How accurate is your raw DNA data, honestly?
Sooner or later everyone who downloads their raw DNA file asks the same question: can I actually trust what's in here? The honest answer is more interesting than yes or no: your file is excellent in one zone and genuinely unreliable in another, and knowing where the line runs matters more than any single result in it.
How a chip decides which letters you carry
Consumer tests do not read your genome letter by letter. Sequencing does that; it is why whole-genome sequencing costs more. A chip works differently: it carries hundreds of thousands of tiny probes, each designed to grab one specific spot of your DNA, and your genotype is called from the pattern of fluorescent signal each probe lights up with. The calling is statistical: your signal is compared against clusters formed by many other samples, and you get assigned to the cluster you land closest to.
That design has a built-in consequence. For a common variant, every batch of samples contains thousands of people in each genotype group, the clusters are dense and well-separated, and calls are extremely reliable. For a rare variant, almost nobody in the batch carries it, the "carrier" cluster is barely a cluster at all, and the algorithm is deciding your result from a handful of noisy points. The chip is not equally good everywhere; it is superb exactly where lots of people share the variant, and shaky exactly where almost nobody does.
The 40% number
In 2018, a clinical genetics laboratory published what happened when people brought raw-data findings in for confirmation with clinical-grade methods. Across 49 samples carrying variants flagged in consumer raw data, 40% turned out to be false positives: the variant simply was not there when tested properly. On top of that, some variants that third-party tools had labeled "increased risk" were classified as benign by multiple clinical labs, being in fact common variants present in population databases.
Read that carefully, because both halves matter. The false positives concentrated in rare variants in serious genes, exactly the ones that make people panic. Nobody is finding 40% error rates in lactose tolerance or caffeine metabolism calls; those are common variants sitting in dense, confident clusters.
Where that leaves your file
- Common variants: trust zone. The findings consumer genetics is actually built for, caffeine response, chronotype, lactose, muscle fiber type, taste, common folate variants, sit on common positions where chips perform at their best.
- Rare, scary-looking variants: verify zone. If a third-party tool tells you your file contains a rare pathogenic variant, treat it as a reason for a clinical test, never as a result. Odds are uncomfortably close to a coin flip that it is not real.
- Absence proves nothing. Your chip measures well under 0.1% of your genome. A variant not listed in your file usually was not measured at all.
- No-calls are normal. The "--" entries scattered through your file are positions where the signal was too ambiguous to call. Every file has them.
How we use this at Helisoma
This asymmetry is not a footnote for us; it is the design. Your report is built almost entirely on common, replicated variants, the zone where chip data is genuinely strong, and we deliberately do not report rare clinical findings, the zone where it is not. Your browser reads the file on your device, the report states each effect honestly, and every card links its study. Accuracy is not just about the chip; it is about only asking the chip questions it can answer.
Sources
- Tandy-Connor S et al. False-positive results released by direct-to-consumer genetic tests highlight the importance of clinical confirmation testing for appropriate patient care. 40% of variants across 49 samples failed clinical confirmation. PubMed 29565420