How we read your DNA, and what we refuse to claim.
Genetic interpretation is easy to overstate, and a polished interface can create more confidence than the evidence behind it supports. So here is the whole method in the open: where each finding comes from, how markers get combined, what we take out of the report on purpose, and exactly where the science stops. None of it is behind the paywall.
Where the findings come from
Every statement in the report traces to a public, versioned source, and every card links that source so you can read it instead of taking our word for it.
Effect alleles, effect sizes and p-values for common-variant associations, read from the EBI and NHGRI catalog rather than retyped from a news write-up.
Reviewed classifications for cataloged variants. We carry ClinVar's review status onto the card verbatim, with its 0-4 star rating, so you can see whether a classification came from an expert panel, a single submitter, or submitters who disagree.
Response annotations carrying their own evidence level. Only the ones about caffeine, alcohol and nicotine reach your report; everything about prescription drugs is filtered out before the report is built.
Gene-to-trait links used as supporting context. Never the sole basis for a finding.
The primary paper behind each curated statement, linked on the card so you can read the source instead of trusting our summary of it.
How a marker becomes a finding
Anchor the effect allele
We store which allele the research actually measured, and which direction it pushes the trait, as two separate facts. The alternate allele is simply the non-reference one and is not automatically the worse one, so a report that treats ALT as "the bad letter" gets roughly half its cards backwards.
Write the reading per genotype
The sentence you read is curated for your exact genotype, not generated from the numbers at display time. That keeps the prose and the data from drifting apart, which is the failure mode that produces confident text about the wrong allele.
Keep the report to everyday wellness
Some research stays out of the report on purpose: cancer risk, chemotherapy toxicity, diagnosis-adjacent claims, and prescription-drug response. What remains covers 128 topics across five areas, and your own file decides how many of those it actually carries.
The part everyone gets wrong: combining markers
When several markers speak to one topic, we combine them into a single lean. That combination is not a polygenic risk score, and we do not dress it up as one. The difference is worth being precise about.
| A published PRS | What Helisoma computes | |
|---|---|---|
| Purpose | Estimate risk in a defined population | Order which findings deserve your attention first |
| Inputs | Thousands to millions of variants, genome-wide | The handful of curated markers your file carries for that topic |
| How it is checked | Against a held-out cohort, calibrated per ancestry | Each card is tied to its published source, and automated tests guard the knowledge base and the report math |
| Output | A score with a population distribution | Three words: favorable, typical, worth a review |
Inside that combination, five rules do the real work. They exist because the naive version, adding up good and bad markers, is wrong in ways that are easy to miss and impossible to spot from the finished page.
155 linkage blocks are defined across the knowledge base for the first rule alone.
What we keep separate
A high-impact variant must never end up buried inside a trait score. Three kinds of genetic information behave nothing alike, so they travel through the report on separate tracks and are never summed together.
Small leans measured across large groups. These are what the report is actually about, and they are stated as leans, not verdicts. One of them changes a habit at most.
Most are left out of the report on purpose.
Not in the report at all. We curate it, we hold the CPIC and PharmGKB evidence levels for it, and we keep it out of every surface you can reach: the interactive report, the PDF, and what your AI is served. How you metabolize a prescription belongs in a conversation with whoever writes it, backed by a clinical test, not in a wellness product you bought online. What stays is substance response: caffeine, alcohol, and nicotine.
Ancestry, and why you will not see odds ratios
Most of the effect sizes behind common-variant research were measured in European-ancestry cohorts. That is a fact about the field rather than about you, and it changes how much any single number is worth.
What your file can and cannot deliver
Consumer arrays, exomes and whole genomes all feed the same pipeline, and each brings its own limits. These are ours.
What the AI is allowed to do with it
The report is built to be handed to your own assistant, which is the point at which a careless product would let free-form medical interpretation back in through the side door. Four rules ship with the connector.
Your data
Your raw DNA file is parsed by your own browser and never uploaded. Only the small set of markers the report uses is sent to us, well under a megabyte, and we never receive or store the file itself. We do not sell personal data. You can delete your genetic data and your account yourself in settings, and the extracted markers are erased immediately and permanently. Reports are not stored; they are rebuilt from your markers each time, so nothing is left to rebuild them from. Transaction records are kept for six years to meet tax obligations and contain no genetic data. The full detail is in the Privacy Policy.
What this is not
Nothing in the report should change a medication, a diagnosis, or a large health decision on its own. It is educational, it is about everyday tendencies, and the interesting questions it raises are good ones to bring to a doctor.
Questions people ask
Is the topic score a polygenic risk score?
No, and we do not present it as one. A published PRS is fitted and calibrated on large cohorts so that it can output a risk figure. Ours does a different job: it orders the handful of markers your file carries for a topic, to answer "what should I look at first", and it outputs three words rather than a number: favorable, typical, or worth a review.
Do you report pathogenic variants, BRCA, or anything a genetic counselor would handle?
No. High-stakes clinical claims are left out of the report, including cancer risk and chemotherapy toxicity. If you want those answers, you want a clinical test, not a consumer report.
Do you tell me how I respond to my medications?
No. We curate prescription-drug pharmacogenomics and hold the CPIC and PharmGKB evidence levels for it, and then we keep all of it out of the interactive report, the PDF, and anything your AI is served. Dosing is a conversation with whoever writes your prescription, backed by a clinical test. What the report does read is substance response: caffeine, alcohol, and nicotine.
How do I know how much review is behind a ClinVar classification?
The card tells you. ClinVar rates its own classifications 0 to 4 stars by how much assertion criteria stood behind them, and we print that rating with ClinVar's wording, unedited: "reviewed by expert panel" and "practice guideline" at the top, "criteria provided, single submitter" low down, "conflicting classifications" when submitters disagree, "no assertion criteria provided" at the bottom. It rates the review, not the size of the effect. Two of the three high-impact variants we do report carry conflicting classifications, and the card says so rather than rounding it up to settled.
Do you impute missing genotypes?
Never. If a marker is not in your file, it produces no finding at all. Imputation would let us claim coverage we do not have, and an imputation error is invisible to the person reading the report.
Does this work for exome or whole-genome data?
Yes, through standard VCF. Drop the file your lab sent, at any size: your browser reads it line by line and keeps only the markers the report uses. The report reads the same marker set either way, so sequencing mostly buys you fuller coverage rather than different findings.
Do you read CNVs or structural variants?
No. Consumer arrays are not built for them and our pipeline does not call them. Everything in the report is a single-nucleotide variant read directly from your file.
Can I see the method applied to a real report before paying?
Yes. The sample report is a full report built from a sample genome, free and without an email, and it opens with how its evidence is assessed and how to read each card.
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