your browser — the file never leaves your device. It tells you which provider and format the file is, how many variant rows it contains, how many no-calls there are, and whether it looks truncated. A text editor works too: a healthy 23andMe file starts with comment lines beginning with #, followed by row after row of four tab-separated values.

• Right file? You want the file named something like genome_Firstname_Lastname_Full_*.txt — not the ancestry composition PDF or a health-report summary.
• Complete file? A full 23andMe export is roughly 20-25 MB and runs to the end of the MT chromosome. If yours is much smaller or stops mid-way, download it again.
• Readable format? 23andMe and AncestryDNA ship tab-separated .txt; MyHeritage ships a quoted .csv. Tools that auto-detect the format save you a conversion step.

What's actually in the file

The 23andMe raw file is a tab-separated text file with four columns:

• rsid — the unique ID of a genetic variant in dbSNP (e.g. rs429358 = the APOE variant).
• chromosome — one of your 23 chromosomes (1-22, X, Y, MT).
• position — the exact base-pair position on that chromosome.
• genotype — the two letters you have at that position (one inherited from each parent).

That's all. It's not your full genome — it's a sample of ~650,000 positions out of 3 billion (~0.02%). But those positions are carefully chosen to cover most known disease-associated variants. For the column-by-column anatomy — genome build, no-calls, chip versions — see the raw data file explained.

Method 1: Look up specific variants by hand

The most direct way is to look up specific rsids you've heard about. For each variant, you can check what your letters mean in three databases:

• SNPedia — a wiki of consumer-relevant variants with plain-language summaries
• dbSNP — the official NCBI database with all known variants
• GWAS Catalog — peer-reviewed disease associations with effect sizes

Famous variants people look up

• rs429358 + rs7412 — combine to define APOE alleles, the strongest common genetic risk factor for Alzheimer's.
• rs6025 — Factor V Leiden, blood-clotting risk.
• rs1801133 — MTHFR variant, folate metabolism (controversial — see SNPedia).
• rs4988235 — LCT, lactose tolerance.
• rs9939609 — FTO, obesity association.
• rs1051730 — CHRNA3, nicotine dependence.
• rs1815739 — ACTN3, the "sprint gene" (sport performance).

This is informative but slow — you can interpret maybe 5-10 variants in an evening, and you have to be careful about ancestry-specific effects and overinterpreting single SNPs.

A worked example: lactose tolerance

Say you want to know what your file says about lactose. The best-studied variant is rs4988235, in the regulatory region of the lactase gene. Here is the whole manual method, end to end:

1. Open your raw file in a text editor and search for rs4988235. You will find a row whose last column is your genotype: AA, AG or GG.
2. Each A allele is associated with lactase persistence — the ability to digest lactose as an adult; GG is associated with lactase non-persistence in the European populations where this was studied.
3. Verify the association in a second source: both the GWAS Catalog and SNPedia document this variant, including which populations the studies recruited.
4. Note the limits before concluding anything: the association is strong in European ancestry and weaker or absent elsewhere, and real-world digestion also depends on your gut microbiome and intestinal health. The genotype is a research association, not a diagnosis.

That is one variant, and it took four careful steps. The method is right — and it is exactly why doing this by hand for hundreds of positions is not realistic, which is where Method 2 comes in.

Method 2: Cross-reference against curated databases

Several tools take your full raw file and automatically match it against curated, peer-reviewed databases. The three databases that matter most are:

1. GWAS Catalog → research associations. Finds which of the hundreds of variants studied for traits like type 2 diabetes, heart disease, Alzheimer's and depression are in your file, and what each study reported for them. It does not add them into a score.
2. PharmGKB → pharmacogenomics. Tells you how your genotype may affect drug response for warfarin, statins, codeine, clopidogrel, SSRIs, metformin and others.
3. ClinVar → clinical variants. Flags positions where you carry a variant labs have classified as pathogenic or likely pathogenic for a specific condition.

This is what our service does. Upload your raw file (zipped or extracted) and get back a structured report covering ten health areas of GWAS research, plus a pharmacogenomics section and clinically-reviewed ClinVar findings, each match shown with the trait its study measured and a link to the source. It reports no overall score. Takes 30 seconds. The 23andMe analysis page describes the report section by section, and what you can and cannot learn draws the honest boundary around it.

Method 3: Whole-genome sequencing

If you want every position in your genome, microarray-based testing is not enough. Whole-genome sequencing services (Nebula, Dante Labs, Sequencing.com and others) deliver the complete picture for a few hundred euros. For most consumer questions, however, microarray data from 23andMe or AncestryDNA is more than sufficient — you only really need WGS if you're investigating rare variants in your family.

How to read an odds ratio without fooling yourself

Most GWAS results are reported as an odds ratio (OR): the odds of the trait in carriers of a variant divided by the odds in non-carriers, as measured in one study. Three habits keep this honest:

1. An odds ratio is not your probability. OR 1.5 does not mean "50% chance", and it does not mean your risk grew by half an absolute point. Applied to a common trait it can shift your personal probability noticeably; applied to a rare one, the same OR moves it by a fraction of a percent.
2. Baseline matters more than the multiplier. Doubling a 1-in-10,000 risk gives 1-in-5,000 — real, but small in absolute terms. Doubling a 1-in-5 risk is a different conversation entirely.
3. One study is a hint, not a verdict. Associations replicate or they fade. A variant with one small study behind it deserves a shrug; one confirmed across many large studies — like the APOE example below — deserves attention. And effect sizes measured mostly in European-ancestry participants may not transfer to you unchanged.

Common mistakes when interpreting raw data

• Reading a no-call as a result. A genotype of -- (23andMe) or 0 (AncestryDNA) means the chip failed to measure that position — not that you lack the variant. Draw no conclusion from it.
• Ignoring the genome build. The positions in your file refer to a reference genome, usually GRCh37. Databases that display GRCh38 coordinates show different numbers for the same variant — match by rsID, which is stable, not by position.
• Confusing the DNA strand. Some databases report genotypes on the opposite strand, where A↔T and C↔G are swapped. An "AA" there can be the "TT" in your file. Reputable tools handle this silently; manual lookups need the check.
• Treating one SNP as a story. Single variants explain tiny fractions of most traits. The interpretable signal is the aggregate — which is what polygenic scores formalise and what curated databases put in context.

A privacy checklist before you upload anywhere

Your raw file is the most identifying document you own — unlike a password, it cannot be reissued. Before uploading it to any service, ours included, four questions should have one-sentence answers:

1. Where is it processed? Look for a jurisdiction you are comfortable with. Our servers are in Germany, under GDPR.
2. What is stored, and for how long? We do not keep the file itself; the extracted variants and your report stay until you delete them, and inactive accounts are purged automatically.
3. Can I export and delete everything? With us: yes, self-service, from your account page. If a service makes you email support to delete your genome, walk away.
4. Is it sold or shared? The answer must be an unqualified no — ours is. The details are in our privacy policy.

The big caveats

Before you go variant-hunting, three things to keep in mind:

1. Most studies are biased toward European ancestry. ~90% of GWAS participants are of European descent. The effect sizes you read about were measured there, and may not apply to you with the same magnitude.
2. A single variant rarely means much. Even the APOE ε4 allele — which is not a single variant but a combination of rs429358 and rs7412, and which this report does not derive — is published as odds ratios that vary widely with the number of copies and the population studied. One meta-analysis reports 3.2 for one copy (ε3/ε4) and 14.9 for two (ε4/ε4) in Caucasian samples, and 5.6 and 33.1 for the same two in Japanese ones. Most disease risk is polygenic — hundreds of small effects added together.
3. This is not medical advice. Your raw data is a starting point for a conversation with a doctor, not a diagnosis. Anything that looks alarming deserves a clinical follow-up, not a panic.

Doing it the easy way

Manual lookup teaches you a lot but takes hours. If you just want the answer, upload your raw file — we cross-reference it against the GWAS Catalog, PharmGKB and ClinVar, then explain each result in plain language. The coverage summary, the clinically-reviewed ClinVar findings and the strong-evidence drug interactions are free; the full report is a one-time 10 €. Equivalent to several hours of manual research, in under a minute.

Frequently asked questions

Related reading

• The 23andMe raw data file explained → — the four columns, the genome build and the no-calls in detail.
• What you can and cannot learn from raw data → — the honest scope of an analysis.
• See an annotated raw-data example → — a synthetic file with the famous variants explained.
• How to download your 23andMe raw data → — step by step, in two minutes.
• Analyse your 23andMe raw data → — what our report covers, section by section.
• 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.